Slotting Introduction

Slotting Introduction
In slotting, we determine for each item its: 
(1) optimal storage mode,
(2) optimal allocation of space
(3) optimal storage location in its appropriate storage mode. 
As a result, slotting has a significant impact on all of the warehouse key performance indicators - productivity, shipping accuracy, inventory accuracy, dock-to-stock time, warehouse order cycle time, storage density, and the level of automation.  Hence, few decisions have more impact on the overall performance of a warehouse than slotting.  Yet when we begin our Slotting projects we typically find that less than 15% of the items are slotted correctly. Consequently, most warehouses are spending 10% to 30% more per year than they should because the warehouse is mis-slotted.  After looking back on all those projects and all those different types of items - cans, bottles, rolls of carpet backing, brake parts, spools of yarn, computer hardware, vials of nuclear medicine, automotive service parts, paper products, frozen food, and chainsaws - I identified the common denominators of the projects, and developed this ten-step slotting methodology and supporting tool to assist in slotting projects. 
Slotting Data
Fortunately, the number of data elements required for slotting is not overwhelming.  For each item we need the following data.
Item number,
Item description,
Material type,
Number of requests,
Total quantity requested,
Storage environment (i.e. Frozen, refrigerated, flammable, hazardous, etc.),
Shelf life,
Dimensions (l, w, h),
Item unit cube,
Units per carton,
Cartons per pallet, and
Base unit of measure
This information should be readily available from the product or item master file.  Just the process of evaluating the accuracy and availability of this data is helpful as a data integrity audit.
 For each customer order we need the customer identification, the unique items requested on the order and the quantities of each, and order date and time. This information should be available from the sales and/or order history file.  The sample size required depends heavily on the seasonality of the industry.  If there are large annual surges of demand, such as in the mail order and retailing industries, then a 12-month sample is necessary.  If the demand is fairly stable over the course of a year, as in automotive service parts, then a three to six month sample will be appropriate. 
Slotting Statistics
 Once the raw data is captured, the computation of slotting statistics is fairly straightforward.  Unfortunately, the natural interpretations and application of the results may be counter intuitive and misleading. 
Unit(s) of Measure
Notes and Comments
Time (Year, Quarter, Month, Week, Day)
The time period for slotting calculations.
Requests per Period
Sometimes referred to as the hits on an item.  Used with volume to determine assignment to a storage mode and location within the storage mode.
Units Shipped per Period
Sometimes referred to as the demand for an item.  Used with unit cube to compute cube movement for storage mode assignment and space allocation.
Unit Cube
Measures the physical size of one unit of a unique item.  The information may already be in a database.  If not, C can be computed by measuring the size of the outer container for the item (pallet, case, tote, bag, etc.) and dividing by the number of units in the container.
Cube Movement
V =
T x C
Sometimes referred to as the volume.  Used to determine the appropriate storage mode and the allocation of space in the storage mode.
Pick Density
D =
P / V
Used in golden zoning.  The items with the highest pick density should be assigned to the most accessible picking locations.
Demand Increment
I =
T / P
Units per Request
Demand Correlation
The probability that item i will appear in order when item j does.
Standard Deviation of Demand
Measure of the daily standard deviation of demand.
These statistics appear on the surface to be self-explanatory.  However, there are some subtle but critical issues surrounding the interpretation of each statistic.  For example, popularity is often incorrectly measured in $sales or unit sales.  The popularity (P) of an item, like the popularity of a song on a jukebox, should be measured by the number of times it is requested.  This indicator is critical since it is a measure of the number of potential times an operator will visit the location for a particular item.  Since most of the work in a warehouse is traveling to, from, and between warehouse locations, knowledge of the potential location visits for individuals and families of items is critical to success in managing the overall work content in the warehouse. 
 Unfortunately, many warehouse managers and analysts stop with popularity in their search for slotting criteria.  Popularity is used singly to assign items to storage modes, to allocate space in storage modes, and to locate items within storage modes.  Let’s consider the example of golden zoning a section of bin shelving.  The objective is to maximize the amount of picking activity that is done at or near waist level.  Assume there is 7 cubic feet of space available in the golden zone.  Suppose there are three items we are considering for slotting.  The slotting statistics for the three items are recorded below.
Item ID
Pick Density
140 requests/month
7 ft3/month
20 requests/ft3
108 requests/month
4 ft3/month
27 requests/ft3
75 requests/month
3 ft3/month
25 requests/ft3
Suppose we decide to store a month’s supply of material in bin shelving.  Item A requires 7 cubic feet, item B requires 4 cubic feet, and item C requires 3 cubic feet.  Suppose we rank the items based on popularity alone to determine the order of preference for assignment into the golden zone.  (Remember the golden zone only has 7 cubic feet of capacity).  With the popularity ranking, item A will be assigned to (and will exhaust the available space in) the golden zone.  There will be 140 visits to the golden zone per month.  (Remember we are trying to maximize the number of trips to the golden zone.)  Is that the best we can do?  Absolutely not!  Suppose we assign items B and C to the golden zone.  In that case, there will be 183 trips to the golden zone.  Had we used pick density as the criteria for the preference ranking, we would have maximized the activity in the golden zone.  That is why that measure of picking activity is so critical to the success of slotting, and consequently why it is so important to have all of the slotting statistics available.
Reslotting Optimization
Source: Right Chain