A fast-scaling European specialty distributor running purchasing across thousands of SKUs, thousands of suppliers, and six regional warehouses was buying almost entirely on spreadsheets and intuition. Purchase orders were triggered manually, communicated over email and fax, and generated only after stock had already run low.
Net Solutions was engaged to design and build a new procurement platform from the ground up: not a UI refresh on top of the existing ERP, but a new product, a forecasting and automation layer engineered to sit alongside it. At the center of the build was a proprietary forecasting algorithm that we designed, tested, and hardened in production to calculate exactly what to buy, when, and how much and to generate the purchase order automatically. This is the story of how that algorithm was engineered, and what it took to get a 100+ person buying organization to trust software with a decision they’d always made themselves.
This wasn’t a “build us a dashboard” engagement. The client’s growth had outpaced what a legacy ERP and human judgment could reliably coordinate:
Reordering was reactive, triggered when stock hit a minimum, not before. That meant missed order windows, rush freight, and strained supplier relationships.
With 100+ buyers each covering different product groups across six hubs, there was no single, structured way to know what should be ordered, from whom, and by when.
Millions of historical sales, stock, and supplier records needed to be evaluated continuously. Something no buyer, however good, could do by hand across tens of thousands of SKUs.
Cost changes, lead times, and delivery dates moved over email and fax, with no audit trail and no visibility for either side.
The brief, in effect, was a new-product-development problem: design a system that could think about procurement the way a good buyer does, at a scale no buyer could sustain, and do it in a way buyers would actually trust enough to act on.
We ran this as a full product engineering engagement, not a scripted integration job:
This was the technical core of the build, and where most of the engineering effort went.
The problem with “just automate it.” A naive automated reorder rule: reorder when stock crosses a threshold was exactly the reactive logic the client was trying to escape. To actually reduce manual work without reducing accuracy, the algorithm had to replicate (and improve on) the judgment a skilled buyer applies: accounting for seasonality, supplier lead times, commercial constraints, and the business’s own past stock-out mistakes.
We engineered the algorithm as a transparent, step-by-step calculation pipeline rather than a black-box model, so every recommendation could be traced back to its inputs:
As a result, the purchase orders that previously required a buyer to notice a shortfall, calculate a quantity by hand, and manually raise an order, are now generated automatically while every number in the calculation remains visible and explainable to the buyer who owns that SKU.
Why the transparency mattered as much as the math. Buyers had spent years developing intuition for their categories, and a system that just outputs “order 480 units” with no explanation was never going to be adopted, regardless of accuracy. We built a calculation walkthrough into the buyer dashboard so every recommendation could be expanded into its component steps: demand, corrections, lead time, constraints, turning the algorithm from a black box into something buyers could audit, challenge, and ultimately trust. That trust is what allowed the organization to shift the default purchasing motion from manual to automated, rather than running the algorithm as a suggestion nobody acted on.
An algorithm is only as good as the data feeding it and the workflow around it, so we engineered the supporting product surface with the same rigor:
This project sits at the intersection of two things we specialize in: product engineering, building a new system from a blank page, architected for a specific business’s scale and constraints and software engineering discipline applied to a genuinely hard algorithmic problem, where correctness, explainability, and performance all had to hold simultaneously.
We don’t treat automation and trust as a trade-off. The engineering choice to make every step of the forecasting algorithm inspectable rather than optimizing purely for model accuracy is what turned a technically sound system into one buyers actually adopted. That’s the kind of problem we like building for: where the algorithm has to be right, explainable, and fast enough to run across tens of thousands of SKUs, every day, without anyone needing to trust a black box.
We design and build predictive, data-driven platforms replacing manual, reactive processes with intelligent systems that forecast demand, automate buying decisions, and scale with you across new suppliers, more SKUs, and growing order volumes.
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