A multi-methodological collaborative simulation for inter-organizational supply chain networks

Long, Quan

doi:10.1016/j.knosys.2015.12.026

Cited by 27 publications

(7 citation statements)

References 36 publications

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“…to cope with complex problems, MAM has become an effective and popular paradigm penetrating into the field of supply chain. With respect to MAM, many problems on supply chain have been studied, such as platform supply chain networks [38], [39], production scheduling [40], [41], and products management [42], [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Backup or Reliability Improvement Strategy for a Manufacturer Facing Heterogeneous Consumers in a Dynamic Supply Chain

Jiang

2019

IEEE Access

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This paper examines a manufacturer's supply management strategies for mitigating yield risk in a complex dynamic supply chain. Two strategies can be adopted for the manufacturer: backup and reliability improvement. Consumers may select to leave (instant consumers) or wait (delaying consumers) when they confront the manufacturer's insufficient inventory. Utilizing the method of multi-agent modeling, a manufacturer and a supplier are modeled as the intelligent agents with the reinforcement learning behavior. The study shows that: 1) when the number of instant consumers is small, reliability improvement strategy should be selected; otherwise, the manufacturer should adopt a backup strategy; 2) only when mean yield is large enough, reliability improvement strategy is the optimal choice; and 3) if yield uncertainty is small, the manufacturer should choose reliability improvement strategy; otherwise, it is suitable to use a backup strategy. In addition, when the main supplier can determine its own wholesale price, it is found that: 1) when the mean yield is small, a lower wholesale price should be designed for the main supplier, to induce higher order quantity under backup strategy; and 2) the impact of yield uncertainty on the manufacturer's supply management strategy can be changed by the main supplier's adaptive pricing behavior. INDEX TERMS Consumer behavior, multi-agent modeling, supply chain management, yield risk.

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Section: Literature Reviewmentioning

confidence: 99%

Backup or Reliability Improvement Strategy for a Manufacturer Facing Heterogeneous Consumers in a Dynamic Supply Chain

Jiang

2019

IEEE Access

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“…Conventionally, NPD (new product development) is often thought of as a stage-gate based process wherein product design concepts are selected based on broad business and technical needs, followed by realization of physical prototypes, testing and, validation, mass production, and finally product launch (Ulrich and Eppinger, 2017). However, in order to improve TTM (time to market), reduce life cycle costs, and facilitate smooth launch of new products, OEMs (original equipment manufacturers) have been increasingly taking a concurrent view of product development to include supply chain partners that are internal to the organization and external vendors, for advance and proactive validation of technical and commercial concerns (Petersen, et al, 2005 andLong, 2016).…”

Section: Introductionmentioning

confidence: 99%

Decision modeling and analysis in new product development considering supply chain uncertainties: A multi-functional expert based approach

Goswami

Daultani

2021

Expert Systems with Applications

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Successful new product development projects and extant research literature advocate for inclusion of inputs pertaining to the supply chain at early stages of product development to proactively identify risk averse product design concepts. To this end, we devise an analytical framework to converge upon product design concept(s) that would be associated with lesser supply chain risks, usually function of both technical and commercialization considerations. The high-level and constituent lower-level supply chain risks are represented by parent and root nodes respectively within the devised Bayesian network driven research framework. Thereafter, a quantitative measure denoted as SCRI (supply chain risk index) is evolved that yields overall composite risk numbers corresponding to respective design concepts at different risk states. Validation and comparison of the devised method with an extant study illustrates the consistency and reliability of the study. It is found that the risk propensity of a particular design concept is inversely related to the probabilistic utility of that particular concept. The case of a construction power tool of a global firm is used to demonstrate the methodology. Our research addresses an important future research pathway as argued by Hosseini et al., (2020) that extant research literature is devoid of decision-making frameworks focused on measurement and analysis the propagation of risks on complex networks.

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“…A supply chain includes a set of suppliers, manufacturers, distribution centers, and transfer channels. Each member plays a distinct role in manufacturing final products from raw materials according to the needs of the consumer [16,28,36]. In recent years, the globalization of trade, competition, and the integration of supply chains (SCs) have made organizations pay more attention to their production plans and the other related members in the SC [10,14,16,28].…”

Section: Introductionmentioning

confidence: 99%

A bi-objective production-distribution problem in a supply chain network under grey flexible conditions

et al. 2021

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One of the main topics discussed in a supply chain is the production-distribution problem. Producing and distributing the products plays a key role in reducing the costs of the chain. To design a supply chain, a network of efficient management and production-distribution decisions is essential. Accordingly, providing an appropriate mathematical model for such problems can be helpful in designing and managing supply chain networks. Mathematical formulations must be drawn close to the real world due to the importance of supply chain networks. This makes those formulations more complicated. In this study, a novel multi-objective formulation is devised for the production-distribution problem of a supply chain that consists of several suppliers, manufacturers, distributors, and different customers. Also, a Mixed Integer Linear Programming (MILP) mathematical model is proposed for designing a multi-objective and multi-period supply chain network. In addition, grey flexible linear programming (GFLP) is done for a multi-objective production-distribution problem in a supply chain network. The network is designed for the first time to cope with the uncertain nature of costs, demands, and capacity parameters. In this regard, due to the NP-hardness and complexity of problems and the necessity of using meta-heuristic algorithms, NSGA-II and Fast PGA algorithm are applied and compared in terms of several criteria that emphasize the quality and diversity of the solutions.

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A multi-methodological collaborative simulation for inter-organizational supply chain networks

Cited by 27 publications

References 36 publications

Backup or Reliability Improvement Strategy for a Manufacturer Facing Heterogeneous Consumers in a Dynamic Supply Chain

Backup or Reliability Improvement Strategy for a Manufacturer Facing Heterogeneous Consumers in a Dynamic Supply Chain

Decision modeling and analysis in new product development considering supply chain uncertainties: A multi-functional expert based approach

A bi-objective production-distribution problem in a supply chain network under grey flexible conditions

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