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Comparison between Two Types of Inventory Targets under Variability of a Semiconductor Supply Chain

Received: 13 October 2014     Accepted: 27 October 2014     Published: 11 December 2014
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Abstract

As an innovation in the semiconductor industry grows speedy, supply chain processes have not followed up. The variability in semiconductor supply chain have increased and been more complicated. These results in accurately forecast demand and set inventory target. Demand and supply are more and more stochastic and non-stationary. Inventory is one of the methods that companies are able to buffer themselves from complex and variable environment, while still being able to satisfy customer needs. We explore the variability of semiconductor industry in automotive industry. On the supply side, we evaluate variability in complexities of manufacturing process and also products are composed with multiple parts efforts to stochastic production lead-time. However in this paper, we disregard the variability arising from supply side so we assumed lead-time is fixed at 16 weeks. For demand side, the phenomenon is known as the bullwhip effect, the demand variability increases as one move up a supply chain, severely effects to semiconductor supply chain. This results the stochastic demand process is not well understood. Thus we evaluate the stochastic in demand as two aspects: 1) the dispersion of historical demand data from its mean which denoted as standard deviation of demand, 2) the difference between the actual demand and forecast data which denoted as standard deviation of forecast error. We use them as a proxy for demand variability. Then we apply the data to the base stock model. Then, we determine what each variability parameter contributes to inventory. The inventory model represents the semiconductor manufactory’s inventory with actual statistical data which provided from semiconductor company to calculate inventory target required to meet the desired customer service level.

Published in International Journal of Business and Economics Research (Volume 3, Issue 6-1)

This article belongs to the Special Issue Supply Chain Management: Its Theory and Applications

DOI 10.11648/j.ijber.s.2014030601.21
Page(s) 74-80
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Demand Fluctuation, Base Stock Model, Inventory Targets

References
[1] S. Graves and Willems S., “Optimizing Strategic Safety Stock Placementin Supply Chains,” Manufacturing & Service Operations Management Vol. 2, No. 1, Winter 2000, 2000, pp. 68–83
[2] G. Guillermo and Garrett J., “Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite horizons,” Management Science, Vol. 40, No. 8, 1994, pp.999-1019
[3] E. Hans, “The Bullwhip EffectAnylogic Conference Supply Chain Innovation,” Berlin, Germany,AnyLogic Conference 2012, www.anylogic.com/alc-2012
[4] M. Kojima and Nakashima. K and Ohno.K., “Performance Evaluation of SCM in JIT envi-ronment”. International Journal of Production EconomicsVol.115, No.2, 2008, pp 439-443.
[5] F. L. Marshall and Janice H. Hammond., “Making Supply Meet Demand in an Uncertain World,” Harvard Business Review, Vol. 94302, 1994, pp83-93
[6] M.P. Manary and Willems S., “Setting Safety-Stock Targets at Intel in the Presence of Forecast Bias,” Management Science, Vol.38/2, 2008, pp.112–122
[7] K. Nakashima, Kojima M. and S. M. Gupta, “Management of a Disassembly Line using Two Types of Kanbans,” International Journal of Supply Chain Management, Vol.1, No.3, 2012, pp. 11-19.
[8] K. Nakashima, Thitima S. Hans E. and G. Yachi, “Stochastic Inventory Control Systems with Consideration for the Cost Factors Based on EBIT,” International Journal of Supply Chain Management, Vol.3, No.3, 2014, pp. 68-74.
[9] J. Neale and Willems S., Managing Inventory in Supply Chains with Nonstationary Demand, Interfaces Vol. 39, No. 5, 2009, pp. 388–399
[10] N. Thomopoulos, Safety Stock comparison with Availability and Service level, The International Applied Business Research (IABR) Conference in Cancun, Mexico -- March 20 - 24, 2006
Cite This Article
  • APA Style

    Kenichi Nakashima, Thitima Sornmanapong, Hans Ehm, Geraldine Yachi. (2014). Comparison between Two Types of Inventory Targets under Variability of a Semiconductor Supply Chain. International Journal of Business and Economics Research, 3(6-1), 74-80. https://doi.org/10.11648/j.ijber.s.2014030601.21

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    ACS Style

    Kenichi Nakashima; Thitima Sornmanapong; Hans Ehm; Geraldine Yachi. Comparison between Two Types of Inventory Targets under Variability of a Semiconductor Supply Chain. Int. J. Bus. Econ. Res. 2014, 3(6-1), 74-80. doi: 10.11648/j.ijber.s.2014030601.21

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    AMA Style

    Kenichi Nakashima, Thitima Sornmanapong, Hans Ehm, Geraldine Yachi. Comparison between Two Types of Inventory Targets under Variability of a Semiconductor Supply Chain. Int J Bus Econ Res. 2014;3(6-1):74-80. doi: 10.11648/j.ijber.s.2014030601.21

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  • @article{10.11648/j.ijber.s.2014030601.21,
      author = {Kenichi Nakashima and Thitima Sornmanapong and Hans Ehm and Geraldine Yachi},
      title = {Comparison between Two Types of Inventory Targets under Variability of a Semiconductor Supply Chain},
      journal = {International Journal of Business and Economics Research},
      volume = {3},
      number = {6-1},
      pages = {74-80},
      doi = {10.11648/j.ijber.s.2014030601.21},
      url = {https://doi.org/10.11648/j.ijber.s.2014030601.21},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.s.2014030601.21},
      abstract = {As an innovation in the semiconductor industry grows speedy, supply chain processes have not followed up. The variability in semiconductor supply chain have increased and been more complicated. These results in accurately forecast demand and set inventory target. Demand and supply are more and more stochastic and non-stationary. Inventory is one of the methods that companies are able to buffer themselves from complex and variable environment, while still being able to satisfy customer needs. We explore the variability of semiconductor industry in automotive industry. On the supply side, we evaluate variability in complexities of manufacturing process and also products are composed with multiple parts efforts to stochastic production lead-time. However in this paper, we disregard the variability arising from supply side so we assumed lead-time is fixed at 16 weeks. For demand side, the phenomenon is known as the bullwhip effect, the demand variability increases as one move up a supply chain, severely effects to semiconductor supply chain. This results the stochastic demand process is not well understood. Thus we evaluate the stochastic in demand as two aspects: 1) the dispersion of historical demand data from its mean which denoted as standard deviation of demand, 2) the difference between the actual demand and forecast data which denoted as standard deviation of forecast error. We use them as a proxy for demand variability. Then we apply the data to the base stock model. Then, we determine what each variability parameter contributes to inventory. The inventory model represents the semiconductor manufactory’s inventory with actual statistical data which provided from semiconductor company to calculate inventory target required to meet the desired customer service level.},
     year = {2014}
    }
    

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  • TY  - JOUR
    T1  - Comparison between Two Types of Inventory Targets under Variability of a Semiconductor Supply Chain
    AU  - Kenichi Nakashima
    AU  - Thitima Sornmanapong
    AU  - Hans Ehm
    AU  - Geraldine Yachi
    Y1  - 2014/12/11
    PY  - 2014
    N1  - https://doi.org/10.11648/j.ijber.s.2014030601.21
    DO  - 10.11648/j.ijber.s.2014030601.21
    T2  - International Journal of Business and Economics Research
    JF  - International Journal of Business and Economics Research
    JO  - International Journal of Business and Economics Research
    SP  - 74
    EP  - 80
    PB  - Science Publishing Group
    SN  - 2328-756X
    UR  - https://doi.org/10.11648/j.ijber.s.2014030601.21
    AB  - As an innovation in the semiconductor industry grows speedy, supply chain processes have not followed up. The variability in semiconductor supply chain have increased and been more complicated. These results in accurately forecast demand and set inventory target. Demand and supply are more and more stochastic and non-stationary. Inventory is one of the methods that companies are able to buffer themselves from complex and variable environment, while still being able to satisfy customer needs. We explore the variability of semiconductor industry in automotive industry. On the supply side, we evaluate variability in complexities of manufacturing process and also products are composed with multiple parts efforts to stochastic production lead-time. However in this paper, we disregard the variability arising from supply side so we assumed lead-time is fixed at 16 weeks. For demand side, the phenomenon is known as the bullwhip effect, the demand variability increases as one move up a supply chain, severely effects to semiconductor supply chain. This results the stochastic demand process is not well understood. Thus we evaluate the stochastic in demand as two aspects: 1) the dispersion of historical demand data from its mean which denoted as standard deviation of demand, 2) the difference between the actual demand and forecast data which denoted as standard deviation of forecast error. We use them as a proxy for demand variability. Then we apply the data to the base stock model. Then, we determine what each variability parameter contributes to inventory. The inventory model represents the semiconductor manufactory’s inventory with actual statistical data which provided from semiconductor company to calculate inventory target required to meet the desired customer service level.
    VL  - 3
    IS  - 6-1
    ER  - 

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Author Information
  • Department of Industrial Engineering and Management, Kanagawa University, Yokohama, Japan

  • Department of Industrial Engineering and Management, Kanagawa University, Yokohama, Japan

  • Infineon Technologies AG, Neubiberg, Germany

  • Infineon Technologies AG, Neubiberg, Germany

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