In today’s business environment, a vast amount of information is generated every day. The same applies to companies operating warehouses, where despite the availability of information systems such as WMS and ERP platforms, as well as data analysis tools, both operational and strategic decisions are often still made based on experience rather than measurable data.
In this article, we will explore what Key Performance Indicators (KPIs) are and which metrics can be applied within a warehouse environment. We will then examine how to select the right KPIs according to the needs of a business and what critical insights can be derived from them. Finally, we will see how the use of a Warehouse Management System (WMS), combined with the adoption of a business intelligence platform such as Power BI, can significantly enhance the way warehouse data is utilized and transformed into actionable business intelligence.
What Are Key Performance Indicators (KPIs)?
Key Performance Indicators (KPIs) are quantitative pieces of information — numerical values associated with measuring a process or an outcome. Put more simply, they are metrics that help us determine whether what we are doing is being done effectively or not. [1] KPIs can take the form of absolute values, percentages, ratios, or time/rate-based measurements.
A business may process orders and manage the movement of goods and personnel every day, without having a clear understanding of whether it is on a path toward stability or growth. Without collecting data and consistently monitoring and interpreting KPIs, a company cannot truly assess how effectively its processes and employees perform, identify where time and resources may be wasted, or detect operational issues and hidden costs. Beyond these operational advantages, data-driven companies have been shown to achieve profits that outperform their competitors by approximately 25%. [2].
To summarize, KPIs provide a company with the following capabilities [3]:
- Performance measurement becomes objective, as each KPI is based on a clearly defined method of calculation. Depending on the type of KPI, the resulting measurements can also be interpreted in a structured and objective manner.
- Each KPI and its calculation method can be communicated to employees. In this way, transparency around critical information and business outcomes is ensured. At the same time, teams and departments can align more effectively around shared goals and expectations.
- For managers and decision-makers, KPIs and their monitoring overtime serve as dual-purpose tool. On one hand, they support more informed short-term and long-term decision-making. On the other hand, KPIs act as early warning indicators when performance begins to deviate from targets. This enables organizations to intervene promptly, identify the root causes of deviations, and take corrective action.
Which KPIs are used in a Warehouse?
The importance of KPIs in warehouse operations is clearly highlighted in a 2023 scientific study [4]. The researchers reviewed 203 published articles and identified approximately 70 performance indicators that are regularly used in warehouses to measure business performance. Among them, 52 were financial KPIs — the most widely adopted category — while the remaining indicators were related to companies’ performance in social and environmental areas.
Financial KPIs include:
- Time-based performance indicators, such as metrics measuring order picking and receiving times.
- Cost-based performance indicators, such as storage costs and direct labor costs
- Indicators related to warehouse information systems and technologies, such as system/algorithm reliability and barcode/QR/image scanning speed and
- General warehouse performance indicators, such as order picking accuracy and inventory turnover. These indicators are more comprehensive and operational, and their measurement is linked to more than one dimension (time, cost, quality, or technology).
Which KPIs should I ultimately choose for my warehouse?
Each warehouse has its own tasks and specific characteristics. Therefore, its managers must align KPIs with this reality. Depending on the industry and the nature of the business, what is valuable can vary significantly. For example, a company with more than one warehouse gains added value by monitoring the Stock Transfer Rate between its facilities. This KPI can highlight potential imbalances in inventory distribution, demand forecasting issues, or replenishment needs between warehouses. However, the same indicator would be unnecessary if each warehouse operated independently for business or geographical reasons, or if each warehouse managed different product categories. Ultimately, there is no single answer to the question above, but rather general guidelines that each business must follow to benefit from the use of appropriate KPIs. More specifically, the selection of the right KPIs can be based on [3, 5]:
- The core functions available in the warehouse, since it is not given that all of them are performed or that all of them should be monitored through KPIs. A process-based approach is perhaps the most classic way of performance evaluation in warehouses.
- The performance dimensions, such as time, cost, accuracy, as well as performance in quantitative quality characteristics.
- The link between a KPI and decisions and potential actions. A common assumption in business is that if something is not measurable, it is not manageable. At a deeper level, however, the real value of a KPI does not lie simply in whether something can be measured, but in whether it can ultimately lead to a meaningful business decision and improvement intervention.
- The criticality of the KPIs. Monitoring many indicators also produces many interpretations, which reduces the effectiveness of analysis. For this reason, prioritization and focus on the most critical KPIs is recommended.
- The broader business model of the company, as well as the degree and way in which warehouse operations support its execution.
Five useful KPIs for the warehouse and how to use them
Inventory Accuracy
This KPI measures the difference between physical stock and system-recorded stock, and it is typically calculated during annual or cycle counting processes. It is arguably the KPI that improves the most using a WMS [6].
On a day-to-day level, it helps identify and correct inventory discrepancies, while large or recurring deviations indicate the need to improve warehouse processes and flows.
At a strategic and broader level, inventory accuracy ensures high levels of customer service, keeps storage and handling costs under control, and prevents the business from losing sales—and therefore profit—due to stockouts.
Order lead time
This KPI measures the total order fulfillment time, from the moment an order is entered until it is ready for dispatch. Depending on the warehouse processes, this time includes picking, packing, loading, and shipment activities. It is a KPI that requires technological infrastructure to be properly tracked.
On a day-to-day level, this indicator helps optimize resource allocation, ensuring that delays are avoided and that orders do not remain pending for execution.
At a strategic level, clearly measured lead times enable the definition and improvement of service levels. Order fulfillment time has become a critical differentiating factor for businesses and reducing it can translate into a significant competitive advantage in the market [7].
Picks per Hour
This KPI measures employee productivity and, ultimately, the performance of the entire warehouse team during the picking process [8].
On a day-to-day level, it helps monitor warehouse and team performance, while also highlighting training needs.
At a strategic level, it reveals opportunities to improve picking methods and tools, such as wave picking or cart-assisted picking. In larger-scale operations, it can also highlight the need for greater automation.
Receiving Efficiency
This KPI measures the speed and accuracy of the goods receiving process and is typically calculated during inbound operations. In some companies, it also includes the time required to put away stock. This KPI requires appropriate technological support for accurate tracking and measurement.
On a day-to-day level, it helps monitor delays during receiving and identify supplier-related errors [9].
At a strategic level, it can support supplier evaluation and negotiation, the redesign of inbound warehouse processes, as well as the potential adoption of cross-docking operations.
Inventory Turnover Ratio
This KPI measures how quickly inventory is sold and replenished over a specific period, and it is not directly linked to a single warehouse process [2].
It is a strategic indicator, as it shows how efficiently a company’s capital tied up in inventory is being utilized. It helps identify overstock situations, slow-moving inventory that ties up working capital, or, conversely, stockouts that lead to lost sales.
Ultimately, it has a significant impact on purchasing policy, working capital management, and decision-making regarding promotional activities for inventory clearance.
The role of a consultant in combination with WMS & Power BI
For small and medium-sized enterprises, the above steps for selecting KPIs and the information behind them are conceptually clear. However, in practice they often appear demanding to implement, especially when there is limited time and no structured management methodology in place.
At this point, the role of a business consultant can prove decisive, as they are able to identify the specific needs, complexity, and day-to-day reality of each business and place them into an applicable and realistic framework of KPIs and monitoring. However, the effectiveness of such a framework is not sufficient on its own. Business also needs a more systematic way to act and make decisions.
Modern Warehouse Management Systems (WMS) and data analytics tools (Power BI) contribute in a complementary and meaningful way both to data collection and to the organization and transformation of data into useful information [10, 11]. On the one hand, WMS can ensure the correct execution and recording—collection of warehouse process data and inventory movements. Many of these systems also provide businesses with useful standard reports.
On the other hand, Power BI extends the use of this data. Within a WMS, information typically refers to what is happening in real time or what happened at a specific point in the past (e.g., a day, month, or period). Power BI operates at a higher analytical level and can provide more strategic direction. It can combine and transform historical data and data coming from different sources and systems into dynamic analyses and correlations, while presenting them in a user-friendly way through various visualization options such as charts and dashboards. It supports both the understanding of trends and the identification of causes and relationships between factors that affect warehouse performance.
A company can start gradually by reviewing the current evaluation of its warehouse processes. It can then proceed with relevant improvement actions and subsequently adopt a WMS. Over time, it becomes able to understand the power of data in a structural way, and at a cultural level it is more mature to also adopt Power BI, as well as more advanced performance indicators that provide deeper business insight.
INGENIO is a unified partner for businesses. With many years of experience in process redesign and experienced professionals, we provide both WMS and Power BI services, covering the entire warehouse ecosystem. For more information, please visit our respective websites WMS & Power BI.
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Sources:
[1] Meier, H., Lagemann, H., Morlock, F. & Rathmann, C. (2013). Key performance indicators for assessing the planning and delivery of industrial services. Procedia CIRP, 11, 99 – 104. https://doi.org/10.1016/j.procir.2013.07.056.
[2] OPEX Corporation. (2025). Top 10 metrics to measure Operational Success: A Warehouse KPI Checklist. https://www.opex.com/insights/warehouse-kpi-checklist-for-operational-success/
[3] Aithal, P.S. & Aithal, S. (2023). Key Performance Indicators (KPI) for Researchers at Different Levels & Strategies to Achieve it. International Journal of Management Technology and Social Sciences, 8(3), 294 – 325. https://doi.org/10.47992/ijmts.2581.6012.0304
[4] Faveto, A., Traini, E., Bruno, G. & Chiabert, P. (2024). Review‑based method for evaluating key performance indicators: an application on warehouse system. The International Journal of Advanced Manufacturing Technology¸130, 297 – 310. https://doi.org/10.1007/s00170-023-12684-4[5] Midor, K., Sujová, E., Cierna, H., Zarebinska, D. & Kaniak, W. (2020). Key Performance Indicators (KPIs) as a Tool to Improve Product Quality. New Trends in Production Engineering, 3(1), 347 – 354.
[6] Wasp Barcode Technologies. Top 10 Inventory Management KPIs – Key Performance Indicators for Maximizing Profit and Efficiency in Your Warehouse Operations. https://assets-us-01.kc-usercontent.com/ad8d8eda-c21e-000c-a5e6-7478633c60f3/5379fe4a-cce8-4f6c-bdce-edb282399055/Top%2010%20Inventory%20KPIs%20White%20Paper-web.pdf
[7] Villarreal, B. & Salido, L. (2009). Improving Order Lead Time: A Case Study. College Teaching Methods & Styles Journal, 5(1), 21 – 28. https://doi.org/10.19030/ctms.v5i1.5038
[8] Piasecki, D. (2001). Order Picking: Methods and Equipment for Piece Pick, Case Pick, and Pallet Pick Operations. https://www.logsuper.com/storage/ueditor/php/upload/file/20200308/1583653677859231.pdf
[9] Kalluru, S.R. & Gurijala, P.K.R. (2024). Increasing Efficiency of Goods Receipt with Mobility Solutions. International Journal of Computer Trends and Technology, 72(5), 182 – 187. https://doi.org/10.14445/22312803/IJCTT-V72I5P122
[10] Pacheco, D., Clausen, D.M. & Bumann, J. (2022). A multi-method approach for reducing operational wastes in distribution warehouses. International Journal of Production Economics, 256(1), 108705. https://doi.org/10.1016/j.ijpe.2022.108705
[11] Caridade, R., Pereira, T., Pinto Ferreira, L. & Silva, F.G.H. (2017). Analysis and optimisation of a logistic warehouse in the automotive industry. Procedia Manufacturing, 13, 1096 – 1103. https://doi.org/10.1016/j.promfg.2017.09.170