A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management

Caiado, Rodrigo Goyannes Gusmão; Scavarda, Luiz Felipe; Gavião, Luiz Octávio; Ivson, Paulo; Nascimento, Denise Cristina de Oliveira; Garza‐Reyes, Jose Arturo

doi:10.1016/j.ijpe.2020.107883

Cited by 200 publications

(116 citation statements)

References 59 publications

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“…Gartner, an international consulting firm, proposed a demand-driven value network maturity model based on the DDVN model system in 2003, which divides supply chain maturity into five maturity stages: passive-responsive, internal functional division, integrated, demand-driven value network, and network value creation, and the maturity of these five stages increases in turn, and accordingly, the supply chain operational capability of enterprises are also stronger [18]. Kevin McCormak in 2004 proposed a supply chain business process maturity model in his study of supply chain management maturity models, which analyzed the relationship between business processes and supply chain performance by landing on coordinated organizations and business processes formed by strategic partners with an emphasis on competition [19][20][21]. The institute for Business Value investigated the supply chain evolution and transformation process of leading companies in the industry and proposed the IBM supply chain maturity model for continuous improvement in 2005, which is used to measure the degree of customer centricity and responsiveness of the supply chain and the supply chain process integration strategies under different stages [22][23][24].…”

Section: Supply Chain Maturitymentioning

confidence: 99%

“…The supply chain reduces resource waste by about $7 billion. Therefore, it is important to strengthen the information platform, so that the actual demand information and the enterprise's forecast of the future demand can be transmitted to suppliers promptly, and at the same time, establish an inventory information-sharing mechanism with suppliers, so that enterprises can keep abreast of the suppliers' supply capacity, to reasonably arrange procurement activities [19,[40][41]. For non-bidding procurement, the implementation of supply-demand synergy is the key to reduce the waste of supply chain resources, and better utilize its advantages of short duration, convenience, and high efficiency, thus realizing the enterprise's intelligent procurement.…”

Section: Figure 2 Intelligent Procurement Synergy Mechanism Constructionmentioning

confidence: 99%

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Enterprise Intelligent Procurement Management Model Exploration

Yang¹,

Yang²

2021

Preprint

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With the development of big data analysis, blockchain and other technologies, the supply chain of enterprises is transforming to lean and intelligent. As an important link in the enterprise supply chain, the intelligent transformation of procurement plays an important role in the improvement of the supply chain efficiency, therefore, the construction of a common method supporting the intelligent upgrade of the enterprise procurement business has become a key concern for enterprise managers. Based on the balanced scorecard theory and the supply chain maturity model, this study combines the actual situation of procurement management in Chinese energy enterprises and constructs a procurement benchmarking system that balances the development direction of the industry and the actual needs of enterprises. Meanwhile, based on the grounded theory, three major themes of the intelligent procurement system (digital business module, procurement synergy mechanism and procurement ecosystem) are extracted to provide a methodological reference for the construction of intelligent procurement systems of energy enterprises. The study concludes with a case study of China National Energy Group Materials Company to demonstrate the application of the intelligent procurement system built in this paper, with a view to providing methodological reference for the intelligent procurement management in energy enterprises.

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Section: Supply Chain Maturitymentioning

confidence: 99%

Section: Figure 2 Intelligent Procurement Synergy Mechanism Constructionmentioning

confidence: 99%

Enterprise Intelligent Procurement Management Model Exploration

Yang¹,

Yang²

2021

Preprint

View full text Add to dashboard Cite

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“…The development of 5G communication networks will make it possible to use the broadband Internet and the capabilities of artificial intelligence systems for business management [35], create new business models that allow implementing breakthrough production technologies, combining intelligent operations and supply chain management [10]. The use of new economic and mathematical models [28], including the methods of fuzzy logic and multi-agent modeling, also makes it possible to improve the management processes of virtual business, reduce the number of errors in decision making, entrusting it to information technologies [36].…”

Section: Intellectual Activity and Artificial Intelligencementioning

confidence: 99%

System analysis of digital economy virtualization processes

Simchenko¹,

Apatova²,

Korolev³

2021

P Sci Edu

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The virtualization of socio-economic processes is expressed in the development of various types of economic activities in the Internet computer network, acting as the infrastructure of the modern economic system, a catalyst for globalization processes, and the main means of communication between computers, information systems of various purposes and complexity, and people. The study of the theoretical and methodological problem of digital economy virtualization processes is due to the importance of determining the structure of virtualization as a set of interconnected industries, consumption, markets, and finance on the Internet. The elements of the system combine the types of economic activities, which include entrepreneurship on the Internet, as well as information and intellectual activities. The study of this problem was carried out using empirical methods of cognition of the structure of the system analysis of digital economy virtualization processes, along with the use of scientometric methods for reviewing the methodology of the educational process for training digital economy personnel and improving their qualifications as a precursor to the design of production processes, consumption, market and financial processes occurring in a digital environment. The conducted studies make it possible to obtain scientific results in the field of structuring the socio-economic processes of virtualization, namely: a dichotomy of virtualization processes in the digital economy is proposed, taking into account the factor of education; the positive and negative consequences of virtualization for the economy and society as a whole are identified. It was concluded in the course of the study that the system analysis of digital economy virtualization processes should be based on the development of the educational component and reflect the environment of virtualization processes, including digital platforms, which, first, form the global infrastructure of the world economy; second, they contribute to the development of entrepreneurship; third, they provide production, consumer, market and financial functions and corporate governance; fourth, they are a source of network externalities that create new social and economic problems. The prospect for further research is the development of a model for assessing the impact of the structure of virtualization processes on economic growth in the digital economy.

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“…In Figure 5, the elements of the cloud of set A intersect with the elements of the cloud of set B, forming an intersection of sets of the fuzzy type, which is not exact, but rather diffuse, similar to human reasoning. In this sense, fuzzy logic is a way to manage uncertainties (Caiado et al, 2021;Takáč, 2014) and its use is very appropriate to treat data collected from the Human Sciences and measure the levels of uncertainty inherent to human communication.…”

Section: Bibliometricmentioning

confidence: 99%