Computer-Aided Ordering for Perishable Goods.
A store-level demand forecasting engine that cuts perishable waste at the shelf — one model per SKU per store, retrained daily, driving dynamic reorder and price actions.
The challenge
Why retail & cpg teams get stuck.
Every store has a different demand curve
One central forecast for all locations always over-orders for the small stores and under-orders for the busy ones. Perishables are the categories where that mistake shows up as visible waste.
Bakery, dairy and produce have brutal shelf-life economics
Ordering short costs sales. Ordering long costs write-offs. The optimal number changes with day-of-week, weather, local events, and promotional overlap — none of which a static reorder point captures.
Price and demand are entangled
A markdown at 6pm on Friday clears the shelf but destroys next week's model. Nothing in the store's POS stream tags 'this was clearance' — the model needs to know.
The approach
How Initium would build this on SAP BTP.
A concrete solution pattern our SAP BTP architects would design and deliver for you. Not a slideware pitch — an implementable reference architecture.
One forecast per store × SKU × day
Time-series ML in HANA Cloud generates a per-store, per-SKU daily demand forecast. Inputs: multi-year POS, weather, local events, promotion calendar, and shelf-life economics of the category.
Reorder + markdown recommendations
The forecast drives two decisions each morning: what to order for tomorrow, and what to mark down today. Store managers see the recommendation with confidence bands, not a black box.
Feedback loops that improve the model
Manager overrides get captured (with reason codes). Actual sell-through feeds back into the next retrain. Model drift is monitored per store.
Boardroom view of category waste
SAC dashboards show waste-percentage trends by category, store, and region. Category managers can see which stores need which interventions.
The SAP BTP stack
The components we'd use — and why.
Data & Analytics
SAP HANA Cloud
Multi-year POS + weather + calendar unified model
AI & ML
SAP Predictive Analysis Library (PAL)
Per-store, per-SKU time-series forecasting
Data & Analytics
SAP Analytics Cloud + Digital Boardroom
Category waste dashboards for merchandising and ops
Integration
SAP Integration Suite
POS ingest, weather feeds, promotion calendar sync, S/4HANA reorder push
Application
SAP Build
Store-manager app for reorder / markdown recommendations
The value
What the numbers look like.
Directional ranges based on comparable SAP BTP deployments in this pattern. Your baseline will define your actual delta.
20-35%
reduction in perishable waste
Store-level forecasting hits closer to actual demand than any central reorder point.
3-8%
uplift in perishable sales
Better shelf availability at peak hours captures sales that dropped through the floor before.
1 view
of waste by store × category
Merchandising finally sees which stores have systemic waste and can act on it.
How we'd deliver
From discovery to production, without the six-month RFP.
Phase 01
3 weeksDiscover
Pick 2-3 pilot stores, categorise perishables, measure current waste baseline, size the data pipe.
Phase 02
8-10 weeksPilot
Ship the forecast for 3 categories in the pilot stores. Manager app in production. Measure waste-percentage delta.
Phase 03
6-9 monthsRollout
Scale to remaining stores and categories. Add markdown automation. Retire the legacy reorder tool.
Want to explore what this looks like
in your landscape?
30-minute discovery call. We'll walk your team through the reference architecture, size the pilot for your data volumes, and share a fixed-fee scope for the first phase.
Related use cases
Ready to Build Intelligence Into Your SAP Landscape?
Talk to our SAP BTP and AI specialists. Most engagements go from discovery to first deployment in 4 weeks.