Semiconductor supply planning by considering transit options to take advantage of pre-productions and order cancellations

Abstract One of the objectives of supply planning is to find when and how many productions have to be started to minimize total cost. We aim to find the optimum. Base data like the length of transit time are important parameters to plan for the optimum start of production. In this research, we considered two kinds of transit options: normal transit and emergency transit, and we distinguished between planned and executed transit. The decision regarding which transit option to choose for the execution is trivial because emergency is only used when it is needed since normal transit is more cost efficient. However, for planning purpose, it is more difficult to decide which transit option should be used since the uncertainty in demand and supply between plan and execution can allow to plan an emergency transit but to execute the delivery with normal transit, which is a huge benefit in the competitive capital intensive semiconductor industry. If we planned an emergency, we can save inventory and production cost as we can delay the start of production. In contrast, we need pay additional transit cost in case that emergency transit is actually executed. Many characteristics of the semiconductor industry, which might be the front runner for many other industries, are considered in this model such as demand uncertainty, supply uncertainty and economic volatility. In our numerical experiments, we could gain the optimum, depending on each economic situation. Furthermore, we conducted sensitivity analysis of the effect of demand and supply uncertainties on total cost.

[1]  Peter Williams,et al.  Modeling supply contracts in semiconductor supply chains , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[2]  Charles H. Fine,et al.  UPSTREAM VOLATILITY IN THE SUPPLY CHAIN: THE MACHINE TOOL INDUSTRY AS A CASE STUDY , 2000 .

[3]  Lars Mönch,et al.  Heuristic approaches for master planning in semiconductor manufacturing , 2012, Comput. Oper. Res..

[4]  Kurt Siehr Survey of problems , 1993 .

[5]  Guoqi Zhang,et al.  More than Moore: Creating High Value Micro/Nanoelectronics Systems , 2009 .

[6]  Ming Dong,et al.  A stochastic dynamic programming approach-based yield management with substitution and uncertainty in semiconductor manufacturing , 2011, Comput. Math. Appl..

[7]  Susana Relvas,et al.  Alexandre Dolgui and Jean-Marie Proth, Supply Chain Engineering - Useful Methods and Techniques , Springer-Verlag (2010) 541 pp., ISBN: 978-1-84996-016-8 , 2010, Eur. J. Oper. Res..

[8]  Kut C. So,et al.  Impact of supplier's lead time and forecast demand updating on retailer's order quantity variability in a two-level supply chain , 2003 .

[9]  Lars Mönch,et al.  Using iterative simulation to incorporate load-dependent lead times in master planning heuristics , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[10]  Reha Uzsoy,et al.  Production planning for semiconductor manufacturing via simulation optimization , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[11]  M. D. Byrne,et al.  Production planning using a hybrid simulation – analytical approach , 1999 .

[12]  Ozgur M. Araz,et al.  Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations , 2011 .

[13]  Mark Stevenson,et al.  A review of production planning and control: the applicability of key concepts to the make-to-order industry , 2005 .

[14]  Yi-Feng Hung,et al.  Determining safety stocks for production planning in uncertain manufacturing , 1999 .

[15]  Mir Saman Pishvaee,et al.  A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment , 2011, Adv. Eng. Softw..

[16]  M. Khouja A Note on the Newsboy Problem with an Emergency Supply Option , 1996 .

[17]  Yavuz Acar,et al.  A decision support framework for global supply chain modelling: an assessment of the impact of demand, supply and lead-time uncertainties on performance , 2010 .

[18]  E. W. Barankin,et al.  A DELIVERY-LAG INVENTORY MODEL WITH AN EMERGENCY PROVISION , 1961 .

[19]  Alexandre Dolgui,et al.  Supply Chain Engineering , 2010 .

[20]  John W. Fowler,et al.  A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations , 2011, J. Sched..

[21]  John W. Fowler,et al.  Analysis of a customer demand driven semiconductor supply chain in a distributed simulation test bed , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[22]  Samuel Kortum,et al.  Moore's Law and the Semiconductor Industry: A Vintage Model , 2005 .

[23]  Hans Ehm,et al.  Towards a supply chain simulation reference model for the semiconductor industry , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[24]  J. Proth,et al.  Supply Chain Engineering : Useful Methods and Techniques , 2009 .

[25]  Ting Zhang,et al.  The impact of collaborative transportation management on supply chain performance , 2010, 2010 8th International Conference on Supply Chain Management and Information.

[26]  Guoqi Zhang,et al.  More than Moore: Creating High Value Micro/Nanoelectronics Systems , 2009 .

[27]  R. Leachman,et al.  Integration of speed economics into decision-making for manufacturing management , 2007 .

[28]  Xinhui Zhang,et al.  A stochastic production planning model under uncertain seasonal demand and market growth , 2011 .

[29]  Lee W. Schruben,et al.  Operational modeling and simulation in semiconductor manufacturing , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[30]  Jafar Heydari,et al.  A study of lead time variation impact on supply chain performance , 2009 .