Comparison of performance measurement-systems used for order-picking

In consequence of rapidly changing market demands companies are permanently encouraged to review their own processes and structures and to modify them. Being one of these developments, order-picking is involved as part of an intra-logistics system. But to take appropriate actions, system performance and system costs have to be measured permanently. Concerning this the use of performance measurement-systems as further development of traditional systems of key figures is suitable. In this paper various performance measurement-systems are compared and their suitability for an implementation in order-picking systems is estimated. On the basis of the result of the evaluation a first concept of a performance measurement-system for order-picking will be developed by using typical key figures that are mentioned in academic literature. Finally, hints for a necessary detailed implementation and evaluation in practice will be given.

Calculation of the average travel distance in a low-level picker-to-part system considering any distribution function within the aisles

The estimation of the average travel distance in a low-level picker-to-part order picking system can be done by analytical methods in most cases. Often a uniform distribution of the access frequency over all bin locations is assumed in the storage system. This only applies if the bin location assignment is done randomly. If the access frequency of the articles is considered in the bin location assignment to reduce the average total travel distance of the picker, the access frequency over the bin locations of one aisle can be approximated by an exponential density function or any similar density function. All known calculation methods assume that the average number of orderlines per order is greater than the number of aisles of the storage system. In case of small orders this assumption is often invalid. This paper shows a new approach for calculating the average total travel distance taking into account that the average number of orderlines per order is lower than the total number of aisles in the storage system and the access frequency over the bin locations of an aisle can be approximated by any density function.