1. bookVolume 45 (2021): Issue 340 (August 2021)
Journal Details
License
Format
Journal
eISSN
2256-0939
First Published
30 Aug 2012
Publication timeframe
2 times per year
Languages
English
access type Open Access

Project Valuation and Risk Assessment in Food Product Development: Evidence from Fuzzy Real Option Valuation

Published Online: 11 Aug 2021
Volume & Issue: Volume 45 (2021) - Issue 340 (August 2021)
Page range: 38 - 52
Received: 02 Feb 2021
Accepted: 07 Apr 2021
Journal Details
License
Format
Journal
eISSN
2256-0939
First Published
30 Aug 2012
Publication timeframe
2 times per year
Languages
English
Abstract

To survive in today's competitive environment, companies must continuously develop and offer customers new products. To increase the probability of a successful business case of investing in the development of a new product, careful attention must be paid to risk analysis in terms of the present value of future potential income. The article considers an example of the research work of the Latvia University of Life Sciences and Technologies, in the framework of which a technical and technological project was developed to produce a new product, like Mediterranean anchovy, from cheaper Baltic sprats. The main goal of this work is to explore the application multi-factor sensitivity and fuzzy real option analysis to the valuation of new product development project.

The multivariate analysis of the sensitivity of the financial model of the greenfield production project described in this article revealed the main risk groups, as well as their degree of influence on the assessment of the Net Present Value of the project by a potential investor. The use of Fuzzy Real Option Valuation made it possible to evaluate the project with uncertain parameters, as well as to calculate the potential upside from preliminary refinement of parameters to eliminate negative scenarios.

The described approach is applicable to risk assessment of new food product development and allows investors to make a more informed decision about participation in such projects.

Keywords

Ahuja, G. (2000). Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study. Administrative Science Quarterly, 45 (3): 425–55. https://doi.org/10.2307/2667105.10.2307/2667105Search in Google Scholar

Anderson, Ph., Tushman, M.L. (2001). Organizational Environments and Industry Exit: The Effects of Uncertainty, Munificence and Complexity. Industrial and Corporate Change, 10 (3): 675-711. https://doi.org/10.1093/icc/10.3.675.10.1093/icc/10.3.675Search in Google Scholar

Borgonovo, E., Peccati, L. (2004). Sensitivity Analysis in Investment Project Evaluation. International Journal of Production Economics, 90 (1): 17–25.Search in Google Scholar

Borgonovo, E., Peccati, L. (2006). Uncertainty and Global Sensitivity Analysis in the Evaluation of Investment Projects. International Journal of Production Economics, Strategic Issues and Innovation in Production Economics, 104 (1): 62–73. https://doi.org/10.1016/j.ijpe.2005.05.024.10.1016/j.ijpe.2005.05.024Search in Google Scholar

Carlsson, Ch., Fullér, R., Heikkilä, M., Majlender, P. (2007). “A Fuzzy Approach to R&D Project Portfolio Selection.” International Journal of Approximate Reasoning, Fuzzy Decision-Making Applications, 44 (2): 93–105. https://doi.org/10.1016/j.ijar.2006.07.003.10.1016/j.ijar.2006.07.003Search in Google Scholar

Cooper, R.G., Kleinschmidt, E.J. (1995). Benchmarking the Firm’s Critical Success Factors in New Product Development. Journal of Product Innovation Management, 12 (5): 374–91. https://doi.org/10.1016/0737-6782(95)00059-3.10.1111/1540-5885.1250374Search in Google Scholar

Cox, J.C., Ross, S.A., Rubinstein, M. (1979). Option Pricing: A Simplified Approach. Journal of Financial Economics, 7 (3): 229–63. https://doi.org/10.1016/0304-405X(79)90015-1.10.1016/0304-405X(79)90015-1Search in Google Scholar

Dodgson, M., Rothwell, R. (1991). External Linkages and Innovation in Small and Medium-Sized Enterprises. SSRN Scholarly Paper, ID 1506762. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=1506762.Search in Google Scholar

Freeman, Ch., Soete, L. (1997). The Economics of Industrial Innovation - 3rd Edition. Third edition edition. Cambridge, Mass: The M.I.T. Press.Search in Google Scholar

Haahtela, T.J. (2010). Regression Sensitivity Analysis for Cash Flow Simulation Based Real Option Valuation. Procedia – Social and Behavioral Sciences, Sixth International Conference on Sensitivity Analysis of Model Output, 2 (6): 7670–71. https://doi.org/10.1016/j.sbspro.2010.05.171.10.1016/j.sbspro.2010.05.171Search in Google Scholar

Haaker, M.P.R., Verheijen P.J.T. (2004). Local and Global Sensitivity Analysis for a Reactor Design with Parameter Uncertainty. Chemical Engineering Research and Design, 82 (5): 591–98. https://doi.org/10.1205/026387604323142630.10.1205/026387604323142630Search in Google Scholar

Ho, Sh.H., Liao, Sh.H. (2011). A Fuzzy Real Option Approach for Investment Project Valuation. Expert Syst. Appl., 38: 15296–302. https://doi.org/10.1016/j.eswa.2011.06.010.10.1016/j.eswa.2011.06.010Search in Google Scholar

Jerrard, R., Hands, D. eds. (2007). Design Management: Exploring Fieldwork and Applications. 1 edition. London; New York, NY: Routledge.Search in Google Scholar

Jovanović, P. (1999). Application of Sensitivity Analysis in Investment Project Evaluation under Uncertainty and Risk. International Journal of Project Management, 17 (4): 217–22. https://doi.org/10.1016/S0263-7863(98)00035-0.10.1016/S0263-7863(98)00035-0Search in Google Scholar

Kahraman, C., Ruan, D., Tolga, E. (2002). Capital Budgeting Techniques Using Discounted Fuzzy versus Probabilistic Cash Flows. Information Sciences, 142 (1–4): 57–76. https://doi.org/10.1016/S0020-0255(02)00157-3.10.1016/S0020-0255(02)00157-3Search in Google Scholar

Kahraman, C. (2008). Fuzzy Engineering Economics with Applications. Springer.10.1007/978-3-540-70810-0Search in Google Scholar

Keizera, J.A., Halman, J.I.M., Song, M. (2002). From Experience: Applying the Risk Diagnosing Methodology. Journal of Product Innovation Management, 19 (3): 213–32. https://doi.org/10.1016/S0737-6782(02)00138-8.10.1111/1540-5885.1930213Search in Google Scholar

Kim, Y.J, Vonortas, N.S. (2014). Managing Risk in the Formative Years: Evidence from Young Enterprises in Europe. Technovation, Risk and Uncertainty Management in Technological Innovation, 34 (8): 454–65. https://doi.org/10.1016/j.technovation.2014.05.004.10.1016/j.technovation.2014.05.004Search in Google Scholar

Klingelhöfer, H.E. (2009). Investments in EOP-Technologies and Emissions Trading – Results from a Linear Programming Approach and Sensitivity Analysis. European Journal of Operational Research, 196 (1): 370–83. https://doi.org/10.1016/j.ejor.2008.03.016.10.1016/j.ejor.2008.03.016Search in Google Scholar

Mansor, N., Yahaya, S., Okazaki, K. (2016). Risk Factors Affecting New Product Development (NPD) Performance In Small Medium Enterprises (SMES), International journal of recent research and applied studies, 27(1): 8. https://www.arpapress.com/Volumes/Vol27Issue1/IJRRAS_27_1_03.pdfSearch in Google Scholar

Meyer, M.H., Roberts, E.B. (1986). New Product Strategy in Small Technology-Based Firms: A Pilot Study. Management Science, 32 (7): 806–21.Search in Google Scholar

Milliken, F.J. (1987). Three Types of Perceived Uncertainty about the Environment: State, Effect, and Response Uncertainty. The Academy of Management Review, 12 (1): 133–43. https://doi.org/10.2307/257999.10.2307/257999Search in Google Scholar

Moenaert, R.K., De Meyer, A., Souder W.E., Deschoolmeester, D. (1995). R D/Marketing Communication during the Fuzzy Front-End. IEEE Transactions on Engineering Management, 42 (3): 243–58. https://doi.org/10.1109/17.403743.10.1109/17.403743Search in Google Scholar

Moskowitz, H.R., Saguy, I. S., Straus, T. eds. (2009). An Integrated Approach to New Food Product Development. 0 ed. CRC Press. https://doi.org/10.1201/9781420065558.10.1201/9781420065558Search in Google Scholar

Mu J., Peng G., MacLachlan D.L. (2009). Effect of Risk Management Strategy on NPD Performance. Technovation, 29 (3): 170–80. https://doi.org/10.1016/j.technovation.2008.07.006.10.1016/j.technovation.2008.07.006Search in Google Scholar

Muzzioli, S., Reynaerts, H. (2008). American Option Pricing with Imprecise Risk-Neutral Probabilities. International Journal of Approximate Reasoning, Special Section on Logical Approaches to Imprecise Probabilities and Special Section on Imprecise Probabilities in Finance and Economics, 49 (1): 140–47. https://doi.org/10.1016/j.ijar.2007.06.011.10.1016/j.ijar.2007.06.011Search in Google Scholar

Muzzioli, S., Torricelli, C. (2004). A Multiperiod Binomial Model for Pricing Options in a Vague World. Journal of Economic Dynamics and Control, Financial decision models in a dynamical setting, 28 (5): 861–87. https://doi.org/10.1016/S0165-1889(03)00060-5.10.1016/S0165-1889(03)00060-5Search in Google Scholar

Park, Y.H. (2010). A Study of Risk Management and Performance Measures on New Product Development. Asian Journal on Quality, 11 (1): 39–48. https://doi.org/10.1108/15982681011051813.10.1108/15982681011051813Search in Google Scholar

Perez-Villarreal, B., Pozo, R. (1992). Ripening of the Salted Anchovy (Engraulis Encrasicholus): Study the Sensory, Biochemical and Microbiological Aspects. Developments in Food Science. http://agris.fao.org/agris-search/search.do?recordID=US201301770474.Search in Google Scholar

Sabovics, M., Mugurevics, A., Silovs, M., Dmitrijeva, O. (2019a). Development of Technology and Recepies of Baltic Anchovy in Oil Preserves (Salted Products) and Baltic Anchovies Paste from Sprattus Balticus as Analogous to Traditional Italian Canned Anchovies in Oil and Mechanization of Technological Processes for Their Production”, project no. 17-00-F01101-000003, Latvia University of Life Sciences and Technologies.Search in Google Scholar

Sabovics, M., Pilvere, I., Muizniece-Brasava, S., Silovs, M., Dmitrijeva, O. (2019b). Baltic sprat (Sprattus sprattus balticus) as a raw material for developing “anchovy” analogue. In: 19th International multidisciplinary scientific GeoConference SGEM 2019: conference proceedings, Albena, Bulgaria, 9-11 December, 2019/Bulgarian Academy of Sciences Sofia, 2019. Vol.19, Issue 6.3: Micro and nano technologies. Advances in biotechnology. Green buildings technologies and materials. Green design and sustainable architecture; pp.43.-50. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092461894&partnerID=40&md5=2862942d40932719fd7159140870beef ISBN 9786197408997.Search in Google Scholar

Sari, I.D., Kuchta, D. (2012). Fuzzy Global Sensitivity Analysis of Fuzzy Net Present Value. Control and Cybernetics, Vol. 41, no. 2: 481–96.Search in Google Scholar

Sheen, J.N. (2005). Fuzzy Evaluation of Cogeneration Alternatives in a Petrochemical Industry. Computers & Mathematics with Applications, 49 (5): 741–55. https://doi.org/10.1016/j.camwa.2004.10.035.10.1016/j.camwa.2004.10.035Search in Google Scholar

Taylor, R.B., West, T.M. (1992). Development of a Spreadsheet-Based Capital Investment Model with Sensitivity Analysis. Computers and Industrial Engineering, 23 (1–4): 431–433. https://doi.org/10.1016/0360-8352(92)90153-B.10.1016/0360-8352(92)90153-BSearch in Google Scholar

Van Groenendaal, W.J.H., Kleijnen, J.P.C. (2002). Deterministic versus Stochastic Sensitivity Analysis in Investment Problems: An Environmental Case Study. European Journal of Operational Research 141 (1): 8–20. https://doi.org/10.1016/S0377-2217(01)00236-3.10.1016/S0377-2217(01)00236-3Search in Google Scholar

Yeo, K. T., Qiu, F. (2003). The Value of Management Flexibility – a Real Option Approach to Investment Evaluation. International Journal of Project Management, 21 (4): 243–50. https://doi.org/10.1016/S0263-7863(02)00025-X.10.1016/S0263-7863(02)00025-XSearch in Google Scholar

Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8 (3): 338–53. https://doi.org/10.1016/S0019-9958(65)90241-X.10.1016/S0019-9958(65)90241-XSearch in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo