Modeling Warehouse Capacity to Support Monthly S&OPGlobal retailer uses detailed DC models to predict capacity bottlenecks and support monthly S&OP process and long-term investment and tactical planning

Challenge

Solution

A large global retailer of athletic shoes and apparel wanted to have a more detailed view into capacity within its four primary European DCs in order to improve resource utilization, support monthly S&OP planning and reduce penalty costs. The company had monthly volume/productivity data but no real connection to corresponding capac-ity. High intra-month variations in processing capacity as well as machine bottlenecks were constraining DC productivity.

Product is sourced globally, stored and processed in four European DCs and then shipped to customers via 3PLs. Each of the four DCs is dedicated to a specific product category. Processing at DCs includes:

•Fullcase

•Repackandpicking

•Value-addedservices(VAS)includingcustomerlabeling/taggingandhanging

•E-commerce

The company worked with the LLamasoft solutions team to build a detailed short-term model of processing activity within the four DCs, using LLama-soft® Supply Chain Guru®. The model included a weekly view of volume requirements and a detailed view of product flow in each DC as well as 3PL capacity in order to:

•Predictcapacityrequirementsforexpected

customer deliveries

•Identifythebestsplittoimproveresource

utilization and level processing

•SupportthemonthlyS&OPprocess—supportingdecisionsontheamountof

producttobepulledforward(i.e.soldearlier),orholdoffinbound(i.e.processitlater)

Model inputs included staff productivity, staff to infrastructure ratio, infrastructure efficiency and number of “effective” available work hours per period.

CASE STUDY: REtAIlInduStRy

©2014llamasoft,Inc.Allrightsreserved.v.031114

High-level model flow

ResultsBy separating capacity data by DC, the model allowed the company to balance inbound and outbound volumes, thus creating a positive or negative net effect on inventories. This model is used on an ongoing basis to support monthly decision making. Benefits include:

•Improvedaccuracyinpredictingcapacitybottlenecks

•Fasteranalysisturnaround

•Betterlevellingofprocessingrequirementsthroughoutthemonthand

throughout DCs

•Reducedfrequencyandvolumeofpenaltycostsforovertime,offsite

storage and trailer rentals

CASE STUDY: REtAIlInduStRy

LLamasoft, Inc.

201 South Main Street, Suite 400

Ann Arbor, Michigan 48104, USA

Phone: +1 866.598.9831

LLamasoft.com

Info@llamasoft.com

©2014llamasoft,Inc.Allrightsreserved.v.031114

•Modelingframeworkformakinglong-termwarehouseinfrastructureimprovements

Examplemodeloutputgraphic