What are the Key Takeaways from this Executive Summary?
Quick answer
- Static safety stock trades one failure for another. Add weeks and you tie up cash. Cut them and you stock out.
- Sensing is not forecasting. A forecast says what the history implies. Sensing says what is happening now.
- The levers are familiar: segmentation, order quantity, dynamic buffers, postponement. What decides whether they work is the freshness of the signal and who is allowed to change a parameter.
Why Do Traditional Inventory Methods Fail in Volatile Markets?
Quick answer
Every inventory manager knows the pattern. The quarterly plan lands. Safety stock is set from the history. Within weeks the numbers are stale.
A competitor runs a flash promotion. A vessel skips a port. A warm spell moves what people buy. None of that was in the history.
The IHL Group estimates that inventory distortion — the combined cost of overstocks and out-of-stocks — exceeds $1.8 trillion a year worldwide. That is not a forecasting slip. It is what the method produces.
Three things make fixed methods age badly.
The bullwhip effect. A small move at the till becomes a larger order to the warehouse, and a larger one again to the supplier. Each tier adds its own caution.
Long lead times. An ocean lane can run six to eight weeks. You commit with a booking and a letter of credit long before you know what sells.
Season timing. Build too little and you miss the season. Build too much and you carry it for months, then discount it.
The usual answer is more weeks of cover. That buys calm and pays for it in cash.
What Is the Difference Between Demand Forecasting and Demand Sensing?
Quick answer
The difference is the planning horizon.
A forecast works on a frozen window. Moving averages and similar methods do well when demand is steady. When it is not, their strength becomes the problem: they only know the past.
Sensing closes that gap with signals that arrive continuously. Till data shows what shoppers actually bought before the orders flow through. Weather moves demand for anything temperature-sensitive. Carrier arrival feeds show where your inbound stock really is. Search and social trends flag a shift — a viral mention, a competitor recall — before it shows up in orders.
Reductions in forecast error are claimed for demand sensing, and the claims are not comparable with each other because error is measured differently in each. The test that settles it for you is a backtest: hold out the last eight weeks, run both methods forward over them, and compare each against what actually sold at the weekly SKU-location level. That is a week of work and it answers the question for your own demand rather than for somebody’s sample.
The change for the team is a cadence change. Monthly set-and-forget becomes a weekly loop: signal, check, adjust.
What Inventory Optimization Levers Should Leaders Prioritize?
Quick answer
Sensing gives you the signal. These four levers turn it into a decision.
Segment by value and by variability. Rank items by what they contribute, then by how erratic their demand is. A high-value, predictable item can run lean. A low-value, erratic one may not be worth stocking at all; order it in or drop-ship it. Set a different service target for each group rather than one target for everything.
Recalculate order quantity against today’s costs. Order quantity maths uses a carrying cost. Most teams use last year’s average. When storage tightens in peak, the real cost of an extra pallet changes, and so does the right order size.
Let safety stock move. Replace fixed weeks of cover with a buffer that responds to two things: how much demand varies, and how unreliable the lane is. When a carrier’s schedule reliability drops on a lane, the buffer for that lane should rise. When till velocity slows, it should fall.
Postpone the final step. Hold goods generic — unlabelled, unpacked, unconfigured — until you know which variant sells. It cuts the part of the commitment that depends on a guess.
How Do You Measure Whether Any of This Is Working?
Quick answer
Most inventory teams are not short of data. They are short of a way to read it together, quickly enough to act.
The warehouse system holds what is on hand. The transport system tracks what is inbound. The planning system holds orders and the plan. The web store reports what sold an hour ago. Each holds a piece. None holds the position.
Where demand sensing and inventory optimisation are put together, the money shows up as inventory-related working capital and in the cash conversion cycle. Both are already on your balance sheet, so the honest way to size the opportunity is to record them before you change anything and read them again two quarters later — not to apply somebody else’s percentage to your own inventory.
Pulling the pieces together is what Runink FACE does. It reads till feeds, carrier milestone events, warehouse capacity records and supplier performance history out of the systems that already hold them, and produces a forecast at the SKU-location level with the records behind it attached. A proposed change to a safety stock level, a reorder point or an allocation arrives as a draft for the planner who owns that SKU to approve, edit or reject. The planner applies it, because a safety stock change is a decision about cash.
Before any of that, get your own baseline. Two figures, both in your systems: the days between a sell-through signal and a parameter change, and how much of your stock is covered by a parameter nobody has reviewed this year.
Conclusion
Quick answer
The teams pulling ahead are not the ones adding weeks of buffer. They are the ones shortening the gap between a signal and a decision.
The cost of leaving it alone is countable: cash sitting in stock, fill rates slipping, and a planning team working from last month’s picture.
So start with one figure. How many days pass in your operation between a sell-through signal being recorded and a replenishment parameter changing because of it? Get in touch if it would help to work that number out together.
Sources
- APICS / ASCM inventory management body of knowledge — frameworks for segmentation, order quantity and safety stock
- IHL Group inventory distortion study — source for the $1.8 trillion figure quoted above
- McKinsey: working capital management — source for the working capital figure quoted above
