Share this article
Follow us on Linkedin
If your plan follows shipments, you’re already late.
Across consumer goods, retail, and ecommerce, many organizations still anchor their demand plan to shipment orders. It’s understandable: shipment orders feel tangible, auditable, and familiar.
But shipment orders are a proxy for demand—and a late one. In fast-moving markets, that proxy breaks.
Why omnichannel demand broke shipment-led planning.
Consumer demand is now shaped in real time by promotions, marketplace placement, social sentiment, weather, micro-trends, and even diet shifts.
Shipment orders, by contrast, are clunky and policy-driven—batch cycles, minimums, safety stocks, and calendar rhythms all distort true sell-through. Planning on shipments alone is like steering by the rearview mirror: you’ll move, but you’ll miss the bend ahead.
Two patterns show up again and again:
- Over-shipping. Inventory stalls, cash is locked, damages and obsolescence creep in, and markdowns become the escape hatch.
- Under-shipping. Shelves go empty, service drops, and operations scramble with overtime, changeovers, and costly freight for rushing delivery orders.
Different symptoms, same root cause: the plan is anchored to a lagging indicator instead of the actual consumer demand.
What “good” looks like: POS and other retail-level data as your primary demand signals.
Modern demand planning starts with the end consumer and enriches that signal with context—weather events, social buzz, dietary shifts, and trend data. Practically, this means:
- Making real-time POS the primary demand input.
- Letting shipments play a supporting role, not the lead.
When actual demand becomes the heartbeat of the plan, planners stop guessing and start seeing what the full operation truly needs for balancing demand and supply, with improved planning of material inventory requirements.
A historical barrier no longer there.
The historical barrier was a “plumbing” issue. Getting POS clean and consistent across retailers used to mean manual files and one-off mappings. Today, platforms automate POS collection and harmonization, so data arrives ready for planning.
With that foundation, companies such as Westernacher Consulting and Alloy.ai bring both the consulting and integration expertise to integrate POS data and other retail-level data into robust solutions like SAP Integrated Business Planning for supply chain (“SAP IBP”)—not as a dashboard, but as core inputs, which can improve forecast accuracy significantly and, consequently, drive important supply chain performance results.
Where AI fits—and where it doesn’t.
AI isn’t a magic button. It only works when the data foundation is solid—clean, timely POS routed into planning workflows with governance and guardrails. Without that, AI just moves noise faster.
Used well, AI is an accelerator that helps teams utilize and harmonize the data they already have by:
- Cleansing demand: filtering outliers from promos, calendar effects, and one-off events.
- Choosing the right model for each segment: no single algorithm wins everywhere.
- Managing scale: keeping thousands of demand streams tuned without endless manual intervention.
A practical blueprint: from shipments to POS + AI.
1. Assess the gap. Compare shipment-led forecasts to POS sell-through; quantify volatility by category, channel, and lane.
2. Wire the data. Automate POS ingestion across retailers; harmonize definitions, calendars, and hierarchies.
3. Operationalize in SAP IBP. Feed POS into demand planning and tune the process, so planners act on the signal, not just view it.
4. Pilot for proof. Select a handful of products/locations and measure accuracy, stockouts, waste, and freight before and after.
5. Layer AI thoughtfully. Automate cleansing and model selection where scale demands it; keep human oversight where context matters.
6. Embed the change. Shift cadence (daily/weekly, not just monthly), update S&OP touchpoints, and coach planners on consumer-first behaviors.
Bottom line.
Shipment-led planning isn’t “wrong”—it’s late. Teams that shift to consumer-led, POS-driven planning and then layer AI on top of a reliable data foundation see improvements where it counts: forecast accuracy, inventory turns, waste, logistics costs, and ultimately, margin.
