Improving energy efficiency has taken center stage as industries adopt advanced technologies, particularly within smart factories guided by Industry 4.0 principles. This transformation aims not only at increasing productivity but at fostering sustainable manufacturing practices.
The emergence of smart factories—with characteristics like performance self-optimization and the integration of the Internet of Things (IoT)—offers opportunities to revolutionize production. These factories allow for rapid responses to changing consumer demands and operational efficiencies, but they also introduce new challenges, especially when it focuses on decision-making under uncertainty. Therefore, energy efficiency is becoming increasingly relevant, as it directly impacts operational costs and environmental responsibility.
Researchers have developed a novel multi-criteria decision-making (MCDM) framework using intuitionistic fuzzy sets (IFS) to manage the ambiguity inherent to evaluating various energy-saving alternatives. This innovative framework improved the assessment process by using newly developed aggregation operators and applying the method based on the removal effects of criteria (MEREC), which facilitates criteria weight assignment, ensuring accurate representation of importance.
Active technological adoption, including artificial intelligence and big data analytics, supports operational enhancement and flexibility. But the complexity of energy management within these dynamic environments calls for advanced decision frameworks. The newly proposed methodology provides substantial benefits, assisting organizations through systematic evaluation of energy efficiency alternatives geared for Industry 4.0.
A case study executed under this framework demonstrated the application of five distinct energy-saving solutions judged against eight criteria, emphasizing how systematic decision-making processes can effectively guide manufacturers toward sustainability. The research showcases the feasibility and efficacy of MCDM techniques, highlighting renewable integration as the most effective choice for improving energy efficiency, thereby minimizing the dependency on fossil fuels and lowering carbon emissions.
With operational costs being critically influenced by energy consumption, this framework resonates significantly with researchers and industry leaders alike. By prioritizing criteria like operational costs, carbon emissions, and energy consumption under the new MCDM, the study demonstrates significant potential to streamline operations and direct factories toward environmentally friendly practices.
The authors shed light on how the framework’s novel aggregation operators—intuitionistic fuzzy softmax Dubois-Prade (IFSDP), intuitionistic fuzzy softmax interactive Dubois-Prade weighted average (IFSIDPWA), and others—each play pivotal roles when coupled with the RAFSI method, ensuring decisions align with both immediate operational demands and long-term sustainability goals.
Numerous challenges continue to loom around conventional decision-making techniques, especially their inability to adapt to multifaceted criteria reflective of smart factory operations. The study’s focus on alleviating uncertainties through IFSs draws attention to the pressing need for adaptive frameworks. The researchers posit, "Advanced manufacturing facilities have the potential to greatly increase sustainability and energy efficiency, as shown by the case study’s results." This assertion captures the heart of their findings, emphasizing the necessity of integrating intelligent systems capable of leveraging data and anticipated outcomes effectively.
The framework, by addressing potential biases associated with subjective judgment through objective methods like MEREC, presents decision-makers with clearer pathways to energy-efficient practices, positioning them ideally to fulfill the sustainability objectives set forth by global initiatives.
Industry stakeholders must now embrace this study’s approaches to navigate unique technological challenges, demonstrating how informed decisions can not only bolster economic performance but also contribute to sustainable manufacturing ecosystems. By enhancing energy efficiency, industries can leverage technology as both savior and rocket booster for ambitious sustainability goals.
While the study centralizes around five energy-saving alternatives, its findings offer invaluable insights relevant across diverse industrial applications globally. Ensuring these methods are flexible enough to accommodate varying contexts will refine the benefits realized through advanced decision-making frameworks, fostering practices ripe for the future.