Influence of Industry 4.0 on the Response Speed of LSCM

The technologies analyzed in the literature that can potentially contribute to response speed in LSCM contexts are: (1) IoT; (2) RFID; (3) Cloud Computing; (4) AI; (5) AV; (6) Big Data; (7) Additive Manufacturing. The role that these IT play in enhancing the SC’s response speed in LSCM contexts, according to the analyzed literature, is described below.

Internet of Things (IoT)

IoT has sufficient potential to impact almost all sectors, from retail to the automotive industry. The contributions that its implementation makes in LSCM contexts are related to significant improvements in capacity and rapid adaptation to changes in demand due to greater anticipation and faster response to unforeseeable situations. IoT enables data to be collected and analyzed automatically, as well as managed remotely, by providing real-time visibility, traceability, and control of SC processes (Xu et al. 2018). When focal companies, suppliers, and customers implement this IT, it provides a substantial opportunity to generate constant information flows (continuous data vs. discrete data) and to fully or partially automate several communication functions across the SC (Dave et al. 2016). It is then possible to monitor not only the progress of processes and the flow of physical elements in real time but also the information associated with them, enabling the Lean principle of visual management through IoT devices (Dave et al. 2016; Sanders, Elangeswaran and Wulfsberg

  • 2016) . It makes physical and information flows visible at key points in the SC at all times, resulting in real-time monitoring and control of stock levels and precise location (Hofmann and Riisch 2017). The generation of information flows and real-time data transmission between the focal company and its suppliers and customers enable much more effective and efficient short-term planning (Yerpude and Singhal 2017). All of this enables reductions in inventory levels, makes inventory easier to manage, and helps reduce response times and lead times, thus significantly contributing to improving the SC’s response time (Hofmann and RUsch 2017; Yerpude and Singhal
  • 2017) .

Radio Frequency Identification (RFID)

RFID is an IT that is very closely linked to IoT. It enables objects to be identified with no human intervention, thus giving real-time visibility to physical flows along the SC thanks to the accurate and detailed information that the technology provides (Saygin and Sarangapani 2011; Powel and Skjelstad 2012). RFID systems have contributed to improving the LSC’s response speed thanks to faster information capture and transmission in conjunction with greater information integration. It, therefore, affords the SC new capabilities for a much more agile flow of products, whether goods or services, such as in the case of automatic picking, for example. So, in LSCM contexts, RFID has a knock-on effect on response speed to address frequent market changes and reduce the time needed for product movement and distribution (Otamendi, Garcla-Higuera and Garcla-Ansola 20П). Thus, for instance, Just-in-Time and the constant visibility of information provided by RFID systems allow SC members to reduce waiting and processing times (waste), to improve inventory management, to leverage the continuous flow principle (Saygin and Sarangapani 2011) and, in the final instance, to improve the LSC’s response speed (Otamendi, Garcla-Higuera and Garcla-Ansola 2011). In addition, the opportunities that RFID systems offer for information to be quickly and autonomously captured in LSCM environments have enabled the automation of processes such as the monitoring and intelligent reallocation of orders, which make Just-in-Time and Just-in-Sequence significantly easier (Shin et al. 2011; Nabelsi and Gagnon 2017; Sanders, Elangeswaran and Wulfsberg 2016; Tsao, Linh and Lu 2017). Improvements to the real-time trace- ability and visibility of physical flows provided by RFID use have resulted in better inventory control, with reductions in customers’ and suppliers’ lead times and the time associated with inventory-related decision-making -e.g., in case of suppliers’ delays (Dai et al. 2012; Huang et al. 2012; Zelbst et al. 2014; Moon et al. 2018). All the above means that an LSC can operate with greater agility and speed.

Cloud Computing

Cloud Computing represents a leap forward from the traditional communication mechanisms between the various SC members. These systems and the use of intelligent devices such as smartphones and tablets enable faster access to information anytime, anywhere (Sanders, Elangeswaran and Wulfsberg 2016). In contexts where LSCM has been adopted, Cloud Computing contributes to response speed, which enables large volumes of data to be stored and processes to be monitored in real time, with all SC members able to access information anytime, anywhere (Vazquez-Martinez et al. 2018; Xu et al. 2018). Customers and suppliers can use Cloud Computing to manage heterogeneous assets and exchange information, with the agents involved able to share and act upon the same information in real time. This improves communication and cooperation between them and, in short, improves the time needed to respond to any changes that might occur in the environment (Hofmann and Riisch 2017; Xu et al. 2018). Specifically, Vazquez-Martinez et al. (2018) found that certain Cloud solutions could even reduce the negotiation to a partner-to-partner scheme, which translates into a minimization of coordination and synchronization requirements to support the data exchange among partners and, therefore, into a rapid response to changes.

Artificial Intelligence (AI)

The current environment in which organizations operate is characterized by complexity, dynamism, and uncertainty. These characteristics are even more marked in the context of the SC. In this sense, organizations are developing powerful Al-based support systems for decision-making throughout the SC (Liu et al. 2013). So, for example, Al-based systems are used to intelligently map routes and these systems have great potential for improving deliveries in LSCM environments such as Just-in- Time and Just-in-Sequence (Giiner, Murat and Chinnam 2012; Hofmann and Rilsch 2017). Intelligent routing systems based on real-time traffic information have enabled improvements to delivery speeds throughout the SC, reductions in lead times, and greater customer satisfaction (Giiner, Murat and Chinnam 2012). They have also allowed appropriate transport to be selected based on specific order requirements (quantities, delivery dates, etc.), and so the use of these systems means that transportation processes are no longer planned individually but in an integrated way throughout the entire SC (Hofmann and Riisch 2017). In objective criteria and knowledge-based decision-making processes, the use of Al-based systems is enabling the elimination of any type of activity that does not add value along an LSC. Any wastage, such as excess production and inventory, can be eliminated, whilst lead times and the unnecessary movement of materials and products can be reduced. All of this has a direct impact on the LSC’s response speed and, consequently, drives up profitability and productivity (Liu et al. 2013).

Autonomous Vehicles (AV) – Autonomous Carriers (AC)

The literature has analyzed the use of a specific type of autonomous vehicle in LSCM contexts: Autonomous Carriers (AC) automatically distribute materials to production chains and thus involve the suppliers of these materials. Vehicles of this type contribute to improving the SC’s response time and their use is associated with rapid decision-making for smart individual entities and fast operations, as components and raw materials are supplied automatically. In addition, logistics process times are shortened, as the movement rate is automatic and the vehicles identify local obstacles and manage them by responding to them (self decision-making and control), which enables logistics process times to be substantially reduced. In short, the use of these systems in LSCM results in increased performance, effectiveness, efficiency, and faster response speed and reduced lead times throughout the SC (Mehrsai, Thoben and Scholz-Reiter2014). A broad range of simulated experiments using mathematical calculations have amply proven AC’s capabilities and their use has led to improvements in the required response capability and performance increases in dynamic circumstances and real-time work. AC have, therefore, been seen to contribute directly to raising the response speed in LSCM contexts by permitting a continuous material flow, shorter processing time in logistics operations, reduced lead times and work in progress, greater machine utilization and the avoidance of idleness in queues (waste) (Mehrsai. Thoben and Scholz-Reiter 2014).

Big Data

The application of new technologies such as Big Data is transforming SCs, with major changes from what we know today. In a sustainable world, the SC needs to be designed from the customer backwards (demand pull) instead of from the factory outward (supply push), making it responsive to customer demands and reducing waste and returns (Christopher and Ryals 2014). Emerging new technologies such as Big Data enable huge volumes of customer data to be processed and their needs anticipated, with demand identified much more accurately (Christopher and Ryals 2014). Processing large amounts of customer data also enables the value proposal to be better specified through the Lean lens and extending Big Data usage to SCs allows these to operate with less inventory and the SC as a whole to respond more rapidly to any changes demanded by customers (Christopher and Ryals 2014). As a result, Big Data enables changes in consumer preferences to be identified and any need for new products or opportunities in new markets to be identified. In LSCM contexts, Big Data will enable faster decision-making based on information analysis and will be aligned with any changes in the environment, thus facilitating a realtime response to address these changes (Christopher and Ryals 2014). So, in LSCM environments these technologies will enable the development of dynamic capabilities that will allow companies to rapidly respond to any changes in the environment (Christopher and Ryals 2014).

Additive Manufacturing

Other manufacturing technologies that are emerging in Industry 4.0, such as additive manufacturing/3D printing, have also been analyzed by the extant literature in LSCM contexts. Additive manufacturing has been found to shorten product development, production, and commercialization times, thus improving shortterm response time (Christopher and Ryals 2014). In this sense, digital designs enable additive manufacturing to postpone production to the very last possible moment in the SC (Christopher and Ryals 2014). This reduces any wastage linked to the unnecessary movement of goods and so shortens the times associated with materials supply and increases speed in Just-in-Time environments. At the same time, AM enables small-footprint manufacturing changing the traditional paradigm of the “economies of scale” to “economies of scope” and, ultimately, allows the ability to achieve mass-customization in LSCM environments. Another advantage of additive manufacturing in LSCM environments is the possibility of simplifying the generation of prototypes, with substantial reductions in the times associated with this activity and also reductions in production time and costs associated with products (Christopher and Ryals 2014). As occurs with Big Data, in LSCM contexts, these technologies enable dynamic capabilities to be developed that improve the response speed to environmental changes (Christopher and Ryals 2014).

 
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