Optimizing an Apparel Company’s Supply Chain by Combining Agent-Based Modeling

with Geographic Information Systems

Beth Tyrie

Manager, Data Science

11/4/2015

• 4 Separate Business Units

• SKU and retailer intensive company

Problem Statement

• Will it benefit the company in terms of cost and supply chain optimization to add a new distribution center (DC)

on the East coast or West coast or will it benefit the company to redistribute products to a pre-existing DC?

Simulation Modeling Importance

• Solves real-world problems without “real-world” experiment costs

• Enables abstraction

• Simplifies complex systems by parameterizing only relevant details

• Simulations can be conducted faster than real time

• Allows the testing of multiple variations of the experiment

Systems Engineering Fundamentals.Defense Acquisition University Press, 2001

Modeling Software: AnyLogic

“The only simulation tool that supports Discrete Event, Agent Based, and System Dynamics Simulation”

Supply Chain Agent-Based Modeling

• Supply chain participant examples:

• Producers (Cotton farmers)

• Processors (Yarn mills)

• Companies ( FOTL distribution centers)

• Wholesalers

• Retailers

• Each have their own goals and rules and can naturally be represented as agents

Data Collection

Data Required

• DC Customer Shipment Data

• High Demand Customer locations

• Total shipments, total units

• Shipment Types and Rates

• Truckload, LTL, Rail

• Distance from DC to Customers

• Overhead Cost Estimates

• Cost to build new DC

• Fixed and variable costs per products

AnyLogic

DC to Customer Shipment

Data

Shipment Types and

Rates

Distance

DC->

Customers

Overhead Cost

Estimates

Collaborative Data Collection

• Data Warehouse

• Logistics Planning and Analysis

• Transportation Analysis

• Process Engineering

• Simulations MIS dept

• IT- In Transit

• Business Solutions Manufacturing Systems

http://pardington10.wikis.birmingham.k12.mi.us/Collaboration+Techniques

Data Exploration: Outlier Detection

GIS (Geographic Information Systems)

• Geographically referenced data (i.e. customer location data) can be spatially visualized and analyzed

• Spatial relationships, patterns, and trends are revealed that are not readily apparent in spreadsheets leading to:• Cost reduction• Identification of

opportunities• Streamlined operation

http://www.esri.com/

GIS Network Analysis of Original DC to Customers

Note: Mock Data

Chosen Location: McCook, NE

GIS Network Analysis to Determine Optimal Location for DC based on Weighted Distances

Note: Mock Data

Model Calculations• Model Probabilities:

• Percentage of Shipments per Customer:

• Demand per Shipment in Units per Customer:

• Benefits:

• Model does not depend on single orders

• Model can be flexible in terms of total units and shipments

• Model is equipped to be predictive

custNumShipments

totalShipments

(custNumShipments/ totalCustUnits)

totalShippedUnits

Model Assumptions

• Latitude/Longitude calculation by Haversine Formula used by NOAA and NASA

• Cannot take into account road distances; however, 325 mile error was reasonable in terms of total cost

• Currently, model does not take into account small package or consolidated shipments

• Approximately 85% of shipments modeled

Model Agents• Distribution Centers

• Parameters: location, units, overHeadCost, startUpCost

• Customers

• Parameters: location, demandRate, totalShipments, distance, freightRate, shipmentType

• Trucks

• Parameters: location, units, owner, destination

• Trains

• Parameters: location, units, owner, destination

Demo

Simulation Results Excel File

Code Behind Simulation: Action Charts

Java Code Behind Simulation: Events

Java Code Behind Simulation: FunctionsCalculate DC with Shortest Distance to Customer Create New DC

Manufacturers to DCs Model• Manufacturers

• Distribution Centers

• Parameters: location, shipPercent, distance

• Loading Ports

• Parameters: location, annual volume

• Ports of Discharge

• Parameters: location, distance

• Vessels

• Parameters: location, owner, destination, dc, oceanRate

• Trucks

• Parameters: location, owner, destination

Conclusions

• Simulations used as exploratory research tool for the business to investigate feasibility of recommendations

• Data-driven insights from GIS and AnyLogic paired with business knowledge creates a full-fledged approach to develop informed supply chain decisions

Thank you!