Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of BWM and interval valued intuitionistic fuzzy TODIM (original) (raw)
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Although small and medium-sized enterprises (SMEs) shape the cornerstone of economy, they encounter various challenges in transitioning to Industry 4.0. This issue represents an ill-structured problem under uncertainty, which requires a specific set of tools to be solved. This study relied on a hybrid method composed of the interval type-2 fuzzy BWM (IT2F-BWM) and the interval type-2 fuzzy DEMATEL (IT2F-DEMATEL) method, to handle the complexities that SMEs experience in transitioning to Industry 4.0. The results of the IT2F-BWM revealed the importance of the “organizational” criterion, in comparison to “technological” and “strategic” criteria. Furthermore, the IT2F-DEMATEL results showed that the “organizational” dimension exerted the highest degree of influence. The most effective criteria (sub-dimensions) were “a lack of skillful management team”, “the need for advanced skills”, and “having insufficient knowledge of and little interest in Industry 4.0 and its outcomes”, which fell...
Industry 4.0 Adoption Framework in Msmes Using a Hybrid Fuzzy Ahp-Topsis Approach
Proceedings on Engineering Sciences, 2023
This work presents an outline for adopting industry 4.0 enabling technologies, and appropriate strategies are prioritized to implement them. A hybrid fuzzy Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches are applied to achieve the objectives. The enabling technologies and strategies were identified based on the literature review and expert's opinions, a total of 26 enabling technologies and eight strategies were identified. Later fuzzy AHP technique is used to rank the enablers and TOPSIS is applied to order the implementation strategies. From 26 enablers, a total of ten enabling technologies were found to be the most effective. Artificial intelligence (AI), top management commitment and support, virtual reality, and enterprise resource planning (ERP) systems were the top-ranked enablers in the list, whereas edge computing was the least effective enabler. Among the strategies, lean manufacturing, green supply chain and logistics, and integrated and smart manufacturing systems were the top priorities in implementing industry 4.0, while recruiting and managing talents was the least important strategy in the study. The findings from this framework will provide a deep insight to the managers and practitioners of MSMEs to adopt the industry 4.0 technologies in their organizations
Journal of Industrial Engineering and Management, 2022
Purpose: Although the decision to adopt Industry 4.0 is commonly strategical, the selection and implementation of technology are the responsibilities of the tactical level management. The tactical level management will also directly experience the impact of adopting the technology towards the organizational performances in their functional areas. The comparative survey study aims to measure the tactical level management’s sense of urgency of the nine pillars in three plants of a single manufacturing organization.Design/methodology/approach: The research methodology starts with a literature review to collect the criteria appertaining to the pillars. Based on the 95 constituting criteria, the second step prepares and conducts a questionnaire survey with 32 participants on three sister plants. Next, rough BWM-CRITIC-TOPSIS ranks these plants at the pillar and criteria levels. The ranking method integrates Best-Worst Method (BWM), Criteria Importance Through Intercriteria Correlation (C...
American Journal of IR 4.0 and Beyond
The movement of the Fourth Industrial Revolution is touching the manufacturing and processing industries in Bangladesh. The research uses the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) geometric mean technique, a Multi Criteria Decision Making (MCDM) methodology, to identify, analyze, and prioritize the key obstacles to Industry 4.0 implementation in Bangladesh’s Ready Made Garments (RMG) industries. Another triangular type Fuzzy-AHP extent analysis approach is applied to evaluate the minimum degree of possibilities by using fuzzy appropriateness indices and to determine the weights of assessment criteria. Pairwise comparisons are used to collect 11 experts’ preferences in verbal and numerical terms from different industries. The four main obstacles identified from related review studies are used as input variables in the Fuzzy-AHP methods to measure the intensity level of obstacles. The results have shown that the main four obstacles for Industry 4.0 are “Lack of Top Management...
Advances in Production Engineering & Management, 2019
Manufacturing is currently at a turning point from mass production to customized production. The implementation of the Industry 4.0 concept, leading to automation and digitalization of manufacturing processes, is therefore considered vital for companies that aim to follow emerging trends in production. Research in this field is primarily focused on companies from developed countries, while companies from transition countries have difficulties to adapt to new business environment. The aim of this paper is to evaluate the use of advanced digital technologies in manufacturing companies from transition countries (i.e. Serbia) in the context of Industry 4.0. To address this problem, an evaluation method based on Fuzzy Analytic Hierarchy Process (FAHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) is proposed. FAHP was used to determine criteria weights as an input for PROMETHEE method which was then used to evaluate advanced digital technologies. For this purpose, the dataset from the European Manufacturing Survey gathered in 2018 from Serbian manufacturing companies is used. The results of this empirical research revealed that production planning and scheduling, digital exchange of data with suppliers/customers, and production control systems play vital role for manufacturers in the context of industry 4.0. These results could serve to manufacturers for their strategic orientation and decision making.
A Spherical Fuzzy Multi-Criteria Decision-Making Model for Industry 4.0 Performance Measurement
Axioms
In recent years, efficient processes have become increasingly important because of high-level competition in the production industry. The concept of Industry 4.0 is a relatively new and effective method for managing production processes. Because the Industry 4.0 implementation process includes connections between objects, humans, and systems, it is quite difficult to evaluate and measure the performance. At this stage, performance criteria can be applied. However, linguistic evaluation of criteria makes the problem too complicated to solve. The purpose of this paper is to present a novel fuzzy performance measurement model for Industry 4.0 in small and medium-sized manufacturing firms. A hybrid spherical fuzzy analytic hierarchy process (SF-AHP)—weighted score methodology (WSM) is proposed for the performance measurement and scoring process. In the application part of this paper, the propounded methodology was applied to five companies. The results of this study can be used as a ref...
Benchmarking: An International Journal, 2020
Purpose-The purpose of the paper is to visualize the priorities of important factors towards the status of Industry 4.0 implementation in Indian manufacturing industries by utilizing the analytic hierarchy process (AHP) and analytic network process (ANP) techniques. Design/methodology/approach-Based on a review of relevant literature, the authors recognized four criteria and 16 critical factors that have been validated by academic and industry experts. AHP and ANP models are then developed to evaluate the requirements and essential elements of the Industry 4.0 implementation. The outcomes were validated with the aid of sensitivity analysis. Findings-The above models offer the industry a clear and straightforward way to understand the critical factors in the status of Industry 4.0 implementation. The two techniques have emerged to be influential in deciding the status of Industry 4.0 implementation. The two methods yielded almost identical outcomes. Both methods take into account the industry's specific needs based on their available capacity. Practical implications-Decision-makers and specialists will use the strategies discussed in this paper to effectively include the implementation status of Industry 4.0 in their industries, based on their ability to make arrangements for proper implementation of Industry 4.0 and to concentrate on top priority factors when implementing Industry 4.0 at their workplace. Originality/value-The contribution of this research is that it is the first to be carried out with a view to both AHP and ANP to analyze important factors regarding the implementation status of Industry 4.0 and authentication through sensitivity analysis methods; this is a recent initiative in Industry 4.0.
Fuzzy Cognitive Mapping Approach for Assessing Industry 4.0 Tendency
Scientia Iranica
The correct understanding of the conceptual and practical counterpart of Industry 4.0 is of great importance because global competition has made the technology-based production a necessity. The aim of this study is to propose a model that will predict the companies' existing and predicted Industry 4.0 levels. The changes of the concepts are examined and interpreted for 3 different hypothetically prepared scenarios. In the first scenario, an organization that is poorly managed in terms of the development of Industry 4.0 is considered. The industry 4.0 tendency was obtained as 0.04 reaching a steady state after 12 time periods using the fuzzy cognitive maps algorithm. Moderate and well-managed organizations are considered in Scenario 2 and 3 respectively. The industry 4.0 tendency reached 0.12 after 15 time periods for Scenario 2. The tendency is calculated as 0.95 at the end of 5 iterations in the third scenario, which has well-managed concept values in the current situation. In addition to the scenario analysis, strategy and organization, smart operation, and smart factory concepts are found to provide the most significant contribution over the industry 4.0 level as a result of static analysis section.
Annals of Operations Research
Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization's decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer's, policymaker, and researchers' viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process.