Industry 4.0 Technologies: Sustainable Manufacturing Supply Chains

Indian coal industry has very significance importance in Indian economy. In Indian economy core sectors like thermal power plant, cement industry, steel industry, and paper industry are still very much coal dependent. The use of big data analytics and internet of things (IoT) are still at very nascent stage for Indian coal industry. In this chapter, a framework for implementation of big data analytics and IoT in the Indian coal industry and across its supply chain is proposed. At present very limited research is undertaken in the field of application of Industry 4.0 in Indian coal industry and across its supply chain. Big data-driven supply chain and suggesting performance measures will give immense opportunity for coal industry and its consumer to improve the supply chain. The enablers, barriers of big data analytics, and IoT are identified across supply chain of Indian coal Industry.

In the current setup, industries are doing many innovations and coming out with many new and innovative products. Industrial revolution is a driving force for innovations as it provides intelligent technologies that helps in new product development. In the current fourth industrial revolution, industries are becoming smart by adopting digital technologies. the prime focus on adopting digital technologies is to achieve sustainable benefits. Intelligent technologies namely machine learning, big data analytics, additive manufacturing and internet of things help in effective resource utilization and minimizing waste thereby achieving sustainability in manufacturing. In this regard, this chapter aims to highlight the importance of smart manufacturing by identifying various technologies adopted in industries to become smart. Various challenges faced by industries in adopting digital technologies were also highlighted in the study. The present chapter will help research practitioners, and industrial experts to achieve sustainability through adoption of digital technologies in industries and making them smart industries.

Due to the complexity of current manufacturing systems, they are constantly facing challenges in terms of their dynamic and unpredictable nature. The development of artificial intelligence (AI) has shown that it can help solve these issues. With the help of these advanced prediction models, the analysing capability of those evolved models can be transformed into a powerful tool for analysing and improving the manufacturing processes. This chapter aspires to provide a wide-ranging overview of the various applications of AI in manufacturing, especially in contemporary machining namely wire electrical discharge machining and advanced joining processes such as laser welding. It also explores the potential of AI to enhance the competence of manufacturing. The chapter adopts a hierarchical structure to reveal the various interdependencies in a manufacturing plant's operations. The chapter covers a wide range of topics related to manufacturing, such as quality, throughput, and development of intelligent decision-making tools. It also explores the applications of AI in manufacturing engineering to improve the efficiency of factories.

Businesses and governments worldwide are implementing measures to tackle the significant sustainability challenges in the manufacturing sector, including environmental, health and safety and productivity related. As the sector moves toward Industry 4.0, Artificial Intelligence (AI) is viewed as a promising solution to address these challenges. However, current knowledge on these technologies and their interplay with the triple bottom line (TBL) sustainability dimensions is scattered and unclear. This study seeks to bridge this gap by creating a comprehensive AI implementation framework consisting of application, data and computation layers. The AI applications are categorized into virtualization, forecasting, automation and intelligent environment, while the computation layer comprises machine learning, deep learning, computer vision and natural language processing. These in turn use multimedia, time-series manufacturing, product parameter, sensor and location-based data inputs. Moreover, the framework assesses the impact of AI technologies on enhancing TBL sustainability, covering environmental, social and economic aspects. This novel and comprehensive framework, which is not seen in the previous literature, can support the development of policy interventions and support systems to promote AI adoption in the manufacturing sector, while also achieving TBL sustainability goals.

Small and medium-sized enterprises (SMEs) are rapidly contributing to economic growth. With the ever-demanding growth in the market and changes occurring in the market, not a single company can be left behind in terms of the improvement of its processes. In this chapter, the focus is to understand how the case company evaluates the challenges that it faces in incorporating the amalgamated process of Lean principles in the Industry 4.0 scenario. Lean manufacturing broadly focuses on the fact that there are limited wasteful activities in the process. In addition to that if we were to consider the Industry 4.0 scenario, where emerging technologies such as artificial intelligence, cyber-physical spaces, big data, and other associated technologies could be studied. The complicated challenging situations that the SMEs need to consider while developing their products is what we are considering to evaluate for the company. The real-life condition of the cement industry is taken into account, and the criteria for a Lean 4.0 setup in manufacturing are evaluated using the Grey-DEMATEL technique.

The SME sector is India's economic backbone and plays a critical role in insulating the nation from shocks and difficulties in the global economy. The development of India's economy depends heavily on the country's small and medium-sized businesses. Because of this, SMEs must embrace lean to survive in today's global business environment. Companies need new standards to address the requirements of increased customization and product variety; diversified markets; shorter product life span and development time; responsiveness toward customers; and waste minimization, which are increasingly becoming key features. These requirements are needed to survive in the present global scenario. By concentrating on their plans for continuous improvement in all areas of business and simultaneously improving their manufacturing processes by reducing waste prevalent at all levels, companies can improve their performance in the aforementioned areas. It is necessary to pinpoint the problems that, in the context of the Indian SME sector, would result in the approval of lean. Concentrating on Indian small and medium-sized business organizations, this chapter tries to identify the obstacles to lean 4.0 implementation and rank the factors that affect the system deployment in a business organization. The study emphasizes the difficulties Jharkhand's SMEs had implemented the lean method.

Now a days Small and Medium sized Enterprises (SMEs) are also interested in lean with smart manufacturing due to the increasing demand of the product and customer satisfaction in the developing country. In that situation, most of the SMEs addressed in the developing countries are facing lots of hurdles and challenges for converting their traditional manufacturing environment into a smart environment. The most important reason behind that digital transformation is the impact and the application of the recent technologies of the Industry 4.0 with the smart and autonomous systems in the SMEs. Maintenance is the most important activity of all the large and small-scale manufacturing industry in and around the world. The unexpected machine fault, causing machine down time and delay of maintenance actions leads to major losses in the industry. This study is to investigate the optimal decision support with smart maintenance management systems for SMEs. This study identified the most critical systems and their subsystems based on their performance. The most critical systems and their subsystems were monitored and implemented the IIoT based continuous real-time health monitoring approaches. Using artificial intelligence techniques and machine learning algorithms, the proposed methods have helped to predict the Remaining Useful Life (RUL) of those critical systems and their subsystems in the SMEs. The result of this study, maintenance personnel are scheduled and assigned to service actions at the right time automatically. Based on the optimal availability and RUL, it also identifies real-time health degradation and potential disturbances of critical subsystems.

Supply chain digitalization is increasingly critical to the competitiveness of micro, small, and medium enterprises (MSMEs). However, knowledge about it, including the technologies, enablers, and benefits from the perspective of these enterprises is limited, particularly in developing economies. Addressing this knowledge gap is essential, as around 95% of enterprises in these economies are MSMEs. This study aims to bridge this gap using Thailand as a developing economy case study, gathering relevant information through a survey (with 574 responses) and semi-structured interviews. The findings reveal that most Thai MSMEs use basic digital tools, while the adoption of intermediate and advanced technologies is moderate and low respectively. Consumer and competitor pressures primarily drive digitalization efforts, with the government playing a somewhat limited role. These (digitalization) efforts were found to have accelerated during the COVID-19 pandemic, despite budgetary constraints. From a benefits perspective, digitalization was found to improve business processes and enhance sales and customer satisfaction. These findings offer insights for emerging economy practitioners and policymakers to develop suitable policy interventions and support mechanisms for MSME digitalization. This study makes a novel and significant contribution to the literature, as no previous studies have comprehensively investigated digitalization in emerging economies or in Thailand.

Transforming a more adaptable and resilient supply chains in the pandemic (COVID-19) environment and understanding how similar future events can be managed has been a great challenge faced by the sustainable supply chain (SSC) processes. With this aim, we have identified studies in sustainable supply chain (SSC) literature to tackle this specific challenge. Hence this research intends to determine and enhance the Blockchain technology research and practise of sustainable supply chain (SSC), making it less vulnerable to environmental risks, such as the pandemic. The preliminary review of the papers has indicated four themes presented across the literature which are as follows: benefits of utilising Blockchain in optimising sustainable supply chains; the role of digitalisation in sustainable supply chains; challenges in supply chains and the impact of Blockchain; and encouraging sustainable supply chains development. Positioned in the literature review we established a framework conceptually to evaluate the context of a hybrid blockchain in the context of SSC arena.

The coronavirus illness epidemic of 2019 (COVID-19), the most devastating to world health, has affected not only demand but also supply. It has evolved into an economic shock that has had a significant impact on our daily lives and worldview. The economic fallout has posed significant challenges regarding raw materials and final product flow, thereby affecting manufacturing. In this paper, a simulation model of the susceptible-exposed-infectious-recovered (SEIR) network is built, which forecasts how infected and healed people will act. The graphs for each parameter are generated from the SEIR model output behavior, and the model is then used to identify the behavior of patients who are vulnerable, exposed, infected, and recovered. Analyzing the graph makes it simple to comprehend the behavior and prepare backup facilities, helping to reduce patient fatalities. With the help of the SEIR simulation model and its output behavior, an attempt has been made to establish a perfect supply chain mechanism in a different pandemic situation. These models can also be applied to predict the peak stage of any pandemic and improve the existing supply chain.

Digital twins, the virtual replicas of physical assets and processes are revolutionizing supply chain management by improving overall visibility, enabling accurate data-driven decision-making leading to operational efficiency. The chapter discusses the various definitions of digital twins, their types, and their potential benefits in specific to supply chain management are explored. Specific areas where digital twins have a significant impact on supply chain management such as how digital twins enhance supply chain visibility, increase efficiency and productivity, enhance collaboration and communication, and help in risk management and mitigation and contribute towards better customer satisfaction with lowered overall costs are detailed. Further, a ten-step process of implementing digital twins in existing supply chains and the associated challenges in implementation is presented in this chapter. To summarize, this chapter showcases the importance of digital twins as an enabler of digitalization in the supply chain by highlighting the potential benefits of using digital twins and how managers can unlock new opportunities for supply chain optimization and innovation in the digital era.

The commencement of Industry 4.0 is dramatically changing the method of working in the manufacturing sector and it is anticipated to lead the way for forthcoming intelligent industries with smart machines and interconnected networks to accomplish higher productivity, profitability, and operations flexibility without affecting the ecosystem and society. The adoption of Industry 4.0 are crucial to achieving sustainable development. This is because the technological developments in Industry 4.0 not merely encourage the sustainable initiatives to increase the financial benefits besides diminishing the impact on the environment and exerting an influence on the development of society nearby. But the Industry 4.0 adoption in the manufacturing sector to limit non-eco-friendly practices have substantial challenges to face. To address the above problem, this chapter attempts to elucidate the dimensions and requirements available from the literature for Industry 4.0 adoption in the manufacturing supply chain. The identified requirements from this chapter were categorized into five dimensions from the context of sustainable, organizational and technological. The requirements identified from this study can assist managers and policymakers for the adoption of Industry 4.0 especially helpful to organizations keen to enhance their sustainability from the aspect of competitive advantage.