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Benchmarking Industry 4.0 readiness evaluation using fuzzy approaches
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PurposeThe purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.Design/methodology/approachI4.0 implies fourth industrial revolution that necessitates vital challenges to be dealt with. In this viewpoint, this article presents the evaluation of I4.0 Readiness Index. The evaluation includes two levels with appropriate criteria and factors. Fuzzy logic approach is used for assessment. Furthermore, the results obtained from fuzzy logic have been benchmarked with multi-grade fuzzy approach.FindingsThe proposed assessment model has successfully utilized fuzzy logic approach for assessment of I4.0 readiness index of automotive component manufacturing organization. Based on fuzzy logic approach, readiness index of I4.0 has been found to be (4.74, 6.26, 7.80) which is further benchmarked using multi-grade fuzzy approach. Industry 4.0 readiness index obtained from multi-grade fuzzy approach is 6.258 and thus, validated. Furthermore, 20 weaker areas have been identified and improvement suggestions are provided.Research limitations/implicationsThe assessment module include two levels (Six Criteria and 50 Factors). The assessment model could be expanded based on advancements in industrial developments. Therefore, future researchers could utilize findings of the readiness model to further develop multi-level assessment module for Industry 4.0 readiness in organization. The developed readiness model helped researchers in understanding the methodology to assess I4.0 readiness of organization.Practical implicationsThe model has been tested with reference to automotive component manufacturing organization and hence the inferences derived have practical relevance. Furthermore, the benchmarking strategy adopted in the present study is simple to understand that makes the model unique and could be applied to other organizations. The results obtained from the study reveal that fuzzy logic-based readiness model is efficient to assess I4.0 readiness of industry.Originality/valueThe development of model for I4.0 readiness assessment and further analysis is the original contribution of the authors. The developed fuzzy logic based I4.0 readiness model indicated the readiness level of an organization using I4RI. Also, the model provided weaker areas based on FPII values which is essential to improve the readiness of organization that already began with the adoption of I4.0 concepts. Further modification in the readiness model would help in enhancing I4.0 readiness of organization. Moreover, the benchmarking strategy adopted in the study i.e. MGF would help to validate the computed I4.0 readiness.
Title: Benchmarking Industry 4.0 readiness evaluation using fuzzy approaches
Description:
PurposeThe purpose is to assess Industry 4.
0 (I4.
0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.
Design/methodology/approachI4.
0 implies fourth industrial revolution that necessitates vital challenges to be dealt with.
In this viewpoint, this article presents the evaluation of I4.
0 Readiness Index.
The evaluation includes two levels with appropriate criteria and factors.
Fuzzy logic approach is used for assessment.
Furthermore, the results obtained from fuzzy logic have been benchmarked with multi-grade fuzzy approach.
FindingsThe proposed assessment model has successfully utilized fuzzy logic approach for assessment of I4.
0 readiness index of automotive component manufacturing organization.
Based on fuzzy logic approach, readiness index of I4.
0 has been found to be (4.
74, 6.
26, 7.
80) which is further benchmarked using multi-grade fuzzy approach.
Industry 4.
0 readiness index obtained from multi-grade fuzzy approach is 6.
258 and thus, validated.
Furthermore, 20 weaker areas have been identified and improvement suggestions are provided.
Research limitations/implicationsThe assessment module include two levels (Six Criteria and 50 Factors).
The assessment model could be expanded based on advancements in industrial developments.
Therefore, future researchers could utilize findings of the readiness model to further develop multi-level assessment module for Industry 4.
0 readiness in organization.
The developed readiness model helped researchers in understanding the methodology to assess I4.
0 readiness of organization.
Practical implicationsThe model has been tested with reference to automotive component manufacturing organization and hence the inferences derived have practical relevance.
Furthermore, the benchmarking strategy adopted in the present study is simple to understand that makes the model unique and could be applied to other organizations.
The results obtained from the study reveal that fuzzy logic-based readiness model is efficient to assess I4.
0 readiness of industry.
Originality/valueThe development of model for I4.
0 readiness assessment and further analysis is the original contribution of the authors.
The developed fuzzy logic based I4.
0 readiness model indicated the readiness level of an organization using I4RI.
Also, the model provided weaker areas based on FPII values which is essential to improve the readiness of organization that already began with the adoption of I4.
0 concepts.
Further modification in the readiness model would help in enhancing I4.
0 readiness of organization.
Moreover, the benchmarking strategy adopted in the study i.
e.
MGF would help to validate the computed I4.
0 readiness.
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