Case study deck · 9 slides
Food manufacturing
A food & baking company
The challenge
The demand plan was rebuilt from scratch every quarter, always looking backward, and took the planning manager three weeks to compile from three disconnected systems. Pumpkin spice loaves ran at the summer average and were still baking in September; a dinner roll SKU stocked out three times during a festival promotion the historical average never saw. Waste and lost sales ate margin from both directions at once.
The approach
We put three years of shipment data, promo dates and seasonal windows into one SKU-week table, which showed the old averages were smearing promo spikes and seasonal cliffs into one flat number. We built a forecast treating seasonality and promotions as separate effects and ran it in shadow mode for six weeks, catching that festival-driven spikes were underweighted, before switching the planning meeting over to it.
What we built
We redesigned the workflow around the model, then built the pieces that make it run every day.
SKU-week demand model with separate seasonality and promo terms
Regional-event flagging for festival-driven spikes the average missed
Weekly forecast-review view with flagged exceptions by SKU
Feed pushing the approved forecast into batch scheduling
Inside the build
How the planning cadence itself changed, from a three-week manual rebuild once a quarter to a weekly forecast review production now runs on.
Tools & systems
Manual quarterly compile
Planning manager pulls shipment history and builds SKU averages by hand across three disconnected systems, taking roughly three weeks per quarter.
Flat-average production plan
Production runs off a single quarterly number that blends seasonal spikes and ordinary demand, driving both overproduction and stockouts.
Shadow-mode validation
New SKU-week forecast model runs alongside the manual plan for six weeks; a regional-event flag is added after festival-driven demand spikes are found underweighted.
Model goes live in planning meeting
The forecast becomes the starting point for the weekly production discussion, replacing the manual export as the first draft.
Monday exception review becomes routine
Planning manager reviews a short flagged-exception list each Monday instead of rebuilding the full plan, with production pulling numbers directly off the weekly feed.
Results
Improvement in forecast accuracy with the data-driven demand model
Reduction in excess inventory across SKUs
Reduced spoilage, with profit margins improving
Time from data to production plan
How it stuck
Three weeks a quarter became ninety minutes every Monday: the planning manager checks the handful of flagged exceptions and overrides in seconds, everything else rolls through untouched. Production picked it up just as fast, once the batch schedule stopped lagging demand by a full quarter.
“I used to hand production a number I already knew was three weeks stale. Now I hand them Monday's number on Monday, and I decide the five exceptions that need a human.”
Demand planning manager
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