Supply management in assembly systems with random yield and random demand

Gurnani, Haresh; Akella, Ram; Lehoczky, John P.

doi:10.1080/07408170008967428

Cited by 120 publications

(72 citation statements)

References 9 publications

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“…Also, it would be useful to extend our model and analysis to the case where both components are subject to supply uncertainty. However, analytical results for such case appear to be very limited, as the corresponding cost functions become very complex and di cult to analyze; see Gerchak et al (1994) and Gurnani et al (2000). …”

Section: Discussionmentioning

confidence: 99%

“…Gerchak et al (1994) analyze a single-period lot sizing decision model for an assembly system with random yields. Gurnani et al (2000) study the supply management of a two-component assembly system with stochastic demands and random yields. Pan and So (2010) study a centralized assemble-to-order system, in which one of the components faces supply quantity uncertainty and demand is assumed to be price-dependent.…”

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Component procurement strategies in decentralized assembly systems under supply uncertainty

Pan

2015

IIE Transactions

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In this article we analyze the interactions among the assembler and two component suppliers in their procurement decisions under a Vendor-Managed Inventory (VMI) contract. Under the VMI contract, the assembler rst o ers a unit price for each component and will pay component suppliers only for the amounts used to meet the actual demand. The two independent component suppliers then decide on the production quantities of their individual components before the actual demand is realized. We assume that one of the component suppliers has uncertainty in the supply process, in which the actual number of components available for assembly is equal to a random fraction of the production quantity. Under the assembly structure, both component suppliers need to take into account the underlying supply uncertainty in deciding their individual production quantities, as both components are required for the assembly of the nal product. We rst analyze the special case under deterministic demand and then extend our analysis to the general case under stochastic demand. We derive the optimal component prices o ered by the assembler and the corresponding equilibrium production quantities of the component suppliers. . IntroductionSourcing components from a complex global supplier network can lead to a high degree of uncertainty in the supply process. Various supply chain glitches such as unexpected supply disruptions, insu cient supplier capacity, or transportation delays across borders can cause unexpected shortfalls in the required components and halt the assembly of the nal products. At the same time, long procurement lead times in a global supply network make it di cult and expensive to deal with such component shortfalls using emergency orders. Consequently, rms need to e ectively manage supply uncertainty in their component procurement decisions to avoid such potential component shortfalls. This is especially critical for products with rapidly changing technology or short life cycle such as electronic products, as it is expensive to keep safety stock of components due to high obsolescence costs.In this article we analyze the interactions among the assembler and component suppliers in their procurement decisions under a Vendor-Managed Inventory (VMI) contract. Specifically, we assume that the assembler needs to procure two required components from two independent suppliers to assemble the nal product. Under the VMI contract, the assembler rst o ers a unit price for each component and will pay component suppliers only for the amounts used to meet the actual demand. Based on the unit component prices o ered by the assembler, the two independent component suppliers then decide on the production quantities of their individual components and ship the components to the assembler before the actual demand is realized. Here, we assume that the production CONTACT Kut C. So rso@uci.edu Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/uiie. and/or transportation lead times are long,...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Component procurement strategies in decentralized assembly systems under supply uncertainty

Pan

2015

IIE Transactions

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“…We also allow α and γ to add up to less than unity to capture the fact that buyers usually reserve the right not to award the full tender originally announced. 10 Suppliers U and R face the costs of production C U (α) and C R (γ) respectively, where…”

Section: The Modelmentioning

confidence: 99%

A Procurement Auction Model Under Supplier Uncertainty

Ray

2003

SSRN Journal

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As business-to-business commerce shifts to the Internet, newer suppliers with cheaper but unreliable technologies enter the market place to win orders from firms by beating the price of their perfectly reliable (but expensive) competitors. The dilemma facing purchasing firms is the allocation of the tender across suppliers of varying supply reliability. We model the procurement problem as a sealed-bid auction where the buyer has to allocate purchases between an expensive but reliable supplier and a cheaper but unreliable supplier, and the suppliers specify prices for different proportions of the tender awarded to them. A unique feature of our model is that it allows the purchasing firm to reserve the right to change the size of the total tender awarded depending on the nature of the bids received from the suppliers. We prove that the set of Nash equilibrium outcomes coincides with the set of efficient outcomes, and for strictly convex cost functions, the outcome is unique. Further, we show that the possibility of implicit supplier collusion is strengthened in that the suppliers may structure their bids forcing the buyer to allocate the tender resulting in the worst-case (highest price) scenario for him. We also show that the Anton-Yao (A-Y, 1989) model can be interpreted as a limiting case of our model and that the efficient outcome derived in this paper is the only robust outcome in the A-Y model. * We would like to thank Sudipto Dasgupta, Jae Hyon Nahm, Larry Qiu, Kunal Sengupta and Wen Zhou for helpful comments and suggestions on an earlier draft.

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“…Regarding the supply side, business risks primarily result from yield uncertainty which is typical for a variety of business sectors. It frequently occurs in the agricultural sector or in the chemical, electronic and mechanical manufacturing industries (see Gurnani et al 2000;Jones et al 2001;Kazaz 2004;Nahmias 2009). Here, random supply can appear due to different reasons such as weather conditions, production process risks or imperfect input material.…”

Section: Introductionmentioning

confidence: 99%

Supply chain coordination by contracts under binomial production yield

Clemens

Inderfurth

2015

Bus Res

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Supply chain coordination is enabled by adequately designed contracts so that decision making by multiple actors avoids efficiency losses in the supply chain. From the literature it is known that in newsvendor-type settings with random demand and deterministic supply the activities in supply chains can be coordinated by sophisticated contracts while the simple wholesale price contract fails to achieve coordination due to the double marginalization effect. Advanced contracts are typically characterized by risk sharing mechanisms between the actors, which have the potential to coordinate the supply chain. Regarding the opposite setting with random supply and deterministic demand, literature offers a considerably smaller spectrum of solution schemes. While contract types for the well-known stochastically proportional yield have been analyzed under different settings, other yield distributions have not received much attention in the literature so far. However, practice shows that yield types strongly depend on the industry and the production process that is considered. As consequence, they can deviate very much from the specific case of a stochastically proportional yield. This paper analyzes a buyersupplier supply chain in a random yield, deterministic demand setting with production yield of a binomial type. It is shown how under binomially distributed yields risk sharing contracts can be used to coordinate buyer's ordering and supplier's production decision. Both parties are exposed to risks of overproduction and under-delivery. In contrast to settings with stochastically proportional yield, however, the impact of yield uncertainty can be quite different in the binomial yield case. Under binomial yield, the output uncertainty decreases with larger production quantities while it is independent from lot sizes under stochastically proportional yield. Consequently, the results from previous contract analyses on other yield types 123Business Research (2015) 8:301-332 DOI 10.1007 may not hold any longer. The current analytical study reveals that, like under stochastically proportional yield, coordination is impeded by double marginalization if a simple wholesale price contract is applied. However, more sophisticated contracts which penalize or reward the supplier can change the risk distribution so that supply chain coordination is possible also under binomial yield. In this context, many contract properties from planning under stochastically proportional yield carry over. Nevertheless, numerical examples reveal that a misspecification of the yield type can considerably downgrade the extent of supply chain coordination.

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Supply management in assembly systems with random yield and random demand

Cited by 120 publications

References 9 publications

Component procurement strategies in decentralized assembly systems under supply uncertainty

Component procurement strategies in decentralized assembly systems under supply uncertainty

A Procurement Auction Model Under Supplier Uncertainty

Supply chain coordination by contracts under binomial production yield

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