How to Forecast Demand

Peer Discussion

I think the example you cite of ordering shirts for your son’s baseball tournament is an excellent example of the problems of inventory control. Under-ordering can fail to capitalize upon a financial opportunity as well as disappoint customers. Over-ordering, as you note, can render a great deal of worthless inventory, for which there will be little demand after the fact. Similarly, businesses must make decisions about seasonal inventory, such as Christmas ornaments and Halloween-themed candy. Thus, even single-period inventory questions can be critical for ensuring businesses stay in the black (Jacobs & Chase, 2020). With multiperiod inventory monitoring for year ‘round products like soft drinks, of course, these types of products are likewise critical to ensure customer loyalty through reliability, rather than capitalizing sudden upticks in demand. Inventory forecasting based upon past demand is paramount, although other forms of environmental scanning such as weather or the introduction of new products can likewise impact demand.

Reference

Jacobs, F. R., & Chase, R. B. (2020). Operations and supply chain management (16th ed.). New

York, NY: McGraw-Hill.

Peer Discussion 2

Without effective inventory analysis, a company cannot be profitable. Especially today, when people expect they can buy products online whenever they want them, a physical store can offer the advantage of a customer being able to buy something right here and right now. If a store cannot be relied upon to provide highly desirable products in a consistent fashion. But no store can afford to order everything. Having too much inventory has an opportunity cost, just as much as any other economic decision. Inventory must be maintained, and will eventually go obsolete and stale, although this is more likely with some products more than others. One-time purchases enable stores to capitalize upon unique events, as you know, while fixed ordered methods are triggered based upon specific conditions (Jacobs & Chase, 2020). For example, it might behoove a store to order more ice cream during a summer which has a predicted heat wave or batteries during a likely active hurricane season.