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How to Improve Your Warehouse Management System: 7 Practical Fixes

By Simon Kish, AVP, Consulting Services 10 min read
Long distribution center aisle lined with orange pallet racking stocked with boxed goods

Quick answer

Improve a warehouse management system (WMS) in this order:

  1. Measure performance against published benchmarks.
  2. Clean item and inventory data.
  3. Re-slot inventory and rebalance labor.
  4. Replace custom code with configuration.
  5. Connect systems through a reusable integration layer.
  6. Test for peak volume and every release.
  7. Put managed support behind the live system.

In Everest's view, replace the platform only when it blocks growth, automation, or vendor support.

At a glance

  • 76%

    of supply chain and logistics leaders surveyed report notable workforce shortages, and 56% report shortages in warehouse operations.

    Descartes, 2024

  • 58%

    of those respondents say workforce shortages have hurt service levels.

    Descartes, 2024

  • 99.68%+

    is best-in-class order-picking accuracy, and best-in-class on-time shipments are 99.5% or higher.

    WERC, 2025 DC Measures Report

  • Half

    of new warehouses in developed markets will be designed as human-optional facilities by 2030.

    Gartner, 2026

Everest's view: most of the work sits in data, configuration, integration, and testing before it sits in new software.

Why Improving Your Current WMS Matters Now

Three pressures are pushing operators to get more from the WMS they already run:

  • Labor is tight.

    The Descartes survey covered 1,000 decision-makers in the U.S., Canada, and nine European countries.

  • Service levels are exposed.

    More than half of those respondents link shortages to weaker service.

  • Automation is coming.

    Gartner describes these human-optional facilities as robot-centric. A WMS built for people-only workflows will need to coordinate equipment as well.

Before changing anything, decide whether the system needs tuning or replacing.

Defining the Terms

Warehouse management system (WMS)
Software that directs how inventory is received, stored, picked, and shipped in a distribution center (DC).
WMS optimization
Improving how an existing WMS performs through data, configuration, integration, testing, and support, without replacing the platform.
Warehouse execution system (WES) and warehouse control system (WCS)
A WES coordinates work across the WMS, labor, and automation. A WCS directs automated equipment in real time.
Material handling equipment (MHE)
Conveyors, sorters, robots, and similar equipment.

Optimize or Replace? A Quick Check

This table is Everest's working checklist, not an industry standard.

Optimize or replace: what each signal points toward

What you seePoints towardFirst step
KPIs below benchmark, vendor still supports your version Optimize Baseline, then fix data, slotting, and configuration
Heavy core customization Optimize first, then reassess Inventory every modification
Integrations rebuilt for each new system Optimize Move to a reusable integration layer
Platform cannot connect to planned automation or new sites Replace or modernize Run an assessment
Version nearing end of support Plan a migration Check the support policy for your exact version

If several rows point to replacement

Read 7 Signs It's Time to Move Off a Legacy WMS.

Otherwise

The seven fixes below are the place to start.

7 Practical Fixes for Your Warehouse Management System

Work through these in order. Each fix makes the next one easier to measure.

The seven-fix sequence for improving a warehouse management system Seven-step sequence for improving a warehouse management system: baseline against benchmarks, clean data, re-slot and rebalance labor, reduce customization, connect systems, test for peak and every release, and run as a managed service. Each step sits one level above the last, and Fix 7 reviews KPIs against the baseline recorded in Fix 1. The seven-fix sequence Work through the fixes in order. Each fix makes the next one easier to measure. 1234567 Baselineagainstbenchmarks Cleandata Re-slot andrebalancelabor Reducecustomization Connectsystems Test for peakand everyrelease Run as amanagedservice Fix 7 reviews KPIs against the baseline recorded in Fix 1 The seven-fix sequence for improving a warehouse management system Seven-step sequence for improving a warehouse management system: baseline against benchmarks, clean data, re-slot and rebalance labor, reduce customization, connect systems, test for peak and every release, and run as a managed service. Fix 7 reviews KPIs against the baseline recorded in Fix 1. The seven-fix sequence Work through the fixes in order. Each fix makes the next one easier to measure. 1234567 Baseline against benchmarks Clean data Re-slot and rebalance labor Reduce customization Connect systems Test for peak and every release Run as a managed service Fix 7 reviews KPIs against the baseline recorded in Fix 1
Fig. 1The seven-fix sequence. Each fix builds on the one before it, and every result is read against the same baseline.

1. Baseline performance against published benchmarks

  • Pull a full year of data for on-time shipments, order-picking accuracy, inventory count accuracy by location, dock-to-stock cycle time, and capacity used.
  • Compare each against WERC's 2025 DC Measures. Best-in-class order-picking accuracy (by order) is 99.68% or higher. Best-in-class on-time shipments are 99.5% or higher.
  • Split results by DC, shift, and customer type to see where the gap sits.
  • Record the baseline. Every later fix needs a before-and-after number.
Baseline scorecard against WERC best-in-class marks A blank scorecard for a full year of data. Best-in-class marks from WERC's 2025 DC Measures: on-time shipments 99.5% or higher, order-picking accuracy by order 99.68% or higher, average warehouse capacity used 90% or higher, peak warehouse capacity used 100% or higher. Inventory count accuracy by location and dock-to-stock cycle time are tracked as part of the baseline. Each result has an empty column for your baseline and columns to split it by DC, shift, and customer type. Record the baseline: every later fix needs a before-and-after number. Baseline scorecard Pull a full year of data. Compare it with WERC's 2025 DC Measures. Split each result to find where the gap sits. Measure Best-in-class Your baseline By DC By shift By customer type On-time shipments Order-picking accuracy (by order) Inventory count accuracy by location Dock-to-stock cycle time Capacity used, average Capacity used, peak ≥ 99.5%≥ 99.68%≥ 90%≥ 100% Track itTrack it your number Record the baseline. Every later fix needs a before-and-after number. Before After Baseline scorecard against WERC best-in-class marks A blank scorecard for a full year of data. Best-in-class marks from WERC's 2025 DC Measures: on-time shipments 99.5% or higher, order-picking accuracy by order 99.68% or higher, average warehouse capacity used 90% or higher, peak warehouse capacity used 100% or higher. Inventory count accuracy by location and dock-to-stock cycle time are tracked as part of the baseline. Split each result by DC, shift, and customer type, and record the baseline: every later fix needs a before-and-after number. Baseline scorecard Pull a full year of data and compare it with WERC's 2025 DC Measures. Split each result by DC, shift, and customer type. On-time shipments Order-picking accuracy (by order) Inventory count accuracy by location Dock-to-stock cycle time Capacity used, average Capacity used, peak BEST-IN-CLASSYOUR BASELINE BEST-IN-CLASSYOUR BASELINE BEST-IN-CLASSYOUR BASELINE BEST-IN-CLASSYOUR BASELINE BEST-IN-CLASSYOUR BASELINE BEST-IN-CLASSYOUR BASELINE ≥ 99.5%≥ 99.68%≥ 90%≥ 100% Track itTrack it your 12-month number Record the baseline. Every later fix needs a before-and-after number.
Fig. 2A baseline worksheet. The red marks are WERC's 2025 best-in-class figures; the dashed fields are what your own full year of data fill in.

Everest noteThe assessment phase starts with visits to flagship DCs to find pain points and redundancies.

2. Clean up item, location, and inventory data

  • Check item dimensions, weights, units of measure, and packaging. Cartonization, slotting, and capacity planning all depend on them.
  • Name one system of record for each data field, and one owner for changes.
  • Fix errors at the source before adjusting counts in the WMS.
  • Track inventory count accuracy by location as a standing KPI.

3. Re-slot inventory and rebalance labor

  • Re-slot by current demand, not last year's layout.
  • Review labor standards and task assignment through labor management.
  • Watch capacity used. WERC's best-in-class marks are 90% or higher on average and 100% or higher at peak.
  • With 56% of respondents reporting warehouse labor shortages (Descartes, 2024), every wasted step matters more.

4. Replace custom code with configuration and update-safe extensions

  • List every modification: what it does, why it exists, and whether the base product now covers it.
  • Retire what the platform handles natively.
  • Rebuild what is truly unique as extensions on the platform's own framework.

Manhattan SCALE and WMOS

Extensions are typically customizations to the core that must be retested at each upgrade.

Manhattan Active®

Built so version updates apply without re-implementation.

Know the difference before you rebuild anything. Source: Everest Technologies.

5. Connect the WMS to everything around it

  • Map every link: ERP, order management, transportation, labor, and automation.
  • Move point-to-point links onto a reusable integration layer. Everest builds these with MuleSoft, Boomi, and Kafka.
  • Monitor latency and failures. A slow integration can look like a slow WMS.
  • Synchronize WMS logic with MHE so equipment follows the same task sequencing. For heavily automated sites, check whether a WES or WCS is needed. See Supply Chain Strategy with Automation and How Much Autonomy Should an AI Agent Have in Your Warehouse?
From point-to-point links to a reusable integration layer Left: point-to-point. The WMS connects to ERP, order management, transportation, labor, and automation through five separate links, each built and maintained on its own, and a slow link can look like a slow WMS. Right: a reusable integration layer, which Everest builds with MuleSoft, Boomi, and Kafka. The WMS connects once to the layer, the layer connects to the same five systems, latency and failures are monitored, and automation equipment follows the same task sequencing as the WMS. Point-to-point Each link built and maintained on its own Reusable integration layer Everest builds these with MuleSoft, Boomi, and Kafka WMS ERPOrder managementTransportationLaborAutomation (MHE) A slow link canlook like aslow WMS WMS INTEGRATION LAYER ERPOrder managementTransportationLaborAutomation (MHE) follows WMS task sequencing Latency andfailuresmonitored From point-to-point links to a reusable integration layer Top: point-to-point. The WMS connects to ERP, order management, transportation, labor, and automation through five separate links, each built and maintained on its own, and a slow link can look like a slow WMS. Bottom: a reusable integration layer, which Everest builds with MuleSoft, Boomi, and Kafka. The WMS connects once to the layer, the layer connects to the same five systems, latency and failures are monitored, and automation equipment follows the same task sequencing as the WMS. Point-to-point Each link built and maintained on its own WMS ERPOrder managementTransportationLaborAutomation (MHE) A slow linkcan look likea slow WMS Reusable integration layer Everest builds these with MuleSoft, Boomi, and Kafka WMS INTEGRATION LAYER ERPOrder managementTransportationLaborAutomation (MHE) follows WMS task sequencing Latency andfailuresmonitored
Fig. 3One layer instead of five separate builds. The same layer is where latency and failures are monitored, and where automation equipment follows WMS task sequencing.

6. Test for peak and test every release

  • Test the full workload at once, including integrations and automation, at peak volume.
  • Automate regression tests so every release is checked the same way. Everest builds these frameworks.
  • Keep a production-like test environment.
  • Schedule changes away from peak.

Everest noteTesting covers functional, integration, end-to-end, user acceptance, and performance work, including load testing for peak volumes and warehouse automation.

7. Run the WMS as a managed service after go-live

  • Set service levels for response and resolution.
  • Trace incidents to root cause instead of repeating repairs.
  • Review KPIs against the baseline on a fixed schedule.
  • Keep release management and monitoring in one accountable team.

Everest noteSLA-backed L2 and L3 support, production monitoring, incident and release management, and continuous optimization are available for Manhattan Active® environments. Everest also supports Manhattan SCALE, WMOS, Infios, and Made4Net, so one team can cover more than one platform.

These fixes cover tuning. The examples below show what is possible when the platform itself is the constraint.

What Improvement Looks Like in Practice

These three engagements involved modernizing or replacing a legacy platform, not tuning an existing one.

Midwest supermarket chain

  • 219 stores
  • 7 DCs

Starting point

Fragmented legacy systems, and a previous integrator that did not deliver.

What Everest did

Led Manhattan Active® WM design, build, and testing across 7 DCs using MuleSoft integration.

Results

  • 28%omni-channel improvement
  • 17%DC productivity gain
  • Zerohypercare defects at DCs 4 and 5

Specialty furniture retailer

  • 120 stores
  • 4 major DCs

Starting point

AS400 systems more than 35 years old.

What Everest did

Converted them to Manhattan Active®, as full integrator from Phase 0 through hypercare.

Results

  • 55% to 95%inventory accuracy
  • 20%+rise in customer satisfaction
Inventory accuracy rose from 55% to 95% Two rows of 20 squares, each square worth 5 points of inventory accuracy. Before: 11 of 20 squares filled, 55%. After the move to Manhattan Active: 19 of 20 squares filled, 95%. Before 55% After 95% Each square is 5 points of inventory accuracy
Fig. 4Inventory accuracy at the specialty furniture retailer, before and after the move to Manhattan Active®.

Seasonal retailer

  • 60 3PL facilities

Starting point

A seasonal, high-velocity 3PL model.

What Everest did

Built a custom WMS for that model.

Results

  • 4.6M+inbound cartons processed during peak cycles
  • 4.6M+outbound cartons processed during peak cycles
  • 850,000+cartons, weekly peak volume

Read the full case study.

Source: Everest Technologies, Supply Chain case studies.

Where Everest Can Help

  • 300+WMS go-lives delivered by Everest Technologies since 1997
  • 150+go-lives on Manhattan Active®, 65 of them in 2024 and 2025 alone
  • PlatinumManhattan Associates Partner and two-time Partner of the Year (Commerce Services, 2022; Collaboration Excellence, 2023)
  • SilverInfios (Körber) Partner

Where Everest fits in the sequence

  • Assess and plan Assessment visits and Phase Zero. Fix 1
  • Configure and extend Certified teams, with 100+ custom extensions delivered. Fix 4
  • Connect Integration, Data & Analytics. Fix 5
  • Test Quality Engineering. Fix 6
  • Support Managed services across the platforms above. Fix 7
  • Take over Everest regularly stabilizes live environments implemented by other partners. Live systems

If your WMS is underperforming its benchmarks, Everest's supply chain consultants can walk through whether to tune, extend, or modernize it.

Talk to an Everest WMS expert
FAQs

Frequently Asked Questions

Work in this order: baseline performance against published benchmarks, clean item and inventory data, re-slot inventory and rebalance labor, replace custom code with configuration or update-safe extensions, connect systems through a reusable integration layer, test for peak volume and every release, and put managed support behind the live system.

WMS optimization is improving how an existing warehouse management system performs through better data, configuration, integration, testing, and support, without replacing the platform.

Optimize first when the vendor still supports your version and the gaps sit in data, configuration, slotting, or integrations. Consider replacing or modernizing when the platform cannot connect to planned automation or new sites, or when your version is nearing end of support. Heavy core customization usually calls for optimization first, then a reassessment. This is Everest's working view, not an industry standard.

WERC's 2025 DC Measures Report tracks more than 30 metrics. Useful starting points are on-time shipments (best-in-class 99.5% or higher), order-picking accuracy by order (99.68% or higher), average warehouse capacity used (90% or higher), and peak warehouse capacity used (100% or higher). Also track dock-to-stock cycle time and inventory count accuracy by location.

On earlier on-premise platforms such as Manhattan SCALE and WMOS, extensions are typically customizations to the core that must be retested and reapplied at each upgrade. Manhattan Active® is built so version updates apply without re-implementation. (Everest Technologies)

Test the full workload at once: functional, integration, end-to-end, user acceptance, and performance testing, including load testing for peak volumes and warehouse automation. Automated regression frameworks let each release be checked the same way. (Everest Technologies)

Everest's support for Manhattan Active® environments includes SLA-backed L2 and L3 application support, production monitoring, incident and release management, and continuous optimization, with 24/7 post-production coverage. Everest also supports Manhattan SCALE, WMOS, Infios, and Made4Net.

Sources

  1. Descartes Systems Group, "Descartes' Study Reveals 76% of Supply Chain and Logistics Operations are Experiencing Notable Workforce Shortages," January 30, 2024 (1,000 decision-makers surveyed in late 2023 across nine European countries, Canada, and the U.S.). https://www.descartes.com/resources/news/descartes-study-reveals-76-supply-chain-and-logistics-operations-are-experiencing
  2. WERC, "WERC Releases 2025 DC Measures Report," June 5, 2025. https://werc.org/news/702949/WERC-Releases-2025-DC-Measures-Report-with-a-Focus-on-Combining-Vision-with-Vigilance-.htm
  3. Yale Materials Handling, "Top 12 distribution center metrics" (best-in-class scores from the 2025 WERC DC Measures Report). https://www.yale.com/globalassets/coms/yale/north-america/documents/white-papers/snack-drawer/YALE-1520_WERC-DC-Metrics-infographic.pdf
  4. Gartner, "Gartner Predicts Half of New Warehouses Built in Developed Markets Will Be Human-Optional Facilities by 2030," April 13, 2026. https://www.gartner.com/en/newsroom/2026-04-13-gartner-predicts-half-of-new-warehouses-built-in-developed-markets-will-be-human-optional-facilities-by-2030
  5. Everest Technologies, Manhattan Active® Implementation Partner (partner status, go-live counts, support scope).
  6. Everest Technologies, Supply Chain & WMS Implementation (methodology, testing, integration, case study results).

About the Author

Simon Kish

Simon Kish

AVP of Consulting Services, Everest Technologies, Inc.

As AVP of Consulting Services at Everest Technologies, Inc., Simon Kish helps retail and supply chain organizations turn complex technology challenges into practical, business-aligned strategies. Over 25 years, he has led digital transformations and IT turnarounds, with deep experience in IT strategic planning, systems integration, and vendor management across the full technology life cycle. He brings that hands-on perspective to Everest's retail and supply chain insights.