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Hierarchical Decision Modeling pp 97–119Cite as

Strategic Planning: A Quantitative Model for the Strategic Evaluation of Emerging Technologies

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Part of the book series: Innovation, Technology, and Knowledge Management ((ITKM))

Abstract

This chapter presents a quantitative model used for evaluating the impact of emerging technologies on a company’s objective. The hierarchical model with four levels (objective–criteria–factors–technology alternatives) is structured to decompose the complex decision problems and incorporate quantitative and qualitative aspects into the evaluation process. A new approach on applying a semi-absolute scale to quantify the values of technologies is proposed in conjunction with the determination of criteria priorities and the relative importance of factors under each criterion. The impact of technologies on a company’s objective is calculated as a composite index called technology value. The improvement gap and improvement priority of each technology are also determined to identify the characteristics of the emerging technologies on which technology-driven companies would focus in order to maximize the impact of those technologies on the company’s strategic objectives. A case study is included in this chapter to illustrate the applicability and computations of the proposed model.

A previous revision of this chapter was presented at Portland International Conference on Management of Engineering and Technology, 2004.

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Notes

  1. 1.

    PCM software is developed by Dundar F. Kocaoglu and coded by Bruce J. Bailey. The software is used to facilitate the computation process of constant-sum pairwise comparison method by converting judgments into numerical values [23].

References

  1. Betz, F. (1998). Managing technological innovation (p. 369). New York, NY: John Wiley & Sons, Inc.

    Google Scholar 

  2. Sugiura, H. (1990, Fall). How Honda localizes its global strategy. Sloan Management Review, 32(1), 77–82.

    Google Scholar 

  3. Schmitt, R. W. (1985, May–June). Successful corporate R&D. Harvard Business Review, 124–129.

    Google Scholar 

  4. Radhakrishna, A. V., & Vardarajan, A. (1991, November/December). Maximizing innovation in industry and adopting to change. Industrial Management, 19–21.

    Google Scholar 

  5. Kokubo, A. (1992, January-February). Japanese competitive intelligence for R&D. Research-Technology Management, 33–34.

    Google Scholar 

  6. Wheatley, K. K., & Wilemon, D. (1999). From emerging technology to competitive advantage. In Portland International Conference on the Management of Engineering and Technology (PICMET). Portland, OR.

    Google Scholar 

  7. Saaty, T. (1980). The analytic hierarchy process: Planning, priority setting resource allocation (p. 287). New York: McGraw-Hill.

    Google Scholar 

  8. Vargas, L. G. (1990). An overview of the AHP and its applications. European Journal of Operational Research, 48(1), 2–9.

    Article  Google Scholar 

  9. Ramanujam, V., & Saaty, T. L. (1981). Technological choice in the less developed countries: An analytical hierarchy approach. Technological Forecasting and Social Change, 19, 81–98.

    Article  Google Scholar 

  10. Prasad, A. V. S., & Somasekhara, N. (1990). The analytic hierarchy process for choice of technologies. Technological Forecasting and Social Change, 38, 151–158.

    Article  Google Scholar 

  11. Melachrinoudis, E., & Rice, K. (1991). The prioritization of technologies in a research laboratory. IEEE Transactions on Engineering Management, 38(3), 269–278.

    Article  Google Scholar 

  12. Suh, C.-K., Suh, E.-H., & Baek, K.-C. (1994). Prioritizing telecommunications technologies for long-range R&D planning to the year 2006. IEEE Transactions on Engineering Management, 41(3), 264–275.

    Article  Google Scholar 

  13. Albayrakoglu, M. M. (1996). Justification of new manufacturing technology: A strategic approach using the AHP. Production and Invention Management Journal, 37(1), 71–77.

    Google Scholar 

  14. Sharif, M. N., & Sundararajan, V. (1983). A quantitative model for the evaluation of technological alternatives. Technological Forecasting and Social Change, 24(1), 15–29.

    Article  Google Scholar 

  15. Gerdsri, N., & Kocaoglu, D. F. (2003). An analytical approach to building a technology development envelope (TDE) for roadmapping of emerging technologies. In Portland International Conference on Management of Engineering and Technology (PICMET). Portland, OR.

    Google Scholar 

  16. Kocaoglu, D. (1983). A participative approach to program evaluation. IEEE Transactions on Engineering Management. EM-30(3), 112–118.

    Google Scholar 

  17. Ra, J. W. (1987). Analysis of the column-row approach for pairwise comparison, Unpublished dissertation, University of Pittsburgh

    Google Scholar 

  18. Azar, K. (2001). The future of thermal management in the unstable technology market. In Electronics cooling (p. 1).

    Google Scholar 

  19. Khrustalev, D. (2001). Loop thermosyphons for cooling of electronics. Lancaster, PA: Thermacore, Inc.

    Google Scholar 

  20. Intel, Moore's Law. (2001). www.intel.com/research/silicon/mooreslaw.htm

  21. Montgomery, S., et al. (2002). High-density architecture meets electrical and thermal challenges. In Intel Developer Update Magazine, 1–8.

    Google Scholar 

  22. Viswanath, R., et al. (2000). Thermal performance challenges from silicon to systems. Intel Technology Journal, Q3, 1–16.

    Google Scholar 

  23. Bailey, B. (1990) PCM User Manual, Unpublished report, Portland State University.

    Google Scholar 

  24. Christensen, C. M. (2001, Winter). The past and future of competitive advantage. MIT Sloan Management Review, 42(2), 105–109.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Engineering and Technology Management, Portland State U., Portland, OR, USA

    Nathasit Gerdsri

  2. College of Management, Mahidol University, Salaya, Thailand

    Nathasit Gerdsri

Authors
  1. Nathasit Gerdsri
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Corresponding author

Correspondence to Nathasit Gerdsri .

Editor information

Editors and Affiliations

  1. Dept. of Engineering and Technology Mana, Portland State University, Portland, Oregon, USA

    Tugrul U. Daim

Appendices

Appendix 1: Description of 5-Point Scale Specifically Defined For Each Qualitative Factor

Factors

5-point scale

Description

Factor 23: Durability under adverse environmental conditions

Excellent (E)

Cooling systems are durable to operate under all three adverse conditions.

Very Good (VG)

Cooling systems are durable to operate under two out of three adverse conditions.

Good (G)

Cooling systems are durable to operate under one adverse condition only.

Acceptable (A)

Cooling systems are durable to operate under normal office environment.

Poor (P)

Cooling systems are required to operate under special environment like clean room.

Unacceptable (UA)

Hypothetically, cooling system could not be operated under any environment.

Factor 15: Toxicity of cooling media and combustion products

Excellent (E)

Totally clean; no toxic treatment needed.

Very Good (VG)

Low toxicity but still well below the safety allowance limits; no treatment needed.

Good (G)

Toxicity within the safety allowance but close to the limit; no treatment needed.

Acceptable (A)

Protective measures such as thicker tank walls are needed to meet the safety allowance but no specific toxic treatment is required.

Poor (P)

Toxic treatment is required, for example, an ammonia cooling system, which requires an ammonia tank surrounded by water.

Unacceptable (UA)

No toxic treatment is available to make cooling systems useable.

Factor 16: Ease of installation and maintenance

Excellent (E)

Just plug-in.

Very Good (VG)

Only a screwdriver needed; no skills required.

Good (G)

Basic handyman skills with a set of tools required.

Acceptable (A)

Some technical skills with a box full of tools required.

Poor (P)

Extensive technical skills with a box full of tools required.

Unacceptable (UA)

Cooling systems could not be installed or maintained on-site, so systems need to be replaced when maintenance is needed.

Factor 26: Interchangeability

Excellent (E)

System components are interchangeable with same/similar components made by numerous manufacturers commonly available in electronic stores.

Very Good (VG)

System components are interchangeable with same/similar components made by few manufacturers and commonly available in electronic stores.

Good (G)

System components are interchangeable with same/similar components made by few manufacturers and available only in specialized stores.

Acceptable (A)

System components are interchangeable only with same/similar components made by the original manufacturer available only in specialized stores.

Poor (P)

System components are made to order by the original manufacturer.

Unacceptable (UA)

System components have to be specifically redesigned.

Factor 17: Physical moldability

Excellent (E)

Easy to reshape; no tools are needed.

Very Good (VG)

Reshaping requires specific hand tools.

Good (G)

Difficult to reshape but it can be done without going through a machine shop process.

Acceptable (A)

Re-shapeable but it has to go through machine shop process.

Poor (P)

Very difficult to reshape even when going through manufacturing processes.

Unacceptable (UA)

Components could not be reshaped.

Factor 27: Scalability

Excellent (E)

Cooling system can adjust itself automatically according to the change of heat dissipation amount.

Very Good (VG)

Cooling system can adjust itself automatically when the certain limits of changes in heat dissipation amount are reached.

Good (G)

The cooling capacity can be adjusted manually (such as opening valve or throttle wider)

Acceptable (A)

Some components need to be replaced to respond to any change of heat dissipation amount.

Poor (P)

The whole cooling system has to be replaced.

Unacceptable (UA)

Cooling system is not scaleable.

Factor 37: Upgradeability

Excellent (E)

Just remove the existing components and plug the new ones in; no additional adjustment or hardware modification required.

Very Good (VG)

Some adjustments are needed; no hardware modification required.

Good (G)

Some adjustments are needed along with some hardware modification.

Acceptable (A)

The whole cooling system needs to be adjusted along with hardware modification.

Poor (P)

The whole cooling system needs to be replaced.

Unacceptable (UA)

Cooling systems could not be upgraded.

Appendix 2: Desirability Curves

figure a

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Gerdsri, N. (2016). Strategic Planning: A Quantitative Model for the Strategic Evaluation of Emerging Technologies. In: Daim, T. (eds) Hierarchical Decision Modeling. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-18558-3_5

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