To obtain an overview of the current state of knowledge and of the aspects and implications that exist between 14.0 and LSCM, the most relevant contributions have had to be identified in the extant literature in the area that is the object of study. For this, the process proposed for a Systematic Literature Review (SLR) has been applied (Tranfield, Denyer and Smart 2003; Denyer and Tranfield 2009; Thome, Scavarda and Scavarda 2016; Durach, Kembro and Wieland 2017). This methodology enables the current state of existing research into any particular topic to be understood through the application of a structured, rigorous, and objective process. When the proposed SLR process is correctly applied, the most relevant contributions in an area of study can be identified and the existing information regarding a specific research question synthesized, thus providing a general overview of the current state of research (Tranfield. Denyer and Smart 2003; Denyer and Tranfield 2009).

The five proposed stages for an SLR (Denyer and Tranfield 2009) have been followed in this study in conjunction with the recommendations and proposed best practices for its specific application in Operations Management in general (Thome, Scavarda and Scavarda 2016) and the SC in particular (Durach, Kembro and Wieland 2017). In the process that is followed, the research is got underway by establishing a research protocol that details specific issues that have to be taken into account as the research is developed. So, a series of guidelines are laid down to prevent any errors and to guarantee consistency and coherence when a number of different researchers are involved (Thome, Scavarda and Scavarda 2016). This protocol contains the procedure that should be adhered to, to achieve the proposed goals and includes issues related to search strategies, criteria for including and excluding articles, criteria for identifying contributions, and details for coding the information extracted from the articles.

The specific steps followed were taken from (Tranfield, Denyer and Smart 2003; Denyer and Tranfield 2009), as shown in Figure 6.1: (1) research question formulation; (2) locating the literature; (3) study selection and evaluation; (4) analysis and synthesis of results; (5) reporting of results.

With the scope of the study delimited and the research goals specified (1), the literature is located (2). For this, the Web of Science (WoS), Scopus, and ABI Inform databases have been used. The search chains have been designed as combinations of three blocks of words using simple and Boolean operators. The area that is the object of study is the intersect between 14.0, Lean, and SC. The search keywords used are given in Table 6.1. The syntax of the search chains has been adapted to each of the databases used.

The SLR process then continues (3) with the selection and evaluation of the studies identified in the previous step. The objective at this stage is to eliminate any works that appear in the searches, but which are not relevant and/or are unrelated to the object of study (false positives). For this, inclusion and exclusion criteria have been established that determine which studies are to be considered

Systematic Literature Review (SLR)

FIGURE 6.1 Systematic Literature Review (SLR): stages: (Adapted from Denyer and Tranfield 2009).

TABLE 6.1 Search Keywords

Lean: Lean, JIT, Just-in-Time

Supply Chain: supply chain, logistics

Industry 4.0: Industry 4.0, information system, information technology, information and communication technology, ICT, technological innovation, internet of things, IoT, cloud, web, e-business, e-commerce, radiofrequency identification, RFID, business intelligence, virtual reality, augmented reality, robot, artificial intelligence, big data, blockchain, autonomous carriers

in the research, and those that are to be discarded with no additional analysis. To ensure the relevance of the selected works, only articles and press articles published in English between 1996 (first article on LSCM, Lamming 1996) and December 2018 have been considered. The search has been restricted to the areas of research included in the scope of this study. Subsequently, the articles have been evaluated by reading the titles, abstracts, and keywords. Any articles that clearly do not comply with the research question have been discarded, and in cases where doubts have arisen, the theoretical framework and the main results of the article have been read. Furthermore, during this stage, a check has been made to ensure that the selected articles have been published in journals indexed in Journal Citation Reports (JCR) and/or Scimago Journal Rank (SJR) to guarantee the peer review process and the quality of the articles. This process yielded a final selection of 23 articles.

In (4) the analysis and synthesis of the results, each of the selected works has been read in its entirety. As this stage has been conducted by more than one researcher, as specified in the research protocol (Thome, Scavarda and Scavarda 2016), a database has been constructed in Excel with the main ideas, objectives, and contributions of each of the analyzed works to ensure the consistency of the results and enable their interpretation. The main technologies involved have also been identified, as has other interesting complementary information such as the title, author, journal, year of publication, methodology, and journal impact.

The last stage of an SLR consists of (5) reporting the end results of the analysis and synthesis. The following section describes this stage in detail. First, a classification of the existing literature is proposed, followed by the presentation and discussion of the results in line with the proposed classification. Figure 6.2 illustrates the process followed in the SLR.


The systematic literature review and, more specifically, the analysis and synthesis process have enabled a criterion to be identified for grouping the identified articles by the contributions that the specific 14.0 IT make to flexibility and response speed in LSCM contexts. Table 6.2 gives the novel classification of the articles based on this classification criterion.

This classification has enabled the identification of the technologies that contribute flexibility to the LSC that have been the object of attention in the literature. These are IoT, RFID, Cloud Computing, and VR, with Cloud Computing being the technology that has received the most attention from researchers in recent years. With respect to the technologies that contribute to response speed most developed in the literature in the context of LSC, these were IoT, RFID, and AI. On the other hand, technologies such as Blockchain, Big Data, Autonomous Vehicles, and Additive Manufacturing have not been greatly developed in LSCM research. There is, moreover, a consensus among researchers that technologies such as IoT, RFID, and Cloud Computing contribute to both flexibility and response speed in the LSC.

Described in greater detail below are, first, the IT that affect flexibility in LSCM environments and, second, the IT that affect response speed.

Summary of SLR

FIGURE 6.2 Summary of SLR.

Influence of Industry 4.0 on LSCM Flexibility

The IT analyzed in the literature that can potentially contribute to improvements to flexibility in the LSCM framework are: (1) IoT; (2) RFID; (3) Cloud Computing; (4) VR; (5) Blockchain. Described below are the roles played by each in this respect.

Internet of Things (IoT)

IoT contributes to LSCM flexibility by enabling the integration of the different processes that generate value throughout the SC. It does this by connecting and


Classification of Articles





Line of research (first level of classification)



Internet of Things

Dave et al. (2016); Sanders, Elangeswaran and Wulfsberg

  • (2016) ; Hofmann and Riisch (2017); Yerpude and Singhal
  • (2017)

Radio Frequency Identification

Otamendi, Garci'a-Higuera and Garci'a-Ansola (2011); Saygin and Sarangapani (2011); Shin et al. (2011); Powell and Skjelstad (2012)

Cloud Computing

Sanders, Elangeswaran and Wulfsberg (2016); Hofmann and Riisch (2017); Vazquez-Martinez et al, (2018); Xu et al. (2018)

Virtual Reality

Li etal. (2018)


Perboli, Musso and Rosano (2018)



Internet of Things

Dave et al. (2016); Sanders, Elangeswaran and Wulfsberg

  • (2016) ; Hofmann and Riisch (2017); Yerpude and Singhal
  • (2017) ; Xu etal. (2018)

Radio Frequency Identification

Otamendi, Garci'a-Higuera and Garci'a-Ansola (2011); Saygin and Sarangapani (2011); Dai et al. (2012); Huang et al. (2012); Powell and Skjelstad (2012); Zelbst et al. (2014); Sanders, Elangeswaran and Wulfsberg (2016); Nabelsi and Gagnon (2017); Tsao, Linh and Lu (2017)

Cloud Computing

Sanders, Elangeswaran and Wulfsberg (2016); Hofmann and Riisch (2017); Vazquez-Martinez et al. (2018); Xu et al. (2018)

Artificial Intelligence

Giiner, Murat and Chinnam (2012); Liu et al. (2013); Hofmann and Riisch (2017)

Autonomous Vehicles

Mehrsai, Thoben and Scholz-Reiter (2014)

Big Data

Christopher and Ryals (2014)



Christopher and Ryals (2014)

exchanging information, mainly by the technology providing improvements to interoperability between the main information systems (Dave et al. 2016; Sanders, Elangeswaran and Wulfsberg 2016). Information flows generated between SC members as a result of IoT implementation facilitate production planning and control (Dave et al. 2016; Hofmann and Rusch 2017; Yerpude and Singhal 2017) by a precise deduction of demand patterns in real time and, in the final instance, allowing the Lean systems in an LSC to respond more flexibly to evolving customer demands and market turbulences (Hofmann and Rusch 2017; Yerpude and Singhal 2017). The benefits that IoT brings enable the medium- and long-term development of close ties between customers and suppliers, mostly as a result of IoT offering greater transparency in the LSC (Dave et al. 2016; Yerpude and Singhal 2017). Having real data on processes and products at all times means that these can be better planned, and actions can be taken to meet customer needs in the medium or long term (Dave et al. 2016; Yerpude and Singhal 2017). In this regard, Yerpude and Singhal (2017) highlight that Vendor Managed Inventory (VMI) enabled with IoT helps the Lean systems to track inventory in real time (e.g., categorizing it into Fast, Slow and Nonmoving, FSN) and to achieve a more flexible SC through an improved replenish pull system (Sanders, Elangeswaran and Wulfsberg 2016).

Radio Frequency Identification (RFID)

RFID technologies are very closely related to IoT and are used in LSCM to handle information automatically (Otamendi, Garcfa-Higuera and Garcfa-Ansola 2011; Powel and Skjelstad 2012). So, the creation of information management platforms with RFID technology support enables the development of an efficient management framework capable of perfectly integrating the heterogeneous and dynamic environments of the various SC agents. This is an effective support for information exchange and gives the LSC added flexibility (Shin et al. 2011). Furthermore, RFID has a strong impact on stock control by improving the monitoring of material flows across the LSC (Saygin and Sarangapani 2011; Powel and Skjelstad 2012) thanks to the greater visibility given to products and their associated information. This benefits organizations, as they are able to collaborate, plan, supervise, and execute whilst continuously improving the operating features in the value flow (Saygin and Sarangapani 2011; Powel and Skjelstad 2012). In this line. RFID helps boost LSC flexibility, trust, and security by eliminating errors in the capture and processing of product-related information and thus supports the ongoing improvement of an organization’s processes (Otamendi, Garcfa-Higuera and Garcfa-Ansola 2011; Powel and Skjelstad 2012). Thus, RFID systems in LSCM environments have contributed to a better and flexible decision-making process when the initial plan needs to be changed and a more flexible response to changes (Shin et al. 2011) by leveraging LSCM principles (Saygin and Sarangapani 2011) and some backbone techniques such as Value Stream Mapping (Powel and Skjelstad 2012).

Cloud Computing

When implemented in LSCM, Cloud technologies allow the flexible integration of different systems and technologies (Sanders, Elangeswaran and Wulfsberg 2016; Hofmann and Riisch 2017; Xu et al. 2018). Many authors agree on highlighting Cloud technologies’ ability to enable a framework of collaboration between SC members, which results in greater SC synchronization/integration and effective feedback between suppliers and customers (Sanders, Elangeswaran and Wulfsberg 2016; Hofmann and Riisch 2017; Vazquez-Martinez et al. 2018; Xu et al. 2018). Thanks to its all-pervasiveness and interoperability, Cloud Computing enables data to be collected and handled throughout the entire SC. Heterogeneous assets can be managed and information exchanged between customers and suppliers in such a way that they can share and act on the same information flexibly and at a minimum cost, whilst the SC’s transparency and flexibility are increased in such a way as to prevent or reduce the bullwhip effect (Hofmann and Riisch 2017; Xu et al. 2018). This is thanks to all connected agents that would be able to act on real time. In fact, real time consumption of products may automatically trigger supply orders or inform suppliers and, therefore, they may gain additional flexibility (Hofmann and Riisch 2017). In addition, Cloud Computing makes a shared economy possible, reducing both costs and the need for investments in infrastructure and enabling the use of other complementary technologies in small- and medium-size companies such as IoT, which provides configurable and scalable information services along a SC and, ultimately, enables business processes to be managed more flexibly, efficiently, and economically (Xu et al. 2018).

Virtual Reality

When applied in LSCM, VR is closely linked to learning processes in very complicated procedures. So, in Prefabrication Housing Production, for example, it enables the site assembly process and logistics processes to be replicated (Li et al. 2018). And it has proven its utility by providing the agents involved in the process with the necessary skills and an understanding of how processes are executed and familiarizing them with the computer programs used. Training can be done anywhere with consequent savings in space, time, and money (Li et al. 2018). So, in the final instance, VR contributes to increasing the flexibility of the companies that use it. Thus, VR enables the greater adaptation of workers and SC members to new' processes, mitigating any possible uncertainties, detecting constraints, reducing and/ or eliminating any possible errors and, ultimately, improving the decision-making process (e.g., assessment of production quality, remaining time to the site assembly, lead time of responding to changes) and optimizing processes throughout the LSC (Li et al. 2018).


Thanks to its ability to guarantee the inalterability, integrity, and scalability of information, in LSCM contexts, Blockchain builds greater trust between SC members to the benefit of interoperability. The heterogeneous and dynamic nature of the information generated by customers and suppliers throughout the SC and the difficulty of integrating the information systems of all the organizations involved could be mitigated by this technology. If Blockchain is adopted by customers and suppliers, it could guarantee organizations information integration and interoperability with their existing systems (Perboli, Musso and Rosano 2018). Trust between members reduces the need for additional checks and provides for common, inalterable information that confers on organizations a greater medium- or long-term capacity for planning and adaptation to address environmental changes. This greater flexibility derives from the transparency and visibility of information that the technology offers in LSCM environments. Inaccurate product demand and flow' information have short-term negative effects on both customers and suppliers. However, when the entire SC is visible (inbound and outbound processes), manufacturers can optimize production processes in the medium and long term, anticipate any possible fluctuations in demand, and reduce the bullwhip effect, and thus enjoy greater flexibility. In this w'ay, inventories in LSCM environments that are currently maintained at high levels higher to increase protection against the bullwhip effect could be reduced and this would result in a reduction in overall logistics costs and higher margins and profitability (Perboli, Musso and Rosano 2018). The use of Blockchain would, therefore, enhance an LSC’s flexibility. Furthermore, organizations are generally reluctant to share sensitive information with their customers and suppliers. However, using this technology in LSCM contexts would improve security levels in information exchange and, consequently, mitigate these concerns. This would benefit collaborative environments and information sharing and thus drive up the SC’s efficiency (Perboli, Musso and Rosano 2018).

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