Market Preferences and Process Selection (MAPPS): the Value of Perfect Flexibility

1
Market Preferences and Process Selection (MAPPS)
the Value of Perfect Flexibility
This research was partially funded by a College
of BusinessCompetitive Summer Research Grant in
Entrepreneurship
2
Objectives of Research

• Develop a methodology for timing and acquiring
  process technologies and selecting production
  processes
• market evolution is stochastic
• market demands and process capabilities must be
  matched
• Conduct experiments to better understand
  important factors in acquiring process
  technologies (i.e., robust strategies)
• Define and illustrate value of perfect flexibility

3
Problem Statement

• Problem
• Determine the value of perfect flexibility for
  process design conversions when market evolution
  is stochastic
• Perfect Flexibility defined
• Increase in profit that can be obtained a policy
  of perfect flexibility in responding to market
  preferences, compared to a robust policy of
  keeping one process design throughout the
  planning horizon.

4
Application to Entrepreneurship

• Start-up companies must make critical decisions
  regarding technology selection
• Inappropriate technology selection can be
  economically fatal
• Market preferences and market evolution uncertain
  for new products in new industries

5
Assumptions

• Time
• can be discretized (i.e., months, quarters,
  years)
• Markets
• can be modeled as discrete scenarios
• markets move between scenarios as a Markov
  process
• Technologies
• can be modeled as discrete option bundles
• Costs
• The costs associated with market/technology pairs
  can be estimated

6
Prior Research

• Monahan and Smunt, OR (1989)
• Optimal Acquisition of Automated Flexible
  Manufacturing Processes
• Rajagopalan, Singh and Morton, MS (1998)
• Capacity Expansion and Replacement in Growing
  Markets with Uncertain Technological
  Breakthroughs
• Gupta, Gerchak and Buzacott, IJPE (1992)
• The Optimal Mix of Flexible and Dedicated
  Manufacturing Capacities Hedging Against Demand
  Uncertainty
• de Groote, IPJE (1994)
• Flexibility and Marketing/Manufacturing
  Coordination
• Paraskevopoulos, Karakitsos and Rustem, MS (1991)
• Robust Capacity Planning Under Uncertainty
• Mulvey and Vanderbei, OR (1995)
• Robust Optimization of Large-Scale Systems

7
Solution Methodology

• Stochastic dynamic programming
• MAPPS Market Preferences and Process Selection

8
A Simple Example
Market Requirements
High Variety Moderate Variety
Standardized
Job Shop
High Flexibility
Batch Shop
Moderate Flexibility
Process Capabilities
Flow Shop
StandardizedProduction
Hayes and Wheelwright, The dynamics of
process-product life cycles, Harvard Business
Review, March-April 1979
9
A Simple Example
Market Requirements
High Variety Moderate Variety
Standardized
Flexible Shop
Job Shop
High Flexibility
Batch Shop
Moderate Flexibility
Process Capabilities
Flow Shop
StandardizedProduction
Hayes and Wheelwright, The dynamics of
process-product life cycles, Harvard Business
Review, March-April 1979
10
A Simple Example
Market Requirements
High Variety Moderate Variety
Standardized
MassCustomization
Flexible Shop
Job Shop
High Flexibility
Batch Shop
Moderate Flexibility
Process Capabilities
Flow Shop
StandardizedProduction
Hayes and Wheelwright, The dynamics of
process-product life cycles, Harvard Business
Review, March-April 1979
11
Marketing Scenarios

• Discrete market scenarios or states M1,,M
• Market state m?M defined by pertinent market
  variables (product type, product mix, demand
  levels, etc.)
• Scenarios highly dependent on specific
  characteristics of the market under study.
• Model market change as an M ? M transition matrix
  ?.
• Element ?ij ? ? represents the probability that
  the market will evolve to from state i to j in
  one period.

12
Market Scenarios for Example

• Three possible market scenarios
• 1. High variety ? high product variability, price
  not a large facto
• 2. Moderate variety ? medium product variability,
  moderate prices
• 3. Low variety ? standardized commodity product,
  low prices required
• Market scenario transition matrix ?

13
Technology Options

• Assume set of technological options T1,,T
• Option t?T defined by important attributes (e.g.,
  equipment descriptions, process capabilities,
  tolerances, capacity)
• Availability of technological scenarios modeled
  usingT ? T technological possibility matrix ?
• Element ?ij ? ? represents the probability that
  technology j will be available in the next period
  h1
• If option t?T has been available in the past, it
  will always be available in the future.

14
Technology Options for Example

• Four technology options
• 1. Job shop low volumes, high product
  variation, high cost
• 2. Batch shop medium volumes and variation,
  moderate cost
• 3. Flow shop high volumes, low product
  variation, low cost
• 4. Flexible shop moderate/high volume, high
  product variation, moderate cost
• Option selected is a management decision

15
Economic Structure

• Revenues modeled as M ? T matrix R
• element rmt is expected period revenues with
  market scenario m and technology option t.
• Production costs represented as M ? T matrix K
• element kmt represents expected period production
  costs when the market is in state m and
  technology is in state t.
• Technology adoption costs modeled as T ? T
  matrix C
• element cij is cost of switching from option i to
  j.
• Single period operating profit p
• p rmt - kmt - ctt

16
Revenue Production Costs for Example

• Revenue matrix R
• Production cost matrix K

17
Adoption Costs for Example

• Technology adoption and maintenance cost matrix A

18
Dynamic Programming Solution

• In period h?H, state of system is uniquely
  defined by market scenario m?M and technology
  option t?T.
• Expected profits ?h for remaining periods h,
  h1, , H are found by the recursive
  relationship
• m,m'?M and t,t'?T.
• Optimal solution is technology the set of t?T
  that maximize ?0 given h and m.

19
Optimal Strategy for Example
20
Simulation of Optimal Strategy
21
Chart of Optimal Strategy
22
Solution with Perfect Flexibility
23
Robust Solution

• By increasing technology adoption costs, we can
  identify robust strategies

24
Perfect Flexibility

• Theorem 1 A policy of perfect flexibility
  provides an upper bound on profitability for the
  MAPPS problem. That is
• Corollary 2 When technology switching costs are
  free (ctt0, for all t,t?T), then a policy of
  perfect flexibility is optimal.

25
Robust Technology Selection

• Theorem 3 A perfectly robust policy provides a
  lower bound on profitability for the MAPPS
  problem. That is
• Corollary 4 When technology switching costs are
  sufficiently expensive (ctt ? ? for all
  t,t?t?T), then a perfectly robust policy is
  optimal.

26
Contributions of Research

• Demonstrate MAPPS as method for good technology
  acquisition decisions
• Establish robust strategy as a lower bound
• Establish perfect flexibility as an upper bound
• Define value of perfect flexibility
• Provides benchmark for valuing flexibility

27
Future Work

• Increase size and complexity of market scenarios
  and technology options
• include cost models for market scenarios
• include cost models for production and adoption
• include revenue models for market/technology
  pairs
• Fully test and understand implications of MAPPS,
  including the development of analytic results
• Test on industrial problems
• identify an industrial client
• gather data and run model

28
Some other examples...
29
Questions?
Market Preferences and Process Selection (MAPPS)
the Value of Perfect Flexibility