Sustainable Manufacturing Systems Research Articles

Artificial Intelligence Techniques for Sustainable Reconfigurable Manufacturing Systems: An AI-Powered Decision-Making Application Using Large Language Models

Artificial intelligence (AI) offers a promising avenue for developing sustainable reconfigurable manufacturing systems. Although there has been significant progress in these research areas, there seem to be no studies devoted to exploring and evaluating AI techniques for such systems. To address this gap, the current study aims to present a deliberation on the subject matter, with a particular focus on assessing AI techniques. For this purpose, an AI-enabled methodological approach is developed in Python, integrating fuzzy logic to effectively navigate the uncertainties inherent in evaluating the performance of techniques. The incorporation of sensitivity analysis further enables a thorough evaluation of how input variations impact decision-making outcomes. To conduct the assessment, this study provides an AI-powered decision-making application using large language models in the field of natural language processing, which has emerged as an influential branch of artificial intelligence. The findings reveal that machine learning and big data analytics as well as fuzzy logic and programming stand out as the most promising AI techniques for sustainable reconfigurable manufacturing systems. The application confirms that using fuzzy logic programming in Python as the computational foundation significantly enhances precision, efficiency, and execution time, offering critical insights that enable more timely and informed decision-making in the field. Thus, this study not only addresses a critical gap in the literature but also offers an AI-driven approach to support complex decision-making processes.

A framework to assess the effectiveness index (EI) of the Indian SMEs to integrate lean and sustainability through combined fuzzy logic and systems approach

PurposeTo become a sustainable lean manufacturing (LM) system, an organization must be first distinctly cognizant of the terms “lean” and “sustainability” as they relate to the state of affairs of their particular industry and business. Next, the organization must identify and acquire the necessary qualities it needs to become sustainable in lean philosophy and its practices in the organization. The LM paradigm has been a top priority for many businesses; thus, this article is based on actual research done in Indian small and medium scale organizations to see how widely it is understood and implemented.Design/methodology/approachA framework was developed based on literature review and academic research. A preliminary analysis of a small number of small and medium-sized enterprises (SMEs) that, conceptually, summarizes and demonstrates the concerted efforts that a company may undertake to increase its leanness. This conceptual model was employed to create a questionnaire that was administered to survey the SMEs of India. The information gathered through this questionnaire was analyzed using the model developed by the researchers. Then fuzzy logic and systems approach were used to find out the effectiveness index (EI) of the organization.FindingsThe EI for system leanness at different organizational levels within an organization is determined using fuzzy logic and systems approach for 48 SMEs in different clusters. The average EI of the system was found to be 0.336 on a scale of 0–1 which indicates that the current state of lean implementation and its sustainability is very low and poor in Indian SMEs. This article outlines the key model components and describes how they were applied to analyze the data gathered from an industry study.Research limitations/implicationsThe research aims to make lean continuously sustainable by surfacing and eliminating the wastes in the Indian SMEs whenever it appears rather than using it as a cleaning tool. The present study was focused on India’s southern industrial areas and it was difficult to gather the information around the country due to its diverse industrial culture and geography. Hence, more research and the comparative study of the same that takes into account the various regions of the nation’s industrial lean behavior can be conducted.Practical implicationsThe generalized sustainable lean framework analyzed using fuzzy logic and systems approach gives the current effectiveness of the leanness in SMEs of south India. This model can be effectively implemented in other areas of the nation to identify the scenario of lean and its sustainability and a final comprehensive model can be developed.Originality/valueThere is a dearth of comprehensive studies on the assessment of sustainability of the lean philosophy in Indian SMEs. With the help of combined fuzzy logic and systems approach, the model developed in this study evaluates the sustainability of the lean methodology using the EI used in SMEs by taking into account both the lean and sustainability factors as well as enablers like customer satisfaction, ethics, innovation and technology.

Stakeholders’ perspectives and performance outcomes of sustainable market-focused manufacturing system in Indian manufacturing organizations

Sustainability and the ever-changing customer demands are the key drivers for innovation across product, process, system, and supply chain management. Sustainability innovations aim to improve organizational environmental, social, and economic performance. Given the complexity of sustainability and ever-changing business and stakeholders’ requirements, organizations must focus their innovation in the direction of meeting the requirements of their stakeholders and business. Integrated sustainable green lean six sigma agile manufacturing system (ISGLSAMS) is a sustainable market-focused manufacturing system. It provides a sustainability and market-oriented business model and meets the perspectives of the various stakeholders. This study aims to identify and rank the various performance outcomes of ISGLSAMS. The study also explores the correlations among the various performance outcomes of ISGLSAMS. The study helps the practitioners to better understand the ISGLSAMS and stakeholders’ perspectives of ISGLSAMS. This research provides a foundation for practitioners, policymakers, organizations, and value chain partners (i) to formulate policies and roadmaps for the successful adoption of ISGLSAMS and (ii) for fostering a more sustainable development of manufacturing organizations in India.

Sustainable Hybrid Manufacturing and Refurbishing Systems with Substitution

This study considers the planning decisions in a production facility that receives returned products as a contribution to sustainability through the reduction in waste from used products. It involves three processes: the refurbishing and disassembly of returned items and the manufacturing of new items. The process is driven by the demands of new items and collected secondhand items. In this study, we consider a downward substitution, in which new items could be used to meet the demand for secondhand items. The objective is to determine the best production planning schedule to satisfy all demands while minimizing the total costs of production and inventory. We propose and analyze different configurations distinguished by their level of integration and whether they allow substitution. Integration is mainly achieved through the sharing of the inventory of returns between the refurbishing line and the disassembly line, which responsible for feeding the manufacturing process with “as good as new” parts. The best configuration is identified based on the total cost and environmental impact. Four heuristics based on combining relax-and-fix and fix-and-optimize approaches are proposed. Numerical experiments have shown that the heuristics are very efficient, achieving gaps of less than 1% from the optimum in short CPU times for most instances. Numerical experiments have also shown that integration through the sharing of returns inventories leads to considerable cost and environmental benefits.

A competency-based learning model for distance education in the post-COVID-19 era: Sustainable manufacturing systems and the circular economy

A competency-based learning model for distance education in the post-COVID-19 era: Sustainable manufacturing systems and the circular economy

The Overview Literature Review on Competence-Based Education in The Industrial Engineering Using PRISMA 2020

Structured methods for the methodical re-alignment of professional education programs are needed for the comprehensive integration of the human workforce in digital and sustainable manufacturing and logistics systems. It is believed that investing in engineering education is essential to the advancement of both industry and society. Future engineers, though, will need more than simply a solid foundation in technology. The focus on competence-orientation is often neglected, particularly in the field of Industrial Engineering (IE), and empirically proven success criteria that support the holistic reorientation of engineering education are usually studied. The authors of this work perform a thorough literature review on engineering education, focusing on competence-based engineering education within the field of IE. Additionally, the writers offer beneficial implications for the competence-based for Industrial Engineering students and lectures writings.

Multiobjective Optimization in Distributed Industry and Environmental Sustainability: a Systematic Literature Review

With the growth of industrialization, researchers have also become increasingly concerned about environmental protection. Environmental issues have been one of the problems and one of the objectives to consider when it comes to production scaling problems, mainly minimizing energy consumption, and minimizing carbon emissions, as well as various other objectives to optimize. Measures that put pressure on organizations to pay more attention to the environment have been created, along with other measures, not only economic but also social. Good production scheduling allows organizations to be more successful in business, as it contributes to a better environment and society. Therefore, the search for processes that allow for more effective and efficient decision-making is becoming a subject of paramount importance to study. Sustainability is currently an urgent challenge for engineering and organizations. One of the ways to contribute to more sustainable manufacturing systems is the development of intelligent technologies and the sharing of manufacturing systems. This paper studies the literature on production scheduling approaches in distributed companies and their potential benefits for the environment and society, in addition to the economic benefits. In this way, the optimization of environmental, social, and economic measures in the planning and scheduling of production in extended company contexts, using approaches based on multiobjective optimization, is a primary focus of this work.

Emerging Challenges in the Sustainable Manufacturing System: From Industry 4.0 to Industry 5.0

Emerging Challenges in the Sustainable Manufacturing System: From Industry 4.0 to Industry 5.0

Resilience of sustainability for a smart production system to produce biodiesel from waste animal fat

The smart production system is implemented for more effective labor, less waste, and a reduction of energy consumption. Biofuels are significant substitutes for conventional fuels and decreases air pollution and carbon monoxide, can be made through a smart production system with automated machines. Biodiesel can be produced using an assortment of sources that can be both edible and inedible, but animal fat is a sensible alternative among inedible sources because of its affordable price and ease of availability. The model studies the smart biodiesel production system with energy utilization and less carbon emissions to produce pure biodiesel. The multi-objective mathematical model minimizes the total biodiesel supply chain cost followed by minimizing the total biodiesel supply chain environmental impact and enhancing its social impact. The smart production system with a variable production rate produces the biodiesel with less impurities, but still impurities in the biodiesel, that can be detected by a two-stage autonomated inspection system. The impure biodiesel is remanufactured in the biorefineries at the end of every natural production cycle. For the best utilization of energy, various energy costs, such as lighting and air handling costs are calculated. The improved augmented ϵ-constraint approach with lexicographic optimization is utilized to solve the model. The four mathematical examples are studied, and the repercussions of mathematical experiments reveal that the animal fat purchasing cost is 44.32% of the total cost, and the percentage of impurities in biodiesel can be decreased through minimum energy consumption. The managerial insights of the model, graphical representation, and sensitivity analysis of key parameters are provided in this research. The conclusions can be useful to investors in a sustainable smart production system and biofuel policymakers.

A Framework for Sustainable Manufacturing: Integrating Industry 4.0 Technologies with Industry 5.0 Values

The limitations imposed by resource scarcity and the imperative to mitigate adverse environmental and societal impacts have intensified the urgency of developing more sustainable manufacturing systems. Simultaneously, the rapid development and implementation of new technologies is exacerbating the digital divide among vulnerable workers. Concomitantly, the enabling technologies stemming from Industry 4.0 offer significant potential to enhance the competitiveness of manufacturing systems. However, the impact of these enabling technologies on achieving sustainable manufacturing remains uncertain. This paper embarks on a comprehensive exploration to address this knowledge gap. Initially, it assesses the suitability of each enabling technology within Industry 4.0 across the economic, social, and environmental dimensions of sustainability. Subsequently, the needs of the production process are studied to characterize its sustainable performance. For this, the ASTM E3012-22 standard is introduced. Building upon this foundation, the incorporation of Industry 5.0 is introduced to guide the selection of enabling technologies for sustainability based on its core values, encompassing sustainability, human-centricity, and resilience. The integration of new technologies guided by these values can help bridge the technological divide among vulnerable workers. Finally, a theoretical framework is proposed to enable the design of sustainable manufacturing systems guided by Industry 5.0 values. This framework enables the seamless integration of enabling technologies, machinery, and human expertise throughout the system life cycle.

Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience

We propose to use engineering models for Bayesian Network (BN) learning for fault diagnostics at the factory-level using key performance indicators (KPIs) such as overall equipment effectiveness (OEE). OEE is widely used in industry and it measures sustainability by capturing product quality (e.g., less scrap) and measures resilience by capturing availability. A major advantage of the proposed approach is that the engineering models are likely to be available long before the corresponding digitalized smart factory becomes fully operational. Specifically, for BN structure learning, we propose to use analytical queueing theory models of the factory to elicit the structure, and to carry out intervention we propose to use designed experiments based on discrete-event simulation models of the factory. For parameter learning, we apply a qualitative maximum a posteriori (QMAP) method and propose additional expert constraints based on the law of propagation of uncertainty from queueing theory. Furthermore, the proposed approach overcomes the challenge of obtaining balanced-class data in BN learning for fault diagnostics. We apply the proposed BN learning approach to (i) a 4-robot cell in our laboratory and (ii) a robotic machining cell in a commercial vehicle factory. In both cases, the proposed method is found to be efficacious in accurately learning the BN structure and parameter, as measured using structural-hamming distance and Kullback–Leibler divergence score, respectively. The proposed approach can pave the way for a new class of resilient and sustainable smart manufacturing systems.

ASBlock:An Agricultural based Supply Chain Management using Blockchain Technology

Blockchain is a distributed digital ledger technology that allows for the immutable and auditable logging of transactions without needing a central authority or centralized database. First introduced in 2008 as the technology behind the digital currency Bitcoin, blockchain has now found use in a wide range of sectors. Modern agriculture supply networks have emerged to accommodate intricate interactions between regional actors and the agricultural environment. Interactions between various participants in the supply chain, including those involved in manufacturing, processing, and delivery. One of the main reasons fraud happens so often in agriculture is the lack of transparency in the supply chain. Businesses that aren’t transparent run the risk of losing money, alienating their customers, and seeing their brands decline in value. To encourage the growth of an efficient and trustworthy trading environment, several fundamental adjustments to the current supply chain architecture are necessary. The potential for blockchain technology to enhance food and agricultural supply chain transparency is well acknowledged. Companies utilize IoT and blockchain to create more equitable, sustainable, and customer-friendly food manufacturing systems. In this current work, a blockchain-based decentralized application (dApp), ‘ASBlock’, is developed for agricultural-based supply chain management. The primary focus of this developed application is to bring transparency and security to all participating users of the application; the users of this application include farmers or producers, suppliers or retailers, hotels, and end users. The performance analysis shows that the average gas utilization of the proposed ASBlock dApp is ∼ 2.8 ⋆ 104. The average smart contract execution time for all use cases is ∼ 112sec.

Evolutionary design framework and assessment of the sustainable manufacturing systems

Evolutionary design framework and assessment of the sustainable manufacturing systems

Innovative Teaching Method of Circularity Design for Sustainable Manufacturing Systems: an Application on Urban Factories

Laws to mitigate the impacts of the industries and responsible consumption trends have been pushing manufacturers to integrate sustainability in their business models. In this context, circular economy and sustainable manufacturing have become key interests for manufacturers, and thus should be included in higher education curricula for industrial engineering students. However, tools and sources for these new subjects are scarce. Such multidisciplinary topics require practical innovative teaching approaches with suitable evaluation approaches. Such a proposal is presented in this paper to define a set of pedagogical specifications for both hard and soft skills required to design sustainable and circular proposals for urban factories. First, the students are required to define the scope of impact mitigation through the interpretation of life cycle assessment (LCA) results for a case study. Then, a roleplay has been developed with the objective of improving sustainability using sustainable manufacturing practices and circular economy strategies. The design phase is supported by a package of databases and instructions, alongside tools and templates for expected deliverables including LCA and sustainable business model canvas. To enable innovation and design thinking, feedbacks have been given to students on a regular basis with the aim to improve outcomes prior to the final deliverable. Evaluation has been conducted through an oral presentation in front of academic and specialized external experts, followed by a discussion and a deliberation. This proposal has been tested in a class of 40 industrial engineering students during a semester for a course dedicated to urban factories. All groups have successfully achieved the specifications of the course, and have demonstrated additional soft skills such as teamwork, time management, and effective presentation.

From OEE to OSEE: How to reinforce Production and Maintenance Management Indicator Systems for Sustainability?

Sustainability in manufacturing enterprises is considered as a tradeoff between ecological, economic and social factors. Manufacturing enterprises, however, are not aware of holistically sustainable operational strategies that comprehensively include and quantify these factors, while ensuring their competitiveness. In particular, key performance indicators (KPIs) to measure sustainability performance and facilitate root cause analysis on an operational level are missing. Currently, the consideration of ecological and social aspects in production and maintenance management is still rather theoretical, while fostering economical sustainability is omnipresent. To manage a transition to a sustainable manufacturing system, these factors should be orchestrated, and economic sustainability must not be a contradiction to ecological and social sustainability. To pursue the aforementioned line of research in sustainable operations management, this paper aims to introduce a conceptual approach incorporating economic, ecological and human sustainability factors. It supports decision-making in strategic and operational levels and lays the ground for the development of a novel KPI schema, in particular focusing on extension of OEE towards Overall Sustainable Equipment Effectiveness (OSEE) for reinforcing informed decision-making ensuring sustainability goals.

Linguistic pattern-based facility layout optimization in designing sustainable manufacturing systems

Linguistic pattern-based facility layout optimization in designing sustainable manufacturing systems

Evolutionary design framework and assessment of the sustainable manufacturing systems

Evolutionary design framework and assessment of the sustainable manufacturing systems

Multiobjective Optimization in Distributed Industry and Environmental Sustainability: a Systematic Literature Review

With the growth of industrialization, researchers have also become increasingly concerned about environmental protection. Environmental issues have been one of the problems and one of the objectives to consider when it comes to production scaling problems, mainly minimizing energy consumption, and minimizing carbon emissions, as well as various other objectives to optimize. Measures that put pressure on organizations to pay more attention to the environment have been created, along with other measures, not only economic but also social. Good production scheduling allows organizations to be more successful in business, as it contributes to a better environment and society. Therefore, the search for processes that allow for more effective and efficient decision-making is becoming a subject of paramount importance to study. Sustainability is currently an urgent challenge for engineering and organizations. One of the ways to contribute to more sustainable manufacturing systems is the development of intelligent technologies and the sharing of manufacturing systems. This paper studies the literature on production scheduling approaches in distributed companies and their potential benefits for the environment and society, in addition to the economic benefits. In this way, the optimization of environmental, social, and economic measures in the planning and scheduling of production in extended company contexts, using approaches based on multiobjective optimization, is a primary focus of this work.

Industrial Metabolism: A Multilevel Characterization for Designing Sustainable Manufacturing Systems

The development of industrial manufacturing systems has significant implications for society and the environment, often resulting in substantial waste generation. To address this issue and promote sustainable growth, the concept of industrial metabolism offers a promising approach. Industrial metabolism facilitates the circularity of energy and material flows within the industrial environment, contributing to the establishment of more sustainable manufacturing systems. This paper provides a comprehensive analysis of industrial metabolism, highlighting its analogy with natural systems and categorizing models based on their application at different levels: macro (national or regional), meso (eco-industrial park), and micro (manufacturing plant or line). The analysis emphasizes the importance of considering the trophic network and evaluating the efficiency, cyclicality, toxicity, and resilience of industrial metabolic pathways. The proposed characterization of bioinspired industrial metabolism is positioned within the industrial environment. This positioning facilitates the design of manufacturing systems that emphasize circularity, drawing on frameworks applied at different levels within industrial metabolism.

Modelling the inhibitors of integrated sustainable lean manufacturing system in the South Indian SMEs using fuzzy logic

PurposeSmall and medium-scale enterprises (SMEs) that operate with modest financial investments and commodities face numerous challenges to remain in business. One major philosophy used by SMEs these days is the implementation of lean manufacturing to get solutions for various issues they encounter. But is lean getting sustained over time? The purpose of this research is to design a Sustainable Lean Performance Index (SLPI) to assess the sustainability of lean systems and to pinpoint the variables that might be present as potential lean system inhibitors which hinder the sustainability of leanness.Design/methodology/approachA multi-level sustainable lean performance model is constructed and presented based on the literature research, field investigation and survey conducted by administering a questionnaire. Fuzzy logic approach is used to analyse the multi-level model.FindingsSLPI for the SMEs is found using fuzzy logic approach. Additionally, the ranking score system is applied to categorise attributes into weak and strong categories. The performance of the current lean system is determined to be “fair” based on the Euclidean distance approach and the SLPI for SMEs.Research limitations/implicationsThis work is concentrated only in South India because of the country’s vast geographical area and rich and wide diversity in industrial culture of the nation. Hence, more work can be done incorporating the other parts of the country and can analyse the lean behaviour in a comparative manner.Practical implicationsThe generalised sustainable lean model analysed using fuzzy logic identifies the inhibitors and level of performance of SMEs in South India. This can be implemented to find out the level of performance in the SMEs after a deeper study and analysis around the SMEs of the country.OriginalityThe sustainable assessment of lean parameters in the SMEs of India is found to be very less in literature, and it lacks profundity. The model established in this study assesses the sustainability of the lean methodology adopted in SMEs by considering the lean and sustainability attributes along with enablers like technology, ethics, customer satisfaction and innovation with the aid of fuzzy logic.