Warehouse Performance Measurement

God, grant me serenity to accept the things I cannot change, courage to change the things I can, and wisdom to know the difference. Reinhold Niebuhr

Performance foundations

01

🧠 Learning objectives

• Understand the role of KPIs in measuring warehouse performance and driving improvements.
• Learn benchmarking techniques to evaluate and compare warehouse performance.
• Explore methods for selecting and implementing effective KPIs in warehouse operations.

What is a KPI?

• A KPI is a performance measurement tool for an organization or specific activity.
  
• Importance: It reflects the success of activities, assesses key objectives, monitors progress, and evaluates efficiency/productivity.
  
• Challenges: Data collection, selecting the right metrics, and ensuring consistency.

A general warehouse model

Performance is measured in terms of ratios of output and input factors (De Koster 2012).

Figure 1: Example of input and output variables for a general warehouse model by Johnson and McGinnis (2010).

The major functions of a warehouse are to store products in order to make an assortment for customers, to assemble customer orders, sometimes to add value to the orders by customization activities, organize transport to the customers and ship orders timely, in the way desired by the customer. Warehouse performance, therefore, has multiple dimensions. Often, performance is measured in terms of ratios of output and input factors. Output factors include production (shipped orders, lines and units), quality (for example, order completeness, error-free and on time delivery), flexibility (possibility to cope with changes in customer demand), agility (process adaptation to changed environment) and innovativeness (use of new supply chain concepts yielding competitive advantage). Inputs are the resources used to achieve the outputs. These include the number of full-time equivalents (work hours used per year), investment in systems, buildings and IT infrastructure, process organization (i.e. the management) or the assortment carried.

— Warehouse Assessment in a Single Tour (De Koster 2012, 458).

Our model measures warehouse performance based on the ratio of the services produced to the resources consumed. It includes the most costly inputs, particularly those that may be substituted for one another (e.g., labor and equipment, equipment, and space), and the most valuable outputs.

Two of the most important inputs are capital and labor. For our analysis, we measure labor in hours annually and count both direct and indirect labor hours. Capital is divided into inventory, space, and equipment. Inventory is the on-hand inventory measured in dollars and is an average inventory level for the year. The space component is measured in square feet. Equipment is measured using an equipment inventory as shown in Table A1 in the Appendix online.

Outputs are the results of warehousing operations. A warehouse typically exists to fulfill orders and to store products. The orders fulfilled satisfy the downstream customers in the supply chain, and storage is a service provided to the manufacturers. Orders have order lines (or simply, lines), which may require piece picks (piece lines), case picks (case lines), or pallet picks (pallet lines). It is necessary to designate them as different outputs because different types of lines involve different levels of resource commitment.

— Performance Measurement in the Warehousing Industry (Johnson and McGinnis 2010, 224–25).

Metrics → Decisions

• Typical OR view: start by defining an objective function and constraints.
• Real-world: performance is multi-dimensional, so single-objective models miss trade-offs. Options:
  • Use multi-objective optimization techniques (e.g., genetic algorithms, Pareto optimization, weighted sum approaches).
  • Rank objectives by importance (KPI pyramid).

Three ways a metric enters a decision model

1. Objectives to minimize/maximize (e.g., cost, productivity, service).
2. Limits (constraints) such as:
   • service level \(\geq\) threshold
   • damage \(\leq\) cap
   • labor/space/equipment capacities
3. Targets (setpoints) manage deviation from desired values, for example:
   • Storage utilization band (70–90%).
   • Storage utilization of 85%.

Choose a decision model that matches the metric

• Single Criterion Decision Making: Optimize one objective function subject to constraints.
• Multi-Criteria Decision Making: Simultaneously optimize multiple conflicting objectives. Common approaches include:
  • AHP (Analytic Hierarchy Process): Pairwise comparisons to derive weights and rank alternatives (Saaty 1980).
  • Goal Programming: Minimizes deviations from set goals subject to constraints (Charnes et al. 1955).

Efficiency and trade-off methods answer different questions

• Data Envelopment Analysis (DEA): Nonparametric efficiency measurement of decision-making units (see Abraham Charnes et al. 1978).
• Multi-objective Optimization: Pareto-based search for trade-offs (e.g., Deb et al. 2002).
• Best-Worst Method (BWM): Criteria weights from comparisons of best against others and others against worst (see Rezaei 2015). It requires fewer comparisons than AHP and can reduce the burden on decision makers.

Benchmarking warehouse performance

02

Benchmarking

• Benchmarking is the process of comparing one’s products, business processes, or strategies to those of the most successful firms in the same or other industries (Park 2012).
• Common dimensions measured: quality, time, and cost.
• Typically provides a single efficiency score for performance comparison.

• Common quantitative methods include:
  • Data Envelopment Analysis (DEA): Measures efficiency of decision-making units (see A. Charnes et al. 1978).
  • Regression Analysis: Identifies relationships between variables (see, e.g., Klein Koerkamp 2024).

Benchmarking is the process of comparing one’s products, business processes, or even strategies to those of the most successful firms in the same industry or from other industries. Dimensions typically measured are quality, time, and cost. Benchmarking gives a single efficiency score that can be used for comparing performance. There are several types of benchmarking. Metric benchmarking involves using more aggregative cost or operation information to identify strong and weak performing units. The two most common forms of quantitative analysis used in metric benchmarking are data envelope analysis and regression analysis.

— Order Picking: Issues, Systems and Models (printed pp. 12–13; PDF pp. 29–30) (Park 2012, 12–13).

Internal vs. external benchmarks

• Internal benchmarks: Evaluate performance within your warehouse by comparing similar entities such as sites, shifts, zones, or teams to identify top practices and performance gaps.
  • Project comparison: Assess improvements by analyzing pre/post KPI changes; be cautious of inconsistent KPI definitions, lack of standardization, and overlapping effects.
  • Historical comparison: Track performance trends over months or years, accounting for disruptions or changes in scope.
• External benchmarks: Measure performance against industry norms or competitors (e.g., top-tier metrics, market studies). Pay attention to warehouse type/scale, confidentiality constraints, and interpretation challenges.
  • Sources: Industry reports, trade associations, peer panels, 3PL/client councils, MHE/WMS vendor benchmarks, analyst/consulting reports.

Internal (within your network)

• Purpose: Find best observed practice; quantify gaps by site, shift, zone, team.
• Use when: You control data definitions and processes; want fast, low-friction insights.
• How to compare
  • Project comparison: pre/post KPI deltas with identical scope, horizon, and definitions.
  • Historical comparison: MoM/YoY trends with seasonality controls and notes on disruptions.
  • Normalize: per order/line/case, per labor hour, per m²/m³, per equipment hour.
• Watch-outs: KPI drift (changed definitions), mix effects (SKU/channel/customer), spillovers between areas.

External (outside your network)

• Purpose: Calibrate against market (peer median, top quartile, best-in-class).
• Use when: You need validation for targets or investment cases.
• Make it fair
  • Segment: warehouse type (e-com, retail DC, omni), picking unit (each/case/pallet), automation level, service policy (SLA), order profile (lines/order), product/handling risk.
  • Align definitions: match glossary (e.g., OTIF, dock-to-stock, perfect order).
  • Adjust for scale: use intensity metrics (per hour, per m²) and workload bands.
• Sources: industry studies (e.g., WERC DC Measures), trade associations, cooperative peer panels, 3PL/client councils, MHE/WMS vendors’ user benchmarks, analyst/consulting reports.
• Watch-outs: confidentiality limits, small sample bias, non-comparable methods, survivorship bias.

Benchmarking is the comparison of one warehouse with others. One can try to make the comparison more meaningful by suitably restricting the community of warehouses to which comparison is made. Benchmarking may be done internally, on the processes within a single company; or externally, on the same process in other industries; or on the same process in competitors. There are challenges of diplomacy and information-sharing when benchmarking against other companies, especially competitors.

— Warehouse & Distribution Science [Bartholdi and Hackman (2019), printed p. 256; PDF p. 278].

External benchmarks require comparable operations

When benchmarking externally, it is crucial to ensure that comparisons are meaningful. Demirkran and Ozturkoglu (2022) highlight several factors that can significantly influence KPIs:

• Warehouse type: Import, regional, grocery, wholesale, local, omni-channel, e-commerce, etc.
• Industry: Retail, wholesale distribution, 3PL (third-party logistics), manufacturing, pharmaceutical, medical device, utilities/government, etc.
• Handling unit: Broken case, full case, full pallet picking.
• Geography: Developed vs. developing countries, regional differences.

Warehouse performance metrics in literature and practice

• Literature reviews:
  • Staudt et al. (2015) extracted 38 direct and 8 indirect measures across time, quality, cost, and productivity.
  • Chen et al. (2017) linked KPIs, critical success factors, and capabilities, identifying eight KPIs from four case studies.

Warehouse performance metrics in industry

• Industry benchmarking:
  • Hackman et al. (2001) benchmarked 57 warehouses using DEA, finding efficiency negatively associated with size and higher automation levels.
  • Johnson and McGinnis (2010) reported initial multi-site benchmarking results using an internet-based DEA tool for warehouses (iDEAs-W).
  • WERC’s yearly DC Measures reports (e.g., Tillman et al. 2022) show metric priorities shifting over time across DCs.

Industry vs. Academia?

Demirkran and Ozturkoglu (2022) highlight that while previous studies have provided extensive lists of warehouse performance measures, selecting a concise set of metrics that are valuable, balanced (financial and non-financial), goal-oriented, and aligned with operational priorities remains a critical challenge. This requires considering both industry and academic perspectives.

In addition to the aforementioned academic studies on performance indicators in warehouses, the Warehousing and Educational Research Council (WERC) has been conducting a detailed benchmarking study of key performance measures, the adopted overall strategies and technological investments to monitor the warehousing industries mainly in North America. The latest WERC study, “DC Measures 2019,” was finalized in July 2019. WERC (2019) showed that there are changes in the list of top ten metrics compared to WERC (2018). According to these studies, whereas the companies’ focus was on employee-related metrics in 2018, it was on operations in 2019. This was explained by the increase in business activities, especially in e-commerce in 2019 (WERC, 2019).

There are also a few studies that focus on benchmarking warehouses. Hackman et al. (2001) proposed an input-output model to evaluate and compare the efficiencies of 57 warehouses in various fields. This DEA (Data Envelopment Analysis)-based benchmarking model makes it possible to compare the warehouses that incorporate different types of resources such as labor, space, and equipment as input and different types of output measures such as the number of orders, number of lines per order and storage index. Their findings showed that there is a negative association between warehouse size and efficiency and between the level of automation and efficiency. Long travel distances, poor workflows, communications and supervision in them caused the reasons for low efficiency in large warehouses. Moreover, the existence of static automation systems, the lack of maintenance, and the inappropriate selection of the automation systems were the causes of low efficiency.

Although Staudt et al. (2015) and Wudhikarn et al. (2018) presented detailed and comprehensive lists of performance measures (PMs) in warehouses; it was declared that determining few but sufficient valuable, important, focused, financially and non-financially balanced, and goal-oriented performance measures that are also appropriately assigned over existing operations is a critical task (Thakkar et al, 2009). In doing this, the industries’ point of view is as much important as the academies’ view.

— Key Performance Measures and Digital-Era Technologies in Warehouses (Demirkran and Ozturkoglu 2022, 195).

Warehouses are a substantial component of logistic operations and an important contributor to speed and cost in supply chains. While there are widely accepted benchmarks for individual warehouse functions such as order picking, little is known about the overall technical efficiency of warehouses. Lacking a general understanding of warehouse technical efficiency and the associated causal factors limits industry’s ability to identify the best opportunities for improving warehouse performance. The problem is compounded by the significant gap in the education and training of the industry’s professionals. This article addresses this gap by describing both a new methodology for assessing warehouse technical efficiency based on empirical data integrating several statistical approaches and the new results derived from applying the method to a large sample of warehouses. The self-reported nature of attributes and performance data makes the use of statistical methods for rectifying data, validating models, and identifying key factors affecting efficient performance particularly appropriate. This article also identifies several opportunities for additional research on warehouse assessment and optimization.

— Performance Measurement in the Warehousing Industry (Johnson and McGinnis 2010, 220).

We develop an input-output model of a warehouse system to assess operational efficiency. Our model simultaneously accounts for all of the critical resources (labor, space, storage and handling equipment) and the different workload requirements (broken case, full case and pallet picking, storage and order accumulation) of a warehouse. We collected extensive data on 57 warehouse and distribution facilities from a variety of industries, including auto parts, dental and office supplies, electronics, fine papers, hardware, health care, industrial packaging, mail order apparel, office machines, photographic supplies, and wholesale drugs, and used the model to assess and compare their efficiencies.

We offer 3 conclusions based on a statistical analysis of the operating efficiencies obtained from several models:

• Smaller warehouses tend to be more efficient than larger warehouses.
• Warehouses using lower levels of automation tend to be more efficient. This association is more pronounced in small firms.
• Unionization is not negatively associated with efficiency and in fact may actually contribute to higher efficiency.

— Benchmarking Warehousing and Distribution Operations (Hackman et al. 2001, 79–80).

Selecting and improving KPIs

03

Cycle time metrics

• A cycle time is the total elapsed time to complete a process from start to finish (Tillman et al. 2022, 9–11).
• Common warehouse cycle times include:
  • (Total) Order Cycle Time (OCT): Time from customer order to delivery.
  • Internal Order Cycle Time: Time from order release to shipment.
  • Dock-to-Stock Cycle Time: Time from receiving dock to storage location.
  • Pick Cycle Time: Time taken to pick items for an order.
  • Putaway Cycle Time: Time taken to store received items.

Figure 2: Order Cycle Time (OCT) from customer order to delivery. Milestone spacing is illustrative rather than proportional to elapsed time.

What KPIs can be measured from WMS data?

Diamantini et al. (2014) distinguish between goal-driven and data-driven KPI selection methods.

• Goal-driven: Start from management goals, map to fields of interest, then select KPIs that indicate progress toward that goal.
• Data-driven: Select KPIs primarily from data availability and statistical signal in the operational data, without first anchoring to explicit business goals.

See applicability in Franken (2025).

How to close the performance gaps?

Warehousing & Fulfillment Process Benchmark & Best Practices Guide (2007) provides a practical framework to identify and implement best practices in warehousing operations:

1. Localize gaps with a structured self-assessment by process group.
2. Quantify the performance gap with benchmarked KPI bands.
3. Diagnose root causes using process benchmarks and best-practice attributes.
4. Select improvement levers that line up with the measured gap.
5. Prioritize initiatives by impact, effort, and statistical signal.

1. Localize gaps with a structured self-assessment. Use a short self-assessment by process group (e.g., Receiving & Inspection, Material Handling & Putaway) to rate current practices from Poor to Best. Scores map to clear rank bands and highlight “major opportunity” areas that deserve attention first.

2. Quantify the performance gap with benchmarked KPI bands. For each targeted process, compare your metrics to external ranges scaled from Major Opportunity through Best in Class. Example receiving KPI bands include On-time receipts, where thresholds progress from “<85%” to “≥98%,” and Perfect Order Index with analogous cutoffs—making the gap explicit and comparable across sites. These KPI tables are intentionally designed to support opportunity sizing (Major Opportunity → Best in Class) for each process group.

3. Diagnose root causes using process benchmarks and best-practice attributes. Pair KPI shortfalls with the underlying practice gaps surfaced in the self-assessment. For instance, receiving docks often become choke points; best-in-class sites use firm delivery appointments, date codes, labor balancing, and quick unload commitments to relieve constraints, while applying receiving sortation and WMS-managed consolidation to cut travel and touches.

4. Select improvement levers that line up with the measured gap. Tie levers directly to the practice gaps. Examples for receiving: establish supplier SLAs in routing guides, publish internal standards, and implement double-check scanning/weight confirmation to reduce errors—each explicitly called out in the benchmark ladder.

5. Prioritize initiatives by impact, effort, and statistical signal.

• Impact: size the KPI delta to the next band (e.g., from “Common” to “Good”).
• Effort: classify as quick win (process change/config), enabler (WMS parameterization, ASN activation), or structural (layout, racking).
• Statistical signal: when comparing peers or shifts, favor initiatives where the independent variable (e.g., supplier, shift, method) shows a significant relationship with the KPI (from your multivariate analysis), so you improve where causality is likeliest.

Self-assessment begins with a comparison of your company’s processes against the ranked process attributes in the Process Benchmarks table. As you review the attributes for each process group, you can determine in which category your process falls: Poor Practice, Inadequate Practice, Common Practice, Good Practice, or Best Practice. These rankings are assigned a numeric value from 1 to 5. Quantitative industry performance metrics can be a valuable benchmark to compare KPIs that your company may be gathering. Each KPI is scaled from Major Opportunity to Best in Class.

Receiving sortation can improve productivity of the receiving dock area and improve material flow. Sortation in receiving should be performed when materials arrive in partial or mixed pallets where products must be sorted, classified and sent to different areas for storage or when small quantities of materials are received that do not justify an immediate trip to the warehouse. Set up consolidation areas for materials by putaway zone until there is enough material to move to the zone, reducing travel. Temporary consolidations can be maintained in your WMS.

Companies with good to best practice dock management processes use date codes, set firm delivery appointments to optimize dock and yard usage, balance labor to reduce costs, commit to quick trailer unloading times, and maintain enough room to do the work with no traffic jams, confusion or safety issues. Processes should be in place to double check material through label scans, and verify counts through weight checks.

— Warehousing & Fulfillment Process Benchmark & Best Practices Guide (Warehousing & Fulfillment Process Benchmark & Best Practices Guide 2007, Receiving pp. 2, 5, and 10 (PDF pp. 8, 11, and 16)).

Warehouse assessment tools

04

A single tour can structure a warehouse assessment

De Koster (2012) presents a quick and structured method to assess a warehouse’s performance across 11 key areas. Each area is rated on a six-category scale (Poor=1, Below Average=3, Average=5, Above Average=7, Excellent=9, Best in Class=11), enabling a comprehensive evaluation of:

1. Customer satisfaction
2. Cleanliness, environment, ergonomics, safety, hygiene (HACCP)
3. Use of space, building & technical installations
4. Condition & maintenance of material handling equipment (MHE)
5. Teamwork, management & motivation
6. Storage systems & inventory management
7. Order-picking systems & strategies
8. Supply-chain coordination
9. Level & use of IT
10. Commitment to quality
11. Managing efficiency and flexibility

The Assessment. The tool is divided in 11 areas that have to be assessed, each on a six-category scale (see Exhibit 3). Seven areas (1–5, 8 and 10) are more or less generally applicable to industrial facilities and have been adapted to warehouse environments from Goodson (2002). Areas 6 and 7 (storage and order-picking systems) form the heart of any warehouse and must therefore be included in an assessment. Areas 9 (level and use of IT) and 11 (managing efficiency and flexibility) are equally important in an assessment. To aid filling out the assessment form, a number of Yes/No questions have been formulated (Exhibit 2), which serve the purpose of aiding the area scoring. A score is measured on a 6 category ordinal scale and ranges from poor (1 point) to excellent (9 points) with an additional category ‘best-in-class’ (11 points). Best-in-class means that there is no better. We first discuss the areas in more detail and then discuss results as well as further validation of the method.

17.3 Area 1: Customer Satisfaction

17.4 Area 2: Cleanliness, Environment, Ergonomics, Safety and Hygiene (HACCP)

17.5 Area 3: Use of Space, Condition of Building and Technical Installations

17.6 Area 4: Condition and Technical State of Material Handling Equipment

17.7 Area 5: Teamwork, Management and Motivation

17.9 Area 6: Storage Systems and Strategies and Inventory Management

17.10 Area 7: Order-Picking Systems and Strategies

17.11 Area 8: Supply-Chain Coordination

17.12 Area 9: Level and Use of IT

17.13 Area 10: Commitment to Quality

17.14 Area 11: Managing Efficiency and Flexibility, as a Function of Volume, Assortment and Variety

— Warehouse Assessment in a Single Tour (De Koster 2012, 459–69).

WERC benchmarks KPIs by performance quintile

Tillman et al. (2022) present quintile rankings (Major Opportunity, Disadvantage, Typical, Advantage, Best-in-Class) for common warehouse KPIs, grouped by family:

• Customer Metrics: On-time Shipments, Total Order Cycle Time, Internal Order Cycle Time, Backorders as a Percentage of Total Lines
• Operations Metrics:
  • Inbound: Dock-to-Stock Cycle Time (in Hours), Lines Received and Put Away per Hour, Percent of Supplier Orders Received with Correct Documents, Percent of Supplier Orders Received Damage Free, On-time Receipts — Supplier
  • Outbound: Fill Rate–Case, Order Fill Rate—Case, Lines Picked and Shipped per Person Hour, Orders Picked and Shipped per Person Hour, On-time Ready to Ship.

Table 4 provides a summary of all of the metrics from this year’s study presented in seven columns that are aimed to shed light about how companies are performing. Column 1: Metric Being Examined (metric definitions can be found on pages 9–11 of this report). Columns 2–6: Quintile Rankings: Major Opportunity, lowest 20% of responses; Disadvantage, responses in the 20–40th percentile; Typical, responses in the 40–60th percentile; Advantage, responses in the 60–80th percentile; Best-in-Class, top 20% of responses. Column 7: Median (the actual median performance of all respondents).

Customer Metrics: On-time Shipments; Total Order Cycle Time; Internal Order Cycle Time; Backorders as a Percentage of Total Lines.

Operations Metrics—Inbound Metrics: Dock-to-Stock Cycle Time, in Hours; Lines Received and Put Away per Hour; Percent of Supplier Orders Received with Correct Documents; Percent of Supplier Orders Received Damage Free; On-time Receipts—Supplier.

Operations Metrics—Outbound Metrics: Fill Rate—Line; Order Fill Rate; Lines Picked and Shipped per Person Hour; Orders Picked and Shipped per Person Hour; On-time Ready to Ship.

— 2022 DC Measures: Annual Survey and Report on Industry Metrics (Tillman et al. 2022, 7).

WERC measures: financial, capacity, and workforce

• Financial Metrics: Distribution Cost as a Percent of Sales, Distribution Cost per Unit Shipped, Distribution Cost as Percent of COGS, Days on Hand—Finished Goods
• Capacity/Quality Metrics: Average Warehouse Capacity Used, Peak Warehouse Capacity Used, Honeycomb Percentage, Cube Utilization, Inventory Count Accuracy (Percent by Location), Order-picking Accuracy (Percent by Order)
• Employee Metrics: Annual Workforce Turnover, Overtime Hours to Total Hours, Part-time Workforce to Total Workforce, Contract Employees to Total Workforce, Unplanned Absence Percentage, Cross Trained Percentage
• Perfect Order Index Metrics: Percent of Orders with On-time Delivery, Shipped Complete per Customer Order, Shipped Damage Free (Outbound), Correct Documentation (ASM Invoice, etc.)
• Cash to Cash Metrics: Inventory Days of Supply, Average Days Payable, Average Days of Sales Outstanding

Financial Metrics: Distribution Cost as a Percent of Sales; Distribution Cost per Unit Shipped; Distribution Cost as Percent of COGS; Days on Hand—Finished Goods Inventory.

Capacity/Quality Metrics: Average Warehouse Capacity Used; Peak Warehouse Capacity Used; Honeycomb Percentage; Cube Utilization; Inventory Count Accuracy (Percent by Location); Order-picking Accuracy (Percent by Order).

Employee Metrics: Annual Workforce Turnover; Overtime Hours to Total Hours; Part-time Workforce to Total Workforce; Contract Employees to Total Workforce; Unplanned Absence Percentage; Cross Trained Percentage.

Perfect Order Index Metrics: Percent of Orders with On-time Delivery; Shipped Complete per Customer Order; Shipped Damage Free (Outbound); Correct Documentation (ASN, Invoice, etc.).

Cash to Cash Metrics: Inventory Days of Supply; Average Days Payable; Average Days of Sales Outstanding.

— 2022 DC Measures: Annual Survey and Report on Industry Metrics (Tillman et al. 2022, 7–8).

Reported WERC metrics: ranks 1–6

Table 1: WERC metrics ranked 1–6 in 2022 (from Tillman et al. 2022).

Rank	Metric	Category
1	Average Warehouse Capacity Used	Capacity
2	Order-picking Accuracy (Percent by Order)	Quality
3	On-time Shipments	Customer
4	On-time Ready to Ship	Outbound Operations
5	Peak Warehouse Capacity Used	Capacity
6	Dock-to-Stock Cycle Time, in Hours	Inbound Operations

Each year we identify the Top 12 most popular measures based on the number of respondents to report results for each metric. Table 2 shows the Top 12 most popular metrics used and how that has changed since the 2020 study. This year’s most frequently employed metrics were Average Warehouse Capacity, Order Picking Accuracy, and On-time Shipments, suggesting a bit more balanced approach to managing the DC than we have observed over the past couple of years.

Table 2. Top 12 Metrics for 2022: (1) Average Warehouse Capacity Used, Capacity; (2) Order-picking Accuracy (Percent by Order), Quality; (3) On-time Shipments, Customer; (4) On-time Ready to Ship, Outbound Operations; (5) Peak Warehouse Capacity Used, Capacity; (6) Dock-to-Stock Cycle Time, in Hours, Inbound Operations.

— 2022 DC Measures: Annual Survey and Report on Industry Metrics (Tillman et al. 2022, 5).

Reported WERC metrics: ranks 7–12

Table 2: WERC metrics ranked 7–12 in 2022 (from Tillman et al. 2022).

Rank	Metric	Category
7	Supplier Orders Received Damage Free	Inbound Operations
8	Orders with On-time Delivery	POI*/Customer
9	Order Fill Rate	Outbound Operations
10	Shipped Damage Free (Outbound)	POI/Customer
11	Fill Rate—Line	Outbound Operations
12	Shipped Complete per Customer Order	POI/Customer

Table 2. Top 12 Metrics for 2022: (7) Percent of Supplier Orders Received Damage Free, Inbound Operations; (8) Percent of Orders with On-time Delivery, POI/Customer; (9) Order Fill Rate, Outbound Operations; (10) Shipped Damaged Free (Outbound), POI/Customer; (11) Fill Rate—Line, Outbound Operations; (12) Shipped Complete per Customer Order, POI/Customer.

In fact, while Capacity is still top of mind, a greater focus on Operations has significantly changed the make-up of the Top 12 metrics for 2022. It’s clear to see the impact that a shift to Customer Service strategy and organizational goals has affected the metrics identified in the Top 12. Customer metrics, such as On-time Shipments, moved up. So did Outbound Operations metrics focused on Ready to Ship and Fill Rates.

— 2022 DC Measures: Annual Survey and Report on Industry Metrics (Tillman et al. 2022, 5).

*POI = Perfect Order Index. It combines on-time delivery, order completeness, damage-free shipments, and correct documentation. At 95% for each component, POI is \(0.95^4 = 81.4\%\).

References

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