By: Jacob Read
The primary decisions of inventory control are as follows: What products or materials should be made or ordered? How Much of those products and materials should be made or ordered? When should we make or order those products and materials? Reasons that companies will hold inventory are to achieve economies of scale, to protect against variability in demand, lead time, etc., and more. Costs associated with an inventory control system include holding or carrying costs that represent the cost of keeping items in inventory over a period of time, replenishment costs that represent the cost of making or ordering to increase the inventory level, and penalty or shortage costs for not having sufficient inventory to meet demand.
A good tool to use for planning an inventory control system is an appropriate forecasting model of past demand data. Different forecasting models exist for different data patterns, such as level, seasonal, or intermittent demand data. Each model uses different parameters to analyze past demand patterns and forecast future demand points. As time goes on and more data becomes available, the forecasting model can be continuously updated and improved.
Quantitative inventory control methods can utilize forecasting analysis to optimize when, how much, and what products are made or ordered by minimizing the costs associated with these decisions. Just like different forecasting models exist for different data patterns, different inventory control techniques exist for different demand patterns and various levels of uncertainty.
One of the most commonly known inventory control models is the basic Economic Order Quantity (EOQ) model. This model is very simple and easy to use; however, it has several underlying assumptions that do not typically reflect realistic scenarios such as no replenishment lead time or allowing a non-integer order quantity. More complex models that incorporate lead times, both deterministic and time-varying, quantity discounts, inventory shortages, and variable demand patterns can be derived from this model.
When using forecasts and inventory control models, it is important to remember that they are usually wrong. If this is true, then why would you ever want to use them to make decisions? The answer is because they provide insight into making future decisions. “Good” forecasts are accurate and give you an idea of the range for your future demand. These methods shouldn’t be used independently of important market information either, such as the effects of a new popular movie release on the demand for related movie merchandising.
Better control over the inventory system provides benefits beyond the costs of inventory. Holding excess inventory results in greater storage space requirements, which limits space utilization. Read more about this in our Space Utilization post.