Use case

Demand forecasting at SKU-location grain.

From the field, AI native workflow redesign of demand forecasting process within Demand Planning Supply Chain function.

Convolving expertise

A senior Convolving delivery team partnered with the supply chain planning function for one sprint. Operators from our expert network – with forty combined years inside enterprise demand and supply planning – reviewed the redesign at each checkpoint. Forward-deployed engineers built inside the team's planning, ERP, and POS-feed stack. One flat fee, artifact out, no retainer creep.

Situation

Today the planner runs a weekly forecast at the brand-region level. SKU-location accuracy lives in a separate workbook, refreshed by hand.

ERP, WMS, and SCM master data drift. POS feeds arrive with delays. The planner spends most of the week stitching inputs and overriding outliers. Walmart's in-house multi-horizon RNN cuts forecast error roughly thirty percent; Unilever's twenty AI-enabled control towers report twenty-five percent fewer stockouts and ten percent efficiency gain. ASCM and IBF report twenty to forty percent accuracy gains; the legacy stack does not get there because the data does not flow.

Forecast accuracy Baseline Brand-region grain

SKU-location Spreadsheet Side workbook, manual refresh

Planner time >50% On dashboards and rebuild, not exceptions

Stockouts Frequent Long tail of small shortages

Click any node to see the activities and tools behind it. Open the canvas in fullscreen for the horizontal view.

Complication

Largest obstacles and inefficiencies.

SKU-location accuracy stays in a side workbook.

Planning runs at brand-region; the operational truth lives one grain finer. Stockouts and overstock both hide there.

Planners spend most of the week stitching inputs.

Over fifty percent of planner time goes to dashboards and rebuild, not to exceptions and judgement.

Demand shocks break historical models.

Regime changes – pandemic, tariff shifts, channel mix – outpace the baseline. The planner overrides by hand or watches the model drift.

Resolution

The AI-native cycle.

Same five steps. Click any node to see what the redesign does in that step.

Forecast accuracy ▲ 20–40% ASCM/IBF band on AI-redesigned

SKU-location Native From side workbook to first class

Planner time On exceptions From >50% rebuild to <20%

Stockouts ▼ ~25% Unilever-band on AI-enabled

Key changes

What the redesign actually shifts.

Accuracy gain

Forecast error drops twenty to forty percent at the SKU-location grain.
Stockouts fall toward twenty-five percent in the Unilever band.
Driver decomposition explains every weekly delta.

Planner capacity

Planner time on rebuild drops from over half toward under twenty percent.
Exceptions land in a queue with context and recommendation.
Judgement work concentrates where it matters.

Master data discipline

Master data resolves at ingest, not in the spreadsheet.
Lineage holds across ERP, WMS, and SCM.
Stale records surface for retirement.

Audit and control

Every override logs rationale and model version.
Variance reports refresh daily, not monthly.
S&OP reads the same trail as planning.

Deploy this in your team.

The redesign above ships as a step-by-step playbook. Data ingestion spec, demand model documentation, exception rule library, planner workbench prompts, and the rollout cadence we use on engagements.

Get the playbook Or book a coffee