Reducing lead time risk through multiple sourcing: the case of stochastic demand and variable lead time

Glock, C. H.; Ries, J. M.

doi:10.1080/00207543.2011.644817

Cited by 38 publications

(9 citation statements)

References 50 publications

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“…A buyer may have a number of choices when managing the supply risk, including supplier qualification screening, emergency sourcing, flexibility, reorder, penalties levied for supplier non-performance (Hayya, Christy, and Pan 1987), multiple parallel sourcing (Glock and Ries 2013) or increased buffers (Norrman and Jansson 2004). Intuitively, the effectiveness of risk management tools used by the buyer depends on information the buyer has about the supplier.…”

Section: Introductionmentioning

confidence: 99%

Random yield and coordination mechanisms of a supply chain with emergency backup sourcing

Chen

Yang

2014

International Journal of Production Research

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This paper considers a supply chain in which a buyer purchases finished items from a contracting supplier to satisfy a stochastic market demand, where the supplier's production is subject to random yield. We assume that the buyer can make up the shortage by sourcing from an emergency backup supplier. We develop two Stackelberg game models, i.e. buyer-Stackelberg (BS) model and supplier-Stackelberg (SS) model, and find that the decentralised BS model results in a higher stocking factor of supplier's input than the decentralised SS model. Compared with BS model, the buyer in SS model performs more explicit order plan, and we find that only when the actual yield of the supplier is insufficient, the buyer would use emergency backup sourcing to make up the shortage. When the manufacturing operation of the supplier is in the good state, the buyer only orders a certain amount and has some leftover. When the actual yield of the supplier is moderate, the buyer uses up every item produced from the supplier regardless of the yield rate. Comparing both channel structures, SS operation is a more effective way of controlling both inventory cost and backup sourcing cost, and it can be beneficial for each player as well as for the whole channel. Finally, we develop the coordination mechanism for each channel to investigate the issues of risk handling and risk sharing for uncertain demand and uncertain yield.

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Section: Introductionmentioning

confidence: 99%

Random yield and coordination mechanisms of a supply chain with emergency backup sourcing

Chen

Yang

2014

International Journal of Production Research

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“…He developed expressions for the optimal low-cost order quantity and responsive capacity. Glock and Ries (2013) considered a multi-sourcing problem of a buyer, in which lead time is assumed to be deterministic and a linear function of order quantity. They established that mismatch risk can be reduced by splitting the total order quantity among different suppliers, and thereby reducing the individual lead times.…”

Section: Literature Reviewmentioning

confidence: 99%

Dual sourcing under heavy-tailed demand: an extreme value theory approach

Bicer

2015

International Journal of Production Research

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We consider a single-period dual-sourcing problem in which a buyer purchases its products from two different suppliers and sells them to a market with uncertain demand. One supplier is cheaper but less responsive, whereas the other supplier is more responsive but more expensive. The buyer determines the order quantity from the low-cost supplier and the capacity level from the responsive supplier in such a way that maximises its profit. We apply extreme value theory to analyse the impact of tail heaviness of demand distribution on optimal dual-sourcing strategy. We numerically find that tail heaviness moderates the effect of increasing demand uncertainty on optimal order levels. We also show that dual sourcing allows companies to increase their fill rates cost effectively under heavy-tailed demand if the capacity reservation cost of the responsive supplier is relatively low.

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“…(Hatmoko & Scott, 2010) melakukan simulasi berbasis Monte Carlo untuk mengestimasi pengaruh delay rantai pasok pada kinerja waktu proyek gedung. (Berle et al, 2013) mensimulasikan sistem transportasi LNG, sedangkan (Glock & Ries, 2013) mensimulasikan pengurangan lead time pada rantai pasok. (Durowoju et al, 2012) menggunakan simulasi diskrit untuk menginvestigasi dampak gangguan pada aliran informasi kritis pada operasional manufaktur dalam anggota rantai pasok yang berkolaborasi.…”

Section: Model Simulasiunclassified

Model Simulasi Risiko Rantai Pasok Material Proyek Konstruksi Gedung

Hatmoko

Kistiani

2017

MKTS

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(2.20. 17.05, 11.24); steel profiles (2.12, 15.10, 9.75), formwork (1.79, 16.04, 10.45), precast concrete (1.76, 15.61, 10.24 (2,20; 17,05; 11,24); baja profil (2,12 ;15,10; 9,75), bekisting (1,79 ; 16,04; 10,45), beton precast (1,76; 15,61; 10,24). Hasil analisis sensitivitas menunjukkan risiko keterlambatan akibat 'pemesanan tambahan material karena perubahan desain yang mendadak oleh owner' menduduki peringkat tertinggi sebagai risiko paling dominan untuk material baja tulangan, baja profil dan bekisting dengan besar pengaruh sebesar 25,5%, 37,4%, dan 17%. Hasil penelitian ini bermanfaat bagi

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Reducing lead time risk through multiple sourcing: the case of stochastic demand and variable lead time

Cited by 38 publications

References 50 publications

Random yield and coordination mechanisms of a supply chain with emergency backup sourcing

Random yield and coordination mechanisms of a supply chain with emergency backup sourcing

Dual sourcing under heavy-tailed demand: an extreme value theory approach

Model Simulasi Risiko Rantai Pasok Material Proyek Konstruksi Gedung

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