The emergence of Industry 4.0, also referred to as the fourth industrial revolution, has entirely transformed how the industry or business functions and evolves. It can be attributed to its broadening focus on automation, decentralization, system integration, cyber-physical systems, etc. Its implementation promises numerous benefits in terms of higher productivity, greater volatility, better control and streamlining of processes, accelerated enterprise growth, sustainable development, etc. Despite the worldwide recognition and realization of Industry 4.0, its holistic adoption is constrained by the requirements of specific skills among the workforce. The personnel are expected to acquire adaptive thinking, cognitive and computational skills, predominantly in the area of information technology, data analytics, etc. Thus, the universities that laid the foundation for future talents or trends in society have to adapt and modernize the existing programs, facilities, and infrastructure. This reshaping of higher education in consonance with the vision of Industry 4.0 possesses its opportunities and challenges. There are, of course, a multitude of factors involved and they need a reasonable assessment to strategically plan this metamorphosis. Therefore, this work aims to explore and analyze the different factors that influence the progression and enactment of Industry 4.0 in universities for sustainable education. For this purpose, a systematic approach based on a questionnaire as well as a SWOT (strengths (S), weaknesses (W), opportunities (O), and threats (T)) integrated with the analytic hierarchy process (AHP) is adopted. The questionnaires are administered to university employees and students (or stakeholders) to assess their viewpoint, as well as to estimate the priority values for individual factors to be included in SWOT. The AHP is implemented to quantify the different factors in terms of weights using a pairwise comparison matrix. Finally, the SWOT matrix is established depending on the questionnaire assessment and the AHP weights to figure out stakeholders’ perspectives, in addition to the needed strategic scheme. The SWOT implementation of this research proposes an aggressive approach for universities, where they must make full use of their strengths to take advantage of the emerging opportunities in Industry 4.0. The results also indicate that there are fundamental requirements for universities in Industry 4.0, including effective financial planning, skilled staff, increased industrial partnerships, advanced infrastructure, revised curricula, and insightful workshops. This investigation undoubtedly underlines the importance of practical expertise and the implementation of digital technologies at the university level to empower novices with the requisite skills and a competitive advantage for Industry 4.0.

With the development of Industry 4.0 and the emergence of the smart factory concept, the traditional philosophy of manufacturing systems will change. The smart factory introduces changes to the factors and elements of traditional manufacturing systems and incorporates the current requirements of smart systems so that it can compete in the future. An increasing amount of research in both academia and industry is dedicated to transitioning the concept of the smart factory from theory to practice. The purpose of the current research is to highlight the perspectives that shape the smart factory and to suggest approaches and technical support to enable the realization of those perspectives. This paper fills this gap by identifying and analyzing research on smart factories. We suggest a framework to analyze existing research and investigate the elements and features of smart factory systems.

Many advanced product manufacturing approaches have been introduced in the market in recent years. Thus, it is critical to develop modern techniques which can effectively familiarize budding minds with the latest manufacturing procedures. In fact, the contemporary training methods and advanced education practices are crucial to uphold the interest of the new generation as well as to equip them with state-of the art systems. There is a need for innovative ideas and effective methodologies to inculcate the desired competency and prepare students for prospective manufacturing set ups. In the latest Industry 4.0 paradigm, visualization technologies, especially virtual reality, have been emphasized to sustainably train and educate young students. This work presents a technique for utilizing the leading visualization method based on virtual reality in product manufacturing. It aims to acquaint students with the prominent concept of Industry 4.0, the reconfigurable manufacturing system (RMS). The RMS has been a demanding topic for the novice and, most often, amateurs are not able to grasp and interpret it. Therefore, this paper outlines the various steps that can be useful for students in order to anticipate the RMS design, interact with it, understand its operation, and evaluate its performance.

Big data analytics (BDA) is one of the main pillars of Industry 4.0. It has become a promising tool for supporting the competitive advantages of firms by enhancing data-driven performance. Meanwhile, the scarcity of resources on a worldwide level has forced firms to consider sustainable-based performance as a critical issue. Additionally, the literature confirms that BDA and innovation can enhance firms’ performance, leading to competitive advantage. However, there is a lack of studies that examine whether or not BDA and innovation alone can sustain a firm’s competitive advantage. Drawing on previous studies and dynamic capability theory, this study proposes that big data analytics capabilities (BDAC), supported by a high level of data availability (DA), can improve innovation capabilities (IC) and, hence, lead to the development of a sustainable competitive advantage (SCA). This study examines the proposed hypotheses by surveying 117 manufacturing firms and analyzing responses via partial least squares–structural equation modeling (PLS-SEM) statistical software. Findings reveal that BDAC relies significantly on the degree of DA and has a significant role in increasing IC. Furthermore, the analysis confirms that IC has a significant and direct effect on a firm’s SCA, while BDAC has no direct effect on SCA. This study provides valuable insights for manufacturing firms to improve their sustainable business performance and theoretical and practical insights into BDA implementation issues in attaining sustainability in processes.

Lean manufacturing is one of the most popular improvement agents in the pursuit of perfection. However, in today’s complex and dynamic manufacturing environments, lean tools are facing an inevitable death. Industry 4.0 can be integrated with lean tools to avoid their end. Therefore, the primary purpose of this paper is to introduce an Industry 4.0-based lean framework called dynamic value stream mapping (DVSM) to digitalize lean manufacturing through the integration of lean tools and Industry 4.0 technologies. DVSM with its powerful features is proposed to be the smart IT platform that can sustain lean tools and keep them alive and effective. This paper specifically tackles the scheduling and dispatching in today’s lean manufacturing environments, where the aim of this research is developing a smart lean-based production scheduling and dispatching model to achieve the lean target through optimizing the flow along the VSM and minimizing the manufacturing lead time. The developed model, called the real-time scheduling and dispatching module (RT-SDM), runs on DVSM. The RT-SDM is represented through a mathematical model using mixed integer programming. Part of the testing and verification process, a simplified IT-based software, has been developed and applied on a smart factory lab.

The emerging demands of industry for developing the novel materials with superior mechanical properties have successfully resulted in the development of distinct materials such as Al-matrix composites. Among these composites, newly developed Al6061-7.5% SiC holds promising mechanical characteristics. But, the SiC reinforcement in the Al-matrix makes the machining of this composite challenging, thus posing a serious concern regarding its effective utilization. In this research, high-speed wire electric discharge machining (WEDM) was employed for the precise machining of a squeeze casted Al6061-7.5% SiC composite. The cutting performance of the WEDM was assessed in terms of roughness (S_R), cutting rate (C_s) and kerf width (K_W). Experimentation was performed according to the response surface methodology. The experimental findings were thoroughly investigated using statistical, optical and scanning electron microscopic (SEM) analyses. It has been revealed that the voltage is most influential/contributing parameter (having a percentage contribution of 25%) for controlling theS_Rduring WEDM of Al6061-7.5% SiC composite, whereas for theC_SandK_W, pulse and current are the major contributing control variables with percentage contributions of 90% and 84%, respectively. At low magnitude of both current and voltages, the surface quality is improved up to 33.3%. The SEM and optical microscopic evidences reveal shallow craters, small size melt re-deposits and micro globules on the machined surface at lower settings of both the said variables. Contrarily, for achieving higher cutting speed, high values of current and voltage along with low pulse are deemed essential. In case ofK_W, low magnitude of current and voltage along with smaller pulse yields 20% reduction in the kerf width. The analyses revealed the conflicting nature of the studied output responses (S_R,C_sandK_W). Therefore, multi-objective genetic algorithm (MOGA) was used to find a parametric combination. The best combination of WEDM input parameters found is current = 3 A, voltage = 84.999 V and pulse = 10 mu. This combination gives a minimumS_Rof 5.775 μm with aK_Wof 0.3111 mm at aC_Sof 5.885 mm/min. The suitability of the MOGA-proposed parametric combination was witnessed through confirmation trials. Furthermore, the parametric effects have also been mathematically quantified with respect to the defined machinability parameters.