Inventory optimization is a method of balancing capital investment constraints or objectives and service-level goals over a large assortment of stock-keeping units (SKUs) while taking demand and supply volatility into account.
Inventory management challenges
Every company has the challenge of matching its supply volume to customer demand. How well the company manages this challenge has a major impact on its profitability. In contrast to the traditional "binge and purge" inventory cycle in which companies over-purchase product to prepare for possible demand spikes and then discards extra product, inventory optimization seeks to more efficiently match supply to expected customer demand. APQC Open Standards data shows that the median company carries an inventory of 10.6 percent of annual revenues as of 2011. The typical cost of carrying inventory is at least 10.0 percent of the inventory value. So the median company spends over 1 percent of revenues carrying inventory, although for some companies the number is much higher.
Also, the amount of inventory held has a major impact on available cash. With working capital at a premium, it’s important for companies to keep inventory levels as low possible and to sell inventory as quickly as possible. When Wall Street analysts look at a company’s performance to make earnings forecasts and buy and sell recommendations, inventory is always one of the top factors they consider. Studies have shown a 77 percent correlation between overall manufacturing profitability and inventory turns.
The challenge of managing inventory is increased by the “Long Tail” phenomenon which is causing a greater percentage of total sales for many companies to come from a large number of products, each with low sales frequency. Shorter and more frequent product cycles which are required to meet the needs of more sophisticated markets create the need to manage supply chains containing more products and parts. Hence, businesses need to understand how this affects their inventory and how they can seize the opportunities presented by such products.
At the same time, planning frequencies and time-buckets are moving from monthly/weekly to daily and the number of managed stocking locations from dozens in distribution centers to hundreds or thousands at the points of sale (POS). This leads to a large number of time series with a high level of demand volatility. This explains one of the main challenges in managing modern supply chains, the so-called “bullwhip effect”, which often causes small changes in actual demand to cause a much larger change in perceived demand, which in turn can mislead companies to make bigger changes in inventory than are really necessary.
Non-optimized approach
Without inventory optimization, companies commonly set inventory targets using rules of thumb or single stage calculations. Rules of thumb normally involve setting a number of days of supply as a coverage target. Single stage calculations look at a single item in a single location and calculate the amount of inventory required to meet demand.
Deterministic vs. stochastic
Inventory optimization models can be either deterministic—with every set of variable states uniquely determined by the parameters in the model – or stochastic—with variable states described by probability distributions. Stochastic optimization takes supply uncertainty into account that, for example, 6 percent of orders from an overseas supplier are 1–3 days late, 1 percent are 4–6 days late, 5 percent are 7–14 days late and 8 percent are more than 14 days late.
Stochastic optimization also 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.