Visibility into where your inventory sits and where it is over or understocked, with the causes traced rather than argued about, and the policies and targets behind it calibrated so every function is working to the same numbers.
Where inventory sits, where it is rising and falling, where it is overstocked or understocked, and where it carries write-off risk. Cut by site, SKU, segment and end market, on your own definitions.
Over-forecasting, shifting demand patterns, supply disruption, yield variability, asset and physical constraints, overspecified service levels, batch sizes and campaigns, stock duplicated across levels, volume buys made to win a price break. Each cause needs different data to prove, and the application does that work rather than leaving it to a quarterly study.
Segmentation, made-to-order and made-to-stock classification, demand planning effectiveness, stocking targets, replenishment policies, lead times and minimum order quantities, set as policies the whole business can be held to rather than assumptions buried in a planning system.
Every recommendation carries what it costs elsewhere, so a reduction is taken with the service consequence quantified rather than discovered later.
You cannot say where your inventory is. Where is it increasing, where is it alarmingly increasing, what is overstocked, what is at write-off risk, and are you carrying enough on made-to-stock items and too much to support made-to-order.
You are carrying a lot of inventory and still seeing low margins or low service levels, and the explanation changes depending on who you ask.
You pay a substantial annual licence for a planning system and have yet to see the return.
The S&OP process runs, but without clear policies and targets that all functions are actually aligned on.
“Their expertise in supply chain optimization significantly improved our performance while maintaining high customer service levels. One of the most impressive achievements was the sustainable reduction of our inventory by 30%, which exceeded our corporate target. Their strategic approach in defining our manufacturing strategies, whether make to order, make to stock, or make to forecast, streamlined our operations and better aligned them with our business objectives.”
“They helped us develop a replenishment planning and visibility tool that enables effective VMI replenishments based on the latest demand patterns. Beyond planning, it provides real-time visibility into railcar movements, identifies stockout risks, and alerts us to delays, enabling proactive measures.”
The factors that have to be inferred rather than read from master data: campaign preferences, stock that cannot physically be accessed, a quality hold, a port delay, a published change in carrier schedules. Captured as context, so the calculation adjusts instead of flagging the same opportunity every month.
Root cause agents that hold the context of prior investigations, so only genuinely new and valuable findings surface, and what was dismissed does not come back unchanged.
Policy communications with customers, gathering data from other departments, and telling the affected teams what changed. The work that decides whether a policy actually takes effect.
The application detects when it may be time to reclassify a made-to-stock item as made-to-order or to change a stocking policy, and works out what else has to move with it. A supply lead time shifting affects customer lead time, safety stock and the classification itself, and that is a decision path worth evaluating rather than guessing.
Send us an extract as it is. We will come back with where the cash is sitting and what it would take to release it.