1. Warehouse Activity Profiling
Based on Bartholdi & Hackman
Chpt 5

2. Warehouse Activity Profiling
The careful measurement and statistical analysis of the warehouse activity.
The process of understanding the customer orders that drive the system
Sifting through historical data for opportunities and insights that might confer advantage.
WAP
Summary statistics
SKU data
Order data
Distributions
Location
data
“Structural”
Characterizations, e.g.,
prevailing patterns/trends
relations
dominant elements

3. SKU-related data (distributed over a set of data-bases)
SKU ID
text description
product family (product families are defined for each industry and suggest certain types of storage and handling)
Addresses of storage location in the warehouse (zone, aisle, section, shelf, position on the shelf)
For each location storing the SKU:
storage unit
physical dimensions of the storage unit (length, width, height, weight)
scale of the selling unit
number of selling units per storage unit
Date the SKU was introduced (for assessing growth of the corresponding activity)
Max inventory level by month or week (for assessing space needs)

4. Order-related data (coming from sales-transactions databases)
Order ID
SKU ID
Customer ID
Any needs for special handling
Date/time order was picked
Quantity ordered
Quantity shipped
Remark: This set of data can be really large (the corresponding datafile might exceed the 100M) => Needs processing through some specialized Database software.

5. Data Mining
Handling a set of tables in a relational database management system
Table rows: Records with instances of the object/entity stored in that table (e.g., SKU’s, order lines, etc.)
Table columns: Attributes characterizing the considered entity
Typical functionality involved in data-mining
sorting the rows of a table by a certain attribute
selecting a subset of rows of a table, s.t. all isolated entities satisfy a certain property
counting distinct entries in a table meeting a certain condition
performing joins, i.e., combining the information one table with that of another table to create a new table with a different set of attributes
graphing the results
SQL: Structured Query Language

6. Some basic summary statistics
Order-related
average number of SKU’s involved (work and storage complexity)
average number of orders shipped per day (volume of activity)
average number of lines (SKU’s) per order (picking complexity)
average number of units per line
seasonalities (Seasonal Indices: What percentage of a cycle corresponds to a period in the cycle - temporal distribution of the work)
Facility-related data
area of the warehouse
average number of shipments received per day(the “backend” activity)
average rate of introduction of new SKU’s (operational stability)
average number of SKU’s in the warehouse (volume and scope of operations)
distribution of the personnel to the various activities (labor-related costs and opportunities)

7. A closer characterization of the warehouse workload
What drives the entire warehouse activity is the order/pick lines!
Need to understand how these lines are distributed among
SKU’s
product families
storage locations
warehouse zones
time
Activity analysis
Results are communicated as
discrete distributions
Pareto curves, i.e., cumulative distributions where the items on the horizontal axis are arranged in a decreasing order w.r.t. the corresponding value of the distribution.
other plots (e.g., bird’s eye view for characterizing location activity)

8. Graphing the results of the Activity Analysis
Discrete Distribution
% picks
1.0 A B C D
zone
Pareto curve
% picks
1.0
SKU’s
10K
20K

9. Pareto Effect and ABC Analysis
Pareto Effect: A small percentage of the considered entities account for the largest fraction of the activity (20/80 rule)
ABC analysis: Exploit the Pareto effects in order to classify the considered entities into (typically three: A, B and C) categories, such that
the entities in the first category are the ones responsible for most of the activity, and therefore, more closely managed;
the entities in the second category account for most of the remaining part, and therefore, are moderately important;
the entities in the third category are the largest bulk responsible for only a small part of the activity, and therefore, insignificant.
Remark: ABC classification of the same set of entities will differ from activity to activity (c.f. Bartholdi & Hackman, Tables )

10. Work Patterns and their Implications
Distribution of lines per order: What percentage of orders have a single line, two lines, etc. (Reveals possibilities for batching and/or zoning)
Distribution of picks by order-size: What fraction of picks comes from single-line orders, two-line orders, etc. (reveals whether most work is generated by small or large orders, shipping activity)
Distribution of families/zones per order: What fraction of orders involves a single family/zone, two families/zones, etc. (identifies coupling which can be exploited by the picking process)
Family pairs analysis / “order-crossings” (for zones): identify pairs of families/zones with correlated demand (this correlation should be exploited by putting items in each pair close to each other)

11. “Case Study”: Profiling the Activity of a Wholesales Distributor of Office Products
Problem description:
Problem Solution: