CI in Automated Facilities

The Mental Shift

In a manual DC, the primary CI target is human behavior — method, pace, path, tool design. In an automated facility, the system runs at a fixed pace and constrains individual associate behavior. The CI target moves to:

1. Manning: How many associates at each G2P port, pack station, or exception-handling workstation? Are they the constraint (system waiting on them) or are they waiting on the system? This changes by time of day, wave configuration, and SKU mix.

2. Exception handling: How many items are falling out of the automated flow (unscannable barcodes, mislabeled product, oversize items, unexpected stockouts)? How fast are exceptions resolved? In many automated facilities, exception handling is a larger labor cost than port picking — because exceptions are managed manually and no one has systematically measured the exception rate, categorized types, or designed a flow for them.

3. Software configuration: Wave logic, slotting algorithms, lift dispatch rules, pick-path optimization — these are the primary CI variables. Adjusted through WES/WMS configuration, not physical rearrangement. In a mature automated facility, software CI is the primary CI activity.

AS/RS Optimization

Most AS/RS systems run at 60–75% of theoretical maximum throughput in practice, because wave design doesn’t account for system dynamics.

Wave Shaping Principle

Release waves that pull work from distributed locations across the AS/RS — not concentrated in one zone. A wave pulling 80% of its work from three adjacent aisles creates contention at lifts serving those aisles while leaving lifts in other aisles idle.

Lift Dispatch Tuning

The algorithm deciding which shuttle or crane retrieves which item is a legitimate CI project. Most WES platforms expose configuration parameters for dispatch priority rules. A/B test different rule configurations on alternating days and measure throughput results.

Replenishment Sequencing (Mini-Load)

If pick faces run out because replenishment is sequenced around crane availability rather than pick demand, associated port idle time is avoidable waste. CI work: analyze port idle time attributable to replenishment delays → adjust replenishment trigger logic to prioritize high-velocity SKU replenishment.

G2P Port Balancing

In a 6-port G2P operation, if 2 ports are consistently backed up and 4 are consistently idle, the operation has a balancing problem. Throughput is constrained by the 2 busy ports while capacity sits unused.

Diagnostic sequence:

1. Analyze tote flow by port: is the WES distributing work unevenly? Is it intentional (dedicated client pods) or inadvertent?
2. Measure associate performance at each port: cycle time per tote, idle time between totes, exception rate per port
3. Identify items that consistently slow port throughput (secondary verification required, difficult-to-pick items, unusual packaging)
4. Adjust pick port assignment logic in the WES, or adjust staffing to match capacity to demand

AMR Fleet Optimization

For Kiva-style, 6 River Systems, Locus Robotics, or similar AMR fleets:

Lever	Target	Action
Idle reduction	Robot idle time <15%	If higher: over-fleet for current volume, or wave design creating gaps between waves. Fix wave timing before fleet size reduction
Charging schedule	High fleet availability during high-volume periods	Stagger charging windows — don’t schedule all robots to charge simultaneously. Often a configuration change that was never explicitly set
Pod slotting	High-velocity SKUs at correct ergonomic height, accessible pod faces	Same ABC velocity principles as conventional slotting apply directly
Algorithm tuning	System runs optimally for your specific operation	Bring throughput-by-time-of-day data, wave configuration data, associate performance tier data to the vendor’s application engineering team. They have parameters they can adjust; they need your data to adjust them intelligently

A/B Testing Wave Logic in a DC

The core principle: control for variables you are not testing. Run test configurations on alternating days with comparable volume profiles — not Monday vs. Friday, not peak day vs. slow day.

Practical A/B test structure:

1. Define the hypothesis. “Releasing waves in three smaller batches per hour rather than one large wave per two hours will reduce port idle time by more than 10%.”
2. Identify the metric. Port idle time per hour, measured from WES throughput logs.
3. Control for volume. Run Configuration A on days when daily order volume is within ±5% of the 4-week average. Run Configuration B under the same condition.
4. Run for ≥10 operating days per configuration. A 3-day test is not statistically meaningful; 10 days per configuration gives enough variance to draw a real conclusion.
5. Measure against the hypothesis. Did port idle time move in the expected direction? Implement the winner; if inconclusive, identify what the test revealed about how the system actually behaves.

Working with WCS/WES Vendors on Tuning

Vendors support configuration changes within the documented parameter set. They will not redesign proprietary algorithms for a single site. What they will do — when you come prepared with data, a clear hypothesis, and a structured test plan — is engage application engineering to find the right configuration settings.

Come with data; expect a productive conversation. Come with a complaint; expect support ticket management.

Skill Translation from Manual DC

A CI engineer excellent at conventional DC Kaizen will need to rebuild their toolkit before being effective in a fully automated facility:

Conventional DC skill	Automated facility equivalent
Time study on picker path	Tote cycle time analysis at each port
Spaghetti diagram of travel	WES throughput log analysis by time-of-day
5S on pick zone	Exception item categorization and flow design
Kaizen event on pick method	A/B test on wave release configuration
Slotting by velocity/ergonomics	Pod slotting by velocity/ergonomics (same principles, different medium)
Labor standard for case pick	Manning model by port and shift condition

See PLC-WCS Integration for the controls architecture context.