The Effect of Warehouse Cross Aisles on Order Picking Efficiency

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, [email protected]

ABSTRACT and Sharp [13,14] discuss the need to answer these questions as part of the overall design process of an order Slotting is the assignment of items or stock-keeping units picking system. Although these two questions are also (SKUs) to warehouse storage locations. This paper important, they are beyond the scope of this paper and will evaluates several slotting methods to determine which be studied in future research. method minimizes travel distance in a warehouse. The results show that popularity and cube-per-order index result However, what slotting measure or method to use? There in less picker travel than other slotting methods. are several slotting measures available to a warehouse manager to use, including popularity, turnover, cube-per- INTRODUCTION order index, volume, and pick density. The popularity principle refers to the number of requests for a SKU during Today’s warehouses have to execute more, smaller a given period of time. Turnover refers to the demand or transactions, handle and store more products, offer more number of units shipped per period. Cube-per-order index product and service customization, and provide more value- developed by Heskett [3] takes the physical size of the SKU added services, while having less time to process orders and into account as well as the daily demand for the SKU and with less margin for error. While many firms try to solve the average order size of the SKU. Volume refers to the these challenges with more technology, a better solution cubic volume of a SKU shipped per period. Pick density is may result from a careful analysis of customer orders and the number of requests per cubic volume of a SKU. This products in the warehouse. Frazelle [2] notes that most method is sometimes used in “golden zone” picking where warehouses are spending 10 to 30 percent more per year the SKUs with the highest pick density are assigned to the than they should because the warehouse is improperly most accessible pick locations taking ease of reach and slotted. Slotting is the assignment of items or stock-keeping fatigue into account. This paper focuses on evaluating these units (SKUs) to warehouse storage locations. In a typical five slotting measures under a variety of operating warehouse it is estimated that less than 15 percent of the conditions. SKUs are properly slotted. WAREHOUSE SIMULATION AND EXPERIMENTAL The key to proper slotting is a systematic analysis of SKU DESIGN and customer order activity, commonly called warehouse activity profiling. This profiling process is designed to The warehouse for this Monte Carlo simulation is a manual quickly identify root causes of material and information bin-shelving pick area with 10 picking aisles and a front and flow problems and to pinpoint major opportunities for back cross-aisle to allow access to all picking aisles (Figure process improvements. However, the practitioner literature 1). These aisles allow for picking from both sides of the [1,10,11] states the importance of profiling but gives no aisles and are wide enough to permit two-way travel. The tangible information as to how it should be done. The warehouse contains enough storage space to handle 1,000 academic literature on profiling is minimal, except for Yoon SKUs. The demand for these SKUs follows the commonly and Sharp [13,14] that only briefly discuss it. observed 80-20 curve. For each order, the picker travels from the pick-up/drop-off (p/d) point to retrieve all the SKU activity profiling is used to slot the warehouse. This SKUs on the pick list and then returns to the p/d point to means where to store each stock-keeping unit (SKU), how drop-off the SKUs before picking up a new pick list. much of each SKU to store, and what storage mode to use for each SKU. This author and several others have The factors and levels for this experiment are presented in researched the first question extensively [4,5,6,7,9,12]. Table 1 and results in a 5x3x2x3 design with 90 cells. The However, these previous works have focused on evaluating five slotting measures are popularity, turnover, volume, pick storage assignment strategies and not on evaluating the density, and cube-per-order index. The three storage slotting measures that can be used in determining storage assignment strategies are within-aisle, diagonal, and across- assignment. The question of how much of a SKU to store aisle. These three storage assignment strategies are shown includes not only the total amount to store but also whether in Figure 2 with the darkest shading indicating “A” SKUs, a SKU should be stored in one location or in several the medium shading for “B” SKUs, and no shading for the different locations. Determination of the storage mode “C” SKUs. Petersen and Schmenner [7] evaluated these typically depends on the characteristics of the SKU. Yoon storage strategies using volume-based slotting only and density is clearly the worst, but is performance is expected found that within-aisle and diagonal reduced piker travel to improve when evaluated on total time to pick all orders more than the other storage strategies. However, no one has and not just on travel distance. While the cube-per-order tested these storage strategies using other slotting measures. index is the best slotting measure when using within-aisle storage, the popularity measure is generally the best with Figure 1 Warehouse layout either diagonal or across-aisle storage.

Back Aisle Figure 2 Storage Implementation Strategies

Within-aisle

Front Aisle P/D

P/D Table 1 Experimental Factors and Levels Across-aisle Factor Levels Notation or Values Slotting measure 5 Popularity, Turnover, Volume, Pick Density, Cube-per-order index Storage assignment 3 Within-aisle, Diagonal, strategy Across-aisle Routing policy 2 Traversal, Optimal Order size 3 3, 10, 20 SKUs P/D Diagonal 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 P/D 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 It is clear that within-aisle storage is clearly the best storage experiment is the average route length of the order picker to implementation strategy for all every factor and level except complete the 500 orders. when using pick density as a slotting measure. The relative performance of the slotting measures and storage strategies RESULTS AND DISCUSSION does not seem to change whether optimal or traversal routing is used. The results of the experiment are shown in Tables 2 and 3. There are several observations of note. First, the best Table 2 Average Route Length with Optimal Routing (in overall slotting measures appear to be popularity and cube- Feet) per-order index, although turnover is a close third. Pick Within Diagonal Across Average golden zone. Frazelle [2] suggests that golden zone picking used in conjunction with pick density slotting can be used 3 SKUs with SKUs with a high correlation to reduce picker travel. Popularity 99.9 111.9 131.9 114.6 However, no results are presented and this remains an area Turnover 102.7 118.2 139.0 120.0 that needs further study. Volume 112.9 127.8 142.0 127.5 Table 3 Average Route Length with Traversal Routing Density 184.7 187.0 184.5 185.4 (in Feet) Cube 100.2 113.3 132.2 115.3 Within Diagonal Across Average Average 120.1 131.7 145.9 132.6 10 SKUs 3 SKUs Popularity 189.3 234.7 261.3 228.4 Popularity 147.3 222.8 261.5 210.5 Turnover 195.2 247.9 267.6 236.9 Turnover 150.8 231.8 267.2 216.6 Volume 211.9 263.6 282.2 252.6 Volume 169.0 236.6 263.1 222.9 Density 343.1 354.5 348.0 348.5 Density 270.3 269.8 267.7 269.3 Cube 188.7 235.9 261.4 228.7 Cube 146.2 225.4 262.0 211.2 Average 225.6 267.3 284.1 259.0 Average 176.7 237.3 264.3 226.1 20 SKUs 10 SKUs Popularity 257.0 319.6 341.3 306.0 Popularity 240.3 386.2 464.0 363.5 Turnover 261.0 328.0 347.6 312.2 Turnover 245.3 409.0 467.5 373.9 Volume 281.9 352.8 368.0 334.2 Volume 267.2 415.0 472.1 384.8 Density 458.3 473.3 462.5 464.7 Density 473.4 469.3 466.9 469.9 Cube 255.0 320.1 340.5 305.2 Cube 239.4 389.4 464.4 364.4 Average 302.6 358.8 372.0 344.5 Average 293.1 413.8 467.0 391.3 Overall 20 SKUs Popularity 182.1 222.1 244.8 216.3 Popularity 309.3 493.3 569.0 457.2 Turnover 186.3 231.3 251.4 223.0 Turnover 313.3 506.8 575.1 465.1 Volume 202.2 248.1 264.1 238.1 Volume 337.4 524.5 575.3 479.1 Density 328.7 338.3 331.7 332.9 Density 576.2 572.9 575.2 574.8 Cube 181.3 223.1 244.7 216.4 Cube 307.4 491.6 568.7 455.9 Average 216.1 252.6 267.3 245.3 Average 368.7 517.8 572.6 486.4 Overall Popularity 232.3 367.4 431.5 343.8 CONCLUSION Turnover 236.5 382.5 436.6 351.9 The author has conducted some preliminary work on Volume 257.9 392.0 436.8 362.2 evaluating slotting measures and has found that when only Density 440.0 437.4 436.6 438.0 considering travel distance that popularity, turnover, and cube-per-order index result in significantly less picker travel Cube 231.0 368.8 431.7 343.8 than volume and pick density slotting measures. However, Average 279.5 389.6 434.6 367.9 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 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 References available upon request from the author