A warehouse is a complicated and busy place and it can be hard to get an accurate sense of what is happening. WAP makes this hard work easier. Objectives of this slides are to give a Basic Idea about WAP.

1. WAREHOUSE
ACTIVITY PROFILING
Master of Science in Logistics Management
Faculty of Business & Information Systems
GL 006_Warehouse Management
Mohammad Nazmuzzaman Hye
1001748700
Public Talk
1

2. Outline
• Concept of Warehouse Activity Profiling
• Master Data for Warehouse Activity Profile
• WAP Process and Example
2

3. Warehouse Activity Profiling (WAP)
References:
1. J.J. Bartholdi and S. T. Hackman. Warehouse & distribution science. Supply chain and logistics institute, Georgia institute of technology, 2009.
2. E. Frazelle. World-class warehousing and material handling. McGraw-Hill Professional, 2002.
3. D.E Mulcahy. Warehouse distribution and operations handbook. McGraw-Hill New York, 1994.
Warehouse
• A Warehouse is a complex and busy supply chain entity.
• Data Analytics on warehouse comes under activity profiling
Profile
• Profile: an outline/Snapshot of an aspect of any logistics
activity.
• Example: Customer Order Profile (Behavior of Customer
order/Ordering Pattern)
Profiling
• The systematic analysis of item or order activity to identify
root cause , opportunities for improvement and basis for
decision making.
Warehouse Activity
Profiling
• Analysis of Historical data for the purpose of Projecting
Warehouse Activity
• WAP determines Storage Mood , Physical Layout , workflow
process and labor and equipment requirements.
3

4. Warehouse Activity Profiling (WAP)
• What : improving warehouse by understanding natures & exploring patterns
• Idea : data mining with database program
References:
1. J.J. Bartholdi and S. T. Hackman. Warehouse & distribution science. Supply chain and logistics institute, Georgia institute of technology, 2009.
2. E. Frazelle. World-class warehousing and material handling. McGraw-Hill Professional, 2002.
3. D.E Mulcahy. Warehouse distribution and operations handbook. McGraw-Hill New York, 1994. 4

5. Investigation = WAP
Crime Investigation Warehouse Activity Profiling
 Gathering evidence & witness  Gathering Data
 Understanding Motives  Understanding Patterns
 Selecting Suspects  Selection Cause and Solutions
 Capturing Murder  Improving efficiency and productivity
Questions and Data Information Success of Profiling
References:
1. J.J. Bartholdi and S. T. Hackman. Warehouse & distribution science. Supply chain and logistics institute, Georgia institute of technology, 2009.
2. E. Frazelle. World-class warehousing and material handling. McGraw-Hill Professional, 2002.
3. D.E Mulcahy. Warehouse distribution and operations handbook. McGraw-Hill New York, 1994. 5

6. Benefits of Warehouse Activity Profiling
Understand Demands & Patterns
• Layout, Picking Policy, Labor Management
Calculate Key Performance Index (KPI)
• Snap Shot of Warehouse
Managing SKU
• Select Suitable Equipment, packages , slotting, default pick path
Gather Data for Design
References:
1. J.J. Bartholdi and S. T. Hackman. Warehouse & distribution science. Supply chain and logistics institute, Georgia institute of technology, 2009.
2. E. Frazelle. World-class warehousing and material handling. McGraw-Hill Professional, 2002.
3. D.E Mulcahy. Warehouse distribution and operations handbook. McGraw-Hill New York, 1994. 6

7. Master Data
 Master Data = Source Data (Profiling Database)
ITEM
MASTER
LOCATION
MASTER
ORDER
MASTER
Database Related to SKUs Database of inventory at
all storage location
Database of sale in-out to
warehouse
References:
1. J.J. Bartholdi and S. T. Hackman. Warehouse & distribution science. Supply chain and logistics institute, Georgia institute of technology, 2009.
2. E. Frazelle. World-class warehousing and material handling. McGraw-Hill Professional, 2002.
3. D.E Mulcahy. Warehouse distribution and operations handbook. McGraw-Hill New York, 1994. 7

8. Profiling Data: Item Master
 General : SKU ID, Description, Vendor ID
 Bulk Break : Break SKU , Box Per Pallet
 Physical: Volume, width (length X height X weight)
 Time: received date , expired date
 Ordering : min-max , response person
 Others: Packing note , shipping note, lot # , equipment
Source: Warehousing Science http://www2.isye.gatech.edu/j̃jb/wh/book/profile/activities/profilingexercise.html
8

9. Profiling Data: Location Master
 Header : date-time that data are received
 Address : Zone , aisle, section, position
 Unit: quantity, case pallet
Source: Warehousing Science http://www2.isye.gatech.edu/j̃jb/wh/book/profile/activities/profilingexercise.html
9

10. Typical Warehouse Address
10

11. Profiling Data: Order Master
 Header : Order ID , Customer ID
 Detail : SKU ID , Date , Time , Quantity (Qty), Unit
 Note: Largest Database
Source: Warehousing Science http://www2.isye.gatech.edu/j̃jb/wh/book/profile/activities/profilingexercise.html
11

12. Process in WAP
Define
Question
What do we
plan to improve
(Pros. Vs. Cons.)
Gather Data
Meaning of
Data and
finding related
Data:
Static : SKU
related,
Layout-zone ,
Std. time ,
cut-off Time
Dynamic :
Picker
Related, Plan
, OT ,
Schedule
Import Data
Connect With
Database –
basic Statistic
Analysis
Check Data
Inconsistency ,
Outlier 
Clean-Up-Data
Analysis Data
Create and
explain
Distribution
Implementation
Gap Analysis ,
Saving Analysis
12

13. Some basic summary statistics
Order Related Facility Related
 Average number of SKU’s involved  work and
storage complexity
 Area of the warehouse
 Average number of orders shipped per day 
volume of activity
 Average number of shipments received per day
the “backend ”activity
 Average number of lines (SKU’s) per order
Picking Complexity
 Average rate of introduction of new SKU’s 
Operational Stability
 Average Number of Units Per line  Average number of SKU’s in the warehouse 
Volume and scope of operation
 Seasonality (Seasonal indices- what percentage of a
cycle corresponds to a period in the cycle-
Temporal Distribution of the work)
• Distribution of the personnel to the various
activities labor-related costs and opportunity.
13

14. Graphing the result of Activity Analysis
 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)
A Bird’s Eye View of a Warehouse with each
section of self colored in proportion to the
frequency of request for the SKU stored there
in.
14

15. Pareto Effect and ABC Analysis
Classifying items, events, or activities according to their relative importance
 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.
References: J.J. Bartholdi and S. T. Hackman. Warehouse & distribution science. Supply chain and logistics institute, Georgia institute of technology, 2009.
15

16. Example of WAP
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)
16

17. Conclusion
17
An activity profile is essential to really understand what matter in a
warehouse. The Activity Profile will enable us to understand, Mange
and improve use of labor ,space and equipment.
WAP is a special case of data-mining, which is simply the rummaging
through database to look for patterns that might be exploited to
improve operations.