Market Preferences and Process Selection (MAPPS): the Value of Perfect Flexibility Stephen Lawrence University of Colorado George Monahan University of Illinois Tim Smunt Wake Forest University This research was partially funded by a College of Business Competitive Summer Research Grant in Entrepreneurship

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

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.

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

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

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

Solution Methodology Stochastic dynamic programming MAPPS – Market Preferences and Process Selection

A Simple Example Market Requirements High Variety Moderate Variety Standardized Process Capabilities High Flexibility Moderate Flexibility Standardized Production Job Shop Batch Shop Flow Shop Hayes and Wheelwright, “The dynamics of process-product life cycles,” Harvard Business Review, March-April 1979

Flexible Shop A Simple Example Market Requirements High Variety Moderate Variety Standardized Process Capabilities High Flexibility Moderate Flexibility Standardized Production Job Shop Batch Shop Flow Shop Hayes and Wheelwright, “The dynamics of process-product life cycles,” Harvard Business Review, March-April 1979

Flexible Shop A Simple Example Market Requirements High Variety Moderate Variety Standardized Process Capabilities High Flexibility Moderate Flexibility Standardized Production Job Shop Batch Shop Flow Shop Hayes and Wheelwright, “The dynamics of process-product life cycles,” Harvard Business Review, March-April 1979 Mass Customization

Marketing Scenarios Discrete market scenarios or states M={1,…,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.

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 

Technology Options Assume set of technological options T={1,…,T} Option t  T defined by important attributes (e.g., equipment descriptions, process capabilities, tolerances, capacity) Availability of technological scenarios modeled using T  T technological possibility matrix  Element  ij   represents the probability that technology j will be available in the next period h+1 If option t  T has been available in the past, it will always be available in the future.

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

Economic Structure Revenues modeled as M  T matrix R –element r mt is expected period revenues with market scenario m and technology option t. Production costs represented as M  T matrix K –element k mt 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 c ij is cost of switching from option i to j. Single period operating profit   = r mt - k mt - c tt’

Revenue & Production Costs for Example Revenue matrix R Production cost matrix K

Adoption Costs for Example Technology adoption and maintenance cost matrix A

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, h+1, …, 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.

Optimal Strategy for Example

Simulation of Optimal Strategy

Chart of Optimal Strategy

Solution with Perfect Flexibility

Robust Solution By increasing technology adoption costs, we can identify “robust” strategies

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 (c tt ’ =0, for all t,t ’  T), then a policy of perfect flexibility is optimal.

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 (c tt ’   for all t,t ’  t  T), then a perfectly robust policy is optimal.

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

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

Some other examples...

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