warehouse activity profiling

8/12/2019 Warehouse Activity Profiling

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Warehouse Activity Profiling

Based on Bartholdi & Hackman

Chpt 5


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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

SKU data

Order data

Location

data

Summary statistics

Distributions

StructuralCharacterizations, e.g.,

prevailing patterns/trends relations

dominant elements


3/11

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)


4/11

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 somespecialized Database software.


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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

sortingthe rows of a table by a certain attribute

selectinga subset of rows of a table, s.t. all isolated entities satisfy

a certain property

countingdistinct 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 QueryLanguage


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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 thework)

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-relatedcosts and opportunities)


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A closer characterization of thewarehouse 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)


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Graphing the results of theActivity Analysis

Discrete Distribution

1.0

A B C D zone

% picks

Pareto curve

1.0% picks

SKUs10K 20K


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Pareto Effect and ABC Analysis

Pareto Effect:A small percentage of the considered entitiesaccount 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 ofthe 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 foronly 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)


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Work Patterns and their Implications

Distribution of lines per order:What percentage of orders

have a single line, two lines, etc. (Reveals possibilities forbatching 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 largeorders, 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 thepicking 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)


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Case Study:Profiling the Activity of a Wholesales

Distributor of Office Products

Problem description:http://www.isye.gatech.edu/people/faculty/John_Bartholdi/wh/book/profile/projects/projects.html

Problem Solution:http://www.isye.gatech.edu/~spyros/courses/IE6202/WAP-cs.pdf

warehouse activity profiling

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