In high-velocity warehouses, most orders flow smoothly—until they don’t. Short picks, damaged goods, barcode mismatches, inventory variances, delayed scans, failed put-away, and carrier handoff issues can quickly turn into backlog, missed dispatch SLAs, and customer escalations. The problem isn’t only the exception itself; it’s the time lost in triage—figuring out what happened, who owns it, what action is allowed, and how fast it must be resolved.
A multi-node distribution operation processing high daily order volumes faced recurring operational interruptions due to exceptions across inbound, storage, picking, packing, and outbound. Exceptions were managed through spreadsheets, emails, supervisor calls, and ad-hoc decisions. As peak season volumes increased, the exception queue became a major bottleneck.
Key issues included:
Slow exception triage and unclear ownership
Exceptions were discovered by pickers, QA, or dispatch teams, but ownership was not consistent. Problems bounced between inventory control, floor supervisors, and customer service teams.No standardized decision logic
Similar exceptions were handled differently by different shifts. Some issues were over-escalated, others were ignored until they became SLA breaches.Limited system-driven insights
The WMS contained transaction logs, but exception resolution depended on manual lookups across WMS screens, handheld scan histories, and inventory snapshots.High rework and repeat exceptions
Because root causes were not categorized consistently, recurring issues (wrong bin labels, poor barcode quality, cycle count gaps) were not addressed systematically.Customer impact and SLA risk
Exceptions delayed dispatch, increased split shipments, raised cancellations, and created customer support escalations—especially for high-priority or time-sensitive orders.
The business needed a faster, consistent way to detect and triage exceptions—so teams could act in minutes, not hours.
Maayan Technologies implemented a Warehouse Exception Triage & Automation Layer that standardized exception categories, introduced rule-driven routing, and automated allowable recovery actions—while keeping human oversight for high-risk cases.
1) Standardized Exception Taxonomy and Codes
We established a practical exception taxonomy aligned to warehouse reality, such as:
Short pick / item not found
Inventory mismatch / negative inventory
Barcode unreadable / label mismatch
Damage suspected / QA hold
Put-away pending / location blocked
Weight/Dimension mismatch at packing
Carrier scan missing / outbound handoff delay
Temperature control breach (where applicable)
This created a common language across shifts and sites and enabled reliable reporting.
2) Rule Engine for Triage, Ownership, and SLA Routing
A rules engine was configured to automatically determine:
Ownership (inventory control vs floor supervisor vs QA vs dispatch vs customer service)
Priority based on order SLA, customer tier, shipment type, and cut-off times
Next-best action allowed by policy (re-pick from alternate bin, trigger cycle count, hold for QA, substitute item based on rules, split shipment, escalate to supervisor)
Rules reduced the “decision lag” that typically happens when exceptions hit the floor.
3) Automated Data Lookups for Context-Rich Decisions
To minimize manual investigation, the system pulled context automatically from WMS and related systems:
Last scan event chain (who scanned, where, and when)
Bin history and recent replenishments
Inventory snapshot and variance signals
Packing station exceptions and weight checks
Carrier pickup schedule and cut-off window
Exception tickets were enriched with this evidence so the resolver could act immediately.
4) Assisted Resolution with Guardrails
For common, low-risk exceptions, the system suggested or triggered actions automatically:
Initiate re-pick tasks from alternate locations
Trigger directed cycle count for suspected variance
Route damaged items to QA hold and create replacement pick
Hold orders that violate policy until supervisor approval
Notify dispatch when carrier cut-off risk is detected
High-impact actions (substitutions, order cancellation, overriding inventory) required supervisor approval with a documented reason.
5) Visibility and Exception Control Tower
We created a dashboard view for operations leaders showing:
Real-time exception volume by type and location
Aging and SLA risk heatmaps
Top root-cause patterns (labels, bins, SKUs, zones, shifts)
Resolved vs pending performance by team
This enabled proactive management instead of end-of-shift firefighting.
6) Continuous Improvement Loop
Weekly reviews used exception analytics to target the causes behind repeat disruptions:
Label quality fixes, barcode reprints
Bin maintenance and location master cleanup
Replenishment timing improvements
Training for scanning discipline and packing checks
Cycle count schedule adjustments for volatile SKUs
The implementation improved speed, consistency, and SLA stability:
Faster exception triage with rule-based routing and context-enriched resolution tasks.
Reduced manual coordination by automating ownership, prioritization, and standard actions.
Lower repeat exceptions through consistent categorization and root-cause insights.
Improved on-time dispatch performance as exceptions were resolved earlier in the flow.
Better auditability through logged actions, approvals, and standardized reasons.
Higher operational control via dashboards and SLA risk monitoring.
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