scholarly journals Deploying Big Data Enablers to Strengthen Supply Chain Agility to Mitigate Bullwhip Effect: An Empirical Study of China’s Electronic Manufacturers

2021 ◽  
Vol 16 (7) ◽  
pp. 3375-3405
Author(s):  
Chih-Hung Hsu ◽  
Xue-Hua Yang ◽  
Ting-Yi Zhang ◽  
An-Yuan Chang ◽  
Qing-Wen Zheng
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Quality Function Deployment ◽  
Multicriteria Decision Making ◽  
Economic Globalization ◽  
Bullwhip Effect ◽  
Effect Factors ◽  
Manufacturing Enterprises ◽  
Supply Chain Agility ◽  
Equipment Manufacturing

With the development of economic globalization, the uncertainty of supply chains is also increasing, and alleviating the bullwhip effect has become an important issue. From previous discussions on alleviating the bullwhip effect, there was no research on alleviating it by enhancing supply chain agility through improving big data. Moreover, it has not been found that quality function deployment is used to analyze the interdependence between big data and supply chain agility, as well as between supply chain agility and the bullwhip effect. In particular, the interaction of bullwhip effect factors are not considered. In this study, the multicriteria decision-making integrated framework is proposed and the largest relay manufacturer in China is taken to identify key big data enablers to enhance supply chain agility and mitigate the bullwhip effect, thus providing an effective method for electronic equipment manufacturing enterprises to develop a supply chain that can quickly respond to changes and uncertainties. These big data enablers can enhance supply chain agility and reduce the bullwhip effect. This framework provides an effective method for electronic manufacturers to formulate supply chain agility indicators and big data enablers to mitigate the bullwhip effect and also provides a reference for other manufacturing enterprises in supply chain management.

scholarly journals Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective

Logistics ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Hisham Alidrisi
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Multicriteria Decision Making ◽  
Analytic Network Process ◽  
Assessment Model ◽  
Self Assessment ◽  
Interpretive Structural Modeling ◽  
Supply Chain Finance ◽  
Network Process ◽  
Order Preference

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.

scholarly journals Understanding supply chain analytics capabilities and agility for data-rich environments

2019 ◽  
Vol 39 (6/7/8) ◽  
pp. 887-912 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Shahriar Akter
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Equation ◽  
Equation Modeling ◽  
Future Research ◽  
Chain Management ◽  
Content Type ◽  
Supply Chain Agility ◽  
The Usa

Purpose Big data-driven supply chain analytics capability (SCAC) is now emerging as the next frontier of supply chain transformation. Yet, very few studies have been directed to identify its dimensions, subdimensions and model their holistic impact on supply chain agility (SCAG) and firm performance (FPER). Therefore, to fill this gap, the purpose of this paper is to develop and validate a dynamic SCAC model and assess both its direct and indirect impact on FPER using analytics-driven SCAG as a mediator. Design/methodology/approach The study draws on the emerging literature on big data, the resource-based view and the dynamic capability theory to develop a multi-dimensional, hierarchical SCAC model. Then, the model is tested using data collected from supply chain analytics professionals, managers and mid-level manager in the USA. The study uses the partial least squares-based structural equation modeling to prove the research model. Findings The findings of the study identify supply chain management (i.e. planning, investment, coordination and control), supply chain technology (i.e. connectivity, compatibility and modularity) and supply chain talent (i.e. technology management knowledge, technical knowledge, relational knowledge and business knowledge) as the significant antecedents of a dynamic SCAC model. The study also identifies analytics-driven SCAG as the significant mediator between overall SCAC and FPER. Based on these key findings, the paper discusses their implications for theory, methods and practice. Finally, limitations and future research directions are presented. Originality/value The study fills an important gap in supply chain management research by estimating the significance of various dimensions and subdimensions of a dynamic SCAC model and their overall effects on SCAG and FPER.

scholarly journals A multi-agent based system with big data processing for enhanced supply chain agility

2016 ◽  
Vol 29 (5) ◽  
pp. 706-727 ◽  
Author(s):  
Mihalis Giannakis ◽  
Michalis Louis
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Semantic Web ◽  
Supply Chains ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Agent Systems ◽  
Supply Chain Agility ◽  
Multi Agent

Purpose Decision support systems are becoming an indispensable tool for managing complex supply chains. The purpose of this paper is to develop a multi-agent-based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored. Design/methodology/approach For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled. Findings Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility. Research limitations/implications The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems. Practical implications The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis. Originality/value A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

Dynamic Capability Theory as a Lens to Investigate Big Data Analytics and Supply Chain Agility

2021 ◽  
pp. 467-480
Author(s):  
Trevor Cadden ◽  
Guangming Cao ◽  
Raymond Treacy ◽  
Ying Yang ◽  
George Onofrei
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Dynamic Capability ◽  
Supply Chain Agility

scholarly journals Resilience-Enhancing Solution to Mitigate Risk for Sustainable Supply Chain—An Empirical Study of Elevator Manufacturing

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 596
Author(s):  
Chih-Hung Hsu ◽  
Ru-Yue Yu ◽  
An-Yuan Chang ◽  
Wen-Hong Chung ◽  
Wan-Ling Liu
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chains ◽  
Quality Function Deployment ◽  
Manufacturing Industry ◽  
Multicriteria Decision Making ◽  
Sustainable Supply Chain ◽  
Supply Chain Risk ◽  
Job Information ◽  
Disruption Risks

As the complexity of supply chains increases, the enhancement of resilience for mitigating sustainable disruption risks in supply chains is an important issue. Quality function deployment (QFD) has been successfully applied in many domains to solve multicriteria decision-making (MCDM) problems. However, research on developing two houses of quality to connect sustainable supply chain disruption risks, resilience capacities, and resilience-enhancing features in elevator manufacturing supply chains by using the MCDM approach is lacking. This study aims to develop a framework for exploring useful decision-making by integrating the MCDM approach and QFD. By applying the framework, supply chain resilience can be improved by identifying the major sustainable risks and the key resilience to mitigate these risks. Important managerial insights and practical implications are obtained from the framework implementation in a case study of the elevator manufacturing industry. To strengthen resilience and thus mitigate key risks, the most urgent tasks are to connect the working site and the backstage to enhance product development and design and to share real-time job information. When these features are strengthened, agility, capacity, and visibility can be improved. Finally, unexpected events lead to changes in supplier delivery dates, and factors such as typhoon and lack of critical capacities/skilled employees with the greatest impact can be alleviated. This framework will provide an effective and pragmatic approach for constructing sustainable supply chain risk resilience in the elevator manufacturing industry.

Testing the Potential of RFID to Increase Supply-Chain Agility and to Mitigate the Bullwhip Effect

2012 ◽  
pp. 49-68
Author(s):  
Anthony Vance ◽  
Paul Benjamin Lowry ◽  
Jeffrey A. Ogden
Keyword(s):  
Supply Chain ◽  
Empirical Evidence ◽  
Bullwhip Effect ◽  
Inventory Level ◽  
Rfid Technology ◽  
Holding Costs ◽  
Business Agility ◽  
Supply Chain Agility ◽  
Inventory Holding ◽  
Stockout Costs

This study examines the potential of RFID technology to increase the agility of supply-chain e-commerce systems by mitigating the bullwhip effect. The bullwhip effect is a supply-chain phenomenon that reveals a lack of business agility characterized by the amplification of inventory variance. This study employs an experiment involving a modified Beer Distribution Game to simulate an RFID-enabled supply chain. The results provide empirical evidence that RFID technology can increase a supply chain’s agility and reduce the bullwhip effect by reducing inventory holding costs, stockout costs, and inventory-level variances. The results are all the more important when applied to interorganizational e-commerce systems.

Testing the Potential of RFID to Increase Supply-Chain Agility and to Mitigate the Bullwhip Effect

2010 ◽  
Vol 1 (1) ◽  
pp. 48-66 ◽  
Author(s):  
Anthony Vance ◽  
Paul Benjamin Lowry ◽  
Jeffrey A. Ogden
Keyword(s):  
Supply Chain ◽  
Empirical Evidence ◽  
Bullwhip Effect ◽  
Inventory Level ◽  
Rfid Technology ◽  
Holding Costs ◽  
Business Agility ◽  
Supply Chain Agility ◽  
Inventory Holding

This study examines the potential of RFID technology to increase the agility of supply-chain e-commerce systems by mitigating the bullwhip effect. The bullwhip effect is a supply-chain phenomenon that reveals a lack of business agility characterized by the amplification of inventory variance. This study employs an experiment involving a modified Beer Distribution Game to simulate an RFID-enabled supply chain. The results provide empirical evidence that RFID technology can increase a supply chain’s agility and reduce the bullwhip effect by reducing inventory holding costs, stock out costs, and inventory-level variances. The results are all the more important when applied to interorganizational e-commerce systems.

scholarly journals Analysis of the agility of the automotive industry supply chain in times of COVID-19: a case study

2021 ◽  
pp. 112-120
Author(s):  
Fadoua Tamtam ◽  
Amina Tourabi
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Automotive Industry ◽  
Quality Function Deployment ◽  
Business Environment ◽  
Manufacturing Companies ◽  
Supply Chain Agility ◽  
Working Together ◽  
The Many ◽  
Fuzzy Quality Function Deployment

In the early stages of the corona virus pandemic, business environment was changing rapidly. The Moroccan automotive industry was one of the export sectors most affected negatively by the corona crisis; it collapsed during the three months of confinement and the pandemic has created immense uncertainties in demand and disrupted global supply chains. Indeed, to save the automotive industry, Morocco relies on its competitiveness and challenges current supply models for supply chain agility in order to better prepare for future disruptions. Achieving a competitive edge requires aligning company with suppliers and customers as well as working together to achieve agility, organizationally, strategically and individually. However, agile supply chains are the most powerful competitive vehicles of the manufacturing companies. To help automakers deal with the many challenges associated with the pandemic, let’s present this research on the key enablers that will need to be monitored as the situation evolves. Thus, our article presents an original approach which, by linking the competitive priorities, agile supply chain attributes and enablers, aims at evaluates and enhances supply chain agility of a Moroccan automotive factory. Let’s adopt fuzzy quality function deployment (FQFD) approach and, in particular, the two houses of quality (HOQ) with a fuzzy scale in order to identify the most appropriate enablers to be implemented by the factory. This evaluation demonstrates that there are three enablers needing maximum attention: process compliance, logistics and distribution capabilities and supportive information technology. Then, the supply chain agility improvement should be based on these enablers

scholarly journals Big data analytics capability in supply chain agility

2019 ◽  
Vol 57 (8) ◽  
pp. 2092-2112 ◽  
Author(s):  
Rameshwar Dubey ◽  
Angappa Gunasekaran ◽  
Stephen J. Childe
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Dynamic Capabilities ◽  
Big Data Analytics ◽  
Content Type ◽  
Moderation Effect ◽  
Supply Chain Agility ◽  
Analytics Capability

Purpose The purpose of this paper is to examine when and how organizations build big data analytics capability (BDAC) to improve supply chain agility (SCA) and gain competitive advantage. Design/methodology/approach The authors grounded the theoretical framework in two perspectives: the dynamic capabilities view and contingency theory. To test the research hypotheses, the authors gathered 173 usable responses using a pre-tested questionnaire. Findings The results suggest that BDAC has a positive and significant effect on SCA and competitive advantage. Further, the results support the hypothesis that organizational flexibility (OF) has a positive and significant moderation effect on the path joining BDAC and SCA. However, contrary to the belief, the authors found no support for the moderation effect of OF on the path joining BDAC and competitive advantage. Originality/value The study makes some useful contributions to the literature on BDAC, SCA, OF, and competitive advantage. Moreover, the results may further motivate future scholars to replicate the findings using longitudinal data.

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