Forward – reserve allocation
In order to speed up the pick process, it is in many cases efficient to separate the bulk stock (reserve area)
from the pick stock (forward area). The size of the forward area is restricted: the smaller the area, the lower
the average travel times of the order pickers will be. It is important to decide how much of each SKU is
placed in the forward area and where in the area it has to be located. Figure 2 shows three areas in which a
single SKU can be stored and picked, depending on the storage and pick quantity. Dividing a SKU’s
inventory over multiple areas implies regular internal replenishments from the reserve to the forward area.
One of the trade-offs to be made is then to balance additional replenishment efforts over extra pick effort
savings. It may even be advantageous to store some of the SKUs only in the reserve area, for example if
demand quantities are high or if demand frequencies are low. Furthermore, replenishments are often restricted
to times at which there is no order picking activity, which gives additional constraints. The decisions
concerning the problems described here are commonly called the forward-reserve problem. Literature
includes Frazelle et al. (1994) Hackman and Platzman (1990), and Van den Berg et al. (1998). In their book
Warehouse science, Bartholdi and Hackman (2005) devote a full chapter to this problem.
A concept closely related to the forward-reserve problem is dynamic storage. It aims at making the pick area
very small in order to reduce travel time, and bringing the SKUs to the storage locations dynamically, just in
time for the pick (by an automated crane, carousel, or VLM). The number of locations available in the
forward area is usually smaller than the total number of SKUs. As these systems are capable of achieving very
high picker productivity, they are becoming more and more popular (according to the authors’ knowledge at
least 15 implementations the last few years in Western Europe). The interesting decision problems are in the
interaction of the grouping of orders in a batch (more orders means fewer replenishments, but simultaneously
more SKUs are needed implying larger travel distances), the assignment of SKUs to locations, the timing of
the replenishments, and scheduling of the automated crane. This area is still virgin ground for academics.
Forward – reserve allocationIn order to speed up the pick process, it is in many cases efficient to separate the bulk stock (reserve area)from the pick stock (forward area). The size of the forward area is restricted: the smaller the area, the lowerthe average travel times of the order pickers will be. It is important to decide how much of each SKU isplaced in the forward area and where in the area it has to be located. Figure 2 shows three areas in which asingle SKU can be stored and picked, depending on the storage and pick quantity. Dividing a SKU’sinventory over multiple areas implies regular internal replenishments from the reserve to the forward area.One of the trade-offs to be made is then to balance additional replenishment efforts over extra pick effortsavings. It may even be advantageous to store some of the SKUs only in the reserve area, for example ifdemand quantities are high or if demand frequencies are low. Furthermore, replenishments are often restrictedto times at which there is no order picking activity, which gives additional constraints. The decisionsconcerning the problems described here are commonly called the forward-reserve problem. Literatureincludes Frazelle et al. (1994) Hackman and Platzman (1990), and Van den Berg et al. (1998). In their bookWarehouse science, Bartholdi and Hackman (2005) devote a full chapter to this problem.A concept closely related to the forward-reserve problem is dynamic storage. It aims at making the pick areavery small in order to reduce travel time, and bringing the SKUs to the storage locations dynamically, just intime for the pick (by an automated crane, carousel, or VLM). The number of locations available in theforward area is usually smaller than the total number of SKUs. As these systems are capable of achieving veryhigh picker productivity, they are becoming more and more popular (according to the authors’ knowledge atleast 15 implementations the last few years in Western Europe). The interesting decision problems are in theinteraction of the grouping of orders in a batch (more orders means fewer replenishments, but simultaneouslymore SKUs are needed implying larger travel distances), the assignment of SKUs to locations, the timing ofthe replenishments, and scheduling of the automated crane. This area is still virgin ground for academics.
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