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Evaluating and Optimizing the Performance of Complex Multi-stage Supply Chains Under Disruptions. Sanjay Kumar University of Texas at Dallas. Kathryn E Stecke University of Texas at Dallas Thomas G Schmitt University of Washington Seattle Fred Glover University of Colorado at Boulder.

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evaluating and optimizing the performance of complex multi stage supply chains under disruptions

Evaluating and Optimizing the Performance of Complex Multi-stage Supply Chains Under Disruptions

Sanjay Kumar

University of Texas at Dallas

Kathryn E Stecke

University of Texas at Dallas

Thomas G Schmitt

University of Washington Seattle

Fred Glover

University of Colorado at Boulder

a simple multi stage supply chain
A Simple Multi-Stage Supply Chain

Suppliers

Manufacturers

Distributors

Retailers

Customers

Transporters

demand

Supply Chain Management

Demand

Supply

Certain events can disturb the balance of demand and supply.

a simple multi stage supply chain1
A Simple Multi-Stage Supply Chain

Suppliers

Manufacturers

Distributors

Retailers

Customers

Transporters

Natural catastrophes

Accidents

Terrorist attacks

Modern supply chain design

scope of this research
Scope of this Research

Supply Chain Risk Management

Ordering decisions under disruptions

  • Develop optimization tools for making cost-effective decisions under disruptions.
  • Study the effectiveness and rationale of popular disruptions response methods used in supply chains.
  • Explore the vulnerabilities and understand the (long-term) effects of disruptions at various stages of a supply chain.
outline of the presentation
Outline of the Presentation
  • Problem background and motivations
  • The model
  • Literature
  • Solution methodologies
  • Results
  • Conclusions and contributions
recent supply chain disruptions
Recent Supply Chain Disruptions
  • 9/11
    • Economic losses to New York city in the month following the attacks: $1.5 billion
    • Jobs lost in NY: 200,000
    • Estimated total jobs lost in the country: 1.5 million
  • Hurricane Katrina
    • Economic losses to insurance industry far exceeded the losses because of hurricane Andrew, 9/11, and Northridge California earthquake combined together.
  • The 2000 fuel crisis in UK
    • Resulted in disruptions far more severe than 9/11.

Various types of disruptions affect supply chains. For many industries 9/11 was not the most disruptive event.

disruptions response
Disruptions Response
  • Decisions made during disruptions are often based on short-term goals, or lack of foresight.
  • In many cases losses occur because of “poor” or “wrong” response
    • 9/11 and Homeland Security Advisory System
    • Kobe earthquake
  • Does company level decisions made during disruptions also negatively affect the supply chain performance?
    • Ordering and transportation
disruptions response1
Disruptions Response
  • Homeland Security Department:
    • Sandia National Labs started developing models to understand the economic consequences of disruptions in critical infrastructure.
    • The aim was to predict and mitigate the economic effects of disruptions in
      • Manufacturing facilities
      • Transportation
      • Electric power
      • Telecommunications
manufacturing transportation questions to address
Manufacturing/Transportation: Questions to Address
  • What kind of disruptions affect manufacturing/ transportation?
    • Length
    • How often
  • How does present supply chains cope with them?
  • Can we help companies make better ordering decisions during disruptions and even otherwise?
  • Does company level decisions made during disruptions also negatively affect the supply chain performance?
    • Ordering and transportation

Answers to these questions could vary between industries.

why electronics manufacturing supply chains
Why Electronics Manufacturing Supply Chains?
  • Electronics are widespread in the functioning of our society.
    • Since WWII, electronics products have accounted for over 30% of US GDP.
  • Electronics assembly is very susceptible to disruptions.
    • Typical electronics products can have 70-700 components
  • Electronics supply chains involve global, multinational interests that broaden the exposure to disruptions.
    • Over 80% of electronics components are internationally sourced.
  • Modern electronics products have very short life cycles.
    • Less than 4 months for DVD players and Digital Camcorders
slide14
Key Characteristics of an Electronics Supply Chain: Three Case Studies(from a sample of 14,000 electronics firms)
  • Design of supply chain
    • Assembly is an integral part.
    • Often global.
  • Response
    • Each company expedite orders to overcome shortages.
  • The final customer demand follows AR(1) process.
    • The demand across periods are correlated.
  • Supply chain well coordinated
    • Shortages become lost sales only at the retailer.
supply chain
Supply Chain

Suppliers

Manufacturers

Distributors

Assembler

Retailer

Level 3

Level 4

LT: 35

ELT: 30

Level 2

LT: 30

ELT: 15

Level 1

LT: 10

ELT: 6

LT: 42

ELT: 40

Assembly

(Finished Product)

Stage 3A

Stage 4A

LT: 45

ELT: 40

LT: 25 days

ELT: 10 days

LT: 30

ELT: 28

Stage 4B

Stage 3B

Each stage expedites orders to overcome shortages.

The final customer demand is AR(1).

Assembly is an integral part of electronics supply chain.

Both facility and transportation disruptions are critical.

The supply chain is well coordinated. Demand is lost only at the retailer.

problem statement
Problem Statement
  • In a multi-stage model supply chain, determine the cost effective order-up-to levels for each stage considering the costs of
    • Backorders
    • Lost sales
    • Inventory carrying
    • Expediting
literature
Literature
  • Supply chain security: CSI, increased tracking and visibility, product and process standardization (Sheffi, 2003).
  • Inventory policies
    • Single stage
    • Stationary assumptions
    • Nonstationarity is induced by expediting and disruptions
  • Little research to find policies for multi stage and non-stationary supply chains considering bullwhip.
    • Chen et al. (2001), Lee et al. (1997), and Kahn (1987) deal with the existence and quantification of bullwhip.
  • Almost all research articles consider an i.i.d demand.
    • The “best” demand function is correlated across periods.
objective function
Objective Function

A weighted function of the costs of expediting, backorder, lost sales, and inventory holding is minimized.

s.t. Inventory flow constraints are satisfied (next slide)

Lost sales cost

Backorder cost

Expediting cost

Holding cost

Decision variables: Order quantities at each of the six stages of the supply chain.

flow constraints for stage i
Flow Constraints (for Stage i)
  • Inventory and quantity on order

Expedited Shipment

Regular Shipment

Inventory

Previous Period Inventory

flow constraints for stage i1
Flow Constraints (for Stage i)
  • Shipment to Stage i-1

Shipments are minimum of available inventory and the order quantity +backlog

Additional constraint for assembly stage

flow constraints for stage i2
Flow Constraints (for Stage i)
  • Regular Shipment to stage i-1:
  • Expedited Shipment to stage i-1:

If inventory is positive, regular orders are placed

Negative inventory (shortages) results in expedited orders

flow constraints for stage i3
Flow Constraints (for Stage i)

Effective order- inventory

  • Shortages:
  • Backorders and lost sales:

All shortages backordered

A fraction is backordered, rest is lost

solution strategies
Solution Strategies
  • The objective function is non-convex in the order quantities.
  • Certain deterministic cases are NP complete.
  • Solution methods
    • Fibonacci
      • Results in local optimal solutions
    • Genetic algorithms
      • Significantly longer run time
    • Tabu search
comparison of the solution methods
Comparison of the Solution Methods

Fibonacci

(2.8 min)

GA

(139.7 min)

Cost

Tabu

(28.5 min)

expediting vs no expediting
Expediting vs. no Expediting

4.7%

Expediting

No

Expediting

Cost

No-expediting

effect of assembly
Effect of Assembly
  • Assembly stage reduces the order amplification effect
    • The reduction is prominent in the higher stages.
    • The bullwhip-reducing effect of assembly increases with increase in number of components assembled.
  • This provides an explanation for counter-intuitive results of Cachon et al. (2006).

No assembly

2 components assembly

effects of disruption
Effects of Disruption

15 days disruption at retailer

Magnitude of losses

Disruption

15 days disruption at manufacturer

Disruption

Magnitude of losses

conclusions and summary
Conclusions and Summary
  • We developed and implemented a search-based optimization methodology and effectively used it to find order-up-to quantities in a multi stage supply chain.
    • First such method with the potential to help supply chains make ordering decisions considering
      • Nonstationarity
      • Expediting
  • Tabu Search
    • First such application for Tabu search.
    • Developed and adapted Tabu search to be effectively used for this problem.
    • Genetic search is shown to be inferior.
conclusions and summary1
Conclusions and Summary
  • Bullwhip
    • Assembly stage filters the demand thus reducing bullwhip.
    • We provided a possible explanation to Cachon et al.’s findings.
  • Expediting
    • Widely prevalent expediting practice may hurt supply chain performance.
    • Expediting may also result in longer recovery times.