A Distribution-Free Model with Variable Setup Cost, Backorder Price Discount and Controllable Lead Time

Sarkar

2020

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The necessity of coordination among entities is essential for the success of any supply chain management (SCM). This paper focuses on coordination between two players and cost-sharing in an SCM that considers a vendor and a buyer. For random demand and complex product production, a flexible production system is recommended. The study aims to minimize the total SCM cost under stochastic conditions. In the flexible production systems, the production rate is introduced as the decision variable and the unit production cost is minimum at the obtained optimal value. The setup cost of flexible systems is higher and to control this, a discrete investment function is utilized. The exact information about the probability distribution of lead time demand is not available with known mean and variance. The issue of unknown distribution of lead time demand is solved by considering a distribution-free approach to find the amount of shortages. The game-theoretic approach is employed to obtain closed-form solutions. First, the model is solved under decentralized SCM based on the Stackelberg model, and then solved under centralized SCM. Bargaining is the central theme of any business nowadays among the players of an SCM to make their profit within a centralized and decentralized setup. For this, a cost allocation model for lead time crashing cost based on the Nash bargaining model with the satisfaction level of SCM members is proposed. The cost allocation model under Nash bargaining achieves exciting results in SCM coordination.

Section: Scm With Vendor-buyer Coordination Policymentioning

confidence: 99%

Coordination Supply Chain Management Under Flexible Manufacturing, Stochastic Leadtime Demand, and Mixture of Inventory

Sarkar

2020

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“…Furthermore, Malik and Sarkar [8] studied a continuous-review policy for multiple products with uncertain demand, investments for quality improvements and setup cost reduction, and lead-time control with unknown lead-time demand distribution. Malik and Sarkar [9] recently presented a backorder price discount model with controllable lead time and unknown distribution for lead-time demands. A recent study in a similar direction can be found in Dey et al [4].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, researchers adopted a distribution-free approach to solve these types of problems, which is a more realistic approach [3][4][5]. It is proved from the literature that additional investments can reduce the setup cost for the production system [6][7][8][9]. Mostly, researchers consider continuous investments for controlling the setup cost; however, the discrete investment can be more realistic as the industry may not prefer continuous investment; thus, this is another research gap in the literature.…”

Section: Introductionmentioning

confidence: 99%

Coordinating Supply-Chain Management under Stochastic Fuzzy Environment and Lead-Time Reduction

Sarkar

2019

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In this paper, a supply-chain (SC) coordination method based on the lead-time crashing is proposed for a seller–buyer system. By considering different transportation modes, we control the lead-time (LT) variability. For the first time, we have attempted to determine the impact of the reliable and unreliable seller in a continuous-review supply-chain model under the stochastic environment. The authors discussed two reliability cases for the seller. First, we consider the seller is unreliable and in the second case, the seller is reliable. In addition, the demand during the lead time is stochastic with the known mean and variance. The proposed approach tries to find an optimal solution that performs well without a specific probability distribution. Besides, a discrete investment is made to reduce the setup cost, which will indirectly help supply-chain members to increase the total profit of the system. In the proposed model, the seller motivates the buyer by reducing lead time to take part in coordinating decision-making for the system’s profit optimization. We derive the coordination conditions for both members, the seller and the buyer, under which they are convinced to take part in the cooperative decision-making plan. Therefore, lead-time crashing is the proposed incentive mechanism for collaborative supply-chain management. We use a fixed-charge step function to calculate the lead-time crashing cost for slow and fast shipping mode. We give two numerical examples to validate the proposed models and demonstrate the service-level enhancement under the collaborative supply-chain management in case of an unreliable seller. Concluding remarks and future extensions are discussed at the end.

“…Shin et al [30] analyzed the SC model under stochastic demands, service level constraints and lead time control with additional costs. Recently, Malik and Sarkar [31] studied the SC models while considering lead time and setup cost reductions with discrete investments. With lead time reductions, the service level can be improved, and customers' satisfaction may help management to increase their profits.…”

Section: Introductionmentioning

confidence: 99%

A Constrained Production System Involving Production Flexibility and Carbon Emissions

Kim

2020

The proposed study presents an economic lot size and production rate model for a single vendor and a single buyer setup. This model involves greenhouse gas (GHG) emissions from industrial sources. The carbon emissions in this model are considered as two types: direct emissions and indirect emissions. The production rate affects carbon emissions generation in production, i.e., generally, higher production rates result in more emissions, which is governable in many real-life cases. The production rate also impacts the process reliability and quality. Faster production deteriorates the production system quickly, leading to machine failure and defective items. Such reliability and quality problems increase energy consumptions and supply chain (SC) costs. This paper formulates a vendor-buyer SC model that tackles these issues. It considers two decision-making policies: integrated or centralized as well as decentralized, where the aim is to obtain the optimal values of the decision variables that give the minimum total SC cost. It includes the costs of setup, holding inventory, carbon emissions, order processing, production, reworking, and inspection processes. The decision variables are the production rate, lead time, order quantity, the number of shipments, and the investments for setup cost reduction. In the later sections, this paper compares the numerical outcomes of the two centralized and decentralized policies. It also provides sensitivity analysis and useful insights on the economic and environmental execution of the SC.