Warehouse Activity Profiling

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) gt
  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., SKUs,
  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 SKUs involved (work and
  storage complexity)
• average number of orders shipped per day (volume
  of activity)
• average number of lines (SKUs) 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 SKUs
  (operational stability)
• average number of SKUs 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
• SKUs
• 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., birds eye view for
  characterizing location activity)

8
Graphing the results of the Activity Analysis
Discrete Distribution
picks
1.0
zone
A
B
C
D
Pareto curve
picks
1.0
SKUs
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 5.1 - 5.5)

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
• http//www.isye.gatech.edu/people/faculty/John_Bar
  tholdi/wh/book/profile/projects/projects.html
• Problem Solution
• http//www.isye.gatech.edu/spyros/courses/IE6202/
  WAP-cs.pdf