Hindawi Mathematical Problems in Engineering Volume 2022, Article ID 6424869, 24 pages https://doi.org/10.1155/2022/6424869 Research Article A Novel Smart Production Management System for the Enhancement of Industrial Sustainability in Industry 4.0 Varun Tripathi,1 Somnath Chattopadhyaya,2 A. K. Mukhopadhyay,3 Suvandan Saraswat,4 Shubham Sharma ,5,6 Changhe Li,7 S. Rajkumar ,8 and Fasika Bete Georgise9 1 Department of Mechanical Engineering, Accurate Institute of Management & Technology, Greater Noida, Uttar Pradesh, India Indian Institute of Technology (ISM), Dhanbad, India 3 Department of Mining Machinery Engineering, Indian Institute of Technology (ISM), Dhanbad, India 4 Department of Mechanical Engineering, JSS Academy of Technical Education, Noida, India 5 Department of Mechanical Engineering, IK Gujral Punjab Technical University, Main Campus, Kapurthala 144603, India 6 Department of Mechanical Engineering, University Centre for Research and Development (UCRD), Chandigarh University, Mohali 140413, Punjab, India 7 School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China 8 Department of Mechanical Engineering, Faculty of Manufacturing, Institute of Technology, Hawassa University, Hawassa, Ethiopia 9 Department of Industrial Engineering, Faculty of Manufacturing, Institute of Technology, Hawassa University, Hawassa, Ethiopia 2 Correspondence should be addressed to Shubham Sharma; shubhamsharmacsirclri@gmail.com and S. Rajkumar; rajkumar@ hu.edu.et Received 28 October 2021; Accepted 24 February 2022; Published 13 April 2022 Academic Editor: Kuei-Hu Chang Copyright © 2022 Varun Tripathi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In industry 4.0, shop floor management teams are increasingly focused on developing an unprecedented strategy to avoid financial losses and address the challenges and problems encountered in operations management. In the present scenario, the management teams use various process optimization approaches for operational control, including lean manufacturing, smart manufacturing, the internet of things, and artificial intelligence. The process optimization approach is used to maximize productivity within limited constraints on the shop floor. The present research aims to develop a smart production management system and suggest an efficient process optimization approach to enhancing industrial sustainability by identifying problems and challenges encountered in the complex shop-floor conditions in industry 4.0. The developed production management system has been prepared by classifying the challenges and problems found in the previous research work and organizing brainstorming sessions. The developed management system has been validated by a comprehensive investigation of a case study of an earthmoving machinery manufacturing unit. The analysis showed that the developed system could enhance operation excellence and industrial sustainability in industry 4.0 by optimizing the utilization of resources on the shop floor within limited constraints. The authors of the present article strongly believe that the developed production management system will improve operational excellence and would be beneficial for industry personnel and researchers in controlling operations management in shop floor management of heavy machinery manufacturing, including industry 4.0. 1. Introduction In industry 4.0, the advantages of the process optimization methods have attracted industry persons and young researchers’ attention in the shop floor management domain. Shop floor management, as a key component in industries, is used to maintain industrial sustainability and the stability of resource availability [1]. Process optimization approaches are used to improve productivity with limited resources. Process optimization thinking has focused more on 2 Mathematical Problems in Engineering increasing the efficiency of production processes [2]. At present time, several methods are used for process optimization in industries including lean manufacturing, smart manufacturing, internet of things, and artificial intelligence. The process optimization approach is the prevalent strategy in industry 4.0, and it is implemented to improve productivity and optimization of resources [3]. In addition, another author argued that the process optimization approach is applied to meet customer demand in terms of the product as the process optimization approach improves productivity by eliminating waste to achieve the industry’s goal [4]. Striving this goal also provides a number of other benefits, which may include production time reduction and increased quality of the production processes, leading to a higher satisfaction level of customers in terms of product. Figure 1 shows the objectives of process optimization approaches in industry 4.0. According to Tripathi et al. [5], process optimization methods are implemented to improve production efficiency. The main advantages of the process optimization approach in industry 4.0 are below: (1) To achieve a sustainable production management system (2) To maximize the production rate and production flexibility within available resources (3) To improve the flexibility, agility, customization, and adaptability in industry 4.0 (4) Ease of implementing industry 4.0 technologies on the production shop floor to the industry individuals To evaluate the efficiency of production processes, overall production processes and activities are categorized. The categorization helps know if the activities involved contribute to the production process; if so, the activity adds value to the production and is known as value-added activities; and if not, the activity is non-value-added (NVA) and known as non-value-added activities (NVAA) [6]. The elimination of non-value-added activities continuously improves production processes and makes it easier to control production performance for shop floor management [7]. The shop floor management team encourages the synergistic implementation of the Lean concept with industry 4.0 technologies to eliminate waste and enhance industrial sustainability. For this, new models and strategies have been developed by previous researchers to strengthen operational performance and to know the present condition of industry 4.0 technologies. Sony et al. [8] proposed an integration model of industry 4.0 and lean management. The model was developed by reviewing previous literature, and in the study, vertical, horizontal, and end-to-end engineering models were integrated with the lean management methodology. This study provided 15 research propositions to advance the integrative mechanism of industry 4.0 and lean management for enhancing financial profitability by better utilization of resources. Tortorella et al. [9] examined the role of industry 4.0 technologies on the relationship between operational performance and lean production within Brazil. The study has organized a survey on implementing lean and industry Enhancement in operational excellence on the shop floor Complete elimination of wastes on the shop floor Optimize the utilization of resources Quality enhancement Figure 1: Aim of process optimization approach in industry 4.0. 4.0 technologies by performance indicators, including productivity, quality, delivery, safety, and inventory. The collected data has been collected through questionnaires and analyzed by multivariate analysis and contingency theory. The result of the study revealed that entirely technological adoption could not be able to enhance operational performance. However, lean helps in process improvement and support in controlling operation management in industry 4.0. Kamble et al. [10] combined the empirical and exploratory research design to develop a framework for identifying and validating the performance measure for evaluating smart manufacturing systems in Indian small, medium, and microenterprises of auto-components. The data was collected by questionnaire on ten performance dimensions: flexibility, cost, quality, integration, time, optimized productivity, computing, real-time diagnosis and prognosis, and social and ecological sustainability. The result of the study revealed that the proposed performance system was proved able to evaluate the smart manufacturing system and its investments. Amjad et al. [11] developed a comprehensive implementation framework that integrated lean, green manufacturing, and industry 4.0 effectively. The developed framework was validated by implementing it in an autoparts manufacturing firm. The result of the study showed that reduced the value-added time, lead time, non-valueadded time, and greenhouses gases emission effectively by 24.68%, 25.60%, 56.20%, and 55%, respectively. The developed framework was able to achieve optimized and cleaner production with automation-based rapid and environmentally conscious manufacturing. Tortorella et al. [12] investigated the relationship between lean production and industry 4.0 through a survey organized with 110 different sizes and sectors of Brazilian manufacturing companies. The data were collected by a questionnaire and analyzed with the help of multivariate analysis. The result of the study indicates that lean production was positively associated with industry 4.0 techniques, and their integrated implementation can lead to enhance performance improvements. Dahmani et al. [13] developed an eco-design industry 4.0 framework for investigating the relationship between Mathematical Problems in Engineering industry 4.0 strategies and lean eco-design. The developed framework was based on the synergetic use of eco-design, industry 4.0, and lean design. The study revealed that the developed framework was able to provide cleaner products using suitable processes to help manufacturers design products and fulfill customer expectations. Varela et al. [14] proposed a structural equation model to quantitatively measure the effect of lean manufacturing and industry 4.0 on sustainability. The data was collected by 252 questionnaires obtained from industrial companies located in the Iberian Peninsula. The result of the study showed that it was not conclusive that lean manufacturing was correlated with any pillars of sustainability including environmental, economic, and social, whereas industry 4.0 showed a strong correlation with sustainability pillars. Chiarini and Kumar [15] discussed the integration between principles and tools of lean six sigma and industry 4.0 technologies. The data were collected through direct observations and interviewing manufacturing managers of ten Italian manufacturing companies. The result showed that Lean Six Sigma could achieve effective outcomes from industry 4.0 applications. However, the integration needs reinvented mapping tools and implies an end-to-end integration and vertical and horizontal integration. Saxby et al. [16] assessed how well lean management supports continuous improvement in industry 4.0. Semistructured interviews of five quality specialists in manufacturing were used to collect data and opinions for lean management and industry 4.0. The result showed that lean management could integrate new technologies for effectively continuous support in industry 4.0. Ciano et al. [17] developed a framework on the relationship between industry 4.0 technologies and lean techniques. The developed framework focused on six areas: manufacturing equipment and processes, workforce management, shop floor management, customer relationship, supplier relationship, and new product development. The data were collected through semistructured interviews, internal documents, websites, and annual reports on lean and industry 4.0 implementation projects. Implemented the developed framework in eight sectors, and it has been revealed that as a result, industries’ insights strongly believe that the industry 4.0 technologies could empower lean management techniques. Ghaythan et al. [18] examined the impact of the integration of lean manufacturing and industry 4.0 technologies on the sustainable performance of industries. The data required for the analysis were collected from questionnaires collected by 112 petrochemical and plastics industries. The analysis results showed that lean manufacturing and industry 4.0 have a positive impact on sustainability performance. Rossini et al. [19] investigated the impact of interrelation between the industry 4.0 technologies and lean production on the improvement level of operational performance in European manufacturers. The data has been collected by a survey conducted with 108 European manufacturers and analyzed through multivariate technique. The analysis identifies the interrelation according to different contextual factors, including lean production 3 implementation experience, business operating model, type of ownership, and company size. The finding suggests that European manufacturers should apply concurrent lean production of industry 4.0 to achieve a high degree of process improvement. However, only a few studies have been done on the methodology developed to identify an efficient process optimization technique for enhanced productivity within restricted resources. Here, constraints mean the availability of resources for shop floor management. To improve the process, it is necessary to get rid of the challenges and difficulties faced by the industry [20]. Typically, a production system is developed to identify the challenges and difficulties associated with production in the industry, which helps know where to get rid of the challenges and difficulties of production have to improve [21]. Through this system, information about the processes is collected so that the level of the processes can be evaluated and the production can be improved. The objective of the present article is to develop a smart production management system to identify the problems and challenges encountered on the production shop floor in industry 4.0 and suggest an efficient process optimization approach for industrial sustainability. The present article provides a systematic functional approach to provide a sustainable shop floor management system and to further enhance operational excellence within limited constraints. The proposed methodology has been described in Figure 2. There are five stages in the proposed methodology. The first stage focuses on layout draw and preparing a checklist of ergonomics issues by considering shop floor factors, including workplan, working environment, and workload distribution according to different departments. In the second stage, the production conditions are analyzed by calculating different parameters and resources condition by considering shop floor factors, including time, cost, worker, and shop floor congestion in various departments. The third stage reviews the machinery conditions and availability by observing and analyzing different departments and using advanced condition monitoring systems to improve machinery efficiency and utilization. In the fourth stage, the production shop floor planning is modified by brainstorming, meeting, conversations with production management teams, and using the internet of things, digitization, and data acquisition system. Finally, in the fifth stage, production shop floor improvement is validated by comparing proposed, observed, and previous results by considering product performance, approach, throughput, and shop floor management in different departments. As this methodology comprises nineteen different factors and each and every respective factor is crucial while implementing the same in various departments likes in inspection, quality, production, and design in industry 4.0, for example, the work plan sector is highly significant in the inspection department, quality department, and industry 4.0 department. The same other factors are very much prominent the same in the different departments that will provide a sustainable shop floor management system within available resources. Mathematical Problems in Engineering Proposal of ideal production shop floor Validation of enhancement on Production shop floor Throughput Shop floor management Comparison between proposed, observed, and previous researches results Approach Internet of things Identify optimum solution Data acquisition system Modification on shop floor Digitization Condition based sensor Analysis on machinery utilization Machinery efficiency Machinery availability Arrangement of machinery Machinery condition Congestion on shop floor Analyze resources condition Organization meeting Evaluation of machinery health & availability Worker Cost Calculation of production parameters Time Workload distribution FACTORS Working environment Checklist of ergonomics issue Draw layout Work plan STEPS Production analysis Gemba walk STAGES Product performance 4 DEPARTMENTS Inspection Design Production Quality Industry 4.0 Figure 2: Proposed methodology. 2. Development of Novel Research Methodology The literature review demands developing a research methodology to effectively implement the developed production management systems. The developed research framework helps implement the developed production management system and identification of NVAA so that appropriate action can be taken as per the production condition [22]. The present research methodology has been developed from a thorough analysis of factors of the production management system. Figure 3 illustrates the steps followed in the developed research methodology in the present research work. The development of research methodology is a systematic way to implement process optimization methods that the elimination of waste in production can be possible. In previous researches, few researchers developed a methodology to improve the effectiveness of process optimization methods for shop floor management. In methodologies, emphasis was laid on determining the consumption of resources according to production and improving the production process. The following features distinguish the developed methodologies and prove to be important for the implementation of process optimization techniques. (i) The developed methodology helps understand the reason of source of waste and investigated impact of working production processes on productivity in industry 4.0 (ii) The developed methodology provides a systemic illustration of material-flow, process-flow, time parameters (CT, LT, IT, and TT) to control the uncertainty in an advanced production environment (iii) The developed methodology identifies problems and challenges by systematic analysis and helps provide an efficient action plan at beginning of production in industry 4.0 (iv) The developed methodology can be applied to any type of shop floor management in industry 4.0 and control overall process activities within available resources 3. Developments on the Process Optimization Approaches for Industrial Sustainability in Complex Environment Researchers have proposed several methodologies of process optimization approaches for industrial sustainability in a complex environment. The complex environment includes discrepant working conditions, unexpected demand, over workload, lack of shop floor area, continuous working, and nonstandardization of work [23]. Process optimization concept originated from the Toyota production system and was developed by Taiichi Ohno’s notion of “reduce time and cost by eliminating waste.” Lean manufacturing, smart manufacturing, internet of things, and artificial intelligence are process optimization approaches and implemented for the elimination of waste. Waste means unnecessary activities performed in production that do not increase the product value [24]. Wastes have been classified into eight categories: overprocessing, overproduction, transportation, unnecessary motion, waiting, defects, and unutilized skill [25]. The process optimization approach is mostly preferred in industry 4.0 because it is capable of achieving production improvements with confined assets on automated production lines [26] and serves the purpose of process optimization while other techniques can be used in limited Mathematical Problems in Engineering 5 2 3 1 4 5 1. Review of previous research work Discussion on previous research work on process optimization approach for production shop floor management. 2. Development of production management system Proposed a production management system to identify challenges and problems in industry 4.0 3. Deployment of proposed theory Implementation of developed theory in a case example of industry 4.0 4. Observation of production shop floor Observation of production processes and activities by gemba walk, virtual record, data acquisition system and discussion with industry persons. 5. Validation of developed system Industrial sustainability achieved in the production management, and comparative analysis between present and previous research work results. Figure 3: Research methodology. production conditions. Process optimization as an approach uses limited constraints that include the use of machinery, shop floor area, investment, production, process planning, and time [27]. The objective of process optimization is the minimization of waste in terms of time, manpower, machinery, and shop floor area. The concept of process optimization has been implemented by different techniques in industry 4.0; the techniques used in previous decades and in industry 4.0 are illustrated in Figure 4. Lean manufacturing (LM) is a prevalent approach and has been used in most cases as found in the literature review. LM is used for the identification of NVAA by observation of shop floor conditions [28]. The cost incurred in these activities can neither be added to the production value nor paid by the customer [29]. Therefore, eliminating these activities from production is the only solution that is carried out by process optimization techniques [30]. It has been observed that LM is able to provide production enhancement in case of a manual assembly line, but if production line becomes semiautomated or fully automated, it does not work and the production management team must implement new techniques like integration of techniques. In industry 4.0, production management team members emphasize on development of novel techniques and approaches for productivity enhancement in automated production lines. To accomplish this, several research works have been done in different automated production conditions, and smart manufacturing was found efficient in production management on the shop floor [31]. Smart manufacturing uses various techniques to the control management system in industry 4.0, including internet of things, digitalization, asset tracking system, artificial intelligence, and integration of smart manufacturing with other techniques such as lean, machine learning, and simulation. The complexity of the production situation has been increasing steadily over the past decade. Hence, in the current scenario of industry 4.0, more attention is paid to smart manufacturing techniques and hybrid approach with smart concept by the members of the management team. Because smart manufacturing has been found most suitable and efficient approach in productivity enhancement in previous research works. As yet, several process optimization techniques have been implemented to improve the production on the shop floor [32]. An attempt has been made to tabulate all these techniques by Table 1 and presented a description of the application area and the results obtained. Figure 5 shows the techniques implemented in previous research for coping with problems and challenges. Table 2 illustrates the contribution of process optimization techniques in previous research works. In research work over the past decades, authors have praised lean manufacturing for improving production, and other process optimization methods such as Kaizen and total quality management have also been used by some authors [22, 49, 71, 72, 85]. But it has been observed that smart manufacturing becomes the most preferred and prevalent approach in the past five years because of its higher adaptability in industry 4.0. This discussion of previous research endorses the utility of lean and smart manufacturing for process optimization. Researchers are skeptical about implementing procedures presented to address production challenges using lean and smart manufacturing, as the studies presented so far have proposed a specific approach and applied it only in confined situations. The authors of the present study are reviewing the methodologies presented in select previous studies to clarify the message. Following observations and research gap areas are identified: (1) All the researchers that have developed the methodology of process optimization method applications in the manufacturing environment concluded that by improving the workflow on the shop floor, one can improve productivity and also concluded that this is not a generalized conclusion that can apply in all types of production conditions. (2) There is no clarity in research on how to identify production challenges and problems in industry 4.0. 6 Mathematical Problems in Engineering Process optimization approach In previous decades In industry 4.0 Lean manufacturing Hybrid approach Smart manufacturing Hybrid approach Value stream mapping Lean-kaizen Internet of things Lean smart manufacturing Kaizen Lean six sigma Artificial intelligence Machine learning-smart manufacturing Total quality management TQM-TPM Asset tracking system Simulation based smart manufacturing Figure 4: Process optimization techniques before Industry 4.0 (left) and under the framework of Industry 4.0 (right). Therefore, the drawbacks of research to improve production through production mapping are clearly shown. The objective of the present article is to develop a smart production management system to identify the problems and challenges encountered on the production shop floor in industry 4.0 and suggest an efficient process optimization approach for industrial sustainability. The research objective raised questions for the study are as follows: (i) How to demonstrate the key message of process optimization through an efficient technique using a methodology for reducing wastes influencing productivity in industry 4.0 (ii) How to identify wastes in the industry 4.0 production environment by applying the proposed production management system 4. Scientific Gap in the Literature and Conceptual Paradigm Objectives of the Current Research Work In industry 4.0, the production team members face several problems in enhancing operational excellence due to complexity in the shop floor environment [92]. It has been observed that if production teams ignore different circumstances and issues that can be developed by complexity in the shop floor environment, it results in considerable losses in financial profitability [93]. A comprehensive review of the production management systems developed in previous research has been found that the developed systems were not efficient at controlling operational excellence in all types of shop floor environments and could only increase productivity in certain limited production conditions. Therefore, the production management teams emphasize developing an intelligent system to eliminate the problems faced in different circumstances because of the complex shop floor environment [94]. The developed system helps provide a guideline for selecting a suitable approach for enhancing industrial sustainability in industry 4.0. The proposed smart system has been developed by various brainstorming sessions on problems and challenges faced in production shop floor management, including industry 4.0. The developed system provides a guideline for management teams to understand the actual shop floor conditions and help make an action plan at the beginning of production processes to achieve industrial sustainability enhancement. The developed smart production management system has been validated by enhancing industrial sustainability in the case of industry 4.0. It has been found that the developed system provided improvement in production time, worker’s contribution, machinery utilization, operational excellence, and financial profitability by 17%, 18%, 28%, 35%, and 45%, respectively. The present research aims to develop a smart production management system to identify the problems and challenges encountered in production on the shop floor in industry 4.0 and suggest an efficient process optimization approach for industrial sustainability. The developed smart management system can improve operational excellence in any complex shop floor environment within confined assets and the efficient to enhance sustainability in all industries, including automobile, mining machinery, mining, defense, aerospace, pharmaceutical, chemical, and so on. The authors of the present research article that the developed system would be preferred in shop floor management in industry 4.0 because it can implement a suitable approach by identifying exact problems in Mathematical Problems in Engineering 7 Table 1: Description of previous research work, technique, and results. Results Author(s) Year Östlin and Ekholm [33] 2007 Techniques Value stream mapping (VSM) Improvement/reduction Setup time WIP (work in process), inventory, production lead time (LT), processing time Optimized resources Manpower Seth and Gupta [34] 2007 VSM, Kanban Gati-Wechsler and Torres [35] 2008 TPM, 5S, JIT, Kanban Sahoo et al. [36] 2008 VSM, Taguchi’s method 2009 Cellular manufacturing system, VSM 2009 VSM 2010 VSM 2012 VSM 2012 2013 2013 2013 Kanban VSM, radiofrequency identification VSM VSM Setup time, WIP inventory level, defect Lead time (LT), setup time, waiting time, material handling time LT, processing time, WIP Cycle time (CT), idle time (IT), WIP inventory, defect, uptime (UT) Production LT, work-in-process inventory Inventory LT, waiting time Productivity, LT, defect Productivity Jeyraj et al. [45] 2013 VSM Takt time (TT), LT Das et al. [46] Barbosa et al. [47] Kumar et al. [48] Ismail et al. [49] Singh et al. [50] Santos et al. [51] Mwanza and Mbohwa [52] Choi et al. [53] Rohani and Zahree [54] Esa et al. [55] Lu and Yang [56] Tyagi et al. [57] Choomlucksana et al. [58] Naqvi et al. [59] Andrade et al. [60] Thomas et al. [61] Garre et al. [62] Asif and Singh [63] Dadashnejad and Valmohammadi [64] Diaz et al. [65] Méndez and Rodriguez [66] Nagadi et al. [67] 2014 2014 2014 2014 2014 2015 VSM, SMED, Kaizen LM, TPM VSM, method study Lean Six Sigma, VSM LM 5S, VSM, Kaizen, Setup time, WIP inventory Cycle time (CT), product quality LT CT Cycle time, downtime Productivity Manpower, machine, layout Cost Shop floor area Cost Cost Machine, manpower Shop floor area Layout Layout NA Cost Manpower 2015 Total productive maintenance Downtime Machinery 2015 2015 2015 2015 2015 Smart manufacturing VSM, kaizen, Kanban, 5S SMED VSM, Kaizen VSM, Gemba walk Production enhancement LT Setup time Production, CT LT, quality Cost NA Manpower Manpower Cost 2015 Kaizen, 5S, poka-yoke Processing time Cost 2016 2016 2016 2017 2017 LM, Kaizen, 5S VSM Lean Six Sigma 5S, SMED Internet of things LT, production LT, production time Production time Productivity, total cycle time (TCT) Productivity Layout, cost Manpower Cost Layout Cost 2017 VSM, overall equipment effectiveness Productivity, quality Machine 2017 VSM LT, CT, quality Manpower 2017 TPM, overall equipment effectiveness Productivity Manpower 2017 Smart manufacturing Production time Gazra-Reyes et al. [68] 2018 VSM, TPM, just in time, Kaizen Productivity, quality Gijo et al. [69] Stadnicka and Litwin [70] Cannas et al. [71] 2018 Lean Six Sigma Defect Machinery Energy consumption Cost 2018 VSM, system dynamic analysis Work-in-progress Machine 2018 Kaizen, Yamazumi chart, standardization Pattanaik and Sharma [37] Singh Sharma [38] Vinodh et al. [39] Rahman and Al-Ashraf [40] Rahman et al. [41] Chen et al. [42] Bertolini [43] Longhan et al. [44] Kumar et al. [72] Inventory Performance Lead time, cycle time, inventory VSM, Kaizen, poka-yoke, 5-why, brainstorming level, productivity, quality, rework 2018 technique elimination Cost NA NA Layout Manpower Manpower NA Machine, manpower, cost 8 Mathematical Problems in Engineering Table 1: Continued. Results Author(s) Year Suhardi et al. [73] 2019 Saqlain et al. [74] Masuti and Dabede [75] Ramani and Lingan [76] Priya et al. [77] Liao and Wang [78] Shou et al. [79] 2019 2019 2019 2019 2019 2019 Techniques VSM, 5W1H (what, who, where, when, how, why), ECRS (eliminate, combine, rearrange, and simplify) principle Internet of things VSM VSM Lean Six Sigma, just in time Internet of things VSM Improvement/reduction Optimized resources Lead time Manpower Production time Cycle time, lead time Productivity Defect, quality Production time Production time Machinery, cost Cost Cost NA Cost NA Machine, cost, shop floor Machinery, cost Cost, shop floor Layout Cost Machine, shop floor Machinery, cost Cost, manpower Machinery, cost Cost Cost Cost Sharma et al. [80] 2019 5S Productivity Abubakr et al. [81] Torres et al. [82] Prasad et al. [83] Mittal et al. [84] 2020 2020 2020 2020 Smart manufacturing Smart manufacturing VSM, 5S, Kaizen, Kanban, SMED Smart manufacturing Cause and effect diagram, failure mode effect analysis Smart manufacturing VSM, SMED, cellular layout Internet of things Internet of things Lean and industry 4.0 technologies Lean and internet of things Productivity Production time Productivity Production Balamurugan et al. [85] 2020 Chien and Chen [86] Amrani and Ducq [87] Frankό et al. [88] Gaspar et al. [89] Reyes et al. [90] Vlachos et al. [91] 2020 2020 2020 2021 2021 2021 the complex production environment. Figure 6 shows the current model of shop floor management that was being used by the production management team. 5. Proposed Production Management System Industries face several challenges and problems in controlling the shop floor management. Therefore, to identify these problems and challenges, a smart production management system has been developed in the present research. In the production management system, the challenges and problems are classified in observed forms. The developed production management system has been evaluated by problems found in prior research and applied techniques. Table 3 classifies the challenges commonly encountered by industry in production and the problems associated with them. Most of the researchers appreciated lean and smart manufacturing and acknowledged problems and challenges complexities in an industrial environment. Authors mostly suggested lean for improvement in production and shop floor management in the last decades. The smart manufacturing experience of researchers in production improvement is remarkably diverse and endorsed the reliability of smart manufacturing in shop floor management. To get rid of the challenges and problems faced at the production shop floor, in the current research work, an extensive literature review was done on the work done in the production area and industry 4.0, and a production management system has been developed. The challenges and problems were categorized to prepare the developed system and the process optimization techniques applied in the Productivity Production time Setup time, cycle time, defect rate Production time Production time Productivity Productivity previous research were brainstormed for them. A management system has been developed from the findings obtained from the brainstorming analysis. The authors are strongly believed that the developed production management system would be capable of providing higher throughput in all types of conditions in industry 4.0. The developed smart production management system has been illustrated in Figure 7. The proposed smart production management system follows four steps and is developed by multiple brainstorming sessions organized at the different department levels and using previous research works. 5.1. Experimentation of Proposed Theory in a Case Study for Industry 4.0. The present study has been carried out in an earthmoving machinery manufacturing unit in India. The industry currently has 52 people including managers, supervisors, and employees and operates in a single shift with 10 working hours. The present industry manufactures several types of machinery such as the skid-steer loader, cranes, and truck-mounted. This research work deals with skid-steer loader production processes. Skid-steer loader is an earthmoving machinery, and it is based on cutting-edge technology. The industry is facing stiff competition due to high manufacturing cycle time. Typically, production orders are received intermittently and mostly in small quantities. The production lead time and quality are the main factors to face the competition of the industry. When the industry is faced with problems such as high costs and excessive lead time due to wastage, the level of production becomes exceedingly difficult to control. Production management is therefore vigilant about these problems and Mathematical Problems in Engineering 9 15. Taguchi's 16. Method study method 2% 2% 14. TQM 2% 17. LSS 2% 18. SMED 2% 0% 19. 5W1H 2% 20. Kanban 2% 1. VSM 33% 13. Stadardization 2% 12. SLP 2% 11.5S 2% 10. Fish bone diagram 2% 9. Time study 2% 8. FMEA 2% 2. Smart manufacturing 13% 7. ECRS 2% 4. SOP 6% 6. TPM 4% 3. Internet of things 10% 5. Kaizen 4% Figure 5: Implementation of process optimization techniques in previous researches. Table 2: Contribution of process optimization techniques in previous research works. S. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Process optimization techniques VSM Smart manufacturing Internet of things SOP Kaizen TPM ECRS FMEA Time study Fish bone diagram 5S SLP Standardization TQM Taguchi’s method Method study LSS SMED 5W1H Kanban emphasizes the implementation of process optimization methods for production on the shop floor. The problems found in controlling production in the current industry are illustrated by Figure 8. 5.2. Observation of the Production Shop Floor in Present Case Report. According to Womack and Jones [95], production Contribution (%) 33 13 10 6 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 planning plays an important role in achieving customer needs in the context of the product. For this, it is necessary to observe the precise production information and conditions of the industry. So those necessary arrangements can be made to deal with them. The basic production information has been collected by observation of shop floor, questionnaire, data acquisition system, interviews, and discussion with industry persons. The observation of the shop floor has 10 Mathematical Problems in Engineering Shop floor layout Aim Tasks • Sustainable production planning. • Utilization of resources. • Minimization of energy consumption. • Waste elimination. • Productivity enhancement in complex shop floor management system. • Achieve operational excellence within limited constraints. • Enhancement in financial profitability within available resources Production/day 8 units Manpower 52 workers Production time Different department shop floors Available time 520 minutes Break time 40 minutes Reasons for waste production Assembly 1045 minutes Fabrication 990 minutes Painting 2190 minutes Roll off and hot testing 1945 minutes Lack in allotment of operator for material handling. Cabin installment and electric gauge assembly 715 minutes Manual assembly lines. Outsourcing of services. Manual power supply system. Traditional work allocation. Figure 6: Current shop floor management model. Table 3: Categorization of problems and challenges faced in production. S. No. 1 2 3 Challenges Productivity Quality Time Problems Categorization Poor layout P1 Absenteeism P2 Higher downtime P3 Unskilled worker Communication gap More workstation P4 P5 P6 Work overloaded P7 High setup time Lack in worker sill P8 Q1 Lack of standard Q2 Defect Workload distribution Inventory Q3 Q4 Lack in production planning T1 Downtime Machinery utilization Wrong workload distribution Unsystematic layout Absentees Q5 T2 Author and reference Implemented shop floor number management technique Rahman and Al-Ashraf [40]; VSM, smart manufacturing Saqlain et al. [74] Singh and Sharma [38] VSM, 5S Santos et al. [51]; Chien and VSM, smart manufacturing Chen [86] Jeyaraj et al. [45] VSM Esa et al. [55] Standard operating procedure (SOP) Rohani and Zahree [54] VSM, Kaizen Single minute exchange of die Garee et al. [62] (SMED) Esa et al. [55] SMED Seth and Gupta [34] VSM Rahman and Al-Ashraf [40]; SOP, internet of things Asif and Singh [63] Pattanaik and Sharma [37] VSM Suhardi et al. [73] 5W1H technique, ECRS principle Masuti and Dabede [75] VSM Time study, fishbone diagram, Balamurugan et al. [85]; Priya et al. [77]; Gaspar et al. failure mode effect analysis (FMEA), internet of things [89] Barbosa et al. [47] Total productive maintenance T3 Jeyaraj et al. [45] VSM T4 Chen et al. [42] VSM T5 T6 Systematic layout planning Standardization Unskilled worker T7 Communication gap Lack in material handling T8 Ali Naqvi et al. [59] Amrani and Ducq [87] Gati-Wechsler and Torres [35] Esa et al. [55] T9 Frankó et al. [88] Total quality management (TQM) SOP Asset tracking system Mathematical Problems in Engineering 11 Table 3: Continued. S. No. 4 5 Challenges Implemented shop floor management technique Total productive maintenance (TPM), smart manufacturing C2 Rahman et al. [41] Lean manufacturing, Kanban C3 C4 Sahoo et al. [36] Singh and Sharma [38] Kumar et al. [72]; Ramani and Lingan [76]; Liao and Wang [78] Östlin and Ekholm [33]; Liao and Wang [78] Bertoloini [43] Ismail et al. [49]; Gijo et al. [69] Longhan et al. [44] Das et al. [46]; Mittal et al. [84] Taguchi’s method VSM Categorization Downtime C1 Lack in worker participation Defects Inventory Cost Author and reference number Mwanza and Mbohwa [52]; Chien and Chen [86] Problems Unplanned workflow C5 Higher lead time L1 Defects L2 Standard L3 Quantity L4 Design L5 Customer satisfaction level in terms of product 1 VSM, method study, internet of things VSM, internet of things VSM Lean Six Sigma VSM VSM, SMED, Kaizen, smart manufacturing 2 Observation Analyze Shop floor visit and data acquisition system Production analysis program and data analytics Validation of smart production system Modification in shop floor management Production enhancement in a case study of industry 4.0 Lean and smart manufacturing 4 3 Figure 7: Proposed smart management system for industry sustainability in industry 4.0. Problem Higher production time Communication gap between workers . Limited resources availability Higher downtime Higher production cost Figure 8: Observed problems of present industry. been used to understand the working condition of production on the shop floor. The discussion with workers, supervisors, and managers has been used for the analysis of production information and shown by Table 4. 5.3. Analyze Present Production Shop Floor Management. The working condition map of production processes is shown in Figure 9. The working map demonstrates that the manufacturing orders are being received monthly and the 12 Mathematical Problems in Engineering Table 4: Details of production collected from the observation of the shop floor. Name of data Product name Working time Break time Available time Number of workers Number of processes Number of shifts Number of shops Production per day (PD) Shop floor area Production line Production type Material handling equipment Challenge Previous production record Problems Working environment Working temperature Maintenance type Outsourcing services Number of components in the final product Implemented shop floor management technique shop floor management department transmits the manufacturing instructions on daily basis to the supervisor and workers. In present production condition, a total of 18 processes are performed in the production of the skid steer loader, and the industry operates in one shift per day. The total cycle time (TCT) for manufacturing the skid-steer loader is 6,540 minutes whereas the total idle time (TIT) is 470 minutes. The skid-steer loaders are manufactured to the customers’ demands mostly monthly. The industry normally maintains a product inventory of 15 units due to uncertainty in employee availability. In the observed condition, the proposed production management system has been used to identify production problems and elimination of non-value-added activities. For the elimination of problems, the appropriate technique has been selected from the process optimization techniques implemented in the previous research work, as shown in Table 2. Table 5 shows the description of problems and challenges identified in production processes. The production analysis program and data analytics were implemented for the identification of problems and nonvalue-added activities so that they could be eliminated with the appropriate action. Lean and smart manufacturing were found suitable to improve mostly processes, and additionally, internet of things, artificial intelligence, and embedded system have been applied in some other processes. 6. Result and Discussions 6.1. Development of a Modified Production Shop Floor Management. After the analysis of NVAA present in the production processes of skid-steer loader, various proposals for NVAA elimination have been developed by a discussion with the workers of industry, which are presented as follows: Quantity Skid steer loader 560 minutes 40 minutes 520 minutes 52 18 1 5 8 34 meter × 75 meter Semiautomated Pull Hoist for material handling and forklift Productivity, time, quality, customer satisfaction level WT: 580 minutes, BT: 80 minutes, PD: 9 minutes P1, P2, P3, P4, P5, P6, P7, P8, T1, T2, T3, T4, T5, T6, T7, T8, T9, L1, L4 Unfriendly due to more working hours and unplanned work Workable Preventive Painting (chassis and loader arm) Approximate 800 5S (i) Improvement in shop floor management – The principle of lean smart manufacturing has been found effective for implementation at the following processes, namely wheel assembly, chassis manufacturing, cabin installment, and electric gauges assembly (ii) Reduction of a number of workstations – The production planning at workstations has been improved by simulation and machine learning concepts (iii) Reduction of work-in-progress–The unnecessary activities between production processes has been eliminated by the lean concept (iv) Improvement in resource utilization – The utilization of machinery and workers has been improved by using artificial intelligence and the internet of things concept (v) Reduction of high setup time – The internal activities (preparing setup jig and fixture, movement of chassis component, material handling of components by forklift and hoisting equipment, transportation of large parts for painting, and changing attachment tool) have been considered as external activities (vi) Improvement in the communication gap between workers-organized meeting and conducting the training program Table 6 shows the overall production modification suggested for proposed planning. The proposed modified production planning describing the various improvements incorporated in the production processes of the skid steer loader on the shop floor is shown in Figure 10. Mathematical Problems in Engineering 13 Cycle time – 545 Minutes Changeover time – 80 Minutes Uptime – 94.87% STEP 5 Idle time – 90 Minutes STEP 1 Assembly shop Cabin installment & Electric gauge assembly Cycle time – 1830 Minutes Changeover time – 70 Minutes Uptime – 86.54% STEP 4 Idle time – 45 Minutes Roll off & Hot testing Cycle time (CT) – 870 Minutes Changeover time (CO) – 95 Minutes Uptime (UT) – 90.62% Idle time (IT) – 80 Minutes Observed Management system Cycle time - 2010 Minutes Changeover time – 105 Minutes Uptime – 91.40% Idle time – 75 Minutes STEP 2 Fabrication shop Cycle time – 630 Minutes Changeover time – 240 Minutes Uptime – 86.86% Idle time – 120 Minutes STEP 3 Painting shop Figure 9: Observed production management condition. Table 5: Challenges and problems identified in production processes. S. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Process Transmission assembly Manufacturing of loader arm Chassis manufacturing Wheel assembly Chassis and loader arm fabrication Inspection of fabrication Painting (baby parts) Painting (large parts) Engine assembly Hydraulic pump and motor assembly Inspection of assembly and roll off Hot testing Cabin installment Electric gauges assembly Final inspection A similar work has been reported by Müller et al. [96] who discussed how natural language processing could improve the digital shop floor management concept to provide higher value for decision-makers and the shop-floor teams. The study presented a conceptual approach by integrating the fields of natural language processing and digital shop floor management to discuss assistant functions in digital shop floor management on the text data produced during problem-solving. The result of the study revealed that the developed approach was detailed, quick, and accurate by representing an actual condition in the company. Xu et al. [97] investigated the coexistence of two industrial revolution industry 4.0 and industry 5.0. In the study, five questions were selected by different sources, and the questions were rooted in industry and the scientific community. The study showed that the industrial revolution was driven by transformative technological advances that helped improve Problem P1, P2, P4, P5, P6, P7, P8, T1, T2, T4, T5, T6, T7, T8, T9, L1 P4, P5, P6, P8, T1, T3, T4, T7, C2, L1 P1, P2, P3, P4, P5, P6, P7, P8, T1, T2, T3, T4, T5, T6, T7, T8, L1, L4 P5, T8 P1, P3, P4, P5, P8, T3, T7, T9L1 P2, P3, P5, T1, T2, T3, T8 P8 P8 P1, P7, P8, T3, T4, T5, T8, L1 P1, P7, P8, T3, T4, T5, T8, L1 T1 P3, P5, P7, P8, T1, T4, L1 P4, P5, P7, T1, T4, T7, T8 P4, P5, P7, Q1, Q4, T1, T4, T7, T8 P1, P5, P6, P7, T1, T4, T8 fundamental changes in the industry functions. These fundamental changes included social and economic consequences. It also concluded that the revolution upgrade had been required technological pushes and solutions. Mourtzis [98] discussed the benchmarking obtained in the evolution of manufacturing systems simulation technologies and investigated recent research and industrial revolution in the fields of manufacturing. The study showed that digitalization provides data and new technologies to assist in manufacturing simulation and product design in the new era. 6.2. Validation of Proposed Smart Production Management System. Present research methodology demonstrates its usefulness in terms of improved productivity, customer satisfaction level, resources utilization, and production time. 14 Mathematical Problems in Engineering Table 6: Details of proposed production planning from the investigation of the production shop floor. Name of data Quantity Product name Skid-steer loader Working time (WT) 600 minutes Break time (BT) 90 minutes Available time 510 minutes Number of workers 52 Number of processes 10 Number of shifts 1 Number of shops 5 Production per day 12 Shop floor area 34 meter × 75 meter Material handling equipment Hoist for material handling and forklift Challenge Productivity, time, customer satisfaction level Previous production record WT – 520 minutes, BT – 40 minutes, PD – 8 Problems Eliminated Working environment Aesthetic Maintenance type Condition-based maintenance Outsourcing services Painting (chassis and loader arm) Number of components in the final product Approximate 800 Implemented shop floor management approach Lean smart manufacturing, internet of things, artificial intelligence, asset tracking system 1 2 Fabrication CT – 855 Minutes CO – 280 Minutes UT – 81.37% IT – 110 Minutes 3 Assembly CT – 1095 Minutes CO – 190 Minutes UT – 90.68 % IT – 105 Minutes 4 Painting CT –1710 Minutes CO – 45 Minutes UT – 91.18% IT – 40 Minutes 5 Roll-off and hot testing CT – 1860 Minutes CO – 75 Minutes UT – 85.29% IT – 60 Minutes Quality inspection CT – 150 Minutes CO – 5 Minutes UT – 99.02% IT – 10 Minutes Figure 10: Modified production management system. The case study revealed that the proposed production management system can provide precise identification of the challenges and problems responsible for production in industry 4.0 using a lean and smart manufacturing approach. The production management helps the management system implement a suitable shop floor management approach for the elimination of non-value-added activities. To validate the production management system presented, it was implemented in a production condition of industry 4.0, and it was found that production improved as per the standards set by the production management system. Production improvement has been calculated according to various parameters. These improvements are shown in Figure 11. In line with the problems encountered in shop floor management, the present case example shows the elimination of non-value-added activities and the improvement in productivity levels that have been possible through the proposed smart production management system. To substantiate this statement, a comparative analysis was carried out from the present work from previous research work. It was found that the proposed production management system is better in eradicating of all production problems and non-value-added activities. The comparative analysis between some relevant previous research and the present study has shown in Table 7. The related work has been reported by Mourtzis [99] designed a model of a real manufacturing system using discrete-event simulation and evaluated it by obtaining actual data obtaining from the copper industry. The study used the ANOVA method to highlight the effect of each decision variable on the model. The study demonstrated that the manufacturing system could obtain maximum throughput by utilizing actual data and available resources. Mourtzis [100] developed a framework for remote monitoring refrigerator and cold storage systems using wireless sensor networks and cloud technology for predictive maintenance. In the study, wireless sensor networks and the intelligent algorithm were integrated for predictive maintenance. The study showed that the developed framework could provide yielded promising results. Mourtzis [101] discussed the latest advances and challenges of machine tool evolution in the present industrial era in the manufacturing Mathematical Problems in Engineering 15 325 470 IT (Minutes) 6595 PT (Minutes) 7960 595 765 CO (Minutes) NR UT (%) 39 44 27 20 5670 CT (Minutes) 6725 510 520 AT (Minutes) AT (Minutes) CT (Minutes) UT (%) NR CO (Minutes) PT (Minutes) IT (Minutes) Modified 510 5670 27 39 595 6595 325 Observed 520 6725 20 44 765 7960 470 Figure 11: Production improvement by the presented production management system. Table 7: Comparison between previous results and present study investigation. Author’s Cinar et al. [20] Liao and Wang [78] Rahani and AlAshraf [40] Chien and Chen [86] Ismail et al. [49] Gijo et al. [69] Asif and Singh [63] Das et al. [46] Frankό et al. [88] Longhan et al. [44] Choomlucksana et al. [58] Present study Technique Improvement (%) PT CT Defect Parameter Optimized VSM 1.11 2.59 4 Internet of things SMED, poka-yoke, standardization Smart manufacturing LSS LSS Internet of things VSM, SMED, kaizen Internet of things VSM NA NA NA Production time, machinery utilization, quality Product quality NA 43 66 Production time, product quality Cost Production time Production time Quality Production management Production time Logistic task Production time Machinery Cost Cost Cost Cost Machinery, cost Cost Production time Cost VSM NA NA NA 16.79 NA NA NA NA 85.26 NA NA NA 57.07 43.24 NA NA NA NA 6.85 NA NA 8.02 37.05 NA Lean and smart manufacturing, 17.14 15.69 internet of things, VSM 42 domain. In addition, the study recognized emerging opportunities and identified implications from the implementation point of view. 6.3. Enhancement of Industrial Sustainability of the Developed Methodology for Industry 4.0 for Earthmoving Machinery Manufacturing Shop Floor Management System. The present research methodology can be applied to improve all types of earthmoving machinery manufacturing conditions in industry 4.0. This statement has been proved by an example: this example was of an earthmoving machinery manufacturing unit, in which production was improved by the implementation of the developed production management system in industry 4.0. The improvements obtained in the operational performance of production processes by process optimization techniques are shown in Table 8. The planning and execution of research methods in the manufacturing unit, and improvement in shop floor management has summarized in Table 9. Machinery, cost Cost Production time, machinery and Manpower, machinery, manpower utilization, quality cost, shop floor Productivity enhancement has been achieved effectively by the production management system developed in the present case study. The implementation of the developed methodology led to an improvement of up to 35% in the production by a 42% reduction in defects by the implementation of a suitable process optimization approach. The result of the study validates the author’s statement about developed methodology and describes that the developed production management system would provide industrial sustainability in industry 4.0. The related work has been reported by Liu et al. [102] investigated the emerging industrial internet of things implementation in a cloud manufacturing system for addressed the challenge faced in the development of cloud manufacturing systems. The challenges included communication, efficient data acquisition, analysis of field-level manufacturing equipment, and query. Two industrial internets of things gateway for a 3D printer and a CNC machine tool validated the approach. The result showed that integrating various emerging industrial internet of things technologies in manufacturing systems 16 Table 8: Improvements in the parameters of production processes. S. No. 1 2 3 4 5 6 7 8 9 10 12 13 14 15 Process (modified) Transmission assembly Manufacturing of loader arm Chassis manufacturing Wheel assembly Chassis and loader arm fabrication Inspection of fabrication Painting (baby parts) Painting (large parts) Engine assembly Hydraulic pump and motor assembly Quality inspection and roll off Hot testing Transmission assembly Manufacturing of loader arm Chassis manufacturing Wheel assembly Chassis and loader arm fabrication Reduction (available time) minute Improvement (uptime) % Improvement (number of operators) Improvement (changeover time) minute Improvement (cycle time) minute Improvement (production time) minute Improvement (idle time) minute 10 0.56 1 (Increase) 5 45 60 10 10 2.72 1 (Increase) 15 15 35 5 10 0.62 1 (Increase) 5 15 40 20 10 0.88 1 (Increase) 5 25 30 0 10 2.34 2 (Reduce) 0 30 45 20 10 10.53 1 (Decrease) 60 395 35 Painting 300 1 (Increase) Assembly (engine, hydraulic pump and motor) Quality inspection, roll off, and hot testing Cabin installment Cabin installment and electric gauges assembly Electric gauges assembly Final inspection 10 7.16 10 1.24 90 40 0 35 15 525 545 10 10 10 3.29 2 (Reduce) 20 55 100 25 10 1.9 0 10 10 25 5 Mathematical Problems in Engineering 11 Process Mathematical Problems in Engineering 17 Table 9: Implemented actions for improvement in production on the shop floor. S. No. Process Cause of problem Process optimization approach 1 Transmission assembly (ii) Prepared a systematic layout Lean smart manufacturing (iv) Lack of workers (v) Poor planning (i) Higher setup time 2 3 4 Manufacturing of (ii) Lack of workers Lean manufacturing loader arm (iii) Unawareness of the work (iv) Lack of action plan (i) Manual material handling (ii) Unsystematic planning Chassis Value stream mapping (iii) No sequence of manufacturing and internet of things production processes was determined (iv) Lack in worker skill (i) Unawareness of the worker Wheel assembly Embedded system (i) A Longer distance between chassis and loader arm shop 5 Chassis and loader (ii) Higher setup time arm fabrication (i) Higher setup time 6 Inspection of fabrication 7 Painting (baby parts) 8 Painting (large parts) (ii) Unnecessary transportation for the inspection process (iii) Increased number of workers (iv) Improved shop floor planning (v) Automated production line (vi) Asset tracking system (i) Increased number of workers (ii) Organized meeting and training 11.32 9.21 (iii) Improved action plan (i) Prepared a systematic planning (ii) Organized appropriate training program 9.19 (iii) Digitalization (i) Organized meeting (ii) Prepare to code for operations (i) Both the shops were set up side by side in the revised layout (ii) Increased number of Smart manufacturing workers (iii) Dynamic modeling of shop floor (iv) Digitalization (i) Inspection has done at previous workstation (ii) Inspection has done just after completion of Internet of things, fabrication of loader arm artificial intelligence and chassis (iii) No fixed location was decided for inspection (i) Lack of worker 15.78 8.108 (iii) Program for standard specification of product Embedded system (i) Lack of worker (ii) Painting of large parts from other industry Improvement in process time (%) (i) All assembly processes were performed in one shop that also included gearbox assembly (i) There was no fixed place for different assembly processes (ii) Gearbox assembly has done at a different location (iii) Unnecessary transportation due to different locations of processes Suggested action Lean manufacturing (i) Increase worker (ii) Automated production line (i) Increase worker (ii) Both painting processes were started out simultaneously 18.03 18 Mathematical Problems in Engineering Table 9: Continued. S. No. 9 10 11 12 13 14 15 Process Cause of problem Engine assembly (i) Lack of worker (ii) Poor planning (iii) Unawareness of the worker Process optimization approach Suggested action Improvement in process time (%) (i) Increase worker (ii) Improved planning Lean smart manufacturing (iii) Organized meeting (i) Lack of worker (ii) Unsystematic layout Hydraulic pump (iii) Unawareness of the (iii) Organized meeting work and motor Smart manufacturing (iv) Automated assembly production line (v) Digitalization (i) Improved production (i) Lack in production planning planning Quality inspection (ii) Unnecessary Artificial intelligence (ii) Prepare a coding-based and roll off documentations specifications (iii) Digitalization (i) Lack of worker (i) Increase worker (ii) No time limit (ii) Decided a time limit decided for processes Hot testing Embedded system (iii) Drawback in (iii) Improved planning planning (i) Improvement in layout (i) Unsystematic layout (ii) Unawareness of the (ii) Organized meeting work and training Lean smart Cabin installment (iii) Improved shop floor (iii) Lack in shop floor manufacturing planning planning (iv) Embedded system (i) Improvement in layout (ii) Unsystematic layout (ii) Unawareness of the (ii) Organized meeting work and training (iii) Lack in shop floor (iii) Improved shop floor planning Lean smart Electric gauges planning (iv) Cabin installment and manufacturing assembly electric gauges assembly (iv) Unnecessary have been done at one movement workstation (v) Automated machinery (i) Improvement in (i) Lack in planning planning (ii) Repeatedly deploy (ii) Experienced staff have new workers for Smart manufacturing, deployed for inspection inspection Final inspection artificial intelligence, (iii) Unawareness of the (iii) Organized meeting embedded system work and training (iv) Automated machinery and embedded system (v) Digitalization established the connection between the cloud manufacturing platform and field-level manufacturing equipment. Peng et al. [103] proposed a conceptual framework for flexible manufacturing strategy using the industrial internet. The study reviewed industrial Internet-enabled implementations in China in response to COVID-19 and discussed it from 3Rs’ perspective, that is, supply chain resilience, 21.17 (iv) Simulation and modeling of workstation (v) Digitalization (i) Increase worker (ii) Modified layout 21.46 19.05 13.15 manufacturer capacity recovery, and emergency response. The result provided preliminary study reflections and a flexible manufacturing strategy in the wake of the COVID19 pandemic. Meissner et al. [104] identified different starting points for digital shop floor management using a cluster analysis based on survey data. The study showed three initial conditions for implementing digital shop floor Mathematical Problems in Engineering Objectives of the present smart manufacturing system Development of sustainable guidelines for operational control. Productivity enhancement by maximization of resource utilization using industry 4.0 technologies. Operational excellence in complex shop floor mining machinery manufacturing environment. 19 Statistics analysis of the present ingenious smart manufacturing system in comparison with systems from the previous literature in terms of shop floor management parameters Financial profitability has been improved by 45% Production time has been significantly reduced by 17% Worker contribution has been improved by 18% Recommendation of cost-effective strategies in order to enhance the operational effectiveness and financial profitability within accessible resources in industry 4.0. Production enhancement in shop floor production system under limited conditions. Improvement of operational performance on shop floor after in-depth conversation and inspections. Operational excellence in shop floor manufacturing system under specific stipulated conditions. Implementation of suitable approaches for the elimination of shop floor problems precisely. Robust control for all types of shop floor management in mining machineries manufacturing system for industry 4.0. Inference of the developed manufacturing system from previous literature studies Operational effectiveness has been improved by 35% Machinery utilization has been improved by 28% Identification of appropriate approach for production management system under limited shop floor conditions. Applicable in the shop floor production system under limited working condition. Figure 12: Comparatively benchmark analysis of the present smart manufacturing system for industry 4.0. management in the German metal and electrical industry that can describe gradually. Furthermore, the results showed that digital shop floor management could remain competitive over the long term across all companies. 6.4. Sustainable Shop Floor Management System and Industry 4.0 Technologies. Implementing an efficient process optimization approach with industry 4.0 technologies for operational control in a complex environment plays a vital role in shop floor management [2, 3, 11, 17, 25, 32, 39, 55, 69, 82]. Industry 4.0 technologies enormously enhance productivity by maximizing operational excellence, workers’ contributions, overall equipment effectiveness, and process adaptability [1, 3, 20, 26, 30, 88, 90, 92, 105]. The management team members emphasize using the process optimization approaches with industry 4.0 technologies because of problems faced in production control in complex shop floor manufacturing environments [4, 6, 27, 91]. Industry 4.0 technologies mainly include the internet of things, artificial intelligence, artificial neural network, digitalization, and asset tracking system. These technologies help enhance the overall efficacy of the process optimization approach on the shop floor and enhance productivity within limited resources. It has been observed that the production team members feel mental and physical comforted by implementing the process optimization approaches with industry 4.0 technologies in the complex production environment. Therefore, the efficiency of the shop floor management system can be improved by the integration of the process optimization approach with industry 4.0 technologies. Furthermore, the integrated approach can enhance industrial sustainability in complex production systems by establishing a safe and waste-free environment on the shop floor. 6.5. Performance of Lean Approach and Industry 4.0 Technologies. A methodology has been developed in the present research work to enhance industrial sustainability in industry 4.0 by eliminating waste using lean and smart manufacturing. The developed methodology is able to provide a positive working condition by establishing the aesthetic environment in shop floor departments by eliminating waste. The lean approach aims to maximize productivity by avoiding non-value-added activities in production planning on the shop floor. Non-value added activities are types that never provide any value in product/ production processes, so the shop floor management teams always focus on making a strategy for the elimination of waste by avoiding non-value-added activities in production planning. Industry 4.0 technologies boost the effectiveness of the lean approach in operational control on the production shop floor. Industry 4.0 uses various techniques to enhance the lean approach including an asset tracking system, automated guided vehicle, digitization, radiofrequency identification system, smart condition monitoring system, and big data analytics. In the present scenario, the management team prefers industry 4.0 techniques to enhance productivity by maintaining industrial sustainability within limited constraints. It has been observed that satisfactory results may not be obtained by applying the methodologies developed in the previous research works to other management systems [3, 8, 22, 24, 27, 50, 64, 67, 69, 84]. Therefore, in the present research work, such a management system has been developed that can be applied in all types of production conditions. The developed methodology has been verified and proved by implementing it in different actual production conditions. The results show that the developed methodology provided a robust management system by effectively improving production time, worker’s 20 contribution, machinery utilization, operational excellence, and financial profitability by 17%, 18%, 28%, 35%, and 45%, respectively. Therefore, the developed methodology would be preferable by management team members because it helps in the decision-making phase according to problems and wastes found in different industrial scenarios and could be proved to benchmark for control and enhance operational excellence in industry 4.0. Figure 12 describes the benefits of the developed smart management system in comparison to previous systems in terms of production shop floor management factors. 6.6. Relation between Industry 4.0 and Lean Approach. The lean approach can enhance work performance by necessary improvements in the shop floor management system, and industry 4.0 techniques provide a method to improve productivity by maximizing the utilization of resources. The developed innovative system in the present research uses the integration of lean and industry 4.0 techniques to enhance work performance by optimizing the utilization of resources. The developed system provides industrial sustainability by improving various shop floor factors, including production time, work area, worker contribution, available time, and machinery performance. The developed system has been validated by implementing in an actual production condition of industry 4.0. The results obtained by the production system showed that the developed system is able to enhance productivity within limited constraints. Furthermore, the developed system proved costeffective by minimizing unnecessary uses of resources. The lean approach improves work performance by eliminating unnecessary activities in the production processes, while industry 4.0 techniques improve operational conditions by establishing advanced systems on the production shop floor. In the present scenario, the shop floor management teams emphasize establishing industrial sustainability by improving operational excellence with minimum consumption of resources. Therefore, the developed system would be preferred by industry individuals in the shop floor management system because the developed system was found able to enhance industrial sustainability and financial profitability within limited constraints. 6.7. Potential Contribution of the Proposed Smart Manufacturing System in Managerially Impacts for Earthmoving/Mining Machinery Manufacturing Shop Floor Management. The proposed smart production management system has been developed on a lean and smart manufacturing approach to control production processes using limited resources and enhance productivity in existing financial conditions. The developed system provides a sustainable strategy for identifying problems and eliminating waste by monitoring operational performance on the shop floor. The integration of lean and innovative approaches is considered efficient by the shop floor management teams because this integration can enhance productivity and financial profitability within limited constraints [2, 3, 6, 20, 22, 26, 39, 55, 67, 74, 90, 105]. Mathematical Problems in Engineering Furthermore, the developed smart shop floor management system helps in the decision-making stage to implement a suitable approach for maximizing productivity with available resources. Thus, it has been concluded that the lean and smart approach makes the shop floor management system effective and superior for production management by eliminating waste in the industry 4.0 environment. 7. Conclusions and Future Outlook In the present research article, a smart production management system has been proposed to identify problems and challenges faced on the production shop floor in industry 4.0. The main findings obtained by the present research work are as follows: (i) It has been observed that the innovative system developed can efficiently identify problems and challenges at the start of production processes in complex production shop floor conditions, thereby avoiding financial losses from production in industry 4.0 (ii) The developed system provides an agile system and guidelines for enhancement in industrial sustainability in heavy machinery manufacturing units. (iii) It has been found that the developed system provided improvement in production time, worker’s contribution, machinery utilization, operational excellence, and financial profitability by 17%, 18%, 28%, 35%, and 45%, respectively. In addition, it has been proved that using automated equipment in production lines and reduced working hours provide mental and physical comfort to workers. As a result, unprecedented improvement can be achieved in the production shop floor management. (iv) A comprehensive analysis of previous research work found that smart manufacturing, lean smart manufacturing, artificial intelligence, machine learning, and the internet of things are emerging techniques for shop floor management in industry 4.0. They can be applied to maintain industrial stability in all types of production situations. (v) The authors of the present research article strongly believe that the developed system would provide an intelligent key to industry individuals for enhancement in industrial sustainability of industry 4.0. For the future prospects, and to concentrate on the present scenario of industry 4.0, the production shop floor management team members emphasize developing an innovative system to enhance industrial sustainability within available resources. A smart shop floor management system has been developed in the present research work to accomplish this need of the production management teams. The efficacy of the developed model has been tested in an actual complex shop floor condition of an earthmoving machinery manufacturing unit. The result showed that the developed system efficiently controlled the shop floor Mathematical Problems in Engineering management in heavy machinery manufacturing systems, including industry 4.0, by implementing a suitable approach to eliminate production problems and waste elimination. In future research, the adaptability of the developed system may be improved by applying it in other shop floor environments of industry 4.0. Furthermore, the proposed shop floor management system can be improved by integrating with different lean and intelligent approaches. Abbreviations LM: LSS: VSM: SMED: TPM: JIT: TQM: SOP: SLP: LT: PT: CO: NR: CT: AT: TT: IT: UT: TCT: TIT: WT: BT: PD: NVAA: Lean manufacturing Lean Six Sigma Value stream mapping Single minute exchange of die Total productive maintenance Just in time Total quality management Standard operating procedure Systematic layout planning Lead time Production time Changeover time Number of operators Cycle time Available time Takt time Idle time Uptime Total cycle time Total idle time Working time Break time Production per day Non-value-added activities. Data Availability The data presented in this study are available on request from the corresponding author. Conflicts of Interest The authors declare that there are no conflicts of interest. References [1] M. Ramadan, B. Salah, M. Othman, and A. A. Ayubali, “Industry 4.0-based real-time scheduling and dispatching in lean manufacturing systems,” Sustainability, vol. 12, no. 6, Article ID 2272, 2020. [2] V. Tripathi, S. Saraswat, and G. D. 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