Abstract
In Demand-driven Production with Just-in-Time inputs, there are several sources of uncertainty which impact on the manufacturer’s ability to meet the required customer’s demand within the given time frame. This can result in a loss of revenue and customers, which will have undesirable impacts on the financial aspects and on the viability of the manufacturer. Hence, a key concern for manufacturers in just-in-time production is to determine whether they can meet a specific level of demand within a given time frame, to meet the customers’ orders and also to achieve the required revenue target for that period of time. In this paper, we propose a methodology by which a manufacturer can ascertain the probability of not meeting the required demand within a given period by considering the uncertainties in the availability of production units and raw materials, and the loss of financial revenue that it would experience as a result.
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References
Agervald, O. (1980). Demand driven manufacturing offers and delivers improvements. Manufacturing and Logistics IT Magazine. Available at: http://www.logisticsit.com/absolutenm/templates/article-critical.aspx?articleid=480&zoneid=31.
Alfieri A., Tolio T., Urgo M. (2010) A project scheduling approach to production and material requirement planning in Manufacturing-to-Order environments. Journal of Intelligent Manufacturing 16: 1–11
Berndt B. (2006) Accurate planning in a demand-driven world. Quality Control: Manufacturing & Distribution 154: 56–58
Chen M., Dubrawski R., Meyn S. P. (2004) Management of demand-driven production systems. IEEE Transactions on Automatic Control 49: 686–698
Duran H. (1985) A recursive approach to the cumulant method of calculating reliability and production Cost. IEEE Transactions on Power Apparatus and Systems 104: 82–90
Factory Logic Company. (2003). The power of demand driven manufacturing in a down economy (pp. 1–11). Factory Logic, Austin.
Feiring B. R., Sastri T. (2005) A demand-driven method for scheduling optimal smooth production levels. Annals of Operations Research 17: 199–215
Ghrayeb O., Phojanamongkolkij N., Tan B. (2009) A hybrid push/pull system in assemble-to-order manufacturing environment. Journal of Intelligent Manufacturing 20: 379–387
Gnedenko B. V. (1966) Theory of probability, (Vol. 1). Chelsea Publishing Company, New York
Hussain, O. K., Chang, E., Hussain, F. K., Dillon T. S. (2007). Towards quantifying the possible risk in e-commerce interactions for RDSS. In: IEEE (Ed.), Proceedings of the IEEE international conference on e-Business engineering (ICEBE’07) (pp. 89–96). Hong Kong, IEEE.
Hussain, O., & Dillon, T. (2010). Cost-benefit analysis to hedge with third-party producers in demand-driven production. Complex intelligent systems and their applications (pp. 69–81). Berlin: Springer.
Hussain O. K., Dillon T., Hussain F. K., Chang Elizabeth. (2010) Transactional risk-based decision making system in e-business computing. Computer Systems Science and Engineering 25: 15–25
Janaka S. L., Lina X., Floudas C. A. (2007) A new robust optimization approach for scheduling under uncertainty: II. Uncertainty with known probability distribution. Computers & Chemical Engineering 31: 171–195
Jardim-Goncalves R., Sarraipa J., Agostinho C., Panetto H. (2009) Knowledge framework for intelligent manufacturing systems. Journal of Intelligent Manufacturing 16: 1–11
Johnson, D. C. (1997). Implementing a demand driven production system in an automotive assembly plant (p. 83). Sloan School of Management; Massachusetts Institute of Technology. Department of Mechanical Engineering, M.S. Massachusetts Institute of Technology.
Kendall M. G., Stuart A. (1969) The advanced theory of statistics, (Vol. 1). Butler & Tanner Ltd, London
Li D.-C., Fang Y.-H., Liu C.-W., Juang C.-j. (2010) Using past manufacturing experience to assist building the yield forecast model for new manufacturing processes. Journal of Intelligent Manufacturing 57: 1–12
Lin M., Breipohl A., Lee F. (1986) Comparison of probabilistic production cost simulation methods. IEEE Transactions on Power Systems 4: 1326–1334
Mohebbi E., Choobineh F., Pattanayak A. (2007) Capacity-driven vs. demand-driven material procurement systems. International Journal of Production Economics 107: 451–466
Oh W. (2002) C2C versus B2C: A comparison of the winner’s curse in two types of electronic auctions. International Journal of Electronic Commerce 6: 115–138
Qiu M.M., Burch E.E. (1997) Hierarchical production planning and scheduling in a multiproduct, multi-machine environment. International Journal of Production Research 35: 3023–3042
Sanabria L. A., Dillon T. S. (1986) Stochastic power flow using cumulants and Von Mises functions. International Journal of Electrical Power & Energy Systems 8: 47–60
Sanabria L. A., Dillon T. S. (1988) An error correction algorithm for stochastic production costing. IEEE Transactions on Power Systems 3: 94–100
Sanabria L. A., Dillon T. S. (1998) Power system reliability assessment suitable for a deregulated system via the method of cumulants. International Journal of Electrical Power & Energy Systems 20: 203–211
Tan B., Gershwin S. B. (2004) Production and subcontracting strategies for manufacturers with limited capacity and volatile demand. Annals of Operations Research 125: 205–232
Tony, S. (2004). History repeated as Apple slams CPU supplier. http://www.theregister.co.uk/2004/07/15/apple_cpu_grumbles/.
Van Landeghem H., Vanmaele H. (2002) Robust planning: A new paradigm for demand chain planning. Journal of Operations Management 20: 769–783
Vardi J., Zahavi J., Avi-Itzhak B. (1976) The combined load duration curve and its derivation. IEEE Transactions on Power Apparatus and Systems 96: 978–983
Walpole R.E., Myers R.H., Myers S.L. (2001) Probability and statistics for engineers and scientists. New Jersey, Prentice Hall
Weiss N. A., Holmes P. T., Hardy M. (2005) A course in probability. Addison-Wesley, Massachusetts
Yıldırım I., Barış T., Karaesmen F. (2005) A multiperiod stochastic production planning and sourcing problem with service level constraints. OR Spectrum 27: 471–489
Zäpfel G. (1998) Customer-order-driven production: An economical concept for responding to demand uncertainty. International Journal of Production Economics 56(57): 699–709
Zhang P., Lee S. T. (2004) Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion. IEEE Transactions on Power Systems 19: 676–682
Zhang X., Chen R. (2006) Forecast-driven or customer-order-driven? An empirical analysis of the Chinese automotive industry. International Journal of Operations & Production Management 26: 668–688
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Hussain, O.K., Dillon, T., Hussain, F.K. et al. Probabilistic assessment of loss in revenue generation in demand-driven production. J Intell Manuf 23, 2069–2084 (2012). https://doi.org/10.1007/s10845-011-0518-4
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DOI: https://doi.org/10.1007/s10845-011-0518-4