A PRELIMINARY COMPARISON OF WAREHOUSE SLOTTING MEASURES
Charles G. Petersen, Department of Operations Management and Information Systems
Northern Illinois University, DeKalb, IL 60115, (815) 753-1454, cpetersen@niu.edu
ABSTRACT
Slotting is the assignment of items or stock-keeping units
(SKUs) to warehouse storage locations.
This paper
evaluates several slotting methods to determine which
method minimizes travel distance in a warehouse. The
results show that popularity and cube-per-order index result
in less picker travel than other slotting methods.
INTRODUCTION
Today’s warehouses have to execute more, smaller
transactions, handle and store more products, offer more
product and service customization, and provide more valueadded services, while having less time to process orders and
with less margin for error. While many firms try to solve
these challenges with more technology, a better solution may
result from a careful analysis of customer orders and
products in the warehouse. Frazelle [2] notes that most
warehouses are spending 10 to 30 percent more per year
than they should because the warehouse is improperly
slotted. Slotting is the assignment of items or stock-keeping
units (SKUs) to warehouse storage locations. In a typical
warehouse it is estimated that less than 15 percent of the
SKUs are properly slotted.
The key to proper slotting is a systematic analysis of SKU
and customer order activity, commonly called warehouse
activity profiling. This profiling process is designed to
quickly identify root causes of material and information flow
problems and to pinpoint major opportunities for process
improvements.
However, the practitioner literature
[1,10,11] states the importance of profiling but gives no
tangible information as to how it should be done. The
academic literature on profiling is minimal, except for Yoon
and Sharp [13,14] that only briefly discuss it.
SKU activity profiling is used to slot the warehouse. This
means where to store each stock-keeping unit (SKU), how
much of each SKU to store, and what storage mode to use
for each SKU. This author and several others have
researched the first question extensively [4,5,6,7,9,12].
However, these previous works have focused on evaluating
storage assignment strategies and not on evaluating the
slotting measures that can be used in determining storage
assignment. The question of how much of a SKU to store
includes not only the total amount to store but also whether a
SKU should be stored in one location or in several different
locations. Determination of the storage mode typically
depends on the characteristics of the SKU. Yoon and Sharp
[13,14] discuss the need to answer these questions as part of
the overall design process of an order picking system.
Although these two questions are also important, they are
beyond the scope of this paper and will be studied in future
research.
However, what slotting measure or method to use? There
are several slotting measures available to a warehouse
manager to use, including popularity, turnover, cube-perorder index, volume, and pick density. The popularity
principle refers to the number of requests for a SKU during
a given period of time. Turnover refers to the demand or
number of units shipped per period. Cube-per-order index
developed by Heskett [3] takes the physical size of the SKU
into account as well as the daily demand for the SKU and
the average order size of the SKU. Volume refers to the
cubic volume of a SKU shipped per period. Pick density is
the number of requests per cubic volume of a SKU. This
method is sometimes used in “golden zone” picking where
the SKUs with the highest pick density are assigned to the
most accessible pick locations taking ease of reach and
fatigue into account. This paper focuses on evaluating these
five slotting measures under a variety of operating
conditions.
WAREHOUSE SIMULATION AND EXPERIMENTAL
DESIGN
The warehouse for this Monte Carlo simulation is a manual
bin-shelving pick area with 10 picking aisles and a front and
back cross-aisle to allow access to all picking aisles (Figure
1). These aisles allow for picking from both sides of the
aisles and are wide enough to permit two-way travel. The
warehouse contains enough storage space to handle 1,000
SKUs. The demand for these SKUs follows the commonly
observed 80-20 curve. For each order, the picker travels
from the pick-up/drop-off (p/d) point to retrieve all the
SKUs on the pick list and then returns to the p/d point to
drop-off the SKUs before picking up a new pick list.
The factors and levels for this experiment are presented in
Table 1 and results in a 5x3x2x3 design with 90 cells. The
five slotting measures are popularity, turnover, volume, pick
density, and cube-per-order index. The three storage
assignment strategies are within-aisle, diagonal, and acrossaisle. These three storage assignment strategies are shown
in Figure 2 with the darkest shading indicating “A” SKUs,
the medium shading for “B” SKUs, and no shading for the
“C” SKUs. Petersen and Schmenner [7] evaluated these
storage strategies using volume-based slotting only and
found that within-aisle and diagonal reduced piker travel
more than the other storage strategies. However, no one has
tested these storage strategies using other slotting measures.
Figure 1 Warehouse layout
Back Aisle
The results of the experiment are shown in Tables 2 and 3.
There are several observations of note. First, the best
overall slotting measures appear to be popularity and cubeper-order index, although turnover is a close third. Pick
density is clearly the worst, but is performance is expected
to improve when evaluated on total time to pick all orders
and not just on travel distance. While the cube-per-order
index is the best slotting measure when using within-aisle
storage, the popularity measure is generally the best with
either diagonal or across-aisle storage.
Figure 2 Storage Implementation Strategies
Within-aisle
Front Aisle
P/D
Table 1
Experimental Factors and Levels
Factor
Slotting measure
Levels
Notation or Values
5
Popularity, Turnover,
P/D
Across-aisle
Volume, Pick Density,
Cube-per-order index
Storage assignment
3
strategy
Within-aisle, Diagonal,
Across-aisle
Routing policy
2
Traversal, Optimal
Order size
3
3, 10, 20 SKUs
In addition to the optimal routing procedure of Ratliff and
Rosenthal [8], the author chose to use traversal routing
because it is commonly used in warehousing and order
picking. Traversal routing requires that an order picker exit
a picking aisle from the opposite end from which he or she
entered. This also sometimes called serpentine or s-shaped
routing.
The author chose three levels of order size corresponding to
small, medium, and large orders. The literature as shown
that order size (or pick list size if orders are batched) has a
major effect on the performance of routing and storage
policies. For each level of order size, 500 orders were
randomly generated. The performance measure for this
experiment is the average route length of the order picker to
complete the 500 orders.
RESULTS AND DISCUSSION
P/D
Diagonal
P/D
It is clear that within-aisle storage is clearly the best storage
implementation strategy for all every factor and level except
when using pick density as a slotting measure. The relative
performance of the slotting measures and storage strategies
does not seem to change whether optimal or traversal
routing is used.
Table 2
Feet)
Average Route Length with Optimal Routing (in
Within
Diagonal
Across
Average
Popularity
99.9
111.9
131.9
114.6
Turnover
102.7
118.2
139.0
120.0
Volume
112.9
127.8
142.0
127.5
Density
184.7
187.0
184.5
185.4
Cube
100.2
113.3
132.2
115.3
Average
120.1
131.7
145.9
132.6
3 SKUs
the storage location in addition to size and weight of the
SKU. Storage locations above the picker’s shoulder or
below the picker’s waist require more time to retrieve. The
area between the waist and shoulders is called the “golden
zone” and typically SKUs with higher demand are stored
there while other SKUs that are commonly ordered with the
golden zone SKUs are located above or below the golden
zone. Frazelle [2] suggests that golden zone picking used in
conjunction with pick density slotting can be used with
SKUs with a high correlation to reduce picker travel.
However, no results are presented and this remains an area
that needs further study.
Table 3
(in Feet)
10 SKUs
Average Route Length with Traversal Routing
Within
Diagonal
Across
Average
Popularity
189.3
234.7
261.3
228.4
3 SKUs
Turnover
195.2
247.9
267.6
236.9
Popularity
147.3
222.8
261.5
210.5
Volume
211.9
263.6
282.2
252.6
Turnover
150.8
231.8
267.2
216.6
169.0
236.6
263.1
222.9
Density
343.1
354.5
348.0
348.5
Volume
Cube
188.7
235.9
261.4
228.7
Density
270.3
269.8
267.7
269.3
Average
225.6
267.3
284.1
259.0
Cube
146.2
225.4
262.0
211.2
20 SKUs
Average
176.7
237.3
264.3
226.1
Popularity
257.0
319.6
341.3
306.0
10 SKUs
Turnover
261.0
328.0
347.6
312.2
Popularity
240.3
386.2
464.0
363.5
Volume
281.9
352.8
368.0
334.2
Turnover
245.3
409.0
467.5
373.9
Density
458.3
473.3
462.5
464.7
Volume
267.2
415.0
472.1
384.8
473.4
469.3
466.9
469.9
Cube
255.0
320.1
340.5
305.2
Density
Average
302.6
358.8
372.0
344.5
Cube
239.4
389.4
464.4
364.4
Average
293.1
413.8
467.0
391.3
Overall
Popularity
182.1
222.1
244.8
216.3
20 SKUs
Turnover
186.3
231.3
251.4
223.0
Popularity
309.3
493.3
569.0
457.2
Volume
202.2
248.1
264.1
238.1
Turnover
313.3
506.8
575.1
465.1
Density
328.7
338.3
331.7
332.9
Volume
337.4
524.5
575.3
479.1
Cube
181.3
223.1
244.7
216.4
Density
576.2
572.9
575.2
574.8
245.3
Cube
307.4
491.6
568.7
455.9
Average
368.7
517.8
572.6
486.4
Popularity
232.3
367.4
431.5
343.8
Turnover
236.5
382.5
436.6
351.9
Volume
257.9
392.0
436.8
362.2
Density
440.0
437.4
436.6
438.0
Cube
231.0
368.8
431.7
343.8
Average
279.5
389.6
434.6
367.9
Average
216.1
252.6
267.3
CONCLUSION
The author has conducted some preliminary work on
evaluating slotting measures and has found that when only
considering travel distance that popularity, turnover, and
cube-per-order index result in significantly less picker travel
than volume and pick density slotting measures. However,
this experiment needs be expanded to evaluate the total time
to complete a picking tour by taking into account the picking
time difference in storage location. The time to retrieve a
SKU from a storage location is dependent on the height of
Overall
References available upon request from the author