Skip to main content
Log in

A unified strategy for forecasting of a new product

  • Research Paper
  • Published:
DECISION Aims and scope Submit manuscript

Abstract

In the field of new-product forecasting, forecasting by analogy is a well-known technique. In this paper, on the basis of a review of relevant literature, nine desirable characteristics for an analogy-based new-product forecasting technique are purported. Also, a methodology that addresses the purported desirable characteristics for new-product forecasting is proposed. The methodology is demonstrated by considering a total of eleven different items that include consumer durables and information and communication technology (ICT) products and services in India. The proposed methodology offers a unified platform for building an information system that typically involves men and machine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Akkoc S, Vatansever K (2013) Fuzzy performance evaluation with AHP and Topsis methods: evidence from turkish banking sector after the global financial crisis. Eurasian J Bus Econ 6(11):53–74

    Google Scholar 

  • Awasthi A, Chauhan SS (2012) A hybrid approach integrating affinity diagram, AHP and fuzzy TOPSIS for sustainable city logistics planning. Appl Math Model 36(2):573–584

    Article  Google Scholar 

  • Barcus A, Montibeller G (2008) Supporting the allocation of software development work in distributed teams with multi-criteria decision analysis. Omega. 36(3):464–475

    Article  Google Scholar 

  • Bass FM (1969) A new product growth model for consumer durables. Manag Sci 15:215–227

    Article  Google Scholar 

  • Bass FM (2004) A new product growth for model consumer durables: the Bass model. Manag Sci 50(12):1833–1840

    Article  Google Scholar 

  • Chandramouli C (2014) Houses, household amenities and assets data 2001–2011—visualizing through maps. http://censusindia.gov.in/2011-Common/NSDI/Houses_Household.pdf. Accessed 3 June 2014

  • Chang S-I, Yen D-C, Ng C, Chang IC, Yu S-Y (2011) An ERP system performance assessment model development based on the balanced scorecard approach. Inf Syst Front 13(3):429–450

    Article  Google Scholar 

  • Chen CT (2000) Extensions of the TOPSİS for group decision making under fuzzy environment. Fuzzy Sets Syst 114:1–9

    Article  Google Scholar 

  • Cronrath EM, Zock A (2007) Forecasting the diffusion of innovations by analogies: examples of the mobile telecommunication market, presented at the proceedings of the 2007 international conference of the system dynamics society, Boston. http://www.systemdynamics.org/conferences/2007/proceed/papers/CRONR444.pdf. Accessed 4 Nov 2014

  • Csutora R, Buckley JJ (2001) Fuzzy hierarchical analysis: the lambda-Max method. Fuzzy Sets Syst 120(2):181–195

    Article  Google Scholar 

  • Deng H (1999) Multicriteria analysis with fuzzy pairwise comparison. Int J Approx Reason 21:215–231

    Article  Google Scholar 

  • Dodson J (2014) New product forecasting: the Bass model. http://faculty.washington.edu/jdods/pdf/MktgTool_Bass.pdf. Accessed 26 June 2014

  • Easingwood CJ (1989) An analogical approach to the long term forecasting of major new product sales. Int J Forecast 5:69–82

    Article  Google Scholar 

  • Easingwood CJ, Mahajan V, Muller EW (1981) A nonsymmetric responding logistic model for forecasting technological substitution. Technol Forecast Soc Chang 20:199–213

    Article  Google Scholar 

  • Easingwood CJ, Mahajan V, Muller EW (1983) A non-uniform influence innovation diffusion model of new product acceptance. Mark Sci 2:273–296

    Article  Google Scholar 

  • Goldfarb RS, Stekler HO, David J (2005) Methodological issues in forecasting: insights from the egregious business forecast errors of late 1930. J Econ Methodol 12:517–542

    Article  Google Scholar 

  • Goodwin P, Dyussekeneva K, Meeran S (2013) The use of analogies in forecasting the annual sales of new electronics products. IMA J Manag Math 24(4):407–422

    Article  Google Scholar 

  • Green KC, Armstrong JS (2007) Structured analogies for forecasting. Int J Forecast 23:365–376

    Article  Google Scholar 

  • Huang C-C, Chu P-Y, Chiang Y-H (2008) A fuzzy AHP application in government-sponsored R&D project selection. Omega 36(6):1038–1052

    Article  Google Scholar 

  • Hyndman RJ, Athanasopoulos G (2013) Forecasting: principles and practice, otexts. https://www.otexts.org/book/fpp. Accessed 4 Nov 2014

  • Javanbarg MB, Scawthorn C, Kiyon J, Shahbodaghkh B (2012) Fuzzy AHP-based multicriteria decision making systems using particle swarm optimization. Expert Syst Appl 39:960–966

    Article  Google Scholar 

  • Jeuland A (1981) Parsimonious models of diffusion of innovation: derivation and comparisons. Working paper, marketing department. Graduate school of business, University of Chicago, Chicago

  • Kahn KB (2006) New product forecasting: an applied approach. Armonk, NY, ME Sharpe

    Google Scholar 

  • Kanungo T, Mount D, Netanyahu N, Piatko C, Silverman R, Wu A (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892

    Article  Google Scholar 

  • Kapoor V, Singh S (2005) Fuzzy application to the analytic hierarchy process for robot selection. Fuzzy Optim Decis Mak 4(3):209–234

    Article  Google Scholar 

  • Lee WY, Goodwin P, Fildes R, Nikolopoulos K, Lawrence M (2007) Providing support for the use of analogies in demand forecasting tasks. Int J Forecast 23(3):377–390

    Article  Google Scholar 

  • Li S-T, Chou W-C (2014) Power planning in ICT infrastructure: a multi-criteria operational performance evaluation approach. Omega 49:134–148

    Article  Google Scholar 

  • Li Y, Liu X, Chen Y (2012) Supplier selection using axiomatic fuzzy set and TOPSIS methodology in supply chain management. Fuzzy Optim Decis Mak 11(2):147–176

    Article  Google Scholar 

  • Lin H-F (2010) An application of fuzzy AHP for evaluating course website quality. Comput Educ 54:877–888

    Article  Google Scholar 

  • Mahajan V, Peterson RA (1978) Innovation diffusion in a dynamic potential adopter population. Manag Sci 24:1589–1597

    Article  Google Scholar 

  • Neubauer T, Stummer C (2010) Interactive selection of web services under multiple objectives. Inf Technol Manag 11(1):25–41

    Article  Google Scholar 

  • Olson DL (2004) Comparison of weights in TOPSIS models. Math Comput Model 1:1–7

    Google Scholar 

  • Peres R, Muller E, Mahajan V (2010) Innovation diffusion and new product growth models: a critical review and research directions. Intern J Res Mark 27:91–106

    Article  Google Scholar 

  • Rezaie K, Ramiyani SS, Shirkouhi SN, Badizadeh A (2014) Evaluating performance of Iranian cement firms using an integrated fuzzy AHP–VIKOR method. Appl Math Model. doi:10.1016/j.apm.2014.04.003

    Google Scholar 

  • Rogers EM (2003) Diffusion of innovations. Free Press, New York

    Google Scholar 

  • Saaty TL (1995) Decision making for leaders. RWS Publications, New York

    Google Scholar 

  • Saaty TL (2003) The analytic hierarchy process. McGra Hill, New York

    Google Scholar 

  • Sharif MN, Kabir C (1976) A generalized model for forecasting technological substitution. Technol Forecast Soc Chang 8:353–364

    Article  Google Scholar 

  • Srdjevic B, Medeiros Y (2008) Fuzzy AHP assessment of water management plans. Water Resour Manag 22:877–894

    Article  Google Scholar 

  • Sun C (2010) A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst Appl 37:7745–7754

    Article  Google Scholar 

  • Thomas Robert J (1985) Estimating market growth for new products: an analogical diffusion model approach. J Prod Innov Manag 2:45–55

    Article  Google Scholar 

  • Tiryaki F, Ahlatcioglu B (2009) Fuzzy portfolio selection using fuzzy analytic hierarchy process. Inf Sci 179:53–69

    Article  Google Scholar 

  • Vinod S, Prasanna M, Prakash NH (2014) Integrated fuzzy AHP–TOPSIS for selecting the best plastic recycling method: a case study. Appl Math Model. doi:10.1016/j.apm.2014.03.007

  • White M, Braczyk HJ, Ghobadian A, Niebuhr J (1988) Small firms’ innovation: why regions differ. Policy Studies Institute. http://www.psi.org.uk/site/publication_detail/735. Accessed 21 May 2014

Download references

Acknowledgments

Authors acknowledge the support extended by Prof. Deepali Singh (Indian Institute of Information Technology and Management, Gwalior) for her valuable suggestions regarding this work. Authors also acknowledge the support extended by JUET, Guna in providing the online academic resources required for conduction of this research. The authors are grateful to the anonymous referees who provided useful comments on this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shishir Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandey, P., Kumar, S. & Shrivastava, S. A unified strategy for forecasting of a new product. Decision 41, 411–424 (2014). https://doi.org/10.1007/s40622-014-0065-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40622-014-0065-x

Keywords

Navigation