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Optimal platform investment for product family design

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Abstract

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.

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Abbreviations

B :

Budget available for the whole project (input)

α :

Commonality factor ranging between 0 and 1 (decision variable)

i :

Index for the proposal number; 1 ≤ i ≤ P

P :

Number of variants proposed by the engineering department (input)

N :

Number of variants created in the project (decision variable)

V i :

Binary variable. V i  = 1 if the ith proposal for a variant is implemented, 0 if the ith proposal for a variant is not implemented (decision variable)

C i :

Cost to create the ith proposal (variable)

D i :

Development cost of the ith proposal (variable)

V 0 :

Development base value (input)

E i :

Effort factor of the ith proposal (variable)

m i :

Percentage of modules changed in the peripheral set when creating the ith proposal (input)

T i :

Integration and testing cost of the ith proposal (variable)

A 0 :

Integration & testing base value (input)

X i :

Complexity factor of the ith proposal (variable)

f i :

Number of interfaces having to adapt to create the ith proposal (input)

F :

Flexibility factor of the platform (variable)

I 0 :

Initial investment in the platform (input)

β :

Investment ratio of I 0 to B ranging between 0 and 1 (input)

M :

Market coverage in the target market segment ranging between 0 and 1 (output)

a :

Share of variable costs in the development process of variants (input)

b :

Share of fixed costs in the development process of variants (input)

c :

Commonality of benchmark variant for integration and testing (input)

d :

Interface complexity factor (input)

e :

Benchmark constant for β (input)

f :

Target market segment fitting constant (input)

g :

Scaling value for market coverage (input)

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Authors and Affiliations

  1. Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Nicolas A. Zacharias & Ali A. Yassine

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  1. Nicolas A. Zacharias
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  2. Ali A. Yassine
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Correspondence to Ali A. Yassine.

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Zacharias, N.A., Yassine, A.A. Optimal platform investment for product family design. J Intell Manuf 19, 131–148 (2008). https://doi.org/10.1007/s10845-007-0069-x

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