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A simple approach for assessing the cost of system nervousness

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  • Tunc, Huseyin
  • Kilic, Onur A.
  • Tarim, S. Armagan
  • Eksioglu, Burak

Abstract

A well-known problem in coordinating supply chain inventories is that replenishment decisions are revised due to stochastic demands. This issue is often referred to as system nervousness. The literature distinguishes between two types of nervousness: setup-oriented and quantity-oriented. It is widely accepted that cost of nervousness is difficult to measure. We argue that this cost can be evaluated by means of three well-established inventory control strategies: static uncertainty, dynamic uncertainty, and static-dynamic uncertainty. These strategies reflect extreme cases with regard to the setup- and the quantity-oriented nervousness, and provide a simple yet an objective measure to assess the cost of system nervousness. Our results are of practical importance. We highlight that the setup-oriented nervousness, which is considered to be the most critical in practice, can be eliminated with a minor cost penalty. This is, however, not the case for the quantity-oriented nervousness.

Suggested Citation

  • Tunc, Huseyin & Kilic, Onur A. & Tarim, S. Armagan & Eksioglu, Burak, 2013. "A simple approach for assessing the cost of system nervousness," International Journal of Production Economics, Elsevier, vol. 141(2), pages 619-625.
  • Handle: RePEc:eee:proeco:v:141:y:2013:i:2:p:619-625
    DOI: 10.1016/j.ijpe.2012.09.022
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    References listed on IDEAS

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    Cited by:

    1. Kilic, Onur A. & Tunc, Huseyin & Tarim, S. Armagan, 2018. "Heuristic policies for the stochastic economic lot sizing problem with remanufacturing under service level constraints," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1102-1109.
    2. Sereshti, Narges & Adulyasak, Yossiri & Jans, Raf, 2021. "The value of aggregate service levels in stochastic lot sizing problems," Omega, Elsevier, vol. 102(C).
    3. Van Belle, Jente & Crevits, Ruben & Verbeke, Wouter, 2023. "Improving forecast stability using deep learning," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1333-1350.
    4. Carlos Herrera & Sana Belmokhtar-Berraf & André Thomas & Víctor Parada, 2016. "A reactive decision-making approach to reduce instability in a master production schedule," International Journal of Production Research, Taylor & Francis Journals, vol. 54(8), pages 2394-2404, April.
    5. Gurkan, M. Edib & Tunc, Huseyin & Tarim, S. Armagan, 2022. "The joint stochastic lot sizing and pricing problem," Omega, Elsevier, vol. 108(C).
    6. Viktoryia Buhayenko & Dick den Hertog, 2017. "Adjustable Robust Optimisation approach to optimise discounts for multi-period supply chain coordination under demand uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 55(22), pages 6801-6823, November.
    7. Xiang, Mengyuan & Rossi, Roberto & Martin-Barragan, Belen & Tarim, S. Armagan, 2018. "Computing non-stationary (s, S) policies using mixed integer linear programming," European Journal of Operational Research, Elsevier, vol. 271(2), pages 490-500.
    8. Ghazi M. Magableh & Mahmoud Z. Mistarihi, 2023. "Global Supply Chain Nervousness (GSCN)," Sustainability, MDPI, vol. 15(16), pages 1-24, August.
    9. Dural-Selcuk, Gozdem & Rossi, Roberto & Kilic, Onur A. & Tarim, S. Armagan, 2020. "The benefit of receding horizon control: Near-optimal policies for stochastic inventory control," Omega, Elsevier, vol. 97(C).
    10. Choudhary, Devendra & Shankar, Ravi, 2015. "The value of VMI beyond information sharing in a single supplier multiple retailers supply chain under a non-stationary (Rn, Sn) policy," Omega, Elsevier, vol. 51(C), pages 59-70.
    11. Rossi, Roberto & Kilic, Onur A. & Tarim, S. Armagan, 2015. "Piecewise linear approximations for the static–dynamic uncertainty strategy in stochastic lot-sizing," Omega, Elsevier, vol. 50(C), pages 126-140.

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