The challenge
Cash trapped in the wrong inventory, sales lost to the wrong stockouts, supply and demand that never quite line up. The data to fix this already exists. It's just not being used.
Demand you keep guessing wrong
Forecasts built on averages and last year's numbers. So you over-buy what won't sell and run short on what does. Every miss shows up twice: as cash tied up in dead stock and as sales walking out the door.
Inventory decisions based on averages
Stocking too much of what doesn't sell, too little of what does. Average-based replenishment misses the demand patterns hiding in your data. And the working capital and revenue hiding in those patterns.
Supply and demand that never line up
Sales, planning and procurement each work from their own numbers, so allocation and replenishment lag real demand. The plan looks balanced on paper while service levels slip and margin leaks at the edges.
How we solve it
From gut-feel planning to decisions that move margin and free cash. A systematic approach that proves value in one area, then scales across your supply chain.
Your supply chain data, assessed
Your demand, inventory, logistics and procurement data is analyzed to find where better decisions free the most cash and recover the most sales. You see exactly where margin is leaking and working capital is trapped.
Your decision models, built
Models are tailored to the commercial calls that move margin: demand forecasting, lead-time prediction, S&OP balancing, allocation, replenishment and inventory turn. Whatever drives the most value. Built on your data, validated against your reality.
Your planning team, equipped
Models feed directly into your existing tools and decision processes. Forecasts, allocation calls and replenishment recommendations your team can act on immediately. No new systems to learn.
Your approach, scaled
Once proven in one area, the approach scales across business units and geographies. You build a systematic margin and supply chain capability. Not a one-off project.
Proven results
Supply chain teams that freed working capital and recovered lost sales. With measurable impact.
Machinery Manufacturer
39% better supplier lead time prediction. 10% improvement in purchase-to-delivery estimates. Solution started as a pilot and scaled to 5 global teams across the organization.
Lost sales analysis revealed potential to capture 65% of previously lost revenue. Theory of Constraints-based inventory replenishment model. Smarter stock decisions based on real demand patterns.
Equipment Manufacturer
Group-wide data strategy across all business areas. From 50+ ideas gathered with 10+ business leaders, we prioritized 10 data and AI initiatives and built the roadmap - led by demand and sales forecasting at product-group level to sharpen component orders, inventory and production planning, and to free the safety stock tied up by reactive spare-part sales. Identified potential to grow aftermarket revenue up to 8x and margin up to 10x. A prioritized roadmap, not yet a delivered result.
Team background: H&M. Team members led AI-based supply chain decision support driving 200M€+ in revenue increase. Eniram. 1–3% fuel cost reduction for large marine vessels, translating to millions annually.
What you get
You move from gut-feel planning to decisions that free cash and recover sales. With models that get smarter as more data flows through them.
