What are the Key Takeaways from this Executive Summary?
Quick answer
- Close the gap between checkout data and the WMS and TMS, so the people planning freight can see what the stores sold.
- Read the sales feed alongside the forecast, rather than in place of it — the forecast sets the season, the till reading sets the week.
- Watch the measures you already report: on-time in-full (OTIF), order-to-delivery time, fill rate, and what you spend on expedited freight and drayage.
What are the Major Business Challenges of Reactive Supply Chain Planning?
Quick answer
For a Director of Inventory Management or a supply chain planner, a forecast built only from last year’s sales is working at a disadvantage. Traditional models look backward. They process what happened last month or last quarter and smooth out the anomalies. That is useful for capacity and contracts, and less useful for next Tuesday.
When demand moves locally — a weather event, a local fixture, something that spreads online — the time between the sale and the replenishment order is time the shelf is empty. By the time the enterprise resource planning (ERP) system raises the order, the sale has gone elsewhere. The logistics team is then left buying the expensive fix: an expedited Less-Than-Truckload (LTL) shipment, or air freight, to protect the fill rate.
This also feeds the bullwhip effect, where a small change at the till becomes a large, badly timed order upstream. The result is crowded warehouses, longer yard dwell, and detention and demurrage charges — the fees carriers and ocean lines levy when their equipment sits longer than the free time allowed. Work this way for long and OTIF and order-to-delivery times suffer while freight spend rises.
How Does Integrating Store-Level POS Data Bridge the Retail and Logistics Gap?
Quick answer
The gap between the shop floor and logistics is one of the larger blind spots in retail supply chains. POS data usually belongs to merchandising or finance and is read in weekly or monthly aggregates. Closing the gap means sending store-level checkout data to the people who plan freight, and putting it next to the Warehouse Management System (WMS) and the Transportation Management System (TMS).
When that feed arrives daily rather than monthly, planners can see which SKUs are moving and where. Instead of waiting for a store manager to raise a replenishment request, the network sees the product leave the shelf. Consumption starts to inform supply directly.
Most of the work is in the data itself. Till systems differ by banner, by region and by vintage, so the formats have to be standardized before anything downstream can read them. Once they are, inventory managers can see how a promotion or a seasonal change is actually affecting stock on hand rather than inferring it a month later. This is one of our supply chain visibility use cases: it is what lets a network move from pushing product to stores on a calendar toward pulling it through on what sold.
What is Demand Sensing and Why is it Better than Traditional Forecasting?
Quick answer
Demand sensing sits closer to execution than planning does. Traditional forecasting runs time-series analysis on historical data over long horizons. Demand sensing reads current signals — POS data first, sometimes local weather, disruptions or search and social activity — to estimate demand over days and weeks.
The difference is the question each one answers. A forecast tells a supply chain VP roughly how many units a region will consume next quarter, which is what you buy capacity and containers against. A same-day till reading tells a planner something else entirely: this store sold through its facing today, and the next delivery is Thursday. Neither figure substitutes for the other. The till reading is narrower and arrives while you can still act on it, which is why it belongs in execution rather than in the annual plan.
Software earns its place here because of volume, not cleverness. No planner can watch the sales feeds of hundreds of stores and adjust despatch by hand. Statistical and machine-learning models are good at finding the movement in large, high-frequency data and separating it from ordinary variation. They revise the short-term picture and put a recommendation in front of the planner, who decides. To judge whether the recommendations are any good, run them alongside your existing forecast for a season and compare both against what the stores actually sold.
How Can AI and Data Enable Proactive Inventory Positioning?
Quick answer
A signal is worth nothing until it moves freight. Proactive inventory positioning means placing stock in the network before the order is placed, on what the sales data suggests is coming.
With a short-term signal in hand, freight can be planned rather than rescued. Instead of a run of expedited LTL shipments, planners can consolidate replenishment into Full Truckload (FTL) loads and stage the stock at a regional cross-dock. When demand does move in that area, the inventory is miles away rather than states away, and final-mile delivery is short and cheap. The number that shows whether this is working is your expedited freight spend as a share of total freight, read quarter by quarter.
Positioning also changes the yard. If the signal points at specific SKUs, the Yard Management System (YMS) can put the containers carrying those SKUs at the front of the unloading queue. Lower-priority safety stock can wait when the warehouse is tight. The same logic applies at the port: the freight you need is drayed first, so it is not the load accruing demurrage. The constraint is the same either way — you can only sequence what you can see, which is why the records have to line up before the sequencing is worth anything.
What are the Steps to Implement Store-Level Sales Sync in Your Supply Chain?
Quick answer
Moving to demand sensing is a phased piece of work. For Operations leaders, the order of the phases matters more than the speed, and data integrity comes before anything else.
First, set out the data governance in writing: who owns the POS feed, what clean looks like, and who fixes it when it breaks. This usually means working with IT to build secure interfaces that stream checkout data without disturbing the tills. The data then has to be cleaned of known artefacts — a cashier scanning one item ten times rather than ten different flavours — before anything plans against it.
Second, put the feed where the rest of the picture is. On its own it tells you little. Read against stock on hand in the WMS, freight in transit in the TMS and inbound containers in the YMS, it tells you what to do. One agreed set of records, rather than four systems each sure of something different.
Third, pilot on a narrow scope. Pick one product category or one cluster of stores. Let the models produce replenishment recommendations, and have planners review them next to the existing forecast for long enough to cover a seasonal swing. Keep the record of which recommendations were accepted, which were edited and which were rejected — that record is what tells you whether to widen the scope.
Fourth, automate only what the pilot justifies, and only where someone still signs off. Let the models draft cross-dock directives or FTL load plans, and keep a named approver on each. Then widen the scope by category and region, checking the same measures each time.
Conclusion
Quick answer
Supply chain resilience is not only a matter of moving boxes faster. Reacting to stockouts with expedited freight and a scramble in the warehouse is expensive, and most of the cost is avoidable with a signal that arrives earlier. Sending store-level sales data to the people who plan freight is that earlier signal. Software turns the volume of checkout records into something a planner can act on, and the planner still decides where the stock goes.
Demand forecasting from POS and order history is one of the kinds of work Runink FACE does: it reads the records you already hold, produces a forecast at the level you ask for, and shows the records the forecast was built from. What it does not do is act on that forecast by itself — each proposed replenishment or reposition goes to a named person to approve, edit or reject. Contact our logistics team if you want to see the mechanism against your own POS feed.
Sources
- Council of Supply Chain Management Professionals (CSCMP) — Standards and practices in demand planning and logistics execution
- Association for Supply Chain Management (ASCM) — Research on how current inventory records affect OTIF and fill rate
