Optimal production and inventory decisions under demand and production disruptions

Xu, Xiaoling; Shang, Jennifer; Wang, Haiyan; Chiang, Wen–Chyuan

doi:10.1080/00207543.2015.1073402

Cited by 17 publications

(18 citation statements)

References 40 publications

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“…The authors discovered that the optimal decision value after the demand interrupt is a linear function of the demand interrupt and the optimal decision value before it. [49] investigated the problem of model selection in a cooperating system under cap-and-trade regulation with demand disruptions. The authors first determined optimal decisions in the absence of demand disruption and then introduced demand disruption.…”

Section: 2mentioning

confidence: 99%

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The selection of bundling or independent selling mode for new retailing platform with demand disruptions

Wang,

Xu,

Chen

et al. 2024

JIMO

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This study models a manufacturer that sells products and services through a platform with demand disruptions. To examine the selection of the platform operational selling mode, we consider pricing strategies in two selling modes: (1) In bundling selling mode, the manufacturer and platform are the same party, so their optimal decisions match, and they provide the product and service in a bundle, such that consumers can only buy the service together with the product. (2) In independent selling mode, the manufacturer and platform have independent optimal decisions, and they sell the product and service separately with different prices, allowing consumers to self-select whether to buy both or only the product. We show that the total profits of the supply chain in the independent mode will be larger than in the bundling mode when there is a negative demand disruption in either products or services because of the double marginalization effect. When there are demand disruptions, the profit contributed by services may exceed that from products. The numerical studies indicate that the platform would adopt bundling selling mode to maximize the whole profit when experiencing demand disruptions in product where the product demand disruptions are larger than ∆a or the service demand disruptions are larger than kcp − a.

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Section: 2mentioning

confidence: 99%

“…Our model is similar to that those in [50] and [49] since all of the proposed models examine a selection decision when one of the two components has no value in the absence of the other. Following [50], we thus set the product, service (for independent mode only), and bundle (for bundling mode only) prices separately in our model.…”

Section: 2mentioning

confidence: 99%

The selection of bundling or independent selling mode for new retailing platform with demand disruptions

Wang,

Xu,

Chen

et al. 2024

JIMO

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“…Chowdhury and Quaddus (2016) identified that supply chain flexibility and disaster preparation have the highest importance for supply chain readiness, whilst recovery capability has the highest importance to response-recovery. In addition, studies by He et al (2016) and Xu et al (2016) have considered the impact of the reliability of suppliers on supply disruptions. He et al (2016) propose the application of reliability threshold values on buyer pricing and ordering decisions, while Xu et al (2016) build a flexibility production-inventory model to balance the demand and production disruptions.…”

Section: Reliability and Supply Disruptionmentioning

confidence: 99%

“…In addition, studies by He et al (2016) and Xu et al (2016) have considered the impact of the reliability of suppliers on supply disruptions. He et al (2016) propose the application of reliability threshold values on buyer pricing and ordering decisions, while Xu et al (2016) build a flexibility production-inventory model to balance the demand and production disruptions.…”

Section: Reliability and Supply Disruptionmentioning

confidence: 99%

Design of agile supply chains including the trade-off between number of partners and reliability

Barnes

2018

Int J Adv Manuf Technol

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The reliability of supply partners is particularly vital in agile supply chains as it is vulnerable to the inability of a supply partner to meet its high responsiveness and flexibility requirements resulting in the disruption of the whole network. Disruption can have expensive and extensive results for the entire agile supply chain. To mitigate the risk of disruption and improve the reliability of the whole agile supply chain, decision-makers need to pay more attention to supply chain design and construction, whilst simultaneously taking into account the sourcing strategy decisions. This paper proposes a series of models for the design of agile supply chains using dynamic programming modelling. These provide decision-makers with a systematic way of analysing one of the key decisions of sourcing strategy, namely the trade-off between the number of supply partners and reliability. The efficacy of the models is demonstrated through their application to a Chinese bus and coach manufacturer by way of an empirical illustration. The results show that this approach is effective for this application and it can be applied in other related decision-making scenarios. The methods offered in this paper provide managers with a practical tool to design their agile supply chains while considering the trade-offs between the number of partners and the reliability of the entire agile supply chain.

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“…The plant location decision has often been formulated as a cost optimization problem by converting the associated decision attributes into monetary values. This cost optimization model usually involves decision making regarding the following attributes: market demand, production and storage capacity, production cost, and supply reliability [6][7][8][9][10][11][12]. This optimization model has been extended by incorporating uncertainty-variation of population, changeable market trends, and unpredictable demand-into the decision-making process [13].…”

Section: Introductionmentioning

confidence: 99%

Plant Location Selection for Food Production by Considering the Regional and Seasonal Supply Vulnerability of Raw Materials

Park

Kim

et al. 2018

Mathematical Problems in Engineering

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A production capacity analysis considering market demand and raw materials is very important to design a new plant. However, in the food processing industry, the supply uncertainty of raw materials is very high, depending on the production site and the harvest season, and further, it is not straightforward to analyze too complex food production systems by using an analytical optimization model. For these reasons, this study presents a simulation-based decision support model to select the right location for a new food processing plant. We first define three supply vulnerability factors from the standpoint of regional as well as seasonal instability and present an assessment method for supply vulnerability based on fuzzy quantification. The evaluated vulnerability scores are then converted into raw material supply variations for food production simulation to predict the quarterly production volume of a new food processing plant. The proposed selection procedure is illustrated using a case study of semiprocessed kimchi production. The best plant location is proposed where we can reduce and mitigate risks when supplying raw material, thereby producing a target production volume steadily.

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Optimal production and inventory decisions under demand and production disruptions

Cited by 17 publications

References 40 publications

The selection of bundling or independent selling mode for new retailing platform with demand disruptions

The selection of bundling or independent selling mode for new retailing platform with demand disruptions

Design of agile supply chains including the trade-off between number of partners and reliability

Plant Location Selection for Food Production by Considering the Regional and Seasonal Supply Vulnerability of Raw Materials

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