This section aims to highlight which Industry 4.0-enabling technologies are the most studied in the literature for implementation in a GSC. Analyzing the classification of papers based on the technologies described in Section 2, it is shown that 11 different technologies have been identified in 45 of the 75 papers. Among the most mentioned technologies are the IoT, big data, CPS, and AM (see Figure 7 and Table 2). From this classification, it turned out that there are technologies that are usually implemented individually (e.g., AM), whereas technologies such as RFID or CPS are implemented as a combination of technologies.

The IoT is conceived as a tool to facilitate the information flow through the entire GSC. The IoT plays an important role in the data acquisition and analysis related to inventory, transportation, facilities, and customers. For instance, IoT devices installed in material handling equipment allow us to know if equipment characteristics are beyond boundaries, for example the speed [109]. Another example is presented in [79], where the IoT identifies and classifies the components of products to manage recovery operations. In addition, this data collection can be useful in energy management and, therefore, in reducing energy consumption. As it is suggested by [108], the IoT can collect energy consumption information for its visualization. After that, decision-makers can use this information to manage energy-efficient practices. Overall, the IoT enables the real-time exchange of information with reliability [12], improving the efficiency and quality of production processes and decision making [92].

Research findings indicate that big data is an important technology to develop a competitive and green supply chain. The statistical survey conducted by [101] suggests that an appropriate infrastructure of big data can provide a competitive advantage for GSCs. In the same way, the analysis conducted by [102] using a descriptive qualitative method approach in which source and data collection is conducted through company observation, documentation, and interviews with key company managers. This analysis allows them to pose the following hypotheses: “Big data analytics have a positive relationship with I4.0 adoption” and “Sustainable manufacturing has a positive relationship with I4.0 adoption”. In short, the study asserts that the use of big data allows the management of more transparent energy consumption and recycling of resources. The same idea of energy consumption management and optimization through big data technology is presented in [96]. Moreover, big data can help managers through better visualization of the information for decision making [103], analyzing market trends [98], or even comparing the impact on the supply chain before and after implementing GSC aspects [104].

AM enhances a circular production system, since the materials used in AM processes can be recycled [1,92,100]. Apart from that, it is considered as a key technology for reducing cost, energy, and material waste [9,11,93,99] because of its capability for working just-in-time and the elimination of machine setups due to changeovers. Furthermore, it enables a decentralization of the production and shorter distances between end users and manufacturers, reducing transport cost and environmental impact [98,100].

In the same way, cloud computing technologies facilitate the sharing of information during the entire life cycle of products [92], providing real-time information about raw material status and production capacity. This can be used for achieving GSC practices, such as waste management [98].

Although blockchain has found little application in supply chains, it has great potential to increase transparency, reliability, and availability of information [107]. The quantitative, questionnaire-based study conducted by [105] determines that blockchain technology promotes and enables the tracking of pro-environmental behavior. This technology makes it possible to obtain, manage, and use data on a product throughout its life cycle, resulting in better product design, more efficient production planning, and environmentally responsible end-of-life recovery [98]. This behavior leads to improved green supply chain practices. In addition, traceability serves to increase stakeholder confidence in green supply chains [96], for example, by tracking the environmental quality of materials [92] or reducing rework by knowing the full traceability of defective products, which reduces resource use and greenhouse gas emissions [57]. In addition, the actual carbon footprint of products can be tracked, and the exact amount of carbon tax to be charged to each company can be determined [108]. Ultimately, blockchain technology ensures environmentally friendly product design and production [121].

Once the information is acquired, AI plays an important role in decision making [92]. The articles dealing with AI tools present different case studies. They build on previous experiences to create models that detect possible failure patterns. One of the studies has developed a model that predicts chiller operating efficiency and a chiller health assessment model. This prediction allows meeting the chiller load demand and minimizing electricity consumption given the existing infrastructure [91]. Another study presents a system that makes decisions to meet production targets in a production plant in the presence of uncertainties [89]. Finally, ref. [81] proposes the use of AI to solve technical challenges related to process safety. For example, knowledge-based reasoning for process safety or dynamic risk assessment. Hence, these models minimize energy consumption and enable more efficient decision making.

Simulations can be useful in testing and validating tools and systems [110]. This technology allows the measurement of process efficiency before its implementation through the simulation of activities [92]. For example, simulating and testing activities that are supposed to reduce energy consumption [12].

Following the idea of process efficiency, robotics technology is a key element to improve efficiency by reducing resources and errors. Therefore, its implementation can minimize energy consumption and material waste [92,93].

Autonomous vehicles integrate production, logistics, and planning movements. This integration enables the optimization of movements, reducing fuel consumption and emissions [93]. Moreover, their use improves material handling and reduces carbon emissions, improving environmental performance [108].

They are considered key technologies for smart and sustainable factories (as suggested in [16]) because they enable the treatment of data to achieve valuable information that can be seen through cloud platforms [12,80,106,118]. This enhances the development of indicators to avoid waste and reduce emissions [116] and the monitorization of energy consumption, equipment status, or product quality [120].

As case studies, [119,122] combined these four technologies to develop energy monitoring systems and KPIs by acquiring real-time data, whereas [111] implemented big data and IoT technologies to check the quality of products, improving decision making and reducing waste.

Another repeated combination of technologies is IoT and RFID to record real-time information during the entire product lifecycle, reducing the complexity of the recycling process [114,115]. Adding RFID can improve tracing and tracking in reverse logistics, lowering carbon emissions during transportation [95].

Finally, [97] concluded that integrating all of these technologies (IoT, CPS, big data, cloud computing, simulations, AI, AM, and autonomous vehicles) enables human–machine collaboration to automate the process economically and environmentally.

This section aims to identify which technologies associated with Industry 4.0 are used for each GSC practice. Within the sample of sixty-seven papers that specifically talk about GSC aspects (identified in Section 2), five different aspects have been found: reverse logistic, green warehousing, green design, green manufacturing, and carbon management (see Figure 8). These five aspects are developed in the following subsections.

According to Figure 8, the most studied aspect of GSC within the selected papers is green manufacturing, which appears in 46 of the 67 papers.

As we defined in the literature review section, green manufacturing tries to diminish the environmental impact of production processes. Most of the papers focus on energy consumption due to its high impact in green manufacturing [123]; 70% of the energy consumption in industries belongs to machines [124].

Scholars present AM as a smart production system capable of reducing energy consumption, since it reduces the difficulty of manufacturing complex 3D parts, which results in low energy utilization [1,93,94,97,99].

Others point out that one of the keys of investment to lower energy consumption during manufacturing is robotic development [93]. Moreover, robots reduce the number of human resources and, therefore, the heating and lighting costs, as a transition to a “lights out factory” [125].

Furthermore, the use of CPSs and the IoT enables the real-time monitorization and, therefore, the optimization of energy consumption [94], whereas simulations can reduce energy consumption by simulating activities instead of developing them in the physical world [12,91].

Another interesting green manufacturing practice is the adoption of a lean production, which is known as lean and green. Although [126] in their literature review and [127] in their quantitative measurement do not identify the relation between smart factories and the lean and green supply chain, other authors consider that the purposes of implementing Industry 4.0-associated technologies (efficiency, productivity, flexibility, and transparency) must be also considered in lean and green [128]. In the same way, ref. [88] consider that Industry 4.0 technologies enhance the reduction in delivery times, deliver high quality products, minimize waste, and enhance risk aversion, considered lean terms. These terms reduce carbon footprint and GHG emissions at the same time. The authors of [117] revealed that lean terms and Industry 4.0 enabling technologies are beneficial for achieving optimizing and cleaner production. In this case, they reduced the lead time by 25.60% and the carbon dioxide, methane, and nitrous oxide emissions by 55%. The authors of [129] identify efficiency improvement and an increase in flexibility as the outcomes of integrating Industry 4.0 with lean and green practices. Finally, ref. [130] presents a roadmap for integrating lean and green with Industry 4.0 technologies because they consider this as crucial for developing viable GSCs.

Among all the technologies, CPS, IoT, big data, and cloud computing enable higher effectiveness and resource efficiency [116] and are crucial due to the high level of information sharing required by lean and green supply chains [83,118,131].

Finally, green manufacturing aims also to gain production efficiency, which is considered one of the benefits of implementing Industry 4.0 [11,77,106].

According to several authors, adopting CPS, IoT, big data, and cloud computing technologies for real-time data acquisition enhances and speeds up decision making, improving scheduling and operational efficiency [62,78,80,111,112,132].

Our study identifies three cases of AI implementation for this purpose, where AI detects potential failures and enables quicker decision making [81,89,103].

Simulations are also considered interesting for making strategic decisions because risks can be assessed before the implementation of the strategy [113,121,133].

Design for recycling is described by [11] as “Designing in a manner that will enable waste materials with specified properties to be recovered and used again in the manufacturing process or by others is essential”.

This practice has been identified in two papers using AM. First, AM enables the recycling of waste materials to be used as raw material [100]. The second point is the design of new green products. Here, the capability of AM in manufacturing complex and high-resolution features which often cannot be achieved within the constraints of traditional manufacturing methods is crucial [94].

Green design also consists in designing products which reduce the consumption of materials and energy [134]. In this sense, ref. [94] suggest that simulation technologies can introduce product innovations for an eco-friendlier product, saving energy and material consumption.

Finally, ref. [105] propose the use of blockchain to reduce the negative environmental effects of products, which is another purpose of green design, by sharing raw material information in a more secure, reliable, and trusted way.

Reverse logistic is a GSC aspect that covers the level of recovery and return of used products through operations such as tracking and tracing [1].

All the papers which studied this aspect of GSCs suggest that the IoT enhances information management [11,79]. The combination of the IoT with RFID tags enables the information record of products through their life cycle and tracks them in real-time, reducing the uncertainty and complexity of the product recycling process [114,115]). The authors of [9] add CPS and cloud computing technologies to the IoT and RFID to improve these track-and-trace operations by acquiring and sharing information about products and processes.

Another GSC aspect identified in this study is carbon management, particularly in relation to carbon reduction targets. Therefore, the monitorization of carbon footprint is attracting more attention in the manufacturing environment [120]. Technologies such as CPS, IoT, cloud computing, and big data are necessary to achieve this monitorization, since they enable the acquisition of real-time information.

In this sense, the case studies analyzed which consider this aspect of GSC agree on developing KPIs for monitoring carbon emissions. The authors of [10] developed four indicators that together assess carbon dioxide damage based on technology-induced logistics performance indices, economic growth, industry value added, and high-tech industry. The case study reported in [119] evaluated the six big losses of Nakajima in carbon footprint units.

With regard to green warehousing aspects, two interesting practices have been identified. The first practice is the decrease in inventory levels. CPS, IoT, big data, and cloud computing technologies with the help of AI can adjust the allocations and routes of goods knowing in real-time the available resources and space, as suggested by [98].

The other practice proposes the use of technologies to optimize material flows. In particular, autonomous vehicles are considered efficient in material handling operations due to the reduction in human intervention [108]. Moreover, the acquisition of real-time data thanks to the IoT enables faster decision making, thus accelerating material flows. The information collected about material call actions can be used to optimize material flows [92,125].

To summarize, Table 3 illustrates the relationships found in the literature between Industry 4.0 cutting-edge technologies and GSC aspects divided by practices. The value inside the summary cells shows how many papers have cited the implementation of each technology to improve a GSC practice. The results show that RFID is only being considered by scholars in reverse logistic practices, whereas AM is covered in all the GSC aspects except for carbon emissions management. Furthermore, researchers foresee the use of technologies involved in real-time data acquisition and processing, such as CPS, IoT, big data, or cloud computing, as tools to improve efficiency and lean practices in GSCs.

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https://www.mdpi.com/2071-1050/15/12/9784