Sizing an automation investment before signing
A vendor proposal promised throughput that the demand profile never actually required, and understated the staffing needed on the days that mattered.
- Configuration change
- SmallerA reduced module count met peak service in every replication tested
- Bottleneck relocated
- Pack and shipThe constraint moved downstream once picking was automated
- Peak day service confidence
- 95%Interval reported alongside every throughput figure
The situation
A capital request for goods to person automation was built on average daily volume across the year.
The retailer peak was concentrated in eleven days, when the average was roughly meaningless.
No one had modeled what the surrounding manual processes would do once the automated zone absorbed the pick.
What we did
- 1
Built a discrete event model of the full building, not just the automated zone, including receiving, replenishment, pack, and outbound staging.
- 2
Drove the model with hourly order arrivals reconstructed from two years of order timestamps rather than a daily average.
- 3
Ran a thousand replications per configuration to produce confidence intervals on throughput and cycle time.
- 4
Tested downtime, staffing shortfall, and demand surge scenarios against each configuration option.
Other engagements
All work- Network Design
Consolidating a nine site distribution footprint
A network that had grown by acquisition was carrying duplicate coverage in three regions. Optimization showed the footprint could contract without losing next day reach.
- Forecasting
Forecasting a long tail service parts catalog
Exponential smoothing was being applied uniformly to a catalog where most items moved a handful of times a year, producing confident forecasts of zero.
- Inventory
Releasing working capital without cutting service
Every echelon held a full safety buffer sized against the same demand variability, so the network was paying for the same protection three times.