Product-level inventory analysis for companies that need clearer insight into stock levels, capital, warehouse pressure, service performance, logistics activity and emissions.

Inventory management is rarely a simple question of having more or less stock. For companies that manage hundreds, thousands, or even tens of thousands of items, every product can behave differently. Demand patterns vary. Lead times differ. Order quantities change. Some items move every day, while others are slow, irregular, seasonal, or strategically important. When these differences are managed through broad product groups, simplified rules, spreadsheet formulas, or managerial intuition, small inefficiencies can accumulate into serious business consequences: excess stock, tied-up capital, warehouse pressure, avoidable transport activity, higher storage costs, obsolete inventory, waste, and service-level risk.
OptimInventory was developed to analyze inventory decisions at the level of each individual item. Its purpose is to help identify the lowest inventory levels that can still support the required level of business performance. This is an important distinction. OptimInventory does not simply ask how much inventory a company has. It helps examine how much inventory is actually needed for each item under realistic demand, lead-time, service-level, and replenishment conditions. 
The goal is not to reduce inventory at any cost. The goal is balanced improvement: lower unnecessary inventory where possible, sufficient availability where required, and a clearer understanding of the trade-offs behind each decision. OptimInventory is not intended to replace ERP, MRP, WMS, or other existing business systems. It complements them by using data, simulation modeling, and computational processing to understand how inventory and replenishment decisions behave in practice.

In many companies, inventory parameters are still defined manually or semi-manually. Common approaches include historical averages, ABC categories, ERP defaults, safety-stock rules, spreadsheet calculations, or the experience of inventory managers.
These approaches can be useful as a starting point, but they are often too general when applied to large assortments. A product group may contain items with very different demand patterns, supply conditions, and service requirements. A rule that works for one item may be inefficient, risky, or unnecessarily expensive for another.
OptimInventory takes a different approach. Instead of relying only on product groups or representative examples, it supports analysis at the product level. Each item can be evaluated according to its own data, behavior, constraints, and business importance. The analysis can include historical demand, demand variability, target fill rate, supplier lead time, review period, reorder point, order-up-to level, minimum order quantity, working-day constraints, replenishment frequency, order size, holding cost, ordering cost, transport cost, warehouse pressure, and transport-related emissions where relevant. This enables comparison of inventory scenarios in a structured way. For example, the analysis can show how different review periods affect average stock, how longer lead times increase required inventory, how demand variability changes safety requirements, or how replenishment frequency influences transport activity and cost. Instead of asking only whether inventory is “high” or “low”, OptimInventory helps answer more useful questions: which items are probably overstocked, which items are exposed to service-level risk, which parameters create unnecessary cost, and where operational changes could improve the balance between availability, capital, storage, logistics, and sustainability. This type of analysis is especially important because inventory decisions are interconnected. Higher inventory may improve availability, but it also ties up capital and space. Lower inventory may free resources, but it can increase stockout risk. Frequent replenishment may reduce average stock, but it can increase ordering activity, transport frequency, handling effort, and emissions. Less frequent replenishment may reduce logistics activity, but it can require more stock. There is rarely one universal answer. The optimal decision depends on the actual item, its demand pattern, supplier behavior, operating constraints, and required service level.