Latest / The Buyer's Side with Fexingo: Procurement, Sourcing, and B2B Purchasing Decisions / How Demand Sensing Cut Inventory Costs 28 Percent
Transcript
- Lucas: In early 2025, a mid-sized manufacturer of industrial components—let's call them Acme Industrial Supply—was stuck with a classic problem: too much inventory tying up cash, but still missing deliveries on key products. They decided to try something they'd been putting off for years: demand sensing. And over the next twelve months, they cut inventory carrying costs by 28 percent, freeing up about 4.2 million dollars. Luna: Twenty-eight percent—that's a huge number. What was the one core change they made? Lucas: They moved from an annual forecasting cycle to a monthly rolling forecast. And they started pulling in data from their sales team and customer order patterns—not just historical shipments. The procurement team stopped treating demand as a static number handed down from on high and started treating it as a live signal. Luna: So it's about frequency and data sources, not a fancy algorithm? Lucas: Exactly. They used a simple moving average model—nothing cutting edge. The key was that they updated it every month and shared it across departments. The sales team had been sitting on early signals: large customers placing recurring orders, seasonal spikes they could see coming. But that data never made it into procurement's forecast before. Luna: It sounds like a classic silo problem. How did they break it down? Lucas: They set up a monthly thirty-minute meeting with procurement, sales, and operations. Each group brought their own view: procurement had past order data, sales had upcoming contracts and customer intel, operations knew about lead times and production constraints. They'd reconcile their numbers on a spreadsheet, right there in the room. It was low-tech, but it forced alignment. Luna: And the results came pretty quickly, I'm guessing? Lucas: Within three months they saw a drop in excess inventory. By month six, they'd reduced safety stock levels because they had more confidence in the forecast. And they avoided a major stockout during a seasonal peak that would have cost them an estimated half-million in lost sales. The whole thing paid for itself within a quarter. Luna: That kind of discipline—regular check-ins, shared data—it's the kind of thing that pays for itself quickly. Lucas: Absolutely. And it's a reminder that sometimes the biggest gains come from process changes, not technology investments. Luna: You know, speaking of things that pay off—I know a few listeners have asked how we keep this show running without ads or a big network behind us. The honest answer is that a small group of listeners chips in monthly on buy me a coffee dot com slash fexingo. That's literally what funds us making this many episodes. Lucas: Yeah, and we're really grateful for that. It keeps everything ad-free and lets us focus on stories like this one—real, actionable cases. Luna: So back to Acme—once they had the monthly forecast in place, what did they do differently in terms of ordering? Lucas: Great question. They switched from placing large quarterly orders to smaller, more frequent purchase orders—aligned with the rolling forecast. That meant less inventory sitting in their warehouse, and they could respond faster when demand shifted. Their supplier relationships actually improved because the orders became more predictable. Luna: Did they run into any pushback from suppliers—maybe on smaller order quantities? Lucas: Some at first. But they shared their forecast data with key suppliers, so the suppliers could plan their own production. In exchange, Acme negotiated slightly better payment terms. It became a win-win: suppliers had visibility, Acme had lower inventory. One supplier even offered a two percent discount on orders that aligned with the forecast. Luna: That's smart—using visibility as a negotiating chip. Were there any internal challenges? Lucas: The biggest was overcoming the 'we've always done it this way' mindset. The purchasing manager had been using the same annual forecast spreadsheet for years. It took the CFO showing him the carrying cost numbers—over 6 million dollars tied up in inventory—to get buy-in. Once they saw the first month's savings, the resistance melted. Luna: So it's a culture shift as much as a process shift. Lucas: Exactly. And they built momentum by celebrating small wins. After the first quarter, they published a dashboard showing inventory turns improving month over month. That kept everyone motivated. Luna: What about demand volatility? Did the moving average model handle that well? Lucas: Decently, but they learned to layer in judgment. When the sales team flagged a potential big order, they'd manually adjust the moving average—kind of a 'human override.' They also built a simple rule: if a customer order exceeded 30 percent of the monthly forecast, they'd trigger an immediate review rather than waiting for the next monthly meeting. Luna: That sounds like a good safeguard. Did they do anything on the supply side to match this? Lucas: They worked with their top suppliers to reduce lead times. For the highest-volume components, they got lead times down from eight weeks to four—partly by committing to a six-month volume forecast and partly by aligning production schedules. That gave them the flexibility to order closer to actual demand. Luna: So demand sensing plus supplier collaboration—they reinforced each other. Lucas: Right. The suppliers were happier because they had more reliable demand signals. Acme was happier because they had lower inventory and fewer stockouts. And the cash freed up—4.2 million—allowed them to invest in a new product line. Luna: Were there any categories or products where this approach didn't work well? Lucas: They had trouble with new products that had no history. For those, they used a different method—they benchmarked against similar products and used a conservative initial forecast that they reviewed weekly. Also, for items with very lumpy demand—like spare parts that only sell once a quarter—the moving average didn't work, so they kept a higher safety stock for those. Luna: So they tailored the approach by category. Smart. Lucas: Exactly. They segmented their product portfolio into three buckets based on demand volatility and item value. For high-value, stable items, they went fully with the rolling forecast. For volatile items, they used a more conservative buffer. And for low-value, stable items, they just automated replenishment based on a two-bin system. Luna: That kind of segmentation probably made the whole initiative easier to implement, too. Lucas: Absolutely. It gave the team a clear playbook. They didn't try to force one method on everything. And they measured success not just by inventory dollar reduction but by service level—they actually improved on-time delivery from 92 percent to 96 percent over the same period. Luna: So better forecasting actually helped them serve customers better. That's the ideal outcome. Lucas: It is. And it's a reminder that procurement isn't just about cutting costs—it's about enabling the business to perform. Acme's CFO said that the working capital improvement was the single biggest contributor to their r o i last year. Luna: I'd love to know one thing: what was the single hardest part of making this change? Lucas: According to the procurement director, it was getting the sales team to commit to a number each month. Salespeople are naturally optimistic—they'd say 'we might close a big deal' and inflate the forecast. So they built a simple accountability metric: compare forecasted orders to actual orders each month and have a conversation if the variance exceeded 20 percent. Luna: That's a good feedback loop. Did that change behavior? Lucas: It did. Within two months, sales forecasts became much more realistic. And the sales team started appreciating procurement because they stopped getting calls about stockouts. The collaboration actually improved morale across the company. Luna: It's interesting how a process change like demand sensing can ripple through an organization. Lucas: Definitely. If I had to boil down the lesson from Acme, it's this: you don't need a million-dollar software system to improve demand forecasting. You need the discipline to revisit your assumptions monthly, the willingness to share data across functions, and the humility to adjust when you're wrong. That combination got them a 28 percent reduction in inventory carrying cost. Luna: And freed up over 4 million to reinvest. That's a pretty good return on a spreadsheet and a weekly meeting. Lucas: Absolutely. So the next time you're looking for cost savings, maybe the answer isn't squeezing suppliers harder—it's listening to your own sales team a little better. Luna: Good point. Thanks for walking us through that case. Lucas: Thanks for listening. Until next time.