The use of big data and analytics technologies has become increasingly popular across industries. Every day, more and more businesses realize the value of analyzing their own performance to boost strategies and achieve their goals. This is no different in the logistics industry, where warehouse managers track a range of KPIs that help them efficiently manage inventory, transportation, employee safety, and order fulfillment, among others.

With the help of the right logistics analytics tools, warehouse managers can track powerful metrics and KPIs and extract trends and patterns to ensure everything is running at its maximum potential. But how do you know which indicators to track?

To help you in your journey to achieve warehousing excellence, we put together this insightful guide on warehouse KPIs. Keep on reading to learn a definition, benefits, and a warehouse KPI list with the most prominent examples any manager should be tracking to achieve operational success. 

Let’s dive in with the definition. 

What Is A Warehouse KPI?

A Warehouse KPI is a measurement that helps warehousing managers to track the performance of their inventory management, order fulfillment, picking and packing, transportation, and overall operations. It allows for informed decision-making and efficient risk mitigation.  

With the help of the right KPI tools, managers can not only get a real-time view of their warehouse performance but also go deeper into the data and extract powerful insights to improve their strategies, boost productivity, and use resources smartly. Making the use of warehousing metrics a huge competitive advantage. 

That said, it is important to note that even though most warehouse management KPIs can be applied to any warehouse, each business operates with different goals and objectives. Therefore, it is very important to pick your indicators based on your actual needs. We will dive deeper into this point later in the post. Now, let’s look at some benefits to keep putting the power of warehouse key performance indicators into perspective. 

Why Do You Need Warehouse KPIs?

Now that you know the definition of warehouse KPIs, let’s look at some of the benefits you can reap from using them to evaluate your performance. 

• Informed decision-making: The biggest benefit of using KPIs and metrics in your warehousing strategies is informed decision-making. Being able to inform your strategies with actual facts instead of intuition will enable you to optimize your resources and ensure you are continuously improving. 
• Time and cost efficiency: In business, saving as much time and money as possible is of the essence. Making decisions based on your performance will help you avoid wasting monetary and time resources on wrong strategies. With the power of data, you can boost your warehouse efficiency at the lowest possible cost. 
• Boost customer satisfaction: By making informed decisions using powerful metrics, you’ll be able to offer the best shopping experience to your customers with permanent stock, short delivery times, and no surprises. In time, this will help you increase customer satisfaction and skyrocket warehouse ROI. 
• Enhance efficiency and safety: Ensuring your warehouse operations are efficient and safe is another great benefit of using KPIs. These powerful measurements will allow you to track all activities in real-time to ensure everything runs smoothly and safely. 

Top Warehouse KPIs Examples & Templates

The daily operation of a warehouse involves many processes, technologies, and people working simultaneously to ensure all steps are completed successfully. Across the day, products coming from different suppliers and production centers are received and stored in storage facilities with the help of machines and technology. At the same time, other products are picked from storage, carefully packed, and loaded into a transportation vehicle to be sent to the end customer or to a store to be sold. 

With so many processes, products being handled, and resources being spent, ensuring everything runs smoothly and efficiently is paramount. That is where warehouse metrics and KPIs come into play. These measurements allow managers and other relevant warehouse stakeholders to closely monitor the performance of all the aforementioned processes to not only ensure they are running as expected but also to extract more profound conclusions that can help boost efficiency even further. 

To help you keep making sense of the value of these indicators, we’ve put together a list of the top 15 warehouse performance metrics you should track and divided them into the main sections of warehouse management. We arrenged these 15 warehouse KPIs into 4 main categories: inventory, order management, pick and pack as well as transportation. Lets get started!

I. Inventory 

Inventory metrics are measurements that help you monitor and evaluate the stock level in your warehouse. They help accurately plan production levels and make smart strategic decisions to boost revenue. Let’s look at some examples below.

1) Inventory accuracy 

The first KPI for warehouse and logistics that we will cover in this list is inventory accuracy. Considered one of the most critical metrics for inventory management, it monitors that the level of inventory that has been tracked in the system matches the one physically stored in the warehouse. 

Even though most warehouses use automated systems to track their inventory levels, a mismatch can still happen due to various factors, including theft, damaged products, miscalculations, or even shortages from the supplier. 

The consequences of a low inventory accuracy rate can turn into higher costs for the business, an increase in back orders, and a lower customer satisfaction rate, as some customers might not receive their products due to a lack of physical stock. For that reason, it is of utmost importance to keep this rate as high as possible. As seen in the image above, setting a target based on your historical performance and your accuracy efforts is a great way to approach the process.

2) Inventory carrying costs 

As its name suggests, the inventory carrying costs is a warehouse KPI sample that tracks the costs associated with storing unsold inventory. As seen in the image above, these costs can include employee salaries, taxes, insurance, storage, and even the investment opportunities that the business might be losing due to having a lot of resources tight to inventory. 

The inventory carrying costs are considered one of the biggest challenges for efficient warehouse management as they directly affect profitability. The longer the inventory stays in storage, the higher the cost for the warehouse. Therefore, it is important to define the amount of time the business can afford to keep that inventory before thinking of strategies to get rid of it. 

Among the many strategies and technologies organizations use to keep these costs at a minimum, predictive analytics is one of the most effective ones. By analyzing historical demand, they can forecast the inventory level they will need and avoid having high levels of unsold products.

3) Inventory turnover

Next, in our warehouse metrics examples, we have the inventory turnover. This KPI tracks the number of times you sold your entire inventory over an observed period. Meaning that the higher the turnover, the higher the sales, and the lower the turnover, the lower the sales. A high inventory turnover also means the business is good at predicting demand and promoting its products. 

It is important to note that there is no unique benchmark for this KPI as it will vary from industry to industry. For example, a warehouse storing cars might have a lower turnover than one storing sneakers. Therefore, it is important to only compare yourself to other companies in the same industry. 

Just like with the previous KPI, applying forecasting technologies to predict demand and streamlining your inventory management strategies is a good way to keep this rate in check. 

4) Inventory to sales ratio 

The inventory to sales ratio is a KPI for warehouse and logistics that helps you identify overstock levels. It measures the ratio between your available inventory for sale and the amount that has actually been sold over an observed period. Warehouse managers use this indicator to identify potential cash flow issues and plan the level of stock needed to avoid dealing with unsold products and a low inventory turnover.  

A good practice is to visualize this and other inventory KPIs together in an online dashboard to get a complete picture of your inventory management strategies. You can devise a realistic target and track it regularly to track any improvements or other relevant insights. 

II. Order Management

Probably the most important section of these warehouse KPI examples is order management. It tracks all the processes that take place from the moment a customer places an order to the moment they receive it and everything that comes in between. That is why the warehouse metrics templates we will present below are mostly influenced by other factors, such as picking and packing or delivery times, to name a few. 

5) Order cycle time

The first of our warehouse KPIs templates for order management is the order cycle time. It measures the average time it takes to ship an order from the moment it was placed to the time it leaves the warehouse, without considering the shipping time. 

What makes this KPI so valuable is its high influence on customer satisfaction, as customers highly value their orders arriving on time. Any inefficiencies found during the analysis of this indicator can help boost customer engagement and loyalty by offering short delivery times with no unexpected surprises. 

As a standalone metric, the order cycle time does not provide as many insights. A further look into other indicators, such as the picking accuracy or the average dwell time, can help you get a deeper understanding of the reasons for a higher or lower order cycle time. As we discussed with other examples on this list, setting a realistic target for this KPI is a great practice. 

6) On-time shipping

As its name suggests, this straightforward KPI for warehouse management tracks the ratio of total orders that have been shipped before or on the date they were supposed to. It is calculated by dividing the total number of orders by the orders shipped on time. 

As expected, you want to keep this ratio as high as possible, as it directly influences customer satisfaction and engagement. A lower ratio means your warehouse is suffering from efficiency issues, which can lead to orders being late, among other issues. 

The average time to ship an order might vary from product to product. Therefore, it is essential to test different benchmarks and see what works best for your business. 

7) Perfect order rate 

The perfect order rate is probably one of the most important warehouse metrics that come to mind when thinking about efficient order management. As its name suggests, it tracks the percentage of orders that are shipped and fulfilled without any incidents, such as inaccuracies, damaged products, shipping delays, or even packages getting lost in transit. 

The consequences of a low perfect order rate are significant costs to the company in managing returns and damaged orders but, most importantly, damages to customer satisfaction and the overall company reputation. Something that can prevent customers from coming back to purchase again and recommend your company to their friends. 

A good way to reach your expected perfect order rate is to closely monitor each stage of the order management process and attack any inefficiencies as soon as they are detected. As seen in the image above, you can track it monthly and against a target to measure its development more realistically. 

8) Back order rate

The last of our warehouse productivity metrics related to order management is the back order rate. It measures the ratio of your total orders that can not be fulfilled at the time the customer placed the order. 

This metric can become tricky to analyze if you don’t consider its context. A high back order rate can shine a light on inefficiencies in your warehouse processes but also an unexpected rise in product demand. A decreasing back order rate can mean efficient strategies but lower sales. That is why it should always be looked at from the specific context of the organization instead of a standalone value. 

Visualizing this KPI together with the inventory accuracy or the out-of-stock ratio in a professional logistics dashboard is a great way to get the 360-degree view needed to understand if the back order rate is positive, negative, or nothing to worry about. 

III. Pick and Pack 

As its name suggests, picking and packing in a logistics warehouse refers to the process in which a worker or machine finds an item in the warehouse, picks it from storage, and packs it up to be sent to the end customer. The examples below are all about optimizing the pick and pack process to make it as efficient and cost-effective as possible. 

9) Picking accuracy 

The first warehouse management KPI of this section is picking accuracy.  It measures the percentage of orders that are picked without errors from your total number of orders. A low picking accuracy means a higher rate of return and higher costs in managing wrongly shipped items. It can also damage customer satisfaction, which can lead to a decrease in sales. 

As seen in the image above, you can track this rate monthly against a set target. Tracking it every month can help you see if your picking strategies are having any impact or if something needs to be fixed. You can also do random checks in your warehouse to see if the picking process is running smoothly. 

10) Pick and pack cycle time

The pick and pack cycle time is a logistics warehouse KPI that measures the average time in seconds it takes a warehouse employee to pick an item from the shelf to the time the item is packed and ready to be shipped. It is calculated by dividing the total amount of time by the total number of items picked and packed in a set period.

Achieving a perfect picking and packing cycle time is all about testing. In the image above, we can see that this business tracks the KPI for three different lines of work. Lines B and C are below the maximum target of 119 seconds, while line A is around 10 seconds above. That means something must be fixed in that line to ensure maximum efficiency. 

The value of tracking this metric for different lines of work is that you can test different strategies and see which one works best—giving you the flexibility to test different technologies and ideas. 

11) Pick and pack costs 

Moving on with our list of warehouse performance measures, we have the pick and pack costs. As you probably figured by its name, it tracks all costs related to the pick and pack process, including employee salaries, packaging materials, and equipment. 

Just like all other warehousing processes, you want to keep the picking and packing costs to a minimum. This can be achieved by implementing smart strategies that make the process as smooth as possible. 

As in the previous example, you can measure this KPI for different lines of work to get a detailed picture of all costs. You can also track it for different products or product categories to understand where your costs are going up and what areas need improvement. 

12) Use of packaging material 

The use of packaging material tracks the amount of materials being used to pack orders in each line of work of your warehouse. It is an important metric to track not only because it can help reduce pick and pack costs but mostly because of the environmental impact these materials can have.

For a few years now, businesses have started to reduce the sizes of their packages and invested in more environmentally friendly materials to ensure their environmental impact is as low as possible. This has also become a priority for customers who often call out brands for unnecessarily big packaging. 

A good practice to keep the use of materials in check is to set mandatory package dimensions in relation to the size of the product being packed. That way, you’ll avoid employees packing small items in huge boxes. 

IV. Transportation 

The last section of warehouse performance indicators that we will cover in this post is transportation. This is the last step in the order management process, and it involves metrics related to the efficiency of the delivery stage. These are very important indicators to track, as inefficiencies can affect shipping times and customer satisfaction. 

13) Dwell time

Also known as “detention time”, the dwell time is a warehouse KPI that tracks the average time in hours drivers spend in the warehouse waiting for the orders to be loaded or unloaded from the trailer. 

It is a great indicator to track as all the processes that come before need to be aligned to ensure the orders that have to be delivered make it into the trailer as fast as possible. There are various reasons why this indicator can go to the higher side, including vehicle delays, loading complex or heavy orders, tedious check-in processes, and order volume, among others.

That being said, while there are techniques and strategies you can apply to prevent dwell time, having some level of it is unavoidable, and it should be considered in your shipping times. 

14) Transportation costs

Another cost-related KPI, the transportation costs, breaks down all the costs associated with processing an order, including administrative costs and carrying costs of inventory. Tracking this indicator closely is valuable as it can let you analyze the costs of each stage of the process and see what could be optimized to lower costs without sacrificing delivery efficiency. 

A good practice is to also calculate this metric for specific products. That way, you can see how much transporting an item costs you compared to the revenue it brings. 

15) Trailer utilization rate 

Last but not least, in our warehouse metrics examples, we have the trailer utilization rate. It measures the percentage of space that is being utilized in your trailers every month. Monitoring this KPI regularly can help you maximize your trailer space to the fullest while decreasing costs associated with extra fuel and unnecessary wear and tear of the vehicles. Plus, by carrying out a detailed analysis of your trailer utilization rate, you can realize that you might not need as many trailers as you have, which can also decrease costs considerably. 

That being said, ensuring all orders are ready to be loaded into the trailer and shipped to the customers is not always easy. Therefore, you need to test different strategies and evaluate their effectiveness. 

Visualize All Your KPIs Together In A Professional Warehouse Dashboard

As you learned through the list of warehouse KPI examples that we presented above, these measurements are highly valuable to provide businesses with the needed knowledge to optimize their strategies and ensure efficient warehouse operations. 

That being said, most of them need to be analyzed together to get the best insights out of them, as all processes in the warehouse are tight to each other. Analyzing all your KPIs together will help you tell a story and extract the true potential of your warehouse data. And that is done through the use of professional dashboard software. 

Dashboards are interactive and visually appealing tools that provide a centralized view of a business’s most important key performance indicators. The value of a warehouse dashboard lies in its ability to provide a 360-degree view of historical and current data to make accurate decisions. Below, we present you a template that covers a couple of the KPIs that we described earlier in the post. 

**click to enlarge**

Key Takeaways From Warehouse Management KPIs 

The world of data and analytics is here to stay. Using your own business data to inform your strategies and boost growth can turn into a huge competitive advantage, and KPIs are the secret weapon to achieve it. 

Using the right mix of warehousing metrics and KPIs can make a difference in your daily operations and resource management. These measurements help you track every detail of your performance and put you in a position to find improvement opportunities and discover trends and patterns that will help you take your strategies to the next level. 

Using a professional dashboard creator to assemble everything and tell a compelling story will empower every relevant stakeholder to integrate data into their daily operations. Boosting collaboration, communication, and overall efficiency.

To help you keep your mind fresh, here is a summary of the top warehouse KPIs examples: 

1. Inventory accuracy 
2. Carrying costs of inventory 
3. Inventory turnover
4. Inventory to sales ratio 
5. Order cycle time
6. On-time shipping 
7. Perfect order rate 
8. Rate of return 
9. Pick and pack cycle time 
10. Picking accuracy 
11. Pick and pack costs 
12. Use of packaging material 
13. Dwell time 
14. Transportation costs 
15. Trailer utilization rate 

If you are ready to start generating your own warehouse metrics, then try our professional KPI dashboard software for a 14-day free trial and benefit from advanced data analytics! 

The post Top 15 Warehouse KPIs & Metrics For Efficient Management  appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.

Performance tracking has never been easier. With the rise of modern self-service BI tools, everyone can monitor relevant performance indicators in a matter of seconds. But this is not without problems. Having the ability to analyze your data fast and efficiently doesn’t always mean you are doing it correctly. Businesses extract data from several internal and external sources, which makes it difficult but necessary to filter this data and only keep what’s relevant for the company. This is done with the help of KPIs and metrics. 

KPIs and metrics are often considered the same thing in day-to-day business contexts. However, while they work similarly, they are not used for the same purposes. Just memorize this statement for later: all KPIs are metrics but not all metrics are KPIs. That being said, this post will cover the main difference between metrics and KPIs and some examples and tips for efficient performance tracking. 

Let’s kick it off with the answer to the popular question, are metrics and KPIs the same?

What Are KPIs?

Essentially, Key Performance Indicators or KPIs measure performance or progress based on specific business goals and objectives. A pivotal element to consider is the word “key”, meaning they only track what is truly relevant to the company’s strategic decisions. 

A good KPI should help you and your team understand if you are making the right decisions. They act as a map of business outcomes and are the strategic indicators that will move the company forward. Examples of KPIs can be sales growth, customer retention, or customer lifetime value. Companies usually visualize these measurements together with the help of interactive KPI reports. 

What Are Metrics?

Metrics are quantitative measurements used to track the performance of specific business processes at an operational and tactical level. They help provide context to the performance of key business goals but are not critical to its success like KPIs are.

While some of them might be tight to objectives, metrics are not the most important indicators for monitoring strategic actions. However, they are still relevant to informing businesses about the progress of their different activities. Some examples of metrics include the lead-to-conversion ratio, Return Rate, and Acquisition Costs by Marketing Channel.

Now that we have a basic understanding of the definition of both indicators, let’s dive deeper into the difference between KPIs and metrics.

KPIs vs Metrics: What Is The Difference?

KPIs and metrics are often considered synonyms. But this is not how it actually works. While they are both quantitative measurements, they are used for different purposes. Simply put, KPIs need to be exclusively linked to targets or goals to exist, and metrics just measure the performance of specific business actions or processes. Let’s see some of the KPI and metrics differences in more detail. 

• Communication: The first difference between KPI and metrics is what they communicate. As mentioned above, KPIs are strategic indicators exclusively used to communicate the progress of your business goals. On the other hand, metrics are used to track specific areas or processes that might be working towards that goal. For example, let’s say you want to sell 20% more in the next year; your main KPI would be the number of products or subscriptions sold to date. Now, to monitor the progress of that goal in detail, you would need to track various metrics such as the number of website visitors, best-performing sales channels, the performance of your sales agents, and any other that helps you understand which actions are contributing to achieving your goals and what could be improved. In summary, a KPI can be seen as a collection of metrics that impact your journey to achieving your goals. 
• Objective: Another important difference between metric and KPI is the objective. A good KPI is always tight to an outcome. You expect it to go up or down to reach its target. Metrics, on the other hand, measure the impact of the day-to-day performance of different business areas, and, as seen with the sales example, only some of them help you track the success of your strategic actions. The important takeaway here is that metrics and KPIs are not mutually exclusive – that’s why they are often taken as the same thing. A KPI will need a collection of metrics to track its success; you just need to make sure you are using the right metrics to track it. Remember: while all KPIs are metrics, not all metrics are KPIs.
• Focus: Another key difference between metrics and KPIs is their level of focus. KPIs have a high-level perspective. They represent key business goals that are relevant for various departments. On the other side, metrics are considered lower-level indicators, and they track activities or processes that are specific to a department or business area. Following the example of increasing sales by 20%, it is likely that each department will play a role in achieving that goal. For instance, the marketing department might need to focus on boosting promotions, the sales team might need to focus on developing strategies to efficiently turn leads into paying customers, the logistics team can focus on improving the shipping experience, and the product team can focus on finding strengths and weaknesses in production. Consequently, each department must track different metrics that work towards that general business goal. 

KPIs vs Metrics Examples

Let’s put these differences into perspective with some metrics vs KPI examples created with a modern KPI tool: 

1) Sales growth metric and KPI

Let’s start by going a bit more into detail with our example of increasing sales by 20% by the end of the year. A big goal like the one of sales growth is relevant for various departments across a business, such as management, sales, marketing, and production. Each of these departments will track its own metrics to understand how its activities contribute to the general goal. Here we will focus on a sales metric vs KPI example.

• KPI: Sales Growth 

The image above is a visual representation of our main KPI: sales growth. With information such as the current period vs. the previous one, a percentage of sales based on a target, as well as sales revenue by a sales representative, we can see at a glance if targets are being met or not. But, to fine-tune the strategies, we also need to know how the different activities are performing, which can be done with the help of various sales metrics. 

• Metric: Lead to conversion ratio 

A great sales metric to measure for this specific goal would be the lead-to-conversion ratio. It measures the number of interested people who actually end up turning into paying customers. Which eventually translates into an increase in sales. This metric, generated with professional sales reporting software, is useful as it provides deeper insights to make strategic decisions. If your lead conversion rate is low, then you need to think of alternatives to motivate potential customers to become actual customers. Some other metrics to measure for this goal could include the lead-to-opportunity ratio and net profit margin, among others.

2) Customer experience KPI vs. metrics

Studies say that a 5% increase in customer retention can lead to a 25% increase in profit. Now imagine that with this information in mind, you want to set a goal of increasing your retention rates 10% by the end of the year. Now, this goal can also be relevant to different departments. For instance, the marketing team would need to generate attractive campaigns to get customers to buy again, while the product team might need to focus on developing quality products.

• KPI: customer retention 

Customer retention directly affects your revenue. When a customer is happy with your service or product, he or she will likely come back to make another purchase. Since your goal is to increase your retention rates by 10%, the image above would be your main KPI. A good way to measure your success is by setting a target percentage based on market benchmarks and realistic business numbers. Now let’s look at some product-level metrics that are useful for this specific goal.

• Metric: Return Rate & Return Reasons

The rate of return is a great metric to track to understand customer retention. If your clients are returning what they bought, it is likely that they will not come back to make another purchase. In order to extract deeper conclusions from the rate of return, the product team can track the return reasons metric. As seen in the image above, this metric lists the main reasons customers return their products. Here we see that 28% corresponds to defective items. Lowering this by 28% can have a direct impact on increasing the retention rates. Therefore, a focus area would be to improve product quality. Other valuable customer retention metrics for production can include the repeat purchase ratio or the perfect order rate.

3) Logistics KPIs vs metrics 

In logistics and warehouse management, ensuring the entire supply chain is efficient is of utmost importance to achieve success. One of the most popular KPIs to measure success is the order cycle time, which measures the time it takes a company to ship an order from the moment it was placed up to when it leaves the warehouse, without considering shipping time. Naturally, businesses want to keep this KPI as low as possible as it means all areas of the supply chain, including inventory management, picking and packing, and transportation, are working as expected. Let’s explore this in more detail below. 

• KPI: order cycle time 

The order cycle time is an important KPI as it can shine a light on other issues in your supply chain. It is used to evaluate the efficiency of fulfillment processes, and it can significantly influence customer satisfaction. Unlike other KPIs that we mentioned above, the order cycle time needs to be tracked in shorter periods of time, such as weekly or daily, as it can be affected by multiple unexpected factors like an influencer sharing your product and generating an unexpected increase in demand. That being said, without considering unexpected events, this KPI can still be optimized by measuring different metrics. Below, we will discuss an example of one. 

• Metric: dwell time

As mentioned above, there are many processes and people involved in achieving a successful order cycle time. Therefore, there are several metrics, including picking accuracy, shipping time, equipment utilization, inventory accuracy, and many more, that you can measure to evaluate and optimize your order cycle time in a professional online dashboard. Today, we will focus on dwell time. This metric measures the average hours drivers spend in the warehouse waiting for orders to be loaded or unloaded from the trailer. Some of the most common reasons for an increase in dwell time include vehicle delays, loading complex or heavy orders, tedious check-in processes, and order volume, among others. It is important to mention that having some level of dwell time is really unavoidable and should be considered in your order cycle calculations. However, some of the reasons we just mentioned are easily preventable. Therefore, you should keep a close eye on the metric to ensure you can spot solvable inefficiencies as soon as possible. 

4) Customer service KPI vs metrics

Customer service can make a difference regarding how loyal a customer is to your business. If their issues are solved quickly and efficiently, then the customer will likely return to buy again and even recommend your product or service to their friends. On the contrary, a customer who has a bad support experience can be disappointed and never come back to buy again, even if you are offering the best products. With that in mind, a popular KPI for the support department is customer satisfaction. In the example above, we can see a business that has 71% of positive feedback. They want to increase it to 80% by the end of the quarter. Let’s see how they would do it. 

• KPI: Customer service satisfaction rate

As mentioned above, measuring customer satisfaction in your service department is of utmost importance as it can influence your entire customer-business relationship. Usually, this KPI is measured through a survey with the answers divided into different categories from the most negative to positive reviews. It is a great practice to measure this KPI against a target to evaluate the development in a more detailed way. Plus, setting a target can help you be more realistic about what you can actually achieve and avoid setting unattainable goals. 

• Metric: Average resolution time 

There are many metrics you could track to evaluate your efforts toward increasing customer satisfaction, and the average resolution time is one of the best and most important ones. After all, the least you expect from a business when you contact them with an issue is to help you solve it quickly. In the example above, we see the average resolution time in minutes divided by standard and special requests. Each type of request is complemented with a trend line to help you identify pick times and use them as improvement opportunities. For instance, we can see that during weeks 4 and 8, special requests increased their resolution time. This is an important insight to extract as you can prepare your agents to answer these requests more quickly and increase satisfaction rates.

If you want to see more KPI examples like these, check out our library with examples from different industries, functions, and platforms. 

Tips & Best Practices For Measuring KPIs And Metrics In The Right Way

We’ve covered the definition of key performance indicators and metrics and went into the differences of business metrics vs KPIs. In this section of the post, we will go through 5 tips that will help you efficiently measure your goals and performance.

1. Separate metrics from KPIs

Measuring everything really means you are measuring nothing. When it comes to separating KPIs from metrics, you need to consider what is most important for your business. Any type of indicator can be a metric, but if this indicator does not provide any valuable information to make you improve, then you should discard it.

Tracking the wrong metrics can lead to a waste of time and resources that could be easily avoided. Measuring too much can get confusing and misleading. To avoid this, make sure you pick only the KPIs that really bring value to your goals and leave any unuseful information behind. More on this in the next point. 

2. Choose the right KPIs 

Choosing the right KPIs to measure is probably the most important step to track your strategies efficiently. To help with this purpose, there are some KPI tracking techniques that you can use. Here we will explain two of them: the SMARTER and the Six A’s methods. 

• SMARTER: This KPI tracking practice stands for Specific, Measurable, Attainable, Relevant, Time-bound, Evaluate, and Reevaluate. It works as a list of requirements that your KPIs have to meet in order to be considered useful. As mentioned throughout this post, they should be specific to your goals, realistic to your business reality, and flexible to change with the evolution of strategies.
• Six A’s: This method stands for Aligned, Attainable, Acute, Accurate, Actionable, Alive. Just like the SMARTER criteria, this practice also aims to evaluate the relevance of a KPI, and it is useful for businesses that have too many indicators and need to narrow it down to a few. 

By applying these methods, you should be able to narrow it down to around 2-5 critical KPIs per business goal. This helps you keep your analysis process specific and avoid misleading information that can affect the way you interpret your data.

An important thing to keep in mind here is that you should always revisit your KPIs. If you found a better approach to achieve your goals, then you should make sure you are tracking the right data. You can do this by monitoring your KPIs regularly with weekly or monthly reports. Once your KPIs have been defined, you have all the information you need to start making strategic decisions and thinking about long-term actions. 

3. Make your KPIs and metrics visually driven 

Once you’ve selected your KPIs and metrics, it is time to transform them from plain values and numbers into actionable insights. This is done through the use of a range of data visualizations that will help you tell a story with your indicators and collaborate through them. Plus, it is a well-known fact that the human brain processes visual information way faster than numbers and that they are more accessible and easier to understand for a wider audience. Therefore, picking your graphs and charts carefully can make a difference in your analysis process. 

That being said, it is not as easy as picking a KPI and representing it with a pie chart. Each type of graph and chart has its own purpose and use cases, and you should be careful when picking them. We recommend you carefully think about your goals and what you are trying to communicate and then choose the visual that best suits your needs. This is an important point, as picking the wrong visual can end up misleading your analysis and damaging your strategies. 

4. Get a centralized view with an interactive dashboard

KPI and metrics are valuable tools for businesses. While key performance indicators tend to be more important, metrics are also useful to get a bigger picture of the performance of a department or specific area. Today, there are several online data visualization tools that offer a range of dashboard options to visualize your KPIs and metrics in a centralized way. Let’s look at it with an example of digital marketing.

**click to enlarge** 

The example above was created with a professional dashboard generator, and it is the perfect mix of the metrics and key performance indicators needed to track the ROI of your marketing actions. Getting a centralized view like this one helps marketers get a complete picture of their marketing efforts to make smart strategic decisions. 

If you want to see more dashboard examples like this one, then we recommend you take a look at our library with 80+ templates from different industries, functions, and platforms to get inspired! 

5. Rely on interactivity 

Interactive data analysis has become one of the biggest competitive advantages in the analytical world today. Think about your analytical process as a movie. Your KPIs are the main characters that help you achieve your goals, and your metrics are the side characters that will help you measure the performance of your strategies towards achieving those goals. Your dashboards are the scenery where everything comes together, and you can tell your data story. And interactivity will help you bring everything to life in a compelling way. 

Modern KPI reporting tools provide multiple interactivity features to help you navigate and explore your data more thoroughly. For example, a drill down feature enables you to go into lower levels of hierarchical data all in one chart. Let’s say your goal is to increase sales in the US. For that, you are visualizing a chart with sales by country. A drill down would enable you to click on the USA value and adapt the entire chart to see sales by state. Likewise, an even deeper drill down would enable you to see sales by city of each state. Other interactivity options allow you to change the time period, translate the text in your charts, and much more. 

By making your KPIs and metrics interactive, you’ll ensure that you can extract the maximum potential out of them. A static view of data no longer makes the cut in today’s fast-paced world, where decisions must be made in an accurate and agile environment. 

6. Stay away from vanity metrics  

Vanity metrics refer to the indicators that may look good on paper but are not useful to inform future business strategies. In some cases, vanity metrics are used to show improvement, but they are actually indicators that are not actionable or related to anything you can consider really significant. A great example of a vanity metric would be with social media followers. Imagine you implemented a campaign that attracted 10.000 new followers to your Instagram. Now, that might seem like a success at first hand, but if from those 10,000 followers, only 50 bought your products or service, then the metric becomes useless.  

To avoid facing the issue of vanity metrics, you need to keep your analysis as objective as possible. When choosing the KPIs and metrics you will monitor, always ensure they reflect the truth. While metrics such as the number of followers or likes might seem exciting, they can also point you in the wrong direction. BI tools offer various KPI and dashboard templates that can point you in the right direction to avoid making this mistake.

7. Set realistic targets 

The last tip for measuring metrics and key performance indicators correctly is setting achievable targets. For your KPIs and metrics to be efficiently measured, you need to know where you are headed, and targets make this possible. Here you need to be careful not to set unrealistic targets such as a 50% increase in sales in a year when your average increase from the past years has been 5%. When building targets, consider attainable values based on your business context as well as some industry benchmarks. This way, you will ensure you are working towards achievable goals and avoid getting stacked or disappointed by setting unrealistic values. 

8. Define a monitoring schedule 

Another great practice that will help you measure your metrics and KPIs successfully is to define a monitoring schedule. This will help you ensure you can stay on top of any insights while still having time to plan and carry out your strategies. Given that metrics often track activities that are more operational, they can be monitored on a short-term basis and even in real-time. KPIs, on the other hand, often track strategic goals that are more meaningful when tracked for a longer period of time, such as a month, a quarter, or even a year. 

Professional online BI tools, such as datapine, provide you with intelligent data alerts that will notify you as soon as your KPIs and metrics need your attention. All you have to do is predefine a goal or a threshold value, and the tool will notify you as soon as they are achieved. Leaving you more time to focus on other important tasks rather than constantly monitoring your data. 

9. Reevaluate your process  

As you’ve learned by now, choosing KPIs and metrics is not a task that can be taken lightly. You need to line up a well-thought-out plan to ensure you are tracking the data that will help you measure the success of your strategies and goals but also find improvement opportunities to constantly grow. And, just like many other business-related processes, it requires reassessments to be successful. Our advice is to always take the time to rethink your strategy. Are these metrics still valuable for measuring our efforts? Should we add a couple more? Are they still aligned with our goals? 

In doing so, you’ll ensure your resources are used well and that your efforts pay off with successful strategies and continuous organizational growth. 

Key Takeaways From KPIs vs. Metrics 

As we reach the end of this post about key performance indicators vs. metrics, we hope you have a deeper understanding of how these two differentiate themselves. The important takeaway from this post is to remember that there would be no KPIs without metrics; both are critical to ensure a healthy return on investment from your different business activities.

KPIs and metrics are invaluable tools for performance tracking. Every day more and more businesses turn to BI dashboard software to get a centralized view of their most important indicators interactively and intuitively. Getting access to modern dashboard technology allows teams to stay connected and work together towards common business goals.

To keep your mind fresh, here is a small summary of the main differences between metrics and KPIs: 

• KPIs measure performance based on key business goals, while metrics measure performance or progress for specific business activities. 
• KPIs are strategic, while metrics are often operational or tactical.
• Metrics are lower-level indicators specific to a department, while KPIs can be tracked by various departments working towards the same goal. 
• Metrics provide context to your business activities, and KPIs allow for strategic decision-making.  

If you are ready to start generating your own KPIs and metrics, then test our professional KPI tracking software for 14 days free! 

The post KPIs vs Metrics: Understanding The Differences With Tips & Examples appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.

In our cutthroat digital economy, colossal rafts of data are gathered, stored, analyzed, and optimized every minute of every day.  

What do these insights do, exactly? They deliver the best possible experience to customers and partners at every stage of their journey, across every channel or touchpoint. An integral part of business intelligence (BI), inventory metrics are used to help managers and professionals reach (or even surpass) their core goals, optimizing processes and increasing business value in the process.

That said, It’s extremely important to set up and track inventory KPIs for your business in order to evaluate and improve your overall performance. Collecting big amounts of data is not the only thing to consider here: knowing how to process, analyze, and visualize your business’s most essential insights is key. To make informed decisions that will have a positive impact on your business’s bottom line, you need to have the full scope of your data. In this matter, data analysis and dashboard designer software is a precious ally.

In this article, we will take a closer look at inventory management, ask the question, “What are the performance indicators that can help?”, look at how to choose the right inventory metrics, and outline a mix of real-world inventory metrics examples. We will also work through some essential inventory KPI best practices. 

Toward the end of our data-driven journey, we will explore a genuine business dashboard to show you how those indicators work together when developing an inventory data story.

Let’s get started.

What Are Inventory Metrics?

Inventory metrics are indicators that help you monitor, measure, and assess your performance – and thus, give you some keys to optimize your processes as well as improve them. They focus on a specific area and goals in order to spot trends and identify weaknesses.

KPIs for inventory management can be common to different industries, and it is no surprise that you can identify one as a logistic KPI, but also see it listed as a retail KPI for instance.

By giving you clear milestones to hit every week, month, quarter, or year, they help greatly in eliminating the guesswork. With them, you get the data you need to make strategic and better-informed decisions that will positively impact your business. Indeed, they help you drive the most effective behaviors, strategies, and decisions. Among other things, they help in improving on-time deliveries, reducing operating costs, increasing customer satisfaction, and optimizing transport.

How To Choose The Right Inventory KPIs?

To choose the most reliable and efficient inventory management metrics, here are some tips that you need to take into account:

• Avoid vanity metrics: When it comes to picking up indicators with professional KPI tools, everyone can be tempted to use the easiest, most ‘reassuring’ ones that capture efficiency – but it is far more difficult to choose the ones that reflect an improvement in effectiveness. And these ones are more valuable. The same remark goes to the ‘vanity metrics’, which make a process of the department look good but that do not deliver insights on how to enhance the effectiveness of inventory management.
• Focus on answering business questions: Likewise, you should resist the urge to take KPIs that have a scope too broad: the insights they will deliver will not lead to quick and valuable action or reaction. The key to choosing the right indicators is to always keep in mind your business’s strategic objectives and to select them accordingly.
• Don’t forget your customers: What is complex and should not be overlooked in inventory management, is that operational, supply-chain-related, and customer satisfaction metrics are involved. Indeed, your customers are the ones who are going to receive your inventory: if they get the wrong item or late, or it is out of stock for too long, the inventory will look like it is totally unmanaged and will leave them frustrated (and they probably won’t come back so often).
• Monitor trends: Comparing information with your past performance or setting a KPI scorecard template that you can translate to multiple business scenarios about your inventory measures and metrics will help you spot trends or inefficiencies in your processes. If your out-of-stock rate has had issues in the past weeks or months, you can dig deeper and find out why. That will help you take the right action at the right time.
• Don’t rely on numbers only: Numbers are great but your growth should be focused on the quality, end-user, customer, or partner. If you’re centered only on monitoring numbers, without focusing on the human aspect, you risk business bottlenecks in the long run.

Once you have chosen the inventory metrics that will best fit your business and your needs, let’s go over some examples to illustrate them, as well as some best practices to observe.

Inventory Metrics Examples for Better Management

Since both cost factor and customer experience are essential in inventory management for any modern business looking to make an impact in our cutthroat digital age, we’re going to look at the top 25 inventory KPIs that cater to these two most essential areas.

1. Inventory accuracy

Simple and straightforward: you need to know what you have in stock and what is passing through your warehouse. By performing regular cycle counts, equipping yourself with electronic tags will make the work easier and provide you with the data needed to compare your physical inventory to the electronic record.

This is an inventory KPI that can make or break your business: if you do not have in stock what you sell online on your eCommerce platform, for instance, can considerably harm your business. This metric will also help spot issues related to shipping, receiving, or accounting. Try to keep this ratio over 92% as much as possible.

2. Inventory turnover

Our next KPI template is the inventory turnover looks at how many times, over a certain period, your entire inventory is sold. You can calculate it by dividing the costs of goods sold by the average inventory. It is a good indicator when it comes to efficient production planning, process, marketing, and sales management. As a general rule of thumb: the higher, the better.

If you have a low turnover, that might point out difficulties in turning your stock into actual revenue – you then need to investigate where the bottleneck(s) is(are), at any moment in your supply chain process.

3. Out-of-stock rate inventory KPI

Meeting customers’ demands is critical in successfully managing your company’s inventory, especially in the FMCG industry. Events that cause inventory to be exhausted should be avoided at all costs, although it can happen that some products are not available due to numerous reasons. In this case, the point is to monitor this rate and identify when and what is missing.

In our inventory KPI example illustrated above, you can see that the end of the working week has the highest percentage of out-of-stock rates. If you dig deeper into the data on the left, you can immediately spot the exact time of the day when the inventory experiences the highest rates. To be specific, you can see afternoons have a particular spike that will enable you to solve these issues and prepare for the future much better.

4. Customer retention & loyalty

Some studies state that even just a 5% increase in customer retention will bring between 25 to 100% in profits across a wide range of industries. That is some percentage you certainly do not want to miss, and yes customer loyalty is directly linked to your warehouse management and efficiency. Efficient picking, packing, and shipping of accurate orders on time will keep customers coming back.

Besides, a satisfied customer often refers you to friends and relatives, an act much more powerful than any witty advertisement you can make. This is something you can measure with a customer service KPI like the net promoter score or NPS, that evaluates the power of your referral.

5. Carrying cost of inventory

One of the most critical inventory KPI metrics, the carrying cost of inventory, will show you the total of all expenses that occur when storing unsold goods. These costs can include warehousing costs, insurance, employee costs as well as damages of goods, rent, utilities, etc. Usually, carrying costs amount between 20% and 30% of the company’s inventory value, and this significant percentage makes it an essential factor to account for and monitor closely.

This inventory management KPI is crucial to monitor accurately at all times since it will show you if your production should be increased or decreased to keep the balance between income and expenses. Not only that, analyzing this metric with the help of online BI tools to quickly examine where you can make changes in order to reduce costs (as the carrying costs are a large part of the total costs, as mentioned).

Another critical point to consider here is the fact that if you’re in an industry where profit margins are tight, even the smallest costs can play an extremely important role. In short, be mindful of your carrying costs.

6. Warehousing costs

This warehouse indicator evaluates the expenses involved in the management of your warehouse. Ordering, storing, and loading the goods of your inventory – all this needs money to be executed properly. There are also human costs like labor, transport, and delivery that should not be forgotten.

Measuring the warehousing costs is no piece of cake – this is why you should proceed to do it meticulously and not omit or forget any. Once it is done, it smoothens the overall management and adds value, which is often appreciated by senior management and investors.

7. Average time to sell

Inventory KPI examples couldn’t be complete without the average time to sell. Especially in the FMCG industry, where laws related to food safety and management need to be carefully respected in order to avoid potential legal bottlenecks. Therefore, tracking this internationally renowned as well as a US inventory metric is critical for any industry that needs to respect international and local legal frameworks.

In our example above, we can immediately spot that tobacco has the highest average time to sell, followed by personal care, household care, and, finally, food and beverages. This makes sense since food and beverages have shorter freshness longevity and they need to be replaced more often than appliances, for example. This is one of the KPIs for inventory control that will provide you with useful information about your storing processes and give you more information on how to develop your procurement strategies. Additionally, you can connect this metric with a procurement KPI such as the cost reduction to see how you can allocate your savings, and how the average time to sell affects it. In an ideal scenario, the time to sell should be as low as possible.

8. Inventory to sales ratio

The next example in our inventory KPI list is also oriented towards finances. It is one metric helpful to evaluate the overstock, that will also tell you whether your company is able to face unexpected situations. It is measured by dividing the available inventory for sale by the quantity that is actually sold.

Combined with the inventory turnover or the carrying costs of inventory, it will give you a better picture of the financial stability of your business, and also help to define the direction you want to take (like selling your entire inventory as quickly as possible or not).

To analyze the financial perspective of your collected information in more detail, we suggest you take a look at our page focused on business intelligence in finance.

9. On-shelf availability

Any serious company that wants to increase revenue and profits, needs to evaluate demand and act accordingly, meaning keep products on shelves, visibly accessible, and easy to reach. It typically connects to other inventory performance metrics such as the out-of-stock rate and lost sales, for example, and covers 3 different critical points: shelf availability, store, and warehouse availability.

By utilizing advanced analytics solutions to measure such operational metrics, much of the manual work can be minimized. For example, modern software can track data in real time and provide advanced charts that will immediately trigger actionable insights since the user doesn’t have to manually scroll or search for information in numerous papers or spreadsheets. But let’s get back to our visual example.

We can see the analysis expands throughout the weeks and consider previously mentioned products: tobacco, personal care, household care, food, and beverages. That way, you can see the development of each product and examine if there were certain spikes that would cause a loss of customers. The increase in on-shelf availability can directly impact sales, and do it seriously. If you start losing large amounts of sales because of this metric, your whole business can suffer so it’s of utmost importance to put the on-shelf availability on the list of key inventory productivity metrics to measure regularly.

10. Dock to stock 

This inventory management KPI tracks how long it takes for your inventory to be safely stored on your warehouse shelves and ready to be shipped to the customer. It is calculated from the moment the inventory leaves the supplier warehouse, to the unloading and inspection time, until it is ready to be picked, packed, and shipped. 

This indicator can be considered harder to track as most of the steps in the process are not in total control of the company but of the supplier. That being said, poor performance can indicate issues in inventory management that can lead to orders being late and customers being unsatisfied. 

According to experts, the benchmark times for dock to stock will vary depending on the size of the company and the way it manages its stock. For example, big enterprises have a dock to stock of 10 hours, mid-sized or small organizations have one of 12 to 24 hours, and businesses using third-party providers can have up to 48 hours as these providers work with multiple companies.  

11. Dead stock  

As its name suggests, the dead stock tracks the inventory that has reached the end of its lifecycle. Meaning, it has almost no chance of being sold in the near future. This often happens with seasonal products, such as swimwear, Halloween, or Christmas items, just to name a few. 

It is a great indicator to track as dead stock items occupy a lot of space in your warehouse that could be utilized for other top-selling products. Not to mention that keeping them in storage can cost you a lot of money, up to 30% more than the product’s value. Therefore, tracking it closely and getting rid of unnecessary items can make a difference when it comes to your inventory management efforts. 

In most cases, organizations rely on different strategies to keep their dead stock in check. Some of these strategies include returning or exchanging the products with suppliers, selling the items with a discount, donating them, or even destroying them in some cases. Keeping a good SKU categorization is also a great way to mitigate the dead stock levels. 

12. Stockouts 

In short words, stockouts measure the times when a requested item is not available due to a lack of stock. A stockout can happen for a number of reasons including supplier delays, inadequate demand forecasting, supply chain delays, shortage of working capital, or even poor cash flow management, among others. 

The consequences of a stockout can include losses in sales but most importantly in customer satisfaction and engagement. If a customer is looking for a product and finds it out of stock, they will most likely go shop somewhere else, and the chances of them coming back to your company are lower to none. 

A great and innovative approach to avoid stockouts is to invest in an inventory management system that offers real-time tracking to ensure inventory levels are accurately measured at all times. Through this, you can predict stockouts by monitoring inventory levels and analyzing historical data to predict product demand and plan your stock accordingly. 

13. Order status inventory metric

Keeping an eye on your orders’ status in real-time will enable your team to act promptly to any potential negative occurrence. This is a KPI for inventory management that connects your business with your customers. To effectively evaluate your supply chain metrics strategies, you need real-time access to the status of your orders: if the products are shipped, received, in the packaging process, or canceled. That way, you will be able to decrease the restocking and strive to achieve a higher number of new orders with the goal of keeping your business going. If you spot inefficiencies in your supply chain, it will affect your inventory, and, consequently, the overall business.

For example, if you have a higher percentage of orders that are canceled, it would make sense to examine why. Additional adjustments for retail inventory metrics should focus on the reasons behind slow processing, lower amounts of new orders, or higher inaccuracies. Compare this critical KPI inventory with your revenue as it should increase, too.

14. Order cycle time 

The order cycle time is one of the most valuable inventory management metrics to track as it can shine a light on the efficiency of your entire supply chain. It essentially measures the time from when a customer places an order to the time they receive the order. 

While it would be great to measure the order cycle time for each individual order, businesses often define an average order cycle time to keep the measurement more realistic. They do it with the following formula: Average Order Cycle Time = (Delivery Date – Order Date) / Total Orders Shipped. 

As seen in the image above, it is a great practice to track your average order cycle time by day of the week. That way, you’ll be able to dig deeper into the data and find improvement opportunities or conclusions about the values you are tracking. For example, we can see that Monday had a higher order cycle time than expected. To figure out the reasons, you need to analyze how your supply chain performed that day. Maybe you had fewer employees in your picking and packing area or the number of orders increased due to an unexpected event such as an influencer sharing your product on their socials. 

15. Percentage of sold products within freshness date

One of the inventory management KPIs built to reduce waste and enhance operational efficiency, FMCG offers a visual representation of the proportions of your inventory that has moved from the shelves or store and been fulfilled within its official freshness date.

By offering a comprehensive breakdown of how many items have sold within a certain time frame in relation to their expiry date, you will gain the insight required to make strategic tweaks to strike the perfect balance between stock efficiency, waste reduction, and shelf space. With the FMCG inventory metric, you can pinpoint the products or items that might need better quantity management or merchandising while developing promotional initiatives to turn over your inventory within a suitable time frame. A sure-fire way to boost your bottom line.

16. Sell-through rate

An essential part of a retail KPI dashboard, this inventory metric is particularly powerful for driving optimum retail performance. With your sell-through rate, you can visualize the correlation between your inventory received and sold over a specific period.

By gaining access to this invaluable information, you can use this most valuable inventory KPI metrics to monitor your sell-through performance and, ultimately, make targeted improvements to key aspects of your sales and merchandising processes. In turn, you will keep your inventory movement flowing consistently, maintaining productivity as well as income growth. 

This is one of the most vital inventory metrics for retail at your disposal. Track this metric frequently, and you will build solid, fluent foundations for your inventory management strategy while scaling the business with confidence.

17. Average inventory level

Your average inventory level is exactly what you might expect: a clear-cut visualization outlining your mean inventory level over certain time periods.

One of the most useful inventory health metrics for any modern business looking to streamline their operations, average inventory level will give you an accurate outline of how much inventory your business holds throughout the year.

By removing seasonal fluctuations and other variables from the equation, you can use this metric to hone in on any inventory losses that might have happened due to the likes of damage, theft, shrinkage, or waste. If you can pinpoint these problems, you can tackle them head-on, preventing any further losses or inefficiencies in your supply or fulfillment chain.

18. Backorder rate

The backorder rate is an effective way of monitoring how many goods or items can’t be fulfilled at the time a customer confirms or places an order.

By using these most insightful inventory accuracy metrics to your advantage, you can set a definitive fulfillment target and track your progress on a weekly, monthly, or annual basis. In doing so, you will gain the power to pinpoint the exact reasons for an increase or reduction in backorder rate during certain periods and make strategic tweaks to improve or update your supply chain processes for success.

A large backorder rate sometimes means more sales (which is, of course, positive). Understanding this will give you the tools you need to ensure you can fulfill all of those extra orders effectively.

19. Rate of return 

The rate of return is an inventory KPI that measures the percentage of sold products that end up being returned in an observed period. There are many reasons why this rate can go up. Some of them include inaccurate sizing guides, misrepresented colors, or defective items, just to name a few. 

Naturally, you want to keep your return rate as low as possible as it directly affects customer satisfaction and loyalty but also increases the costs of your supply chain by having to deal with storing inventory that was already considered sold.  

As seen in the image above, generated with professional visual analytics software, you can track your return rate by division or also track it for first-order customers. If a customer purchases from your company for the first time and has to return the item, the possibility of them returning to buy again is smaller. Therefore, tracking your return rate together with return reasons can help you understand if there is something you can improve to keep the rate at a minimum. 

20. Week on-hand

A most essential of our inventory metrics for manufacturing, as well as many other inventory-heavy sectors, weeks on-hand, is a metric that quantifies investment in terms of time rather than items or units of currency.

Weeks on-hand provides a definitive outline of the average amount of time your inventory actually sells in a given week. The reason this specific metric is so effective is its ability to weed out any roadblocks slowing down the time it takes for an item to enter your system, sell, and head to its end destination.

If you have slow weeks on hand, it will become clear that your stock or inventory movement is slow and needs attention. By pinpointing these issues quickly, you and your team can work to drive down your weeks on-hand consistently, get your stock moving, and boost your bottom line year after year.

21. Gross margin return on investment (GMROI)

Gross margin return investment (GMROI) is one of those inventory management metrics that shine a light on income versus investment.

Your GMROI will tell you exactly how much money your business has made compared to how much you’ve spent on your stock or inventory over a particular period.

Armed with this information, you can take measures to improve your GMROI by investing in the inventory that offers a consistently solid return while reducing expenditure on the items that are consistently bogging your business down or draining your budget. It’s an excellent efficiency-driving tool for eCommerce businesses, retailers, and even manufacturers.

22. Time to receive

An essential component of any inventory management dashboard, time to receive is a KPI that drills down into stock fluency and movement.

Tracking this metric visually will empower you to understand the rate at which your team or staff bring in stock and add it to the shelves, coupled with the speed or efficiency at which they prepare said stock for sale.

Benchmarking this metric consistently is useful, as you will be able to spot trends as they emerge. If you notice your time to receive rates slowing down, you will know that there is an inefficiency lurking within your warehouse.

Whether the snag is a poor process or a staff training or development issue, you can use your time to receive KPI to uncover it swiftly, tackling the issue before it has a negative impact on your fulfillment strategy.

23. Inventory shrinkage 

This metric tracks the difference between your recorded inventory levels and the volume you actually have stored in your warehouse. These differences can happen due to products getting damaged during shipping, vendor fraud, human error during counting, or even theft. 

At first, inventory shrinkage might not sound like a critical issue. However, if the shrinkage levels are too high or it happens repeatedly it can shine a light on poor inventory management which leads to bigger consequences, such as loss in profits. It can also indirectly affect employee satisfaction as some products might appear available due to inaccurate stock values. It can also happen that products become more expensive due to the businesses having to incur higher costs to avoid shrinkage. 

Some common techniques to keep shrinkage at a minimum are to conduct regular inventory audits, automate counting, install surveillance cameras, constantly review your vendors, and implement theft prevention training for employees, among others. 

24. Days of inventory (DSI) 

The DSI is a popular metric that tracks the average number of days it takes an organization to sell its current inventory. In general, a lower DSI is preferred as it means that the company can turn inventory into sales faster. However, despite expecters saying that an acceptable DSI is between 30 and 60 days, there is no definitive value to use as a benchmark as it will vary from industry to industry. 

This indicator is often confused with the inventory turnover KPI which we discussed earlier in the post. While the two measure very similar things, they differ in the fact that the DSI measures the average number of days it takes a company to turn its inventory into sales, while the turnover ratio shows the number of times the total inventory is sold and needs to be replaced in a specified period, often a quarter or a year.  

25. Labor cost per item 

Also known as unit labor cost, is a KPI that measures the amount of money a company spends in producing a single item from a labor perspective. It is a great indicator to track as labor expenses account for a big portion of your carrying costs of inventory. Plus, tracking labor costs as a single indicator can also help you find ways to optimize expenses in a more detailed way.

To correctly measure this KPI, you should first and foremost define a tracking period which can be weekly, monthly, quarterly, or annually. Once that is defined, you just need to take the total labor costs for the chosen period and divide it by the number of units produced and handled.  

There are benefits that come from tracking this indicator both from a longer and short-term perspective. On one hand, tracking this indicator in a yearly period can help you spot trends and patterns to boost employee productivity. On the other hand, tracking it for a shorter period can help you test different strategies and see how they develop in a decent amount of time. For that reason, tracking it for both long and short-term periods is often the best idea. 

Inventory Metrics Examples on Dynamic Dashboards

Now that we have gone over some metrics, let’s have a look at how it looks on real-world examples and dashboards.

a) Inventory analytics dashboard for supply chain

**click to enlarge**

Here is a good illustration of inventory management performed with the help of business intelligence and data visualization: the performance metrics that matter to the supply chain manager are tracked and displayed on a professional dashboard. In this case, we can see inventory measurement metrics such as the inventory to sales, turnover, carrying costs, accuracy, and the percentage of stock items on a dynamic, online data visualization overview. This visual will enable you to create an effective data story that will translate into positive business results since you will save time in the analysis process and increase productivity.

To find more examples and templates, take a look at our other logistics dashboards.

b) Inventory analytics dashboard for retail

**click to enlarge**

Retail is another industry that heavily relies on inventory availability metrics and optimal management in order to increase revenue and profits. Especially in online retailing, where consumers can access massive amounts of products with a few clicks. That’s why having an online dashboard tool that covers multiple touchpoints and enables retailers to have a clear overview of their inventory management is crucial in our consumer-faced world.

The best metric for managing inventory strategies, the return reasons will help you to identify what kind of products in your inventory don’t fit customers, are damaged, or simply were wrongly delivered. That way, you will be able to manage your inventory more effectively and avoid potential issues in the future, even before they arise.

The top sellers by orders will clearly show what items you need to have in your inventory so that your business and potential revenue doesn’t get affected negatively and retail analytics software will help you automate your processes and track massive volumes of information, which is critical in this competitive industry.

c) FMCG KPI dashboard for a mix of modern industries

Earlier, we talked about the average percentage of items sold within their freshness data (FMCG) KPI. Now, we’re going to look at an entire FMCG dashboard dedicated to the concept.

**click to enlarge**

Featuring a cohesive mix of inventory management KPIs, this dynamic inventory KPI dashboard generated with a professional dashboard builder is a powerful portal for managing every major cog in your supply chain with accuracy and confidence.

Each KPI and visualization is geared towards improving the operability of your inventory management processes, drilling down into areas including average time to sell, out-of-stock rates, items delivered on time and in full, and, of course, the proportion of items sold within their official freshness date.

With each KPI for inventory management occupying a dedicated space on the dashboard, it’s possible to make swift real-time decisions at a glance while examining patterns or trends and making definitive comparisons.

Here, for example, you could deal with an “out of stock” issue as it emerges, putting the appropriate inventory replenishing measures in place while tracking your inventory turnover over the past few months to feed into your fulfillment efficiency initiatives.

Every one of the inventory metrics examples that populate this powerful dashboard helps to take the guesswork out of the “hows” and “whys” of your inventory management strategies. By using this as your logistical nerve center, you can paint a vivid picture of your supply chain and ensure everyone involved is working towards the right targets at the right time.

As a result, you will drive down unnecessary costs, significantly rescue waste or damage, get your stock or inventory flowing like never before, and, ultimately, expand your bottom line – the key ingredients for commercial growth and evolution.

This mix of examples generated with datapine’s professional dashboard software shows how powerful inventory metrics can be when visualized together. Telling a cohesive story with your KPIs will help you make smarter and more agile decisions to boost your performance.

Inventory Management Best Practices

As we have seen all along with this article, inventory management is not something that is limited solely to warehouses; manufacturing companies need data on trends (seasonal information, price point, buying behaviors and patterns, etc.) to make sure they always have enough for retailers. Retailers need data to effectively manage their restocking, and FMCG to bring fresh products and optimize processes every day. To master the art of inventory management, here are a few tips.

a) ABC Analysis for categorization

To start with, a good technique for inventory management is to realize a hierarchy of your most valuable to least valuable items, by dollar value. As in general, not all your stock has equal value, this will help in focusing on the items that bring in the most money. Classify them as follows:

• A-items: best-selling products with the highest priority. Need a permanent quality review and regular reordering
• B-items: valuable but with a medium priority. Monthly reordering is usually sufficient.
• C-items: low-priority stock, generally carried in high volumes and needing minimal reordering.

Thanks to such an organization, you can manage your warehouse according to how your inventory is sold and how much value that item brings to your business. It is good to optimize space, as well as streamline order fulfillment.

b) JIT – Just In Time

Just in Time is another inventory management technique that helps with the cash flow management for retailers. JIT means that you only buy what you need from a vendor when you get an order from a customer. This technique can be a bit trickier for manufacturers because as we stated earlier, you need data on trends to have enough stocks for retailers.

Your business will best benefit from JIT if you are an eCommerce company, or if you build customized products such as jewelry, furniture, luxury cars, etc; but also as a service-based business (events, food, garage, etc).

c) Be data-driven

With the help of what we wrote above, you can choose the KPIs that fit best your business needs, and track them over time. You may start to recognize patterns and trends, but also bottlenecks and inconsistencies. All this will help you figure out how to improve your processes and enhance efficiency.

Benefiting from the numerous advantages data analysis tools provide will make you stand out of the crowd, and ultimately increase your efficiency even more. Data consolidation and analysis are simple, thanks to the intuitive drag-and-drop interface. They will also help you visualize easily your insights and communicate them in a meaningful way through inventory dashboards.

d) Implement quality control

Last but not least, quality control is of utmost importance for any business and should be implemented as early as possible. To ensure customer satisfaction and steady business growth, it is imperative to have quality control processes.

From a procedures checklist to follow at the reception of an item, to damage monitoring and product compliance, make sure all your employees are aware of the entire quality control process that should be observed. When an item doesn’t match the company’s quality standards, they will return them to the supplier, and reduce the stock levels.

Key Takeaways Inventory Metrics

We have explained inventory management performance, provided examples, and best practices, and finalized with dashboards where all those pieces can come together to tell an efficient story that will enable you to optimize your inventory management processes and deliver invaluable results year after year.

When we look at some facts and figures, we find out that for retail, inventory is accurate only 63% of the time on average, or that companies can reap a 25% increase in productivity and a 30% improvement in stock efficiency if they use integrated order processing for their inventory management. These numbers draft out a troubling scenario: most of the inventory management professionals are aware that the situation and their work could be improved, and that software could help – but they are not yet sure how to make it happen. 

The inventory manager has a lot of responsibilities, essentially acting as a business’s air traffic controller. To succeed, inventory managers need to take a collaborative approach to their efforts while using every relevant strand of data to their advantage.

Inventory efficiency metrics and KPI inventory data are your light in the dark – your consultant and guide when it matters most. Squeeze every last drop of value from your inventory dashboard insights, and you will reap great rewards.

Extensive knowledge of supply chain principles and strong analytical skills will amount to consistent success, progression, and growth. Using online BI tools to set up and track the right inventory metrics will ensure you can spot any opportunities as they arise while identifying potential problems as they emerge.

Thanks to this little guide to inventory management best practices and metrics, you have some of the keys in hand for better command over your business – now, the only way is pure how to make it happen.

To see how you can benefit from BI software, simply try out our 14-day free trial!

The post A Beginner’s Guide To Inventory Metrics And Best Practices appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.

With a shocking 2.5 quintillion bytes of data being produced daily and the wide range of online data analysis tools in the market, the use of data and analytics has never been more accessible.

However, with all good things comes many challenges, and businesses often struggle with correctly managing their information. Oftentimes, the data being collected and used is incomplete or damaged, leading to many other issues that can considerably harm the company. Enters data quality management.

What Is Data Quality Management (DQM)?

Data quality management is a set of practices that aim at maintaining a high quality of information. It goes all the way from the acquisition of data and the implementation of advanced data processes to an effective distribution of data. It also requires a managerial oversight of the information you have. Effective DQM is recognized as essential to any consistent data analysis, as data quality is crucial to derive actionable and – more importantly – accurate insights from your information.

There are a lot of strategies that you can use to improve the quality of your information. Engineered to be the “Swiss Army Knife” of data development, these processes prepare your organization to face the challenges of digital age data, wherever and whenever they appear. In this article, we will detail everything that is at stake when we talk about data quality management: why it is essential, how to measure data quality, the pillars of good quality management, and some data quality control techniques. Reporting being part of an effective data quality management model, we will also go through some KPI examples you can use to assess your efforts in the matter. But first, let’s define what it actually is. 

What is the definition of data quality?

Data quality refers to the assessment of the information you have relative to its purpose and its ability to serve that purpose. The quality of data is defined by different factors that will be detailed later in this article, such as accuracy, completeness, consistency, or timeliness. That quality is necessary to fulfill the needs of an organization in terms of operations, planning, and decision-making.

What is the data quality management lifecycle? 

Big data and analytics have become one of the biggest competitive advantages that organizations have today. That being said, while the process of analyzing data has become easier with the use of self service SaaS BI tools, managing data quality still remains one of the biggest challenges for companies of all sizes. But don’t worry; if you are reading this article, you are already on the right path to extract the maximum potential out of your data-driven efforts. Implementing an efficient framework to clean and manage your information is the first step to getting started. We will dive into detail into each step of the data quality process later in the post, but below, we will discuss them briefly to help you understand what you should expect from the process. 

• Extraction: Gathering the necessary information from various internal and external sources. 
• Evaluate: Evaluate if the data you gathered meets the quality requirements. 
• Cleansing: Clean, remove, or delete any information that is duplicated, wrongly formatted, or useless for your goals. 
• Integration: Integrate your data sources to get a complete view of your information.  
• Reporting: Use KPIs to monitor the quality of your data and ensure no further issues happen. 
• Repair: If your reports show data that is corrupt or needs something changed, fix it promptly. 

Why Do You Need Data Quality Management?

While the digital age has been successful in prompting innovation far and wide, it has also facilitated what is referred to as the “data crisis” – low-quality data.

Today, most of a company’s operations and strategic decisions heavily rely on data, so the importance of quality is even higher. Indeed, low-quality data is the leading cause of failure for advanced data and technology initiatives, to the tune of $9.7 million to American businesses each year (not counting businesses in every other country of the world). More generally, low-quality data can impact productivity, bottom line, and overall ROI.

We’ll get into some of the consequences of poor-quality data in a moment. However, let’s make sure not to get caught in the “quality trap,” because the ultimate goal of DQM is not to create subjective notions of what “high-quality” data is. No, its ultimate goal is to increase return on investment (ROI) for those business segments that depend upon data. Paired with this, it can also: 

Improved decision-making process: From customer relationship management to supply chain management to enterprise resource planning, the benefits of data quality management can have a ripple impact on an organization’s performance. With quality data at their disposal, organizations can form data warehouses to examine trends and establish future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. According to a big data survey by Accenture, 92% of executives using big data to manage are satisfied with the results, and 89% rate data as “very” or “extremely” important, as it will “revolutionize operations the same way the internet did”.

Save time and money: As you will see throughout this insightful post, the consequences of using bad quality data to make important business decisions can not only lead to a waste of time in inefficient strategies but to an even higher loss in money and resources. Taking that into account, it is of uppermost importance for companies to invest in the right processes, systems, and tools to make sure the quality of their data is meeting the needed standards. As a result, not only will the business save tons of money and resources, but will also reap the rewards of making informed decisions based on accurate insights.

Competitive advantage: As mentioned in the previous points, the bottom line of being in possession of good quality data is improved performance across all areas of the organization. From customer relations to marketing, sales, and finances, being able to make informed decisions with your own data is invaluable in today’s fast-paced world. Getting a clear picture of what steps you should follow to be successful will lead to gaining a clear competitive advantage that will set the organization apart from the rest.  

Now that you have a clearer understanding of the benefits you can reap from implementing this process in your organization, let’s explore the concept in more detail. 

The 5 Pillars of Data Quality Management

Now that you understand the importance of high-quality data and want to take action to solidify your data foundation let’s take a look at the techniques behind DQM and the 5 pillars supporting it.

1 – The people

Technology is only as efficient as the individuals who implement it. We may function within a technologically advanced business society, but human oversight and process implementation have not (yet) been rendered obsolete. Therefore, several roles need to be filled, including:

DQM Program Manager: The program manager role should be filled by a high-level leader who accepts the responsibility of general oversight for business intelligence initiatives. He/she should also oversee the management of the daily activities involving data scope, project budget, and program implementation. The program manager should lead the vision for quality data and ROI.

Organization Change Manager: The change manager does exactly what the title suggests: organizing. He/she assists the organization by providing clarity and insight into advanced data technology solutions. As quality issues are often highlighted with the use of dashboard software, the change manager plays an important role in the visualization of data quality.

Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business. This individual defines the quality needs from an organizational perspective. These needs are then quantified into data models for acquisition and delivery. This person (or group of individuals) ensures that the theory behind data quality is communicated to the development team.

2 – Data profiling

Data profiling is an essential process in the DQM lifecycle. It involves:

1. Reviewing data in detail
2. Comparing and contrasting the data to its own metadata
3. Running statistical models
4. Data quality reports

This process is initiated for the purpose of developing insight into existing data, with the purpose of comparing it to quality goals. It helps businesses develop a starting point in the DQM process and sets the standard for how to improve their information quality. The data quality analysis metrics of complete and accurate data are imperative to this step. Accurate data is looking for disproportionate numbers, and complete data is defining the data body and ensuring that all data points are whole. We will go over them in the third part of this article.

3 – Defining data quality

The third pillar is quality itself. “Quality rules” should be created and defined based on business goals and requirements. These are the business/technical rules with which data must comply in order to be considered viable.

Business requirements are likely to take a front seat in this pillar, as critical data elements should depend upon the industry. The development of quality rules is essential to the success of any DQM process, as the rules will detect and prevent compromised data from infecting the health of the whole set.

Much like antibodies detecting and correcting viruses within our bodies, data quality rules will correct inconsistencies among valuable data. When teamed together with online BI tools, these rules can be key in predicting trends and reporting analytics.

4 – Data reporting

Data quality reporting is the process of removing and recording all compromising data. This should be designed to follow a natural process of data rule enforcement. Once exceptions have been identified and captured, they should be aggregated so that quality patterns can be identified.

The captured data points should be modeled and defined based on specific characteristics (e.g., by rule, by date, by source, etc.). Once this data is tallied, it can be connected to an online reporting software to report on the state of quality and the exceptions that exist within a data quality dashboard. If possible, automated reporting and “on-demand” technology solutions should be implemented as well, so dashboard insights can appear in real-time.

Reporting and monitoring are the cruces of enterprise data quality management ROI, as they provide visibility into the state of data at any moment in real-time. By allowing businesses to identify the location and domiciles of data exceptions, teams of data specialists can begin to strategize remediation processes.

Knowledge of where to begin engaging in proactive data adjustments will help businesses move a step closer to recovering their part of the $9.7 billion lost each year to low-quality data.

5 – Data repair

Data repair is the two-step process of determining:

1. The best way to remediate data
2. The most efficient manner in which to implement the change

The most important aspect of data remediation is the performance of a “root cause” examination to determine why, where, and how the data defect originated. Once this examination has been implemented, the remediation plan should begin.

Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defective data. These processes could include reports, campaigns, or financial documentation.

This is also the point where data quality rules should be reviewed again. The review process will help determine if the rules need to be adjusted or updated, and it will help begin the process of data evolution. Once data is deemed high-quality, critical business processes and functions should run more efficiently and accurately, with a higher ROI and lower costs.

Data Quality Management Best Practices

Through the 5 pillars that we just presented above, we also covered some techniques and tips that should be followed to ensure a successful process. To help you digest all that information, we put together a brief summary of all the points you should not forget when it comes to assessing your data. By following these best practices, you should be able to leave your information ready to be analyzed. 

• Ensure data governance: Data governance is a set of processes, roles, standards, and KPIs that ensure organizations use data efficiently and securely. Implementing a governance system is a fundamental step to ensuring data quality management roles and responsibilities are defined. It is also fundamental to keep every employee accountable for how they access and manipulate data. 

• Involve all departments: As we mentioned before, there are roles and responsibilities that are required when it comes to dealing with data quality. Some of these roles include a data quality manager, data analyst, and more. That said, while the need for specialized people is a must, it is also necessary to involve the entire organization in the process.  

• Ensure transparency: Expanding on the previous point, to successfully integrate every relevant stakeholder in the process, it is necessary to offer a high level of transparency to them. Ensure all the rules and processes regarding data management are informed across the organization to avoid any mistakes from damaging your efforts. 

• Define a data glossary: As a part of your governance plan, a good practice is to produce a data glossary. This should contain a collection of all relevant terms that are used to define the company data in a way that is accessible and easy to navigate. This way, you make sure there is a common understanding of data definitions that are being used across the organization.

• Find the root causes for quality issues: If you find poor data quality issues in your business, it is not necessary to just toss it all out. Bad-quality data can also provide insights that will help you improve your processes in the future. A good practice here is to review the current data, find the root of the quality issues, and fix them. This will not only help you set the grounds to work with clean, high-quality data but will also help you identify common issues that can be avoided or prevented in the future. 

• Invest in automation: Manual data entry is considered among the most common causes of poor data quality due to the high possibility of human error. This threat becomes even bigger in companies that require many people to do data entry. To avoid this from happening, it is a good practice to invest in automation tools to take care of the entry process. These tools can be configured to your rules and integrations and can ensure your data is accurate across the board. 

• Implement security processes: During your quality control process, you must archive, cleanse, recover, and delete data from many sources. This data might also need to be accessed by a number of people. Therefore, you need to ensure efficient security measures are in place to prevent any breaches or misuse of data. To do so, you can support yourself with modern management tools that offer top-notch security features. 

•  Define KPIs: Just like any other analytical process, DQM requires the use of KPIs to assess the success and performance of your efforts. In this case, it is important to define quality KPIs that are also related to your general business goals. This step is a detrimental part of the process, and we will cover it in detail in the next portion of the post. 

• Integrate DQM and BI: Integration is one of the buzzwords when we talk about data analysis in a business context. Implementing DQM processes allows companies across industries to perform improved business intelligence. That said, integrating data quality management processes with BI software can help automate the task and ensure better strategic decisions across the board.

• Measure compliance: Last but not least, once you’ve implemented your DQM framework, you need to measure compliance in two key areas. First, you need to evaluate if the policies and standards applied in the previous points are being followed from an internal perspective. Then, you need to evaluate if, overall, the company is meeting the regulatory standards for data usage.  

How Do You Measure Data Quality?

To measure data quality, you obviously need data quality metrics. They are also key in assessing your efforts to increase the quality of your information. Among the various techniques, data quality metrics must be top-notch and clearly defined. These indicators encompass different aspects of quality that can be summed up with the acronym “ACCIT” standing for Accuracy, Consistency, Completeness, Integrity, and Timeliness.

While data analysis can be quite complex, there are a few basic measurements that all key DQM stakeholders should be aware of. Data quality metrics are essential to provide the best and most solid basis you can have for future analyses. These indicators can be visualized together in an interactive report with the help of professional BI reporting tools which will also help you track the effectiveness of your quality improvement efforts, which is, of course, needed to make sure you are on the right track. Let’s go over these categories and detail what they hold.

Accuracy

Refers to business transactions or status changes as they happen in real time. Accuracy should be measured through source documentation (i.e., from the business interactions), but if not available, then through confirmation techniques of an independent nature. It will indicate whether the data is void of significant errors.

A typical metric to measure accuracy is the ratio of data to errors, which tracks the number of known errors (like missing, incomplete, or redundant entries) relative to the data set. This ratio should, of course, increase over time, proving that the quality of your data gets better. There is no specific ratio of data to errors, as it very much depends on the size and nature of your data set – but the higher, the better of course. In the example below, we see that the data-to-error rate is just below the target of 95% accuracy:

Consistency

Strictly speaking, consistency specifies that two data values pulled from separate data sets should not conflict with each other. However, consistency does not automatically imply correctness.

An example of consistency is, for instance, a rule verifying that the sum of employees in each company’s department does not exceed the total number of employees in that organization.

Completeness

Completeness will indicate if there is enough information to draw conclusions. Completeness can be measured by determining whether or not each data entry is a “full” data entry. All available data entry fields must be complete, and sets of data records should not be missing any pertinent information.

For instance, a simple quality metric you can use is the number of empty values within a data set. In an inventory/warehousing context, that means that each line of an item refers to a product, and each of them must have a product identifier. Until that product identifier is filled, the line item is not valid. You should then monitor that metric for a longer period to reduce it.

Integrity

Also known as data validation, integrity refers to the structural testing of data to ensure that the data complies with procedures. This means there are no unintended data errors, and it corresponds to its appropriate designation (e.g., date, month, and year).

Here, it all comes down to the data transformation error rate. The metric you want to use tracks how many data transformation operations fail relatively to the whole – or in other words, how often the process of taking data stored in one format and converting it to a different one is not successfully performed. In our example below, the transformation error rate is represented over time:

Timeliness

Timeliness corresponds to the expectation for the availability and accessibility of information. In other words, it measures the time between when data is expected and the moment when it is readily available for use.

A metric to evaluate timeliness is the data time-to-value. This is essential to measure and optimize this time, as it has many repercussions on the success of a business. The best moment to derive valuable information of data is always now, so the earliest you have access to that information, the better.

Whichever way you choose to improve the quality of your data, you will always need to measure the effectiveness of your efforts. All of these data quality metrics examples make a good assessment of your processes and shouldn’t be left out of the picture. The more you assess, the better you can improve.

Uniqueness 

As its name suggests, the next quality dimension helps to determine if a specific value or dataset is recorded twice with the same identifier. It helps avoid duplications or overlaps, as having two or multiple copies of the same value can cause problems during the analysis as some of the copies might not be updated or correctly formatted or might be considered a different value altogether. Uniqueness is often used together with consistency as both help ensure the is unique and correct across sources. 

For example, imagine your company has 100 full-time employees and 80 part-time ones with a total of 180 contracted employees. However, your employee database shows a total of 220 employees. This could mean that an employee called Thomas Smith can be recorded separately as Tom Smith despite being the same person. This means your uniqueness rate would not be 100%. Cleaning the data to erase duplicates is the best way to ensure a high uniqueness score. 

Validity 

This dimension helps evaluate if the data is valid according to its format, database types, and overall definition. For instance, ZIP codes are valid if they have the correct format for a specific region, dates need to be set in the correct order and format, and so on. Failing to do so can lead your database to classify the value as invalid and affect the accuracy and completeness of your data. 

To ensure data validity, you can set rules to tell your system to ignore or resolve the invalid value and ensure completeness. For example, imagine you are generating a database to classify customers with the identifier of name and surname. So, if a customer is called Marie Cooper, the data is valid, and it should be no problem to integrate. Now, if a customer is called Jean Paul Ross. The database might consider it invalid as the accepted format is name and surname. To avoid this, you can set a rule to ensure that customers with up to two names are also considered valid. 

What Are Data Quality Metrics Examples?

Find here 5 data quality metrics examples you can use:

• The ratio of data to errors: Monitors the number of known data errors compared to the entire data set.
• A number of empty values: Counts the times you have an empty field within a data set.
• Data time-to-value: evaluates how long it takes you to gain insights from a data set. There are other factors influencing it, yet the quality is one of the main reasons it can increase.
• Data transformation error rate: This metric tracks how often a data transformation operation fails.
• Data storage costs: When your storage costs go up while the amount of data you use remains the same, or worse, decreases, it might mean that a significant part of the data stored has a quality too low to be used.

Why You Need Data Quality Control: Use Case

Let’s examine the benefits of high-quality data in marketing. Imagine you have a list you purchased with 10,000 emails, names, phone numbers, businesses, and addresses on it. Then, imagine that 20% of that list is inaccurate. That means that 20% of your list has either the wrong email, name, phone number, etc. How does that translate into numbers?

Well, look at it like this: if you run a Facebook ad campaign targeting the names on this list, the cost will be up to 20% higher than it should be – because of those false name entries. If you do physical mail, up to 20% of your letters won’t even reach their recipients. With phone calls, your sales reps will be wasting more of their time on wrong numbers or numbers that won’t pick up. With emails, you might think that it’s no big deal, but your open rates and other KPIs will be distorted based on your “dirty” list. All of these costs add up quickly, contributing to the $600 billion annual data problem that U.S. companies face.

However, let’s flip the situation: if your data quality improvement strategy is on point, then you’ll be able to:

• Get Facebook leads at lower costs than your competition
• Get more ROI from each direct mail, phone call, or email campaign you execute
• Show C-suite executives better results, making it more likely your ad spend will get increased

All in all, in today’s digital world, having high-quality data is what makes the difference between the leaders of the pack and the “also-rans”.

The Consequences Of Bad Data Quality Control

Bad data quality control can impact every aspect of an organization, including:

• How much do your marketing campaigns cost and how effective they are
• How accurately you are able to understand your customers
• How quickly you can turn prospects into leads into sales
• How accurately you can make business decisions

According to recent information published by Gartner, poor data quality costs businesses an average of $12.9 million a year. This not only translates into a loss in revenue but also to poor decision-making, which can lead to many intangible costs.

The intangible costs

We can’t examine the intangible costs directly. However, we can use our intuition and imagination in this area.

Let’s say that you’re striving to create a data-driven culture at your company. You’re spearheading the effort and currently conducting a pilot program to show the ROI of making data-driven decisions using business intelligence and analytics. If your data isn’t high-quality, you’ll run into many problems showing other people the benefits of BI. If you blame the data quality “after the fact”, your words will just sound like excuses.

However, if you address things upfront and clarify to your colleagues that high quality is absolutely necessary and is the cornerstone of getting ROI from data, you’ll be in a much better position.

One huge intangible cost: bad decisions

Maybe you’re not trying to convince others of the importance of data-driven decision-making. Maybe your company already utilizes analytics but isn’t giving due diligence to data quality control. In that case, you can face an even bigger blowup: making costly decisions based on inaccurate data.

As a big data expert, Scott Lowe states, maybe the worst decisions are made with bad data: which can lead to greater and more serious problems in the end. He would rather make a decision listening to his guts than risk making one with bad data.

For example, let’s say you have an incorrect data set showing that your current cash flows are healthy. Feeling optimistic, you expand operations significantly. Then, a quarter or two later, you run into cash flow issues, and suddenly it’s hard to pay your vendors (or even your employees). Higher-quality data could prevent this kind of disastrous situation.

3 Sources Of Low-Quality Data

Image source: TechTarget

We’ve just gone through how to clean data that may not be accurate. However, as the saying goes, an ounce of prevention is worth a pound of cure. With that in mind, here are some of the origins of low-quality data so that you can be mindful about keeping your records accurate as time passes. Remember: keeping your data high-quality isn’t a one-time job. It’s a continual process that never ends.

Source #1: Mergers and acquisitions

When two companies join together somehow, their data tags along into this new working relationship. However, just like when two people with children from prior marriages form a new relationship, things can sometimes get messy.

For example, it’s very possible, and even probable, that your two companies use entirely different data systems. Maybe one of you has a legacy database, while the other has updated things. Or you use different methods of collecting data. It’s even possible that one partner in the relationship simply has a lot of incorrect data.

Data expert Steve Hoberman gives an example of mergers causing difficulty. He writes that when these two databases disagree with each other, you must set up a winner-loser matrix that states which database’s entries are to be regarded as “true”. As you might expect, these matrices can get exceedingly complex: at some point, “the winner-loser matrix is so complex that nobody really understands what is going on”, he says. Indeed, the programmers can start arguing with business analysts about futilities and “consumption of antidepressants is on the rise”.

Action Step: In the event of a planned merger or acquisition, make sure to bring the heads of IT to the table so that these kinds of issues can be planned for in advance -before any deals are signed.

Source #2: Transitioning from legacy systems

To a non-technical user, it may be hard to understand the difficulties inherent in switching from one operating system to another. Intuitively, a layman would expect that things are “set up” so that transitions are easy and painless for the end user. This is definitely not in line with reality.

Many companies use so-called “legacy systems” for their databases that are decades old, and when the inevitable transition time comes, there’s a whole host of problems to deal with. This is due to the technical nature of the data system itself. Every data system has three parts:

1. The database (the data itself)
2. The “business rules” (how the data is interpreted)
3. The user interface (how the data is presented)

These distinct parts can create distinct challenges during data conversion from one system to another. As Steve Hoberman writes, the center of attention is the data structure during the data conversion. But this is a failing approach, as the business rule layers of the source and destination are very different. The converted data is inevitably inaccurate for practical purposes even though it remains technically correct.

Action step: When transitioning from a legacy system to a newer one, it’s not enough that your transition team is an expert in one system or the other. They need to be experts in both to ensure that the transition goes smoothly.

Source #3: User error

This problem will probably never go away because humans will always be involved with data entry, and humans make mistakes. People mistype things regularly, and this must be accounted for. In his TechTarget post, Steve Hoberman relates a story of how his team was in charge of “cleansing” a database and correcting all of the wrong entries.

You would think that data-cleansing experts would be infallible, right? Well, that wasn’t the case. As Mr. Hoberman states, “Still, 3% of the corrections were entered incorrectly. This was in a project where data quality was the primary objective!”

Action step: Create all the forms that your company uses as easy and straightforward to fill out as possible. While this won’t prevent user error entirely, it will at least mitigate it.

Data Quality Solutions & Tools: Key Attributes

So far, we have offered a detailed guide to the data quality management framework, with its benefits, consequences, examples, and more. Now, you might be wondering, how do I make all of this happen? The answer is with big data quality management tools. There are many solutions out there that can help you assess the accuracy and consistency of your information. To help you choose the right one, here we list the top 5 features you should look for in any DQM software worth its salt.

• Connectivity: To be able to apply all quality rules, DQM software should ensure integration and connectivity as a basis. This means being able to easily connect data coming from multiple sources such as internal, external, cloud, on-premise, and more.
• Profiling: Data profiling enables users to identify and understand quality issues. A tool should be able to offer profiling features in a way that is efficient, fast, and considers the DQM pillars.
• Data monitoring and visualization: To be able to assess the quality of the data, it is necessary to monitor it closely. For this reason, software should offer monitoring capabilities using interactive data visualizations in the shape of online dashboards.
• Metadata management: Good data quality control starts with metadata management. These capabilities provide the necessary documentation and definitions to ensure that data is understood and properly consumed across the organization. It answers the who, what, when, where, why, and how questions of data users.
• User-friendliness and collaboration: Any solution that requires the use of data in today’s modern context should be user-friendly and enable collaboration. As mentioned time and time again throughout this post, there are many key players in a corporate data quality management system, and they should be able to share key definitions, specifications, and tasks in an easy and smart way.

Emerging Data Quality Trends To Watch

If you’ve gotten to this point, then you should be aware of the importance of working with clean and secure information. And you are not alone. According to recent reports, 82% of businesses believe quality concerns represent a barrier to their data integration projects. Making DQM systems a key tool to ensure the resources spent on data analytics don’t go to waste. 

But how are the different industries responding to these quality threats? What new technologies are coming up to ensure organizations can work with quality information? Below, we will discuss some of the current trends in the data quality industry to help you get on the right path.  

Artificial intelligence & machine learning

It might not come as a surprise that enterprises are now relying on powerful artificial intelligence and machine learning technologies to support their analytical quest. These technologies have already penetrated the industry with multiple tools, including natural language processing, computer vision, and automated processing of large volumes of data, among others. This is because, today, developing and deploying AI and ML models is easy and not expensive. Plus, the models keep learning and developing on their own, allowing businesses to efficiently automate tasks like data classification and quality control. 

Automation

Expanding on the previous trend, manually extracting, transforming, and loading data is the enemy of quality. That is why automation has remained one of the biggest trends in the industry for the past few years, and not just when it comes to data quality but the entire data management process. Automation ensures complex, monotonous tasks are completed with efficiency and accuracy with the help of AI and ML. Thanks to these technologies, businesses can automate several tasks, including data discovery and extraction, and perform automatic quality checks to ensure they are working with the highest quality information. 

Increased focus on trust architecture and security

As mentioned earlier, implementing a data governance plan has become one of the greatest practices to ensure data is compliant, secure, and efficient. This is especially true considering how complex business data is becoming from a management and regulatory perspective, turning governance initiatives into a mandatory practice rather than a choice. 

To help boost their governance initiatives, organizations have started to invest in a new approach called “trust architecture”, which allows them to build and maintain stakeholders’ trust in their data-driven products and services. McKinsey’s technology trends report for 2023 shows digital-trust technologies have increased their investment to $47 billion in 2022. That being said, a good trust architecture depends mostly on data quality. Therefore, automated data quality management tools will continuously increase their focus on offering governance and trust solutions to drive data quality and security in an automated environment. 

Data Democratization 

Even though the value of data is more than recognized, there are still many organizations that fail in their analytical efforts due to a lack of literacy and accessibility across areas and departments. That is why democratizing data has become such an important aspect of the process. Ensuring all employees have the knowledge but, most importantly, the trust they need to use data for their decision-making process is very important. 

This is no different when it comes to ensuring quality. The grounds for a successful DQM framework rely mostly on full organizational adoption and keeping every relevant stakeholder responsible for ensuring data quality. In that sense, using Low-code/No-code apps has become a great solution. These tools can be used by anyone without any technical knowledge, empowering them to check their data independently, saving the IT team a lot of time and the business a lot of money

To Conclude…

We hope this post has given you the information and tools you need to keep your data high-quality. We also hope you agree that data quality management is a crucial process for keeping your organization competitive in today’s digital marketplace. While it may seem to be a real pain to maintain high-quality data, consider that other companies also feel like DQM is a huge hassle. So, if your company is the one that takes the pains to make it sound, you’ll automatically gain a competitive advantage in your market. As the saying goes, “if it were easy, everyone would be doing it.”

DQM is the precondition to creating efficient business dashboards that will help your decision-making and bring your business forward. To start building your own company dashboards and benefit from one of the best solutions on the market, start your 14-day free trial here!

The post The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.

For years, businesses have experimented and narrowed down the most effective measurements for productivity. Productivity can be measured in many different ways and at different levels, from the raw industrial output of an asset in a manufacturing facility to the specific individual sales performance of a vendor. Today, employee output is no longer represented by vague descriptions but by isolated figures that offer insight for improvement in specific areas, which can be found on an HR dashboard.

There are a lot of KPI examples out there to monitor progress and assess productivity. Likewise, there are a lot of guides on how to be productive at work. Each industry, business, and department has tailored its definition of individual productivity that performance indicators can assess. Let’s take a look at our selection of essential metrics you can use to improve your organization’s performance in several areas.

What Are Productivity Metrics?

Productivity metrics are measurements used by businesses to evaluate the performance of employees on various activities related to their general company goals. These metrics are used to highlight improvement opportunities and ensure maximum efficiency and productivity.

In short words, productivity is the effectiveness of output; metrics are methods of measurement. They are, by definition, how businesses evaluate productivity, usually that of their employees.

Productivity KPIs provide valuable insights into how well resources are being utilized and help identify areas for improvement. But it’s not just about output; productivity metrics for employees go beyond mere numbers. They also consider factors like time management, quality of work, and collaboration.

One commonly used productivity KPI is the time spent on tasks. This helps track how much time is allocated to different activities and reveals potential bottlenecks or areas where businesses can streamline processes. Another example is the number of completed tasks or projects within a given timeframe. This KPI sheds light on overall productivity but is stronger when complemented by other indicators like customer satisfaction or revenue generated. 

Productivity KPIs can often act as intertwining categories. Sales goals and profit margins are all performance metrics examples that businesses reference, but it goes much deeper than that. Sales bring in profits; the management of those profits is heavily influenced by the metrics used to gauge productivity throughout a business.

While measuring productivity through data is important, remember these numbers alone don’t tell the whole story. Productivity doesn’t always equate to success— just because you serve 100 customers in an hour doesn’t mean you’ve done so with accuracy or good service, for example. These metrics should be analyzed in context and supplemented with additional information to learn more about what the numbers are telling you. 

It’s also worth noting that focusing solely on output can lead to burnout or sacrificing quality for quantity. Organizations need to balance tangible results and intangible aspects like innovation, creativity, and employee well-being when using productivity benchmarking and metrics as guiding tools for success.

Because of this, there are some performance metrics examples and indicators to help increase employee productivity for every department. Professionals in human resources, management, customer service, and more can all benefit from the data in their productivity metrics.

How To Measure Productivity? Best Practices to Set Your KPIs

Measuring productivity can mean many different things. Do the employees finish their projects on time and to instruction? What about the output of those efforts? If they do all of those things according to what they are told, but there is still no sales growth, where are adjustments needed? Finding the answer often takes more than one metric. So, how do you track productivity?

Despite different companies needing to track different information, some common guidelines should be followed to define successful productivity measurements. Let’s look at some of them. 

• Define what productivity looks like: How we measure it can vary between businesses. For example, some businesses believe that letting their employees use social media during the workday can be a productive habit, while other employers consider it a reason for disciplinary action. Without these measurements, it would be more difficult to substantiate any assertions, one way or the other.

• Limit your KPI selection: You don’t have to measure everything, nor should you try. Measuring too many KPIs can become a job and take away from more productive work. It can also distract you from making impactful changes because you’re focusing on too many “priorities” at once, which makes it harder to know where to focus your time and resources.

• Don’t focus only on labor: The first thing you should consider when measuring how productive your company is is to use the correct scope. According to an article in Harvard Business Review, measuring productivity in a modern business context is not only about direct labor but about a lot of other non-labor areas. As the author states: “single-minded attention to direct labor can produce unexpected consequences”. For this reason, your analysis scope should include anything that might affect the final output apart from direct labor. 

• Use the output formula: Next, you need to understand the output formula: productivity= units of output/units of input. Traditionally, this formula is used to understand the revenue to total working hours. For this purpose, businesses divide the output (total revenue of a specific period) by the inputs (total worked hours during that period) to understand their overall productivity. That said, and considering the first point, this approach is only useful to monitor labor productivity. To get a wider scope, inputs can be defined as other factors, such as output per machine or output per ton of material. This way, you will ensure you get a full picture of all aspects related to how productive your company is. 

• Consider both leading and lagging indicators: Leading indicators are predictive and can provide early insights into future performance. Examples include website traffic, social media engagement, and customer inquiries. On the other hand, lagging indicators measure historical performance and show results after an event has occurred. Revenue, profitability, or customer churn rate are common examples of lagging indicators. Both are important to paint the full picture of productivity, as understanding cause and effect can help you know what results to expect if you make changes.

• Align your indicators with specific business goals or objectives: Start by identifying the specific outcomes you want to achieve and then narrow down the relevant KPIs to help you monitor progress towards these goals. Remember that not all KPIs will be relevant to every business unit or project. Carefully evaluate each metric based on its applicability and value in driving positive organizational change. 

• Ensure your KPIs are attainable: There’s no point in choosing KPIs your team won’t be able to track or meet minimum performance standards. Understand what data you need to measure a particular KPI, whether that data is readily accessible, and how to surf that data to track it. Bottlenecks in this process could make your KPI useless or at least take away from its value.

• Gather the data: Once you have understood the scope and how to measure it, it is necessary to gather the needed data. The information you use will, of course, depend on your outputs, but it will most likely include worked hours, tasks completed, time spent on each task, revenue generated, and costs, just to name a few. Managing this raw data is not an easy task. To ensure efficient information management, you can support yourself with an online data analysis tool to help structure your data and leave it ready to extract insights. 

Now that you understand productivity measurement for your organization, it is time to look at some examples of productivity KPIs and metrics covering different areas.

Productivity Metrics Examples You Can Use

These productivity metrics examples are as interchangeable as they are targeted. You can conduct customized assessments per department while sharing certain metrics across the board. Below are examples that can be both specific and universal in gauging output across departments. For more metrics, you can find inspiration in our KPI examples list for different industries, platforms, and functions. 

1. Employee Productivity Metrics 

a. Overtime hours

Overtime is a good way to gauge the cost and output of individual employees, though consideration of context is important for this productivity metric. For example, if the company has a spike in sales, people have to work harder to deliver on the promises they are selling. If overtime hours are a direct result of a heightened workload, this may indicate that you need to hire more talented employees instead of reviewing the ones in place.

Also, look closer by evaluating OT with other employee productivity KPI examples such as workload. You can see, and therefore work to prevent, the mistakes that inevitably happen in an overworked team. Another common symptom of an overworked team is a higher rate of absenteeism. You can dig deeper into this topic by looking at our HR reports article, gathering examples and templates.

b. Overall labor effectiveness

Overall labor effectiveness is a multi-faceted KPI that connects a number of details, such as the amount of staff, shift effectiveness, and more. This is essential for human resources departments because it provides the information they need to answer complicated staffing questions.

Calculating the OLE by dividing the total sales by the number of employees is a very straightforward and simple way to achieve the answer. Yet, once again, while this is a great metric, there should always be other indicators to consider that impact productive output, such as the amount of product delivered, quality control, and more. There is a manufacturing element here that draws appeal to all industries. Professionals who calculate overall labor effectiveness are able to understand exactly what the company has achieved and how efficient their workforce is regularly.

c. Part-time employees

Part-time employees work fewer hours than their full-time counterparts, potentially leading to lower productivity. There are several business benefits of having part-time employees but understand that having multiple part-timers as a way to avoid hiring a few full-time employees can have detrimental effects on productivity. Part-time employees may take longer to learn the job, may have higher turnover, and may not be as invested in your company’s culture or mission. Tracking part-time work alongside company performance, results, or employee satisfaction can reveal whether you might need to adjust your employee classification.

2. Recruiter Productivity Metrics

a. Turnover rate

Turnover rate is an essential productivity metric used by human resources professionals to measure employee retention. Turnover is an inherent part of running a business. For better or worse, employees will come and go in accordance with their talents and desires. The turnover rate gives managers the ability to forecast a necessity for talent replacement so that no leftover duty of a leaving employee goes unassigned.

To calculate the turnover rate, choose a time period. Month-to-month is a common method here. From there, divide the number of separations by the number of active employees during that time period.

A low turnover rate is a sign of happy employees. It ultimately leads to, at the very least, lower recruiting and training costs. If your company has a high turnover rate, look to your managers to identify areas that need extra attention.

b. Recruiting conversion rate

This is a unique indicator because it measures the performance of your human resources staff. Also, with this KPI, you will have to draw your own standards; the quality of your hires will determine the right or wrong.

To measure your recruiting conversion rate, simply compare the number of applicants to the number of hires. If you consistently find outstanding people with little effort, stick to the ratio you are currently implementing. If time, talent, and consistency become areas of concern, it may be helpful to spread out that ratio or even isolate those factors with other metrics, such as the following.

c. Time to fill

As the search for the perfect employee continues, the clock ticks. The work can pile up, causing other employees to become overworked and overwhelmed, often resulting in underperformance. In order to set a point of reference for your human resources professionals, the average time to fill is a helpful productivity metric.

To calculate time to fill, record the time it takes from a job posting to be created all the way up until the hire. Keep in mind that there is no standard for time to fill. The number can be lowered or raised so long as the right employees are chosen with limited cost to the company. If either of those factors is not in place, the time to fill is worthy of experiment.

3. Sales Productivity Metrics 

a. Sales growth

For sales departments and most businesses as a whole, this is the most obvious productivity metric worth assessing. In order to measure sales growth in an actionable fashion, track the individual performance of sales employees against their targets and territories. Be flexible, see what’s working and what isn’t, and readjust as necessary to ensure better productivity from your sales employees.

b. Revenue per sales rep 

This next sales KPI is another great productivity indicator as it shows you the ability of your sales representatives to generate revenue for your organization. By setting ambitious but realistic KPI targets and goals for your reps, you can easily measure their performance and find improvement opportunities by comparing productivity against a benchmark month or last period. 

In order to make the most out of this indicator, it is also important to divide your sales rep under different categories such as level of seniority, channel on which they are selling, and any other relevant aspect. This way, you will make sure you are being fair when it comes to evaluating their progress. 

c. Opportunity-to-win ratio

Not all of your qualified leads will result in a sale, but it’s helpful to know how many of them make that full journey. This can shine a light on a sales manager’s effectiveness in closing deals, which tells you how much money they’re driving into your business. A high opportunity to win compared to a low win rate could mean your sales team needs additional training or other resources in order to close more deals.

4. Call Center Productivity Metrics

a. Top support agents

Your top talent deserves to be recognized, and relying on productivity KPIs enables a manager to remain objective when determining the strongest players. Here are some of the customer service-based indicators you can cross-reference to determine your best support agents:

• First call resolution
• Average calls per hour
• Customer satisfaction surveys
• Sales success

These indicators can help you identify the team members most eligible for managerial positions, as well as those who may need a little more training in specific areas.

b. Customer satisfaction

Arguably one of the most important customer service or call center productivity metrics, customer satisfaction can provide several insights into the quality of service your company is providing. Compared to some of the other examples in this post, the satisfaction KPI can be a bit harder to measure as it requires a different approach to data collection, such as performing a feedback survey to your clients. 

That said, many customer service analytics solutions make this process way easier by providing automation technologies for this process. With the help of the right tool, you can set up different instances to gather feedback from your clientele and pinpoint different areas in which your service could be improved. 

c. First call resolution (FCR)

The most efficient thing your call center team can do is to resolve the caller’s issue or request on the first call. Otherwise, your team must escalate tickets, handle callbacks, and further delay problem-solving. A low FCR could indicate the need for more training on communication and resolution options. Call scripts, surveys, and knowledge bases are also helpful tools in improving this metric.

5. Productivity Metrics In Manufacturing 

a. Production volume 

When we are talking about manufacturing KPIs, there is a key one that needs to be tracked to efficiently assess productivity. This is the production volume. Typically, a manufacturing company has to deal with multiple production deadlines to cover the demands of their clients. With that in mind, this straightforward KPI will help you understand if you can meet your targets and how you could improve. 

Now, since the production volume alone won’t tell you enough about where you could improve, it is important to analyze this metric a bit deeper and compare the productivity of different machines. This way, you can see if a specific machine is underperforming and find solutions to ensure your production process is fully optimized. 

b. First Pass Yield (FPY)

Following the same line as our previous example, we have the first pass yield (FPY). As one of the most relevant manufacturing productivity metrics templates, the FPY assesses the quality and performance of your production process. It does this by dividing the number of perfect units (with no reworks or defects) by the total number of produced units entering the same process in a specified time. 

In a perfect world, a company would aim for an FPY of 100%. But, given that this is not entirely realistic, it is a great productivity measure for achieving final production times on schedule and with the required quality. This indicator can be compared to other important ones, such as the throughput or the scrap rate, and visualized together in a professional manufacturing dashboard. 

c. Overall operations effectiveness (OOE)

Getting a bird’s eye view of your entire operation is critical in manufacturing, and that’s precisely what the overall operations efficiency metric measures. It multiplies the performance with quality and availability (availability equals the production time divided by the operating time). Using this metric, you can dial deeper into specific areas of your operations to see how various functions help or hinder OOE.

6. Marketing Productivity Metrics 

a. Goal conversion rate 

The conversion rate is one of the greatest indicators to measure the efficiency and productivity of the marketing department, as a high conversion rate means a successful strategy. In short, a conversion is any desired action made by a visitor, user, or client, as you want to call it. Of course, the type of conversion will depend on the business and the strategy being measured. It can be anything from a newsletter subscription, downloading a guide, or signing up for a free trial, just to name a few. 

By setting clear conversion goals for your different campaigns and channels, you can easily understand what type of strategy works best for your business and optimize resources to ensure an excellent marketing ROI. This leads us to our next example. 

b. Lead-to-MQL-ratio 

MQLs, or marketing qualified leads, are one of the most important performance measurements for the marketing department. Essentially, an MQL is a potential customer that has been recruited by the marketing team and that meets all the criteria to be derived to the sales team to become a paying customer. 

This is done by gathering multiple leads via different strategies such as content marketing, paid search, or social media strategies, and once leads are generated, the marketing team defines which ones will turn into MQLs. The higher the lead-to-MQL ratio, the more successful your company is at qualifying leads.

c. Website-traffic-to-lead ratio

Getting customers to your website isn’t the end goal—you also want as many qualified visitors as possible to take the next step. The website traffic-to-lead ratio measures the percentage of users that take additional action, also called a conversion. This might be to make a purchase, create an account, request a demo, or some other activity that shows they’re interested. A high website traffic-to-lead ratio indicates you’re attracting quality traffic. Conversion rates that decrease over time can be reviewed alongside other marketing metrics to determine the cause and help you rebound.

7. Inventory Productivity Metrics

a. Inventory turnover

The inventory turnover is a productivity metric that measures how many times your total inventory was sold and replaced during a specific period, usually a year. This is a great indicator to measure the efficiency of many business areas, such as production management, marketing, and sales. 

In general, a low turnover rate can mean something is not working at some stage in your supply chain. However, this is not necessarily always the case. Some companies might have a lower turnover because they are selling more expensive products or services that stay longer on the “shelf”. Therefore, it is important to set realistic turnover targets based on the industry. 

b. Inventory to sales ratio

Following on the same line as the turnover rate, another great example is the inventory-to-sales ratio. This most insightful of logistics KPIs tracks the amount of inventory in your stores compared to the number of sales, and it is utilized to identify overstock and measure the performance of your inventory management process.  

Inventory management is one of the most expensive activities for an organization. The more time you have unsold stock in storage, the more it will cost you. With that in mind, a good practice is to keep your inventory and sales volume at a similar level. If you notice that your stock is too high, but you are already reaching your sales targets, you might be producing more inventory than you need, which is costly in time and money. 

c. Inventory accuracy

Inventory accuracy has a powerful effect on sales and customer satisfaction. Tracking this KPI can help you avoid problems caused by inaccurate inventory while maintaining your squeaky-clean reputation as a reliable supplier. A low inventory accuracy measurement could indicate poor bookkeeping practices, inefficiency in your warehouse, or even employee theft, all of which can create their own ripple effects of consequences.

8. Productivity Metrics In Healthcare

a. Patient satisfaction

Moving on with some examples from the healthcare industry, we first have the patient satisfaction KPI. Just like customers are the most important aspect of a business, patients are the most important aspect of a health organization. Therefore, measuring their satisfaction level is a great indicator to assess efficiency and productivity across departments and areas. 

The most important of KPIs for productivity includes anything from the quality of meals, average waiting times, and treatment efficiency, among many others, and it can tell facilities where to focus their improvement efforts. 

b. Hospital readmission rates 

Our final example is the hospital readmission rates tracked in an interactive gauge chart, which provides insights into the quality of your health service. This indicator directly affects your patient satisfaction levels as it measures the number of patients that return to the hospital within a short period of time after being released.

High readmission rates can shine a light on a number of managerial issues, such as understaffing or an overloaded staff neglecting details that make patients have to come back. Identifying these productivity issues can lead to decreasing unnecessary costs from readmissions as well as offering better service in general. 

You can track this and other relevant metrics with the help of an intuitive healthcare dashboard. 

c. Treatment error rate

Mistakes happen, even in critical fields like healthcare. However, with patient lives in your hands, it’s imperative to reduce the potential for error in every way possible, which is why tracking the treatment error rate is so important. This productivity KPI shows the number of mistakes made by clinical staff when treating patients. Errors might include prescribing the wrong medication, incorrect diagnoses, or providing the wrong treatment, among other things.

9. IT Productivity Metrics

a. Mean Time To Detect

The sooner you detect an attack, the faster you can mitigate its impact. The mean time to detect measures how long a security threat goes unnoticed before an IT team member addresses it. Your goal should be to keep this metric as low as possible to ensure safety and privacy across your technology ecosystem. However, several factors could affect this indicator, including the type and complexity of the threat, as well as the availability of your IT staff. Use this metric as a jumping-off point to find areas for improvement.

b. Mean time to repair

Mean time to repair is as much a measure of confidence and competence as it is productivity. It illustrates how quickly your IT team is addressing issues when they’re noticed. This IT KPI encompasses the entire repair timeline, from diagnosing issues to fixing, testing, and calibrating. The results can indicate your team’s capabilities in facing problems in real-time and whether additional training or staffing might be necessary. 

c. Backup frequency

Backing up your data is critical to keeping your business intact. Whether you’re automatically performing data backups or running manual backup sessions, it’s important to know how often your data is being preserved. You never know when a cyber attack might freeze your systems or wipe out critical information, and knowing your backup frequencies can help you plug any holes in your backup strategy.

Enhance Employee Productivity Through Metrics

Now that you’ve acquired the data, it is time to apply that knowledge and make your business more productive. But first, scrutinize your data. Remember that the circumstances surrounding these measurements should always be considered. Chance is certainly one of those circumstances. For example, a customer service agent who fields a significantly larger number of angry customers may not be the one causing the problems. Factors such as time of day, other workers on duty, and more can be influencing that influx of negativity. Furthermore, that very agent dealing with all these scenarios could become your future leader in conflict resolution.

Second, make sure your data covers a reasonable time period for evaluating your chosen productivity metric. Track metrics for months, not days, so that the report is thorough enough to produce actionable feedback.

Finally, we now know that the employees’ well-being plays a big role in their productivity. This is why setting up and tracking employee satisfaction metrics can be a relevant measure for you to take to see how your different initiatives in the matter impact their productivity. An HR analytics software is the ideal tool to manage all the data you will collect after setting up your metrics.

Once you’ve double and triple-checked your productivity KPIs, it is time to start making some changes. There are simple habits you can teach your employees to make them more productive. Perhaps there are changes to be made in staffing, scheduling, or operations. No matter what you decide to do about your data, make sure you keep recording. Future data will prove the effectiveness of your remedies, allowing for more productive solutions in the future.

Find The Right Workforce Productivity Metrics Today

Business efficiency is a concept that is built over time within every company. Employee productivity is one of the biggest drivers of that efficiency. In order to boost performance, cut costs, and retain both customers and employees, productivity KPIs should be an accelerating part of the conversation.

At datapine, we specialize in helping our clients enhance their businesses. To start measuring your productivity and performance, read more about our self service BI tool or sign up for a 14-day free trial today!

The post Top Productivity Metrics Examples & KPIs To Measure Performance And Outcomes appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.