Every methodology below was designed, tested, and run against an actual food manufacturing and distribution business: 4 facilities, 27 production lines, 850+ active SKUs. You'll notice we don't name the company or show its real numbers. That's intentional — it's the same discretion we bring to your data. What we show instead is the scale, the problem, and what the method actually does.
Sales performance was reviewed monthly, after the fact. By the time a report flagged a declining account, it had usually already gone quiet — there was no way to see the drift happening in real time.
A live account-health methodology that flags accounts by ordering-cycle deviation — not a blanket "days since last order" rule — plus territory intelligence, new-customer conversion tracking, and product-level trend detection.
Built and tested against a real $147M+ annual sales portfolio — hundreds of active accounts across multiple territories and product brands, refreshed weekly.
OEE, utilization, and labour cost lived in disconnected spreadsheets, assembled after the week was already over — useful for a monthly review, not for a Tuesday-morning decision.
A live OEE, availability, performance, and quality view down to the individual line, with a full downtime bridge and labour breakdown — the same view a plant manager needs on their morning walk.
Built and tested across 27 production lines in a live multi-facility manufacturing environment.
Purchasing decisions for hundreds of raw material items relied on one buyer's judgment and a weekly meeting, with limited visibility past the next few weeks of coverage.
A five-part purchasing view — annual requirement coverage, 16-week release planning, weekly adjustment thresholds, spot-purchase risk, and overstock categorization — so a buyer sees a problem 16 weeks out, not the week it becomes urgent.
Built and tested against a live purchasing operation covering 850+ active SKUs and their full raw material chain.
15 minutes is enough to know whether there's a fit — and if there is, what a first step would actually look like.
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