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 |
Demand Fluctuation, Base Stock Model, Inventory Targets
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| [3] | E. Hans, “The Bullwhip EffectAnylogic Conference Supply Chain Innovation,” Berlin, Germany,AnyLogic Conference 2012, www.anylogic.com/alc-2012 |
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| [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. |
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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
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
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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Download@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}
}
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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