Designating Industry 4.0 Maturity Items and Weights for Small and Medium Enterprises

Abstract

The vision of Industry 4.0 is an integrated ecosystem in supply chain where every item and human in the plant has an ID in production and works without any external intervention, communicating with each other in every operation. Although such a concept of manufacturing may sound futuristic to many companies, and especially SMEs, the transition to this future is inevitable, and organizations need a roadmap to clearly understand the concepts and effectively execute the applications of Industry 4.0. In this paper, the level of importance of each Industry 4.0 criterion for SMEs is expressed and used to develop a quantitative maturity model. Analytic Hierarchy Process was utilized to calculate the weights of dimensions and maturity items. An iterative procedure led to 9 different dimensions and 33 correlated items. Initial findings showed that the "Strategy and Organization" dimension has the highest impact on maturity level along with the items "Manufacturing Software", "Employees", and "Industry 4.0 Roadmap".

Identifying and Prioritizing Digital Transformation Elements Using Fuzzy Analytic Hierarchy Process

“…In this section, our work will be evaluated by identifying the advantages of this work compared to the research that is most similar to it [8] and comparing the results of our work with the results of AHP, in addition to using the Sensitive Analysis.…”

“…Several studies have proposed various methodologies for prioritizing DT in different domains. For instance, [8] introduced an AHP-based approach for Industry 4.0, [13] employed Fermatean AHP for Supply Chain prioritization, [19] presented a method for technology selection in DT, [20] combined SF-AHP and SF-TODIM approaches in the defense industry, [21] devised a DEMATEL-based method for assessing DT in the health sector, [22] utilized ANP for evaluating DT in manufacturing, [23] introduced a fuzzy TOPSIS-based approach for supplier evaluation in DT within production systems and [24] employed Shannon entropy to calculate Business digital maturity in Europe Analysis of previous research reveals that many studies focused on the private sector, and there is not the same level of interest in the public sector.…”

“…Based on a thorough study of the literature [25-73], including comparisons of digital maturity assessment in the field of DT and expert reviews. This research employs an iterative and tested approach to construct an assessment framework in DT [14][15] and [8]. Overall, research methodology has a two-phase process, namely:…”

“…To handle the large variation between the decision makers' opinions, Saaty [7] proposed AHP in order to streamline complex multi-decision-making processes and make them more systematic. AHP resolves complicated scenarios, including multiple criteria in the decision-making process by converting to a hierarchical structure [7][8]. Following the creation of the hierarchical structure, any two criteria are compared using pairwise comparison.…”

“…There are three primary steps that make up the AHP: 1) define the goal and hierarchical structure of the study, 2) construct pairwise comparisons between criteria at each level of structure, and 3) calculate weight and ranking. AHP is the most widely used among MCDM techniques in domains, such as software [7] and industry [8]. Downsides with uncertainty associated with the decision-makers judgment can be solved by combining AHP and fuzzy set theory [10], [11][12][13][14].…”