Demand Planning & Inventory Optimization

“Reduce inventory levels and free up working capital, but don’t run out of stock”: such is the eternal dilemma of inventory managers, trying to strike the right balance between demand and supply for hundreds of thousands of spare parts and SKUs, each with a specific demand pattern and different service level requirements.

Getting it right should be a science, but for many organizations, the shortcomings of traditional planning solutions and ERP systems, together with an incomplete overview of the supply chain and a lack of understanding of individual SKU behaviors, make it a bumpy road filled with guesswork.

Unreliable forecasts force demand and inventory planners to stay on the safe side, leading to excessive inventory, costs and waste.

It’s simply impossible to optimize inventory without a structured methodology and powerful analytics.

Undoubtedly, using machine learning approaches to model the impact of internal and external variables on demand will significantly improve your forecasting accuracy. But reducing forecast errors and removing uncertainty is only part of the solution.

Many other must be considered in order to make optimal planning decisions, such as supplier lead times and disruptions, inventory movements, production orders, inventory costs, quality and production issues, and bills of material. Collecting all this information is often a time-consuming endeavor.

We solve this problem with an integrated planning process combining all available data in a single system, to get a full picture of what is going on and provide automated and up-to-date recommendations on how many items to keep in stock, minimizing costs but not compromising on service level.

Planners get notified when their input is required. They can react quickly to changing conditions and simulate the impact of their decisions on inventory and supply chain costs in real time.