Opportunities for Supply Chain Improvements

In recent years, increasing environmental concerns have driven the focus within supply chains to introducing sustainability pressures among their agents from product design to delivery, creating the concept of a green supply chain (Srivastava, 2007). However, greater coordination between firms is required to actually achieve further emission and waste reduction - fundamental conditions for establishing a CE - as well as dynamic reverse logistics for remanufacturing processes (Kamble et al., 2018; Cao et al., 2019). When improving connectivity between agents, 14.0 technologies can unleash several opportunities to overcome this challenge.

The use of CDA technologies, not only at the firm level but across supply chains, allows for multidirectional information exchanges, from the first tier of suppliers downward and vice versa. These can extend the benefits of information analysis. Transparency between firms and their response times are increased as connections become smoothed; resource efficiency, decision-making and logistics become optimized across supply chains as firms increase their sustainable performance (de Sousa Jabbour et al., 2018; Dubey et al., 2019). This improvement, highly regarded by customers with sustainability concerns, results in additional created value for firms. In addition, further supply chain integration will result in higher operational performance, improving the competitive advantages of the whole supply chain that can underpin the transition to greener supply chain practices (Lee and Lee. 2015; Bruque-Camara et al., 2016; Dalenogare et al., 2018).

As Li et at. (2020) reveal that the success of digital technologies lies in the establishment of DSNs. Digital technologies must overcome firms’ boundaries and integrate both downstream and upstream supply chain agents to maximize the value of

14.0 effects in highly competitive environments. DSNs thus become a cornerstone of improving economic and sustainable performance, as they are the mediating mechanism between digital technologies and environmental performance.

These changes will radically alter the way supply chains behave. With the appearance of CPSs and AGVs, flows and exchanges are enhanced, not only at the firm level but across the entire supply chain. Notwithstanding this, AM will be the revolutionary technology that sets out disruptive changes in logistics since it entails lighter and fewer deliveries, smaller batches and reductions in manufacturing steps (Sartal et al., 2018; Niaki et al., 2019). Indeed, as Ghobadian et al. (2020) highlight, AM alters the backbone of traditional supply chains; jobbing or batch processes are now replaced by 3D printers that do not cut, bash or weld materials. As a consequence, JIT practices will also be adjusted as firms can manufacture products on demand by loading only the specified design in the printer’s software. Product stock will not be required anymore while inventories move toward storing raw material, shortening supply chains, reducing time to market and reducing the need for transportation (Table 2.1).


Corollary: I4.0 Technologies in CE Environments: Potential Improvements and Key Factors



CE Improvements


Connection and Data Analytics (CDA)

Liu and Xu, 2017 Tao et a!., 2018 Kamble et al., 2018

Frank et al.. 2019 Cao et al.. 2019 Li et al., 2020 De Sousa Jabbour et al., 2018

The Big Internet


Big Data and Analytics (BDA) Cloud Systems (CS)

  • • Production flexibility
  • • Material use efficiency
  • • Information processing efficiency
  • • Decision-making
  • • Emission and waste reduction
  • • Identification of environmental and operational tradeoffs
  • • Eco-practices: life cycle assessment, eco-design, data monitoring and evaluation of KPIs. products' end-of-life recovery
  • • Collaboration across supply chains
  • • Organizational change
  • • Privacy and security
  • • Workforce’s digital capabilities
  • • Technological infrastructure and firm strategies
  • • Need for investments

Autonomously Supervised Plant Systems (ASPS)

Sartal et al.. 2018 Ghobadian et al., 2020

Oztemel and Gursev. 2020 Dalenogare et al., 2018

Quenehen et al.,


Fragapane et al.,


Automatic Guided Vehicles (AGV) Autonomous Robots and Cobots (ARC) Additive Manufacturing (AM)

  • • Material use efficiency
  • • Energy consumption efficiency
  • • Production flexibility and modularity
  • • Production efficiency
  • • Mass customization
  • • Emission and waste reduction
  • • Continuous improvement and self-organization
  • • Flaw spotting and monitoring
  • • Recycling, disassembly, remanufacturing and reuse
  • • Emission and waste reduction
  • • Product performance and durability
  • • Integration between supply chain agents
  • • Lean principles
  • • Organizational changes
  • • Risk of failure due to their still-formative stage
  • • Social, operational and economic disruptions
  • • Employee reluctance
  • • Need for CDA technologies

TABLE 2.1 (Continued)

Corollary: I4.0 Technologies in CE Environments: Potential Improvements and Key Factors



CE Improvements


Virtual Reality and Optimization (VRO)

Qi and Tao, 2018 Kusiak. 2018 Gbededo el ai, 2018

Oztemel and Gursev. 2020 Leng el al„ 2019

Virtual Reality


Augmented Virtual Reality (AVR) Digital Twins (DT)

  • • Green design of products and production systems
  • • Production efficiency
  • • Decision-making and forecasting capabilities
  • • Product life cycle assessment: quality and performance
  • • Eco-design
  • • Need for investments
  • • Workforce’s digital capabilities
  • • Technological advancement and infrastructure
  • • Risk of failure due to their still-nascent development stage
  • • Need for CDA and ASPS technologies
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