Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1731 2024-03-15 12:12:05 |
2 update references and layout -18 word(s) 1713 2024-03-18 03:41:58 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Paraschos, P.D.; Koulinas, G.K.; Koulouriotis, D.E. Industry 4.0 and Smart Manufacturing. Encyclopedia. Available online: (accessed on 20 April 2024).
Paraschos PD, Koulinas GK, Koulouriotis DE. Industry 4.0 and Smart Manufacturing. Encyclopedia. Available at: Accessed April 20, 2024.
Paraschos, Panagiotis D., Georgios K. Koulinas, Dimitrios E. Koulouriotis. "Industry 4.0 and Smart Manufacturing" Encyclopedia, (accessed April 20, 2024).
Paraschos, P.D., Koulinas, G.K., & Koulouriotis, D.E. (2024, March 15). Industry 4.0 and Smart Manufacturing. In Encyclopedia.
Paraschos, Panagiotis D., et al. "Industry 4.0 and Smart Manufacturing." Encyclopedia. Web. 15 March, 2024.
Industry 4.0 and Smart Manufacturing

The manufacturing industry often faces challenges related to customer satisfaction, system degradation, product sustainability, inventory, and operation management. If not addressed, these challenges can be substantially harmful and costly for the sustainability of manufacturing plants. Paradigms, e.g., Industry 4.0 and smart manufacturing, provide effective and innovative solutions, aiming at managing manufacturing operations, and controlling the quality of completed goods offered to the customers.

material management quality assurance raw materials returned products

1. Introduction

In recent years, the emerged customer needs and product trends define the ever-changing nowadays industry, since the technology has made huge leaps towards the future. In order to stay competitive, manufacturers are constantly working towards rapidly introducing and manufacturing new products that meet the customer expectations. To this end, production planning strategies, e.g., mass personalization, could be employed in an effort to enhance the productivity of manufacturing systems and the diversity of the offered products [1]. However, their application involves a series of challenges that increase the corresponding operational costs, e.g., storage costs. These challenges, among others, include the production of low-quality items, raw material consumption, and fluctuating customer demand. If not properly tackled, they could substantially hinder the long-term sustainability and cost-effectiveness of production systems due to the associated rising costs. Furthermore, governments require production plants’ compliance to environmental regulations and policies in an effort to mitigate the effects of climate change and global warming [2][3].
Given the above, the digitization of processes and operations could be considered as an effective approach to achieving a sustainable production environment [4]. This transformation represents one of the aims of Industry 4.0. Often regarded as the fourth industrial revolution, Industry 4.0 is a manufacturing concept that strives to transform traditional production plants into smart ones, aiming to optimize their productivity and cost-effectiveness through intelligent manufacturing [5]. It is a transition achieved through the coupling of integrated manufacturing operations and components, e.g., machines, with intelligent technologies, including Internet of Things (IoT) [6][7]. These devices communicate with the system, gathering significant data [8][9]. With the aid of machine learning, they analyze and utilize the accumulated data to schedule operations, predict failures, manage product inventory, and conduct quality inspections [10][11]. As a result, corresponding predictive models are developed and assume an integral role in the decision-making process conducted within smart production system [12][13].
Along with Industry 4.0, the lean green manufacturing concept could be employed as well to improve both sustainability and output product quality within smart production systems [14][15]. It is a relatively new paradigm that simultaneously exploits the strengths of lean and green manufacturing in order to identify waste and assess environmental effects [16]. To understand the strengths of lean and green manufacturing, let us define each concept separately. Devised by Toyota, lean manufacturing is a manufacturing paradigm that intends to improve the value of a product. It attempts to reduce material waste within manufacturing systems and to support seamless collaboration between entities, including suppliers and customers, involved in manufacturing operations and processes [17][18]. The goal of this application is to promptly and cost-effectively manufacture high-quality items [19]. One of the limitations of lean manufacturing is the lack of performance metrics that assess the environmental impact of manufacturing processes [20]. On the other hand, green manufacturing endeavors to improve the plants’ productivity with circular economy practices, such as recycling and refurbishing [21]. It aims at producing items that demonstrate conformance to the quality and environmental standards by decreasing the negative impact associated with human intervention [22]. However, the implementation of lean and green practices within production systems is still hindered by a plethora of barriers, including the lack of proper training, inventory management, and effective maintenance plans [23]. Industry 4.0 could solve these barriers with its technologies and finally enable the integration of lean green manufacturing in the actual industry [24].
Within this context, the reinforcement learning-based scheduling can be complemented with lean green practices. These practices combine two types of manufacturing; that is, lean manufacturing and green manufacturing. Considering the lean part, pull production and total productive maintenance are adopted, as they are two well-known lean manufacturing techniques [25]. In this respect, pull production is enabled through ad hoc policies, such as Base Stock [26][27], while total productive maintenance is integrated through predictive maintenance policies, e.g., condition-based maintenance, that seek to minimize maintenance costs and improve the availability of processing machines [28]. The implemented ad hoc policies are frequently employed in the real-world industry and the academic literature [29][30]. On the other hand, the green manufacturing part is supported with circular economy policies, namely refurbishing and remanufacturing. These policies aim to minimize the environmental impact of products by extending their lifecycle [31]. In this respect, low-quality or returned material is processed again in order to create new and usable products. This leads to a substantial reduction in material consumption and raw material dependence.

2. Sustainable Production

Sustainable production is mainly characterized by the generation of products using minimal materials and natural resources [32]. It involves a number of activities, e.g., remanufacturing, aiming to extend product cycle and reduce the negative effects of manufacturing processes on the environment, such as material waste and pollution [33][34]. In this respect, Industry 4.0 has introduced new technologies that enable corresponding practices in production plants, contributing positively to the value creation in the economic [35], social [36], and environmental [37] aspects of sustainability. Given these implications, the state-of-the-art literature has implemented the sustainable production concept to improve the sustainability of manufacturing systems, focusing mainly on their energy efficiency [38]. Specifically, these applications utilize detailed probabilistic models, optimized by machine learning algorithms, to make forecasts for energy costs, improving the decision-making carried out in industries and thus paving the way for a sustainable future [39][40]. For example, focusing on the lot-sizing and scheduling optimization problem, Roshani et al. [41] endeavored to minimize operational costs and the energy consumption of the examined single-stage production system. In this effort, they generated manufacturing and remanufacturing plans with a mixed-integer programming modeling approach and metaheuristic algorithms.
Another noteworthy aspect of sustainable production in Industry 4.0 is the integration of lean manufacturing, aimed to improve the final quality of products and satisfy customers [35]. In the pertinent literature, its applications range from applying lean VSM [42] to supply chain management [43]. For instance, Ferreira et al. [42] developed and simulated a VSM model that implements lean manufacturing environments in the context of Industry 4.0. For this effort, it incorporates a variety of decision-making agents, specializing on discrete aspects of manufacturing environments, such as coordination of operations or resource management. Following a sophisticated approach, Soltani et al. [44] presented a lean manufacturing approach that assess the manufacturing sustainability of plants with VSM and suggests solutions for minimizing generated waste through multi-criteria decision analysis methods, e.g., TOPSIS.
In addition to lean manufacturing, green manufacturing practices emerge as an effective manufacturing solution that aims to build a truly circular manufacturing environment with near-zero environmental impact [22]. An example of such an implementation is presented in [45]. In this publication, the authors considered inventory control models that authorize manufacturing and remanufacturing activities, endeavoring to generate innovative green products, i.e., products that are generated using used and returned items. Focusing on car manufacturing, Liu and De Giovanni [46] strove to find an optimal trade-off between economical and environmental effects considering a supply chain. They modeled the performance of the supply chain to evaluate the impact of green process on the production costs.
Moreover, the combined concept of lean and green manufacturing is still studied and evaluated by the literature, with the aim of creating a ubiquitous framework that could be easily implemented in manufacturing plants. For example, Kurdve and Bellgran [47] discussed how the lean green manufacturing combined with circular economy practices can be incorporated in the plants’ shop-floor and presented a framework that could realize that concept. Tripathi et al. [48] presented a process optimization framework integrating lean and green concepts. Furthermore, they provided guidelines for implementing the framework in Industry 4.0-enabled shop-floor systems in order devise corresponding production plans. Duarte and Cruz-Machado [49] presented a framework conceptualizing a lean green supply chain within Industry 4.0 to investigate how these concepts are intertwined with each other and the relationship between them.

3. Reinforcement Learning-Based Scheduling

In the context of Industry 4.0, there are several applications of reinforcement learning in process control and scheduling, ranging from guiding robots [50] to devising policies [51]. These applications are mostly aimed at tackling either job-shop [52], or flow-shop scheduling [53]. For instance, Yan et al. [54] presented a digital twin-integrated manufacturing environment, wherein a reinforcement learning framework optimizes flow-shop scheduling on the basis of machine availability. Furthermore, publications combine reinforcement learning with other methodologies, e.g., evolutionary algorithms, in an effort to tackle dynamic and complex scheduling problems. For example, Li et al. [55] focused on improving the job make-span and workload in uncertain manufacturing environments, coupling reinforcement learning decision-making with evolutionary algorithms. Addressing dynamic scheduling problem, Gu et al. [56] presented a CPS that generates and compiles job scheduling rules by means of genetic programming and reinforcement learning, respectively. Finally, in terms of sustainable production, reinforcement learning has been applied to circular manufacturing environments, focusing on improving resilience and productivity. An example of such an application is presented in [57]. In this study, the authors coupled reinforcement learning with manufacturing control policies endeavoring to enhance the responsiveness of circular manufacturing system under fluctuating customer demand and recurring faults.
Table 1 summarizes the contribution of cited papers in the pertinent literature. According to this table, a large portion of studies focused on the derivation of policies that merely authorize single type of activities on the basis of specific sustainability aspect, such as energy consumption. To this end, they applied complex mathematical models, e.g., mixed integer model, that capture a snapshot of the investigated problem.
Table 1. Comparison table.
Reference Problem Explicit Model Method Control Plan
Duarte and Cruz-Machado [49] Process scheduling No N/A manufacturing, recycling
Gu et al. [56] Process scheduling Yes reinforcement learning/genetic programming manufacturing
Kurdve and Bellgran [47] Sustainable production No N/A manufacturing
Li et al. [55] Process scheduling Yes reinforcement learning/evolutionary algorithms manufacturing
Liu and De Giovanni [46] Process scheduling Yes Pareto optimization manufacturing
Paraschos et al. [57] Process scheduling No reinforcement learning manufacturing, maintenance, remanufacturing, recycling
Ferreira et al. [42] Sustainable production Yes VSM manufacturing
Roshani et al. [41] Sustainable production Yes metaheuristic algorithms manufacturing, remanufacturing
Sarkar et al. [45] Sustainable production Yes Stepwise optimization algorithm manufacturing, remanufacturing
Soltani et al. [44] Sustainable production Yes VSM/multi-criteria decision-making Manufacturing
Tripathi et al. [48] Process scheduling Yes N/A manufacturing
Yan et al. [54] Process scheduling No reinforcement learning manufacturing, maintenance


  1. Hu, S.J. Evolving Paradigms of Manufacturing: From Mass Production to Mass Customization and Personalization. Procedia CIRP 2013, 7, 3–8.
  2. Wen, H.; Wen, C.; Lee, C.C. Impact of digitalization and environmental regulation on total factor productivity. Inf. Econ. Policy 2022, 61, 101007.
  3. Silva, A.; Rosano, M.; Stocker, L.; Gorissen, L. From waste to sustainable materials management: Three case studies of the transition journey. Waste Manag. 2017, 61, 547–557.
  4. Ciliberto, C.; Szopik-Depczyńska, K.; Tarczyńska-Łuniewska, M.; Ruggieri, A.; Ioppolo, G. Enabling the Circular Economy transition: A sustainable lean manufacturing recipe for Industry 4.0. Bus. Strateg. Environ. 2021, 30, 3255–3272.
  5. Dalzochio, J.; Kunst, R.; Pignaton, E.; Binotto, A.; Sanyal, S.; Favilla, J.; Barbosa, J. Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Comput. Ind. 2020, 123, 103298.
  6. Dalenogare, L.S.; Benitez, G.B.; Ayala, N.F.; Frank, A.G. The expected contribution of Industry 4.0 technologies for industrial performance. Int. J. Prod. Econ. 2018, 204, 383–394.
  7. Chen, B.; Wan, J.; Shu, L.; Li, P.; Mukherjee, M.; Yin, B. Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges. IEEE Access 2018, 6, 6505–6519.
  8. Frank, A.G.; Dalenogare, L.S.; Ayala, N.F. Industry 4.0 technologies: Implementation patterns in manufacturing companies. Int. J. Prod. Econ. 2019, 210, 15–26.
  9. Barrios, P.; Danjou, C.; Eynard, B. Literature review and methodological framework for integration of IoT and PLM in manufacturing industry. Comput. Ind. 2022, 140, 103688.
  10. Rossit, D.A.; Tohmé, F.; Frutos, M. A data-driven scheduling approach to smart manufacturing. J. Ind. Inf. Integr. 2019, 15, 69–79.
  11. Demertzi, V.; Demertzis, S.; Demertzis, K. An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT). Algorithms 2023, 16, 378.
  12. Chabanet, S.; Bril El-Haouzi, H.; Thomas, P. Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context. Comput. Ind. 2021, 133, 103529.
  13. Jimeno-Morenilla, A.; Azariadis, P.; Molina-Carmona, R.; Kyratzi, S.; Moulianitis, V. Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review. Comput. Ind. 2021, 125, 103390.
  14. Erro-Garcés, A. Industry 4.0: Defining the research agenda. Benchmarking Int. J. 2021, 28, 1858–1882.
  15. Queiroz, G.A.; Alves Junior, P.N.; Costa Melo, I. Digitalization as an Enabler to SMEs Implementing Lean-Green? A Systematic Review through the Topic Modelling Approach. Sustainability 2022, 14, 14089.
  16. Yadav, V.; Gahlot, P.; Rathi, R.; Yadav, G.; Kumar, A.; Kaswan, M.S. Integral measures and framework for green lean six sigma implementation in manufacturing environment. Int. J. Sustain. Eng. 2021, 14, 1319–1331.
  17. Sundar, R.; Balaji, A.; Kumar, R.S. A Review on Lean Manufacturing Implementation Techniques. Procedia Eng. 2014, 97, 1875–1885.
  18. Mostafa, S.; Dumrak, J.; Soltan, H. A framework for lean manufacturing implementation. Prod. Manuf. Res. 2013, 1, 44–64.
  19. Gupta, S.; Jain, S.K. A literature review of lean manufacturing. Int. J. Manag. Sci. Eng. Manag. 2013, 8, 241–249.
  20. Banawi, A.; Bilec, M.M. A framework to improve construction processes: Integrating Lean, Green and Six Sigma. Int. J. Constr. Manag. 2014, 14, 45–55.
  21. Rathi, R.; Kaswan, M.S.; Garza-Reyes, J.A.; Antony, J.; Cross, J. Green Lean Six Sigma for improving manufacturing sustainability: Framework development and validation. J. Clean. Prod. 2022, 345, 131130.
  22. Touriki, F.E.; Benkhati, I.; Kamble, S.S.; Belhadi, A.; El fezazi, S. An integrated smart, green, resilient, and lean manufacturing framework: A literature review and future research directions. J. Clean. Prod. 2021, 319, 128691.
  23. Singh, R.K.; Kumar Mangla, S.; Bhatia, M.S.; Luthra, S. Integration of green and lean practices for sustainable business management. Bus. Strateg. Environ. 2022, 31, 353–370.
  24. Leong, W.D.; Lam, H.L.; Ng, W.P.Q.; Lim, C.H.; Tan, C.P.; Ponnambalam, S.G. Lean and Green Manufacturing—A Review on its Applications and Impacts. Process Integr. Optim. Sustain. 2019, 3, 5–23.
  25. Pagliosa, M.; Tortorella, G.; Ferreira, J.C.E. Industry 4.0 and Lean Manufacturing. J. Manuf. Technol. Manag. 2019, 32, 543–569.
  26. Koulinas, G.; Paraschos, P.; Koulouriotis, D. A machine learning-based framework for data mining and optimization of a production system. Procedia Manuf. 2021, 55, 431–438.
  27. Paraschos, P.D.; Koulinas, G.K.; Koulouriotis, D.E. Parametric and reinforcement learning control for degrading multi-stage systems. Procedia Manuf. 2021, 55, 401–408.
  28. Samadhiya, A.; Agrawal, R.; Garza-Reyes, J.A. Integrating Industry 4.0 and Total Productive Maintenance for global sustainability. TQM J. 2022, 36, 24–50.
  29. Xanthopoulos, A.S.; Koulouriotis, D.E. Multi-objective optimization of production control mechanisms for multi-stage serial manufacturing-inventory systems. Int. J. Adv. Manuf. Technol. 2014, 74, 1507–1519.
  30. Koulinas, G.; Paraschos, P.; Koulouriotis, D. A Decision Trees-based knowledge mining approach for controlling a complex production system. Procedia Manuf. 2020, 51, 1439–1445.
  31. Dahmani, N.; Benhida, K.; Belhadi, A.; Kamble, S.; Elfezazi, S.; Jauhar, S.K. Smart circular product design strategies towards eco-effective production systems: A lean eco-design industry 4.0 framework. J. Clean. Prod. 2021, 320, 128847.
  32. Amjad, M.S.; Rafique, M.Z.; Khan, M.A. Leveraging Optimized and Cleaner Production through Industry 4.0. Sustain. Prod. Consum. 2021, 26, 859–871.
  33. Jayal, A.; Badurdeen, F.; Dillon, O.; Jawahir, I. Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels. CIRP J. Manuf. Sci. Technol. 2010, 2, 144–152.
  34. Aruanno, B. EcoPrintAnalyzer: Assessing Sustainability in Material Extrusion Additive Manufacturing for Informed Decision-Making. Sustainability 2024, 16, 615.
  35. Kamble, S.S.; Gunasekaran, A.; Gawankar, S.A. Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Saf. Environ. Prot. 2018, 117, 408–425.
  36. Bai, C.; Kusi-Sarpong, S.; Badri Ahmadi, H.; Sarkis, J. Social sustainable supplier evaluation and selection: A group decision-support approach. Int. J. Prod. Res. 2019, 57, 7046–7067.
  37. Green, K.W.; Zelbst, P.J.; Meacham, J.; Bhadauria, V.S. Green supply chain management practices: Impact on performance. Supply Chain Manag. 2012, 17, 290–305.
  38. Moldavska, A.; Welo, T. The concept of sustainable manufacturing and its definitions: A content-analysis based literature review. J. Clean. Prod. 2017, 166, 744–755.
  39. Jamwal, A.; Agrawal, R.; Sharma, M. Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications. Int. J. Inf. Manag. Data Insights 2022, 2, 100107.
  40. Ahmad, T.; Madonski, R.; Zhang, D.; Huang, C.; Mujeeb, A. Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renew. Sustain. Energy Rev. 2022, 160, 112128.
  41. Roshani, A.; Paolucci, M.; Giglio, D.; Demartini, M.; Tonelli, F.; Dulebenets, M.A. The capacitated lot-sizing and energy efficient single machine scheduling problem with sequence dependent setup times and costs in a closed-loop supply chain network. Ann. Oper. Res. 2023, 321, 469–505.
  42. Ferreira, W.d.P.; Armellini, F.; de Santa-Eulalia, L.A.; Thomasset-Laperrière, V. Extending the lean value stream mapping to the context of Industry 4.0: An agent-based technology approach. J. Manuf. Syst. 2022, 63, 1–14.
  43. de Oliveira-Dias, D.; Maqueira-Marin, J.M.; Moyano-Fuentes, J.; Carvalho, H. Implications of using Industry 4.0 base technologies for lean and agile supply chains and performance. Int. J. Prod. Econ. 2023, 262, 108916.
  44. Soltani, M.; Aouag, H.; Anass, C.; Mouss, M.D. Development of an advanced application process of Lean Manufacturing approach based on a new integrated MCDM method under Pythagorean fuzzy environment. J. Clean. Prod. 2023, 386, 135731.
  45. Sarkar, B.; Ullah, M.; Sarkar, M. Environmental and economic sustainability through innovative green products by remanufacturing. J. Clean. Prod. 2022, 332, 129813.
  46. Liu, B.; De Giovanni, P. Green process innovation through Industry 4.0 technologies and supply chain coordination. Ann. Oper. Res. 2019, 1–36.
  47. Kurdve, M.; Bellgran, M. Green lean operationalisation of the circular economy concept on production shop floor level. J. Clean. Prod. 2021, 278, 123223.
  48. Tripathi, V.; Chattopadhyaya, S.; Mukhopadhyay, A.K.; Sharma, S.; Singh, J.; Pimenov, D.Y.; Giasin, K. An Innovative Agile Model of Smart Lean–Green Approach for Sustainability Enhancement in Industry 4.0. J. Open Innov. Technol. Mark. Complex. 2021, 7, 215.
  49. Duarte, S.; Cruz-Machado, V. An investigation of lean and green supply chain in the Industry 4.0. In Proceedings of the 2017 International Symposium on Industrial Engineering and Operations Management (IEOM), Bristol, UK, 24–25 July 2017; pp. 255–265.
  50. Li, C.; Zheng, P.; Li, S.; Pang, Y.; Lee, C.K. AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop. Robot. Comput. Integr. Manuf. 2022, 76, 102321.
  51. Shakya, M.; Ng, H.Y.; Ong, D.J.; Lee, B.S. Reinforcement Learning Approach for Multi-period Inventory with Stochastic Demand. In AIAI 2022: Artificial Intelligence Applications and Innovations; IFIP Advances in Information and Communication Technology Book Series; Springer: Cham, Switzerland, 2022; Volume 646, pp. 282–291.
  52. Matrenin, P.V. Improvement of Ant Colony Algorithm Performance for the Job-Shop Scheduling Problem Using Evolutionary Adaptation and Software Realization Heuristics. Algorithms 2022, 16, 15.
  53. Kayhan, B.M.; Yildiz, G. Reinforcement learning applications to machine scheduling problems: A comprehensive literature review. J. Intell. Manuf. 2023, 34, 905–929.
  54. Yan, Q.; Wang, H.; Wu, F. Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm. Comput. Oper. Res. 2022, 144, 105823.
  55. Li, R.; Gong, W.; Lu, C. A reinforcement learning based RMOEA/D for bi-objective fuzzy flexible job shop scheduling. Expert Syst. Appl. 2022, 203, 117380.
  56. Gu, W.; Li, Y.; Tang, D.; Wang, X.; Yuan, M. Using real-time manufacturing data to schedule a smart factory via reinforcement learning. Comput. Ind. Eng. 2022, 171, 108406.
  57. Paraschos, P.D.; Xanthopoulos, A.S.; Koulinas, G.K.; Koulouriotis, D.E. Machine learning integrated design and operation management for resilient circular manufacturing systems. Comput. Ind. Eng. 2022, 167, 107971.
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , ,
View Times: 70
Revisions: 2 times (View History)
Update Date: 18 Mar 2024