Chapter 01
How the team learned this operation
The Diagnosis Agent reads across Consumer Goods Manufacturer's supply-chain operation: 7 connected systems. Within 2–4 weeks it finds where hours drain away and ranks what to automate first.
Every source system, connected
The agent identifies every platform the operation depends on and links into each one directly. No migration required.
What the scan found
Four figures set the frame for everything that followed.
Where time is being wasted
Exception heatmap, ranked by share of manual work, with annual hours for each.
Prioritized by ROI
The output: where automation pays off soonest, and why. Every opportunity shown in full: pain, automation potential, savings, time to deploy.
Year-1 savings
Current pain: Of the 47 documented exception types, 31 recur in predictable ways. Roughly 60% of a coordinator's day goes to assembling context across systems before any call gets made.
Automatable
Year-1 savings
To deploy
Year-1 savings
Current pain: SKU-level forecasts land at 71% accuracy. The monthly S&OP plan is already out of date by the time it ships, and no live signals feed back into it.
Automatable
Year-1 savings
To deploy
Year-1 savings
Current pain: 23% of POs get amended after issue. MRP quantities ignore live demand signals, so buyers keep applying manual fixes.
Automatable
Year-1 savings
To deploy
Year-1 savings
Current pain: Two people track inbound freight in a shared spreadsheet with no live shipment feed, which pushes labor scheduling 15–20% above what's needed.
Automatable
Year-1 savings
To deploy
Year-1 savings
Current pain: Supplier metrics are scattered over three systems with no consolidated view, and quality results never make it into the scorecards.
Automatable
Year-1 savings
To deploy
Year-1 savings
Current pain: Deciding disposition on a return runs 5–7 days. Classification is done by hand, and nothing flags recurring defect patterns.
Automatable
Year-1 savings
To deploy