Further Developments on Application of Dynamic Fuzzy Cognitive Map Concept for Digital Business Models
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Further Developments on Application of Dynamic Fuzzy Cognitive Map Concept for Digital Business Models Weihong Xie1 • Baharak Makki1
Received: 15 June 2020 / Revised: 10 August 2020 / Accepted: 31 August 2020 Ó Taiwan Fuzzy Systems Association 2020
Abstract Business systems are considered as complex systems and consist of several sub-models such as procurement market, supply process, assembly, distribution and sales market. In these systems, it is important to analyze strength and weakness of each sub-model over other parts in order to obtain information for enhancing the business system performance. To fulfill this aim, in this paper we utilize differential Hebbian learning-based dynamic fuzzy cognitive map techniques to examine the effect of expert’s knowledge as a sort of digital transformation source as well as impact of each sub-model of business system over other sub-models. Finally, the proposed method is illustrated numerically. Keywords Fuzzy cognitive map Business models Digital transformation Differential Hebbian learning
1 Introduction In recent years, the context of business models to enhance economics levels in firms or companies has been widely studied in literature. A primary intention is to integrate the emerging technologies for a particular commercial purpose which may cause an increase in technology readiness level (TRL) and, obviously, product quality enhancement which is called integration readiness level (IRL) [27]. For Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. & Baharak Makki [email protected] 1
School of Economics and Trade, Guangdong University of Technology, Guangzhou 510075, China
instance, the authors in [21] studied effect of digital transformation in the manufacturing technology based on IRL context. In fact, the main challenge is how to incorporate the existing technologies to increase manufacturing industries in order to enhance the MRL index [3, 5, 22]. To this aim, the context of Industry 4.0 was proposed to integrate several concepts such as cyber–physical systems, the Internet of Things, cloud computing and cognitive computing into manufacturing technology using different levels of automation or smartness. On the other hand, implementation of digitalization context in technology has received much attention in various fields; it become apparent that the two factors of technology strategy (TS) and technology management (TM) can highly affect on the performance of firms. For instance, smartness and digitalization are become key points in the manufacturing business to improve production efficiency through an easier and resilient access to system information for a largely distributed production network. Moreover, information given by managers or experts can be called as management knowledge in production line can also directly affect on the industry profit. Therefore, dealing with information and digitalization, the concept of digital transformation (DT) has been introduced for in
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