A conceptual analysis of virtual supply chain collaborative network

Toh, Albert K.

doi:10.1504/ijsss.2014.066642

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…During the 21st century, research on supply chain management has also focused on the supply chain's networked structure. Such a structure of modern supply chains forms a barrier to collaboration [24], due to the players' behavior [25,26]. Lee et al concluded that it is challenging to reach agreements between the parties in the supply chain to implement the bullwhip mitigation solutions in order to improve the overall performance [10,21,27].…”

Section: Overviewmentioning

confidence: 99%

“…2019, 1 1005 In fact, the oil and gas companies belong to a supply network, as shown in Figure 5. As noted in significant research [14,24], it would be interesting to study the correlations between them, and to evaluate if it is possible to study this phenomenon as a supply network, instead of in relation to supply chain behavior, in case significant correlations among companies are found. Figure 5 has been populated with some significant companies to better illustrate this research idea with real cases and provide concrete suggestions for a preliminary investigation.…”

Section: Upstreammentioning

confidence: 99%

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Using Machine Learning for Enhancing the Understanding of Bullwhip Effect in the Oil and Gas Industry

Sousa

Ribeiro²,

Relvas

et al. 2019

MAKE

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Several suppliers of oil and gas (O & G) equipment and services have reported the necessity of making frequent resources planning adjustments due to the variability of demand, which originates in unbalanced production levels. The occurrence of these specific problems for the suppliers and operators is often related to the bullwhip effect. For studying such a problem, a research proposal is herein presented. Studying the bullwhip effect in the O & G industry requires collecting data from different levels of the supply chain, namely: services, upstream and midstream suppliers, and downstream clients. The first phase of the proposed research consists of gathering the available production and financial data. A second phase will be the statistical treatment of the data in order to evaluate the importance of the bullwhip effect in the oil and gas industry. The third phase of the program involves applying artificial neural networks (ANN) to forecast the demand. At this stage, ANN based on different training methods will be used. Further on, the attained mathematical model will be used to simulate the effects of demand fluctuations and assess the bullwhip effect in an oil and gas supply chain.

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Section: Overviewmentioning

confidence: 99%

Section: Upstreammentioning

confidence: 99%

Using Machine Learning for Enhancing the Understanding of Bullwhip Effect in the Oil and Gas Industry

Sousa

Ribeiro²,

Relvas

et al. 2019

MAKE

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“…They also enable different organizations in a SC to share information in a very cost-effective way (Christopher, 2011). Because the competition between organizations is more related to their SC (Toh and Zhang, 2014), supply chain management (SCM) is a vast sector and is the basic skeleton of every industry (Dutta et al, 2020). SCM serves the manufacturing industries to generate a low-cost plan that ensures firms' productiveness and success (Bhuniya et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Optimization of virtual closed-loop supply chain under uncertainty: application of IoT

Shambayati

Nikabadi

Firouzabadi

et al. 2022

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PurposeSupply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.Design/methodology/approachThe proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.FindingsThe findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.Originality/valueThere are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.HighlightsInvestigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

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Collaborative Network 5.0: By Design Human Values and Human-Centred Based Extended Collaborative Networks

Marchetti,

Nikghadam-Hojjati,

Barata

2023

IFIP Advances in Information and Communication Technology

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A conceptual analysis of virtual supply chain collaborative network

Cited by 5 publications

References 42 publications

Using Machine Learning for Enhancing the Understanding of Bullwhip Effect in the Oil and Gas Industry

Using Machine Learning for Enhancing the Understanding of Bullwhip Effect in the Oil and Gas Industry

Optimization of virtual closed-loop supply chain under uncertainty: application of IoT

Collaborative Network 5.0: By Design Human Values and Human-Centred Based Extended Collaborative Networks

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