Cycle counting is a popular inventory counting solution that allows businesses to count a number of items in a number of areas within the warehouse without having to count the entire inventory. Cycle counting is a sampling technique where the count of a certain number of items infers the count for the whole warehouse. This sampling method is used by pollsters every day where they measure the opinion of a small number of the people and infer that is the opinion of the population.
When a cycle count is performed, there are two inferences that are made. The primary inference is that the accuracy of the items in the cycle count can be used to determine the accuracy of the items in the warehouse as a whole. The other inference is that if an error is found in the cycle count then that error could be expected to occur for other items in the warehouse.
Types of Cycle Counting
There are a number of types of cycle counting that can be used:
- Control Group
- Random Sample
- ABC Analysis
Control Group Cycle Counting
When a company starts using cycle counting they may use a control group to test that the method they are using to count items will give the best results. The process usually focuses on a small group of items that are counted many times in a short period. This repeated count process will show any errors in the count technique which can then be corrected. The process is continued until the technique has been confirmed to be accurate.
Random Sample Cycle Counting
When a number of items to be counted are chosen at random, this process is known as random sample cycle counting. When a company’s warehouse has a large number of similar items, they can randomly select a certain number of items to be counted. The count can be performed each day or workday so that a large percentage of the items in the warehouse are counted in a reasonable period.
Two techniques can be used in random sample cycle counting; constant population counting and diminished population counting.
Constant population counting is where the same number of items are counted each time a count is performed. This can mean that certain items are counted frequently and some items are not counted, as the selection of items to be counted is random.
Diminished population counting is a technique where a number of warehouse items are counted and then excluded from being counted again until all of the items in the warehouse are counted. Each count selects items from an ever-decreasing number of eligible items to be counted.
ABC Cycle Counting
ABC cycle counting is an alternative to random sample counting. This method uses the Pareto principle as the basis for this technique. The Pareto principle states that, for many events, roughly 80 percent of the effects come from 20 percent of the causes. The ABC cycle counting method uses this principle to assume that 20 percent of the parts in a warehouse relate to 80 percent of the sales, these are the “A” items. The principle is then extended to two other categories where “B” items account for 30 percent of the items and 15 percent of sales and “C” items represent 50 percent of the items in the warehouse, but only 5 percent of sales.
Before a cycle count can be performed, the items in the warehouse have to be identified as A, B or C items. This is usually achieved with the help of a computer system, such as inventory control software. Once each of the items in the warehouse has been assigned a category, the number of times each category should be counted needs to be determined. The items with the highest sales value should be counted more frequently than items that have low sales. Therefore, an item that has been assigned as an “A” item will be counted more frequently than items that are designated as “C” items.
The ABC cycle count does have issues. Warehouses with a large number of distinct items may find that they are counting many times a day. The warehouse may not have enough resources to complete the required number of counts. Another issue is that due to the infrequency of counting of “C” items, the inventory accuracy of these items may be low.
Updated by Gary Marion, Logistics and Supply Chain Expert.