Inventory Management, also known in the management parlance as Supply Chain Management, refers to the control of material flow from the suppliers of raw materials, to delivery of the finished product to customers.
Because inventory and capital are positively related it is important to understand and apply various inventory management techniques. For example, the higher the inventory, the higher the capital costs. Controlling costs by managing inventory assumes a primary importance in any manufacturing organization. Most inventory management techniques are based on scientific principles and assume prior knowledge of mathematical and probability theories.
Keeping Track of Goods
Inventory management takes into account other functions such as purchasing, production, and marketing. Its techniques aim at balancing out conflicting goals. By virtue of the fact that it has to be based on scientific theories, these techniques are better explained in terms of models which acquire the characteristics of forecasting tools. These models are composed of methods that forecast the demand, purchase orders, and continual monitoring of the reorder points in order to trigger the suggested orders when one reaches the trigger points. Three of the most important methods are defined below.
Top Inventory Management Methods
1. Optimal Order Quality Technique: This technique was developed by Harris in 1913, but given a more substantive form by Wilson in 1934. Otherwise known as a classical economic order quantity formula, it is still applied today in inventory control management. This model makes certain assumptions in the application such as:
- Demand is consistent and continuous
- Ordering and holding costs are constant overtime
- The batch quantity does not need to be an integer and the whole batch is delivered at the same time
- No shortages are allowed
In this model, equilibrium is achieved when holding costs and ordering quantities become exactly equal. However, this model doesn’t hold up if the assumptions made are not valid. One such situation is when even small errors occur in the batch quantities, it can alter the inventory holding costs dramatically. In that instance more realistic techniques should be used.
2. Silver-Meal Heuristic Model: This is a much more intuitive and simple heuristic method. This sequential method determines the delivery in period one, and simultaneously takes into account the successive demands in periods two and three. When considering period two, a simple test is applied to decide whether this period demand should be added to the delivery batch in the first period. The principle behind this is that under the Silver-Meal heuristic model, the cost per period is considered an alternative to costs per unit; otherwise designated as ‘Least Unit Cost’ heuristics. Subject to other things being equal, the Silver-Meal heuristic model is considered superior to ‘Least Unit Cost’ heuristics because it brings a more effective approximation in forecasting.
3. Wagner-Whitin Algorithm: With the application of algorithm principles, this technique provides an almost exact and optimized solution. The Wagner-Whitin method was developed in 1958 and has been continuously refined by Zangwill (1966), and others such as Federgruen & Tzur (1991, 1994,1995), and Wagelmean et al (1992). The superiority of this method can be found in its applying a finite time horizon against an infinite time horizon, which is real but needs to be handled in determining an optimal solution to the lot sizes in an inventory.
Being dynamic in nature and highly sensitive to cost parameters, inventory management techniques have evolved over the years. Each of these three methods touch major areas relating to inventory control as being a vast science that is applied in management. However, a management student would have to delve deeply into scientific literature to learn more about this area.
- Harris, F.W. (1913): How Many Parts to Make at Once: Factory: The Magazine of Management, 10, 135-136, 152
- Silver, E.A. & Meal, H.C. (1973): A Heuristic for Selection Lot Size Requirements for the Case of a Deterministic Time: Varying Demand Rate and Discrete opportunities for Replenishment, Production and Inventory Management, 14, 64-74
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