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Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
Published on: October 6, 2020
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Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks
Rimalini Gadekar1, Bijan Sarkar2, Ashish Gadekar3
1Mechanical Engineering Department, Government Polytechnic, Gondia, Maharashtra India.
Annals of Operations Research
|August 1, 2022
Summary
This study introduces a new model using KPIs to assess risks for Industry 4.0 (I4.0) adoption. It prioritizes technological and social risks, aiding manufacturers and policymakers in successful implementation.
Area of Science:
- Industrial Engineering
- Operations Management
- Decision Sciences
Background:
- Industry 4.0 (I4.0) adoption offers significant benefits but faces implementation challenges due to a lack of systematic frameworks.
- Risk assessment is crucial for the successful execution of Industry 4.0 projects.
- Existing research lacks a comprehensive model for evaluating I4.0 implementation risks.
Purpose of the Study:
- To develop a Key Performance Indicator (KPI)-based sustainable integrated model for assessing and evaluating risks associated with Industry 4.0 implementation.
- To provide a systematic framework for organizations to identify and prioritize risks before adopting Industry 4.0 technologies.
- To support decision-making for manufacturers, policymakers, and researchers regarding Industry 4.0 adoption.
Main Methods:
- Developed an Industry 4.0 risks evaluation model through fifteen expert interventions and a systematic literature review.
- Utilized sixteen KPIs to evaluate six critical risks impacting I4.0 adoption decisions.
- Employed the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to map KPI causal relationships and the Additive Ratio Assessment (ARAS) with interval triangular fuzzy numbers to rank risks.
Main Results:
- Identified information technology infrastructure and prediction capabilities as the most crucial KPIs.
- Determined that technological and social risks are highly significant in the Industry 4.0 implementation decision-making process.
- The developed model effectively handles uncertainties and vagueness inherent in decision-making using fuzzy MCDM techniques.
Conclusions:
- The developed integrated model provides a pioneering and unique approach to Industry 4.0 risk prioritization.
- The model supports manufacturers, policymakers, and researchers in navigating I4.0 adoption, particularly in the post-COVID-19 era.
- This research contributes novel knowledge to the field by offering a comprehensive yet simple model for accelerating Industry 4.0 adoption through effective risk management.
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