E-Logistics Successes and Failures

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Successes and Failures

H. Donald Ratliff

Executive Director

Regents & UPS Professor [email protected]

President & CEO [email protected]

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Copyright April 2002 – H. Donald Ratliff

INFORMS

The Successes and Failures of E-Logistics

H. Donald Ratliff

Regents & UPS Professor of Industrial & Systems Engineering; Executive Director of the Logistics Institute Georgia Institute of Technology

President and CEO, Velant, Inc.

The Internet is revolutionizing the process and economics of logistics. Twenty years ago, the personal computer launched a new generation of logistics technology based primarily on a decentralized planning model. It empowered sophisticated users to make better decisions by giving them flexible modeling

capability, a graphical interface and a moderate amount of computing power. Client-server computers added an element of connectivity that allowed automation of internal company transactions but did little to change decision technology or interactions between companies. E-logistics--the next generation of logistics

technology--will be based on a centralized computing model driven by networked computing. In this session, Ratliff will discuss the evolution of this new model, the successes and failures to date, and the technical and operational issues that must be overcome.

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Internet-Centric Computing

A network of networks

z Public & private z Wired & wireless

Easy access

Computer horsepower

Location neutral

Carrier Vendor DC Plant Store Colocation Center Service Center

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E-Logistics Technology Matrix

B to B B to C C to C Internally integrated Accounting Execute Transactions Shared dynamic Internal dynamic Internal static Monitor Status Mainframe 1960s Centralized Collaborative Hybrid Distributed Interactive Planning& Analysis Internet-Centric 2000s Client-Server 1990s PC 1980s

Transportation Planning

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Transportation Planning

$500,000,000,000 annual US truck transportation spend

(3,000,000+ trucks)

Shipper/Carrier

Transaction Automation

Fleet Optimization

loads, routes, schedules

Fleet

Opt

Mode

Opt

2/3 Dedicated

Fleets

1/3 Commercial

Carriers

In the US there is as much money spent each year on truck transportation than on the combination of warehousing, inventory, and all other forms of transportation

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Transportation Planning

Network Design

Facility location & sizing

_{Channel definition}

Strategic Procurement

Capacity planning

_{Carrier bid optimization}

Optimization

Shipment consolidation

Load planning & scheduling

Mode selection

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Transportation Planning

Network Design

Optimization

Strategic Procurement

Facility location & sizing

Channel definition

Capacity planning

Carrier bid optimization

Shipment consolidation

Load planning & scheduling

Mode selection

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Supply Chain “Design” Software

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Network Optimization Models

Models

z High level z Product “flows” z Time “buckets” z Linear cost

Algorithms

z LP/IP z Pretty fast

Effective with “super-users”

Customer demand Transport cost Transport cost Plant capacity Plant cost DC capacity DC cost

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SC Design Software

Current Offerings

z CAPS Logistics z I2 z Insight z Logic Tools

Solver engines

z ILOG - CPLEX z Dash – Xpress MP z Custom

Future

z Requires high modeling expertise z Not much change

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Transportation Network Design

Pooling networks

zPool points zUPS facility zPostal facility zLTL hub

Cross-docking networks

zFocus on LTL shipments zCross-docks are shipper

controlled

Origins Destinations

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sort receiving shipping

Cross-Docks

No “planned” inventory

z Unload, sort, stage, load

Examples

z Inbound for retail

z Wal-Mart z Home Depot

z LTL and package delivery

z UPS z Yellow Freight z New cars z Ford

Operation

z Schedule-driven z Load-driven

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Design Difficulties

Origin to destination zTL zLTL zMulti-stop route Origin to cross-dock zTL zLTL zMulti-stop route Cross-dock to destination zTL zLTL zMulti-stop route Routing is difficult zStatic zDynamic

Supply chain design models don’t work!!!

zCost are very nonlinear zFlows are dependent zVolume is critical

Origins

Cross-docks

Technology Inadequate!!!

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Network Design

Transportation Planning

Optimization

Strategic Procurement

Facility location & sizing

Channel definition

Capacity planning

Carrier bid optimization

Shipment consolidation

Load planning & scheduling

Mode selection

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Bid Optimization

Red – all or nothing

Software

z

CAPS Logistics

z

I2

Managed service

z

Logistics.com

Bid opportunities

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Network Design

Transportation Planning

Optimization

Strategic Procurement

Facility location & sizing

Channel definition

Capacity planning

Carrier bid optimization

Shipment consolidation

Load planning & scheduling

Mode selection

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Transportation Optimization

Loading A C D B Routing & scheduling

A _B

A _? B

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Mode Optimization Versus Fleet

Optimization

Mode optimization

zCreate routes from LTL

shipments

zTender routes only if less

cost than LTL

zLane based costs zInvolves an external

transaction

Fleet optimization

zAll shipments must be taken zCost based on dedicated

truck

zDriver

zEquipment

zNo external transaction

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Current Technology

Visual algorithms

Requires smart planner

Poor quality

Greedy algorithms

Requires super-user

Marginal quality

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Why is transportation optimization

hard?

Huge number of possibilities

Complex constraints

z

Loading

z

Delivery windows

z

DOT

z

Compatibility

Dynamic requirements

Large amounts of data

Changing service levels

Min E {Σ cx} Ax = b

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be overcome?

Transportation

Management

Center

Client

Systems

Orders

Executive

Order/Asset

Visibility

Field

Operations

Loads Routes Schedules Status Status Analysis & Simulations Optimized Transportation Plan

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Transportation Management

Center

Optimization engines are the key to success!

Workflow Manager (On-line Execution)

Design and Analysis Manager (Off-line Simulation)

• Goals

• Constraints • Rules

Business

Models • Support_{• Automation}

• Loads • Routes • Schedules Results Optimization Engines Data • Orders • Customers • Assets • Roads • Status • History Exception Handling Continuous improvement Step improvement

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Optimization Providers

X Software X X X X Software X X X X

Software Service Service Service

Manugistics G-Log I2 X X X Velant X Logistics.com CAPS UPS-Roadnet Descartes Hybrid Mode Optimization Fleet Planning

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Collaboration

Levels of collaboration

zShare data zStatus zShare plans zFuture availability zFuture requirements zJointly plan zShare assets zShare capacity

Joint planning technology

zInteractive planning

zMore people zMore time

zQuestionable value

zComposite planning

zCombine independent plans

zAutomated collaboration

zDifficult to create zDifficult to maintain

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Network Effects

Capacity sharing

z Visibility z Collaboration z Optimization

Solution providers

z Elogex z Logistics.com z Nistevo z Velant

Limited success

Private Fleet - Backhaul

Commercial Carrier – Continuous Move

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Copyright April 2002 – H. Donald Ratliff 0 2 4 6 8 10 12 Day Trucks Required ATL CHI DAL DET KC LA NY SF

Workload Imbalance

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Transportation Exchanges “Vision”

Sell unused capacity

z Workload imbalance z Backhauls

z Partial loads

Bring together shippers and carriers

z Many small carriers z Many small shippers

Reduce cost

z Brokers fees 13%

z Increase competition - auctions

Leverage information

Exchange

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Transportation Exchanges

More than 100 startups

Most are gone

z Spot market small z Too many players z Too little experience z Weak technology

Survivors

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What about the future?

Network design

zBetter cross-dock planning technology zProblems are really hard

Strategic procurement

zMostly automation

zOptimization is too hard for carriers

Optimization

zTransportation management centers zNew generation of technology

Trends

zCentralized optimization zOptimization outsourcing zLimited collaboration

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