The concept of class-based storage combines some of the methods mentioned so far. In inventory control, a
classical way for dividing items into classes based on popularity is Pareto’s method. The idea is to group
products into classes in such a way that the fastest moving class contains only about 15% of the products
stored but contributes to about 85% of the turnover2. Each class is then assigned to a dedicated area of the
warehouse. Storage within an area is random. Classes are determined by some measure of demand frequency
of the products, such as COI or pick volume. Fast moving items are generally called A-items. The next fastest
moving category of products is called B-items, and so on. Often the number of classes is restricted to three,
although in some cases more classes can give additional gains with respect to travel times.
Based on simulation experimental results, Petersen et al. (2004) show that with regards to the travel distance
in a manual order-picking system, full-turnover storage outperforms class-based storage. The gap between the
two depends on the class partition strategy (i.e. number of classes, percentage of the total volume per class)
and the routing method used. However, they suggest using the class-based method with 2 to 4 classes in
practice as it is easier to implement than the volume-based method; it does not require a complete list of the
items ranked by volume and it requires less time to administer than the other dedicated methods do. While for
AS/RS, Yang (1988) and Van den Berg and Gademann (2000) found that (in their studies) 6-class is the best
among other options. The advantage of this way of storing is that fast-moving products can be stored close to
the depot and simultaneously the flexibility and low storage space requirements of random storage are
applicable. Graves et al. (1977) observe that in order to enable an incoming load to be stored in the correct
The concept of class-based storage combines some of the methods mentioned so far. In inventory control, aclassical way for dividing items into classes based on popularity is Pareto’s method. The idea is to groupproducts into classes in such a way that the fastest moving class contains only about 15% of the productsstored but contributes to about 85% of the turnover2. Each class is then assigned to a dedicated area of thewarehouse. Storage within an area is random. Classes are determined by some measure of demand frequencyof the products, such as COI or pick volume. Fast moving items are generally called A-items. The next fastestmoving category of products is called B-items, and so on. Often the number of classes is restricted to three,although in some cases more classes can give additional gains with respect to travel times.Based on simulation experimental results, Petersen et al. (2004) show that with regards to the travel distancein a manual order-picking system, full-turnover storage outperforms class-based storage. The gap between thetwo depends on the class partition strategy (i.e. number of classes, percentage of the total volume per class)and the routing method used. However, they suggest using the class-based method with 2 to 4 classes inpractice as it is easier to implement than the volume-based method; it does not require a complete list of theitems ranked by volume and it requires less time to administer than the other dedicated methods do. While forAS/RS, Yang (1988) and Van den Berg and Gademann (2000) found that (in their studies) 6-class is the bestamong other options. The advantage of this way of storing is that fast-moving products can be stored close tothe depot and simultaneously the flexibility and low storage space requirements of random storage areapplicable. Graves et al. (1977) observe that in order to enable an incoming load to be stored in the correct
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