Stock Optimization by Types of Inventory

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Inventory Management must consider various types of inventory when looking to optimize it, whether the focus is ensuring the stock is available to the end customer whenever needed, or minimizing carrying costs and product obsolescence, or both.  Depending on the categorization of your inventory (cycle stock or safety stock), the approach to coordinating and optimizing inventory levels is unique.

Types of Inventory

Inventory is purchased and held for a number of reasons, all of which serve to meet demand.  However, supply chains often categorize inventory by how and when it will be used to satisfy the end customer.

Cycle stock is meant to fulfill regular demand for products. It is measured as a function of the minimum order size and is ordered on cadence with lead time.  The image below depicts the proper ordering schedule of cycle stock to fill relatively stable customer demand for a product taking into account the known or anticipated lead times.

Optimizing cycle stock management is a matter of automating the decision-to-purchase process.  Because cycle stock serves the most stable portion of demand, the best lever to pull is planning automation.  Rather than manually coordinating supplier lead times with anticipated demand, the process can be entirely automated with a system that produces and applies accurate demand forecasts to your inventory strategies, in real time, and generates recommended purchase orders so your cycle stock is ordered at the exact right time to fulfill demand – every time.

Safety stock is used by inventory managers to buffer the organization against spikes in demand, ultimately protecting the end customer against stockouts.  However, safety stock does not come without associated purchase and storage costs.  Optimization of safety stock can be a tremendous value-add to supply chains who often carry too much safety stock, or – at times – not enough.  Since the purpose and amount of safety stock are to buffer against uncertainty, reducing safety stock without stocking out of needed products requires better uncertainty modeling.

Stochastic optimization accounts for demand volatility, which is a top priority among the challenges faced by supply chain professionals.  For example, management predicts a 65 percent probability of selling 500 units, a 20 percent probability of selling 400 units, and a 15 percent probability of selling 600 units.  High service levels can be achieved with cost overruns, excessive inventory, and firefighting, but higher profitability can be achieved by understanding the sources of volatility and planning appropriately.  The result is a better understanding of the inventory requirements than with a deterministic approach.

With an inventory optimization approach that combines automated decision-making with stochastic (or probabilistic) demand forecasting, your organization will buffer themselves and your customers against demand uncertainty while reducing costs and remaining profitable.