Weitere Kapitel dieses Buchs durch Wischen aufrufen
Previous research showed that passenger car manufacturing sector gets affected due to economic volatility and customer behavior pattern. In course of economic slowdown, inventory builds up and production rate suffers. While during recovery, backlog increases causing customers to shift brand loyalty. Besides, customer’s preference tends to affect the supply chain of a firm too. In passenger car sector, changes in pre-designed models have significant bearing on the lead time. Delay in adopting changes sought by the customers’ results in longer production time and obsolescence of inventory. The impact of economic variations on sales of cars has been analyzed through multivariate regression, and the dimensions explaining the customers’ buying pattern have been identified through factor analysis of responses obtained from car buyers. The purpose of this chapter is to establish a system dynamics model, to study the effect of economic volatility and customer’s buying behavior on supply chain of passenger car firms. The proposed framework will enable supply chain managers to carry out policy experimentation under different volatile situations arising out of exogenous factors. The proposed model is expected to address the major challenge, i.e., when there is economic instability with changing customer preferences.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Bibhushan, Prakash A., & Wadhwa, B. (2014). Supply chain flexibility: Some perceptions. In Sushil & E. A. Stohr (Eds.), The flexible enterprise, Flexible Systems Management (pp. 321–331). New Delhi: Springer.
Cariolle, J. (2012). Measuring macroeconomic volatility applications to export revenue data, 1970–2005, Fondation Pour Les Etudes Et Recherches Sur Le Development International. Working paper n°I14 “Innovative indicators” series March 2012.
Chopra, S., & Meindl, P. (2001). Supply chain management: Strategy, planning, and operation (p. 457). New Jersey: Prentice Hall, Upper Saddle River.
Christopher, M. (2005). Logistics and supply chain management: Creating value adding networks (3rd ed.), London, UK: Financial Times Prentice Hall.
Coyle, R. G. (1977). Management system dynamics. London: Wiley.
Dargay, J., & Gately, D. (1999). Income’s effect on car and vehicle ownership, worldwide: 1960–2015. Transportation Research. Part A: Policy and Practice, 33(2).
Dargay, J., & Gately, D. (2001). Modelling global vehicle ownership. In Proceedings of the Ninth World Conference on Transport Research (pp. 22–27).
Demiroğlu, U., & Yüncüler, Ç. (2016). Estimating light-vehicle sales in Turkey. Central Bank Review, 16(3), 93–108. CrossRef
Dey, D., & Sinha, D. (2016). System dynamics simulation of a supply chain intelligence model. In A. Dwivedi (Ed.), Innovative solutions for implementing global supply chains in emerging markets (pp. 71–83). UK: University of Hull Business School.
Dhir, S., Aniruddha, N. A., & Mital, A. (2014). Alliance network heterogeneity absorptive capacity and innovation performance: A framework for mediation and moderation effects, international. Journal of Strategic Business Alliances, 3(2–3), 168–178. CrossRef
Dhir, S., & Mital, A. (2013). Value creation on bilateral cross-border joint ventures: Evidence from India. Strategic Change, 22(5–6), 307–326. CrossRef
Eskeland, G., & Feyzioglu, T. (1997). Is demand for polluting goods manageable? An econometric study of car ownership and use in Mexico. Journal of Development Economics, 5(3), 423–445. CrossRef
Forrester, J. W. (1961). Industrial, dynamics (p. 156). Cambridge, Massachusetts: M.I.T Press.
Forrester, J. W. (1968). Principals of systems. Cambridge, Massachusetts: Wright Allen Press.
Greenspan, A., & Cohen, D. (1999). Motor vehicle stocks, scrappage and sales. The Review of Economics and Statistics, 81(3), 369–383. CrossRef
Gunasekaran, A., Dubey, R., & Singh, S. P. (2016). Flexible sustainable supply chain network design: Current trends, opportunities and future. Global Journal of Flexible Systems Management, 17(2), 109–112. CrossRef
Gunasekaran, A., Lai, K. H., & Edwin Cheng, T. C. (2008). Responsive supply chain: A competitive strategy in a networked economy. Omega, 36(4), 549–564. CrossRef
Gunawan, F. E., & Chandra, F. Y. (2014). Optimal averaging time for predicting traffic velocity using floating car data technique for advanced traveler information system. Procedia-Social and Behavioral Sciences, 138, 566–575. CrossRef
Hamilton, B. W., & Macauley, M. K. (1998). Competition and car longevity, Working paper.
Hnatkovska, V., & Loayza, N. (2005). Volatility and Growth. In J. Azeinman & B. Pinto (Eds.), Managing economic volatility and crises. Cambridge, Mass: Cambridge University Press.
Jacobsen, M. R., & Van Benthem, A. A. (2015). Vehicle scrappage and gasoline policy. The American Economic Review, 105(3), 1312–1338. CrossRef
Kahn, J. A. (1986). Gasoline prices and the used car market: A rational expectations asset price approach. The Quarterly Journal of Economics, 101(2), 323–340. CrossRef
Khanra, S., & Dhir, S. (2017). Creating value in small-cap firms by mitigating risks of market volatility. Vision, 21(4), 350–355. CrossRef
Kurien, G. P., & Qureshi, M. N. (2014). Measurement of flexibility and its benchmarking using data envelopment analysis in supply chains. In M. K. Nandakumar, Sanjay Jharkharia & Abhilash S. Nair (Eds.), Organizational flexibility and competitiveness, Flexible Systems Management (pp. 259–272). New Delhi: Springer.
Mukhtar, M., Jailani, N., Abdullah, S., Yahya, Y., & Abdullah, Z. (2009). A framework for analyzing E-supply chains. European Journal of Scientific Research, 25(4), 649–662.
Naik, A. (2016). http://auto.ndtv.com/news/maruti-suzuki-vitara-brezza-wins-cnb-viewers-choice-car-of-the-year-2017-163990, May 2, 2017.
Parameswar, N., Dhir, S., & Dhir, S. (2017). Banking on innovation, innovation in banking at ICICI bank. Global Business and Organizational Excellence, 36(2), 6–16. CrossRef
Patel, J., Modi, A., & Paul, J. (2017). Pro-environmental behavior and socio-demographic factors in an emerging market. Asian Journal of Business Ethics, 6(2), 189–214. CrossRef
Rota, M. F., Carcedo, J. M., & García, J. P. (2016). Dual approach for modelling demand saturation levels in the automobile market. The Gompertz curve: Macro versus Micro data. Investigación Económica, 75(296), 43–72. CrossRef
Senge, P. (1990). The fifth discipline: The art and science of the learning organization. New York: Currency Doubleday.
Sheffi, Y., & Rice, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41–48.
Stevenson, M., & Spring, M. (2007). Flexibility from a supply chain perspective: Definition and review. International Journal of Operations & Production Management, 27(7), 685–713. CrossRef
Sushil, (1993). System dynamics—A practical approach for managerial problems. New Delhi: Wiley Eastern Ltd.
Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents of supply chain agility of a firm: Scale development and model testing. Journal of Operations Management, 24(2), 170–188. CrossRef
Van der Velde, L.N. J., & Meijer, B. R. (2003). A system approach to supply chain design with a multinational for colorant and coatings. Retieved September 20, 2006 from http://ww.ifm.eng.ca.ac.uk/mcn/pdf_files/part6_5.pdf.
Wu, T., Zhao, H., & Ou, X. (2014). Vehicle ownership analysis based on GDP per Capita in China: 1963–2050. Sustainability, 6(8), 4877–4899. CrossRef
- Supply Chain Strategies to Sustain Economic and Customer Uncertainties
- Springer Singapore
- Chapter 10
Neuer Inhalt/© Stellmach, Neuer Inhalt/© Maturus, Pluta Logo/© Pluta, digitale Transformation/© Maksym Yemelyanov | Fotolia