Supply Chain 4.0 challenges

: The Supply Chain has undergone major transformations due to the need to implement new Industry 4.0 technologies, such as Internet of Things, Big Data, Cyber-Physical Systems and Cloud Computing. Thanks to these technologies, as well as to their subsystems and components, full integration of the supply chain is becoming possible. However, it is observed that the real impacts of Industry 4.0 technologies, rather positive or negative, are not yet totally clear and identified. This paper aims to identify and present an analysis of the challenges and obstacles that Industry 4.0 technologies may cause in the Supply Chain. For this, the most relevant papers on the topic were selected and analyzed through a systematic literature review. Twenty challenges grouped into four macrogroups were identified: (1) technical challenges, (2) financial, environmental and legal challenges, (3) technological challenges, and (4) sociocultural challenges. It should be noted that these challenges require greater attention and more in-depth studies on the part of the academy to support industry in order to mitigate them and thus allow better use of the available technological resources and optimize the performance of Supply Chain operations.


Introduction
Supply Chain (SC) has been intensively studied, and great efforts have been made to integrate and strengthen companies' competitiveness.Nevertheless, the SC function, which is responsible for coordinating both the internal activities of companies and the relationship between their customers and suppliers (Schrauf & Berttram, 2016), 2 Literature review SC4.0 is defined as "[…] a series of interconnected activities concerned with coordination, planning and controlling of products and services between suppliers and consumers" (Büyüközkan & Göçer, 2017, p. 01).Its objective is to generate new ways of adding value for customers and suppliers and generating more revenue through the integration and coordination of its processes (Büyüközkan & Göçer, 2017;Tjahjono et al., 2017): forecasting, acquisition, manufacturing, distribution, and sales and marketing (Chan, 2003): Six characteristics of SC4.0 that should cover all customers and suppliers that make up the supply chain are presented by Wu et al. (2016) in their work (Table 1).These characteristics are due to technologies enabled by Industry 4.0.Four of these technologies are considered the technology basis of Industry 4.0: (Teimoury et al., 2013;Macaulay et al., 2015;Reddy et al., 2016).
The Internet of Things (IoT) aims to extend and connect physical objects to the Internet (Uckelmann et al., 2011) by integrating sensors, actuators, and other devices that collect, transmit, and process data (Khanna, 2016).In the SC, this technology brings business and Web applications (such as social media) together with machines, devices, products, materials, and people, making it possible to create an intelligent network that extends across all factory processes and customers and suppliers (Szozda, 2017).
Cyber-Physical systems comprise machines, storage systems, and production facilities that have been digitally developed and present end-to-end integration based on information and communication technologies (Kagermann et al., 2013).They are characterized by decentralization, adaptation, and autonomous behavior (Ivanov & Sokolov, 2012), and they offer SC opportunities to monitor conditions of manufacturing and logistics activities in real time, enabling prognosis and remote diagnosis and control (Lee et al., 2013).
Cloud computing aims to integrate technologies or architectures to provide a platform or solution through the Internet, allowing them to be accessed anytime and anywhere (Raza et al., 2015), generating unprecedented visibility, insights, and flexibility (Büyüközkan & Göçer, 2018).
Big data make up a large set of data from diverse sources, both traditional and digital, where analyses and continuous discoveries can be made (Arthur, 2013).Big data are distinguished in five dimensions-volume, variety, speed, veracity, and value (Yin & Kaynak, 2015)-and can be used for descriptive, predictive, and prescriptive analyses (Wang et al., 2016).In the SC, it presents applications that include material flows (such as production status, process and quality monitoring, inventory handling, logistics, research and development, and collective solutions in procurement and distribution functions), information flows (such as demand management, supply chain event management, vendor negotiation, risk management, problem identification, automated-decision support and customer management) and financial flows (such as customer segmentation, demand modeling, new business model design, pricing and assortment, and financial aspects of human resources).
Other technologies that impact the SC within Context 4.0 are also presented by Ghobakhloo (2018) and provide a source of competitive advantage for the SC: additive manufacturing, automation and industrial robots, augmented reality, cybernetic security, blockchain, Internet of data, people and services, semantic technologies, and simulation and modeling.
Thanks to these technologies and their subsystems and devices, it is possible to integrate the entire SC (not only customers and suppliers but also their assets, products, and operating environment) and generate a larger volume of data with more quality and speed (Wu et al., 2016).In addition, the technologies enable companies to increase flexibility, productivity, reliability, and responsiveness in their operations.Moreover, by enabling the reorganization of the entire operation in real time, companies have the possibility of reducing the bullwhip effect and costs associated with SC operations (Dweekat & Park, 2016).

Method
The systematic literature review method developed by Levy & Ellis (2006) was used to achieve the objective proposed in this work.The method consists of three steps as shown in Figure 1.The first step, named Input, deals with search definitions, such as database choices, search strings, and inclusion and exclusion criteria.Four databases-SCOPUS, Web of Science, Science Direct, and IEEE-were used to search relevant papers about SC4.0 concepts.These databases were selected because they are considered the most important databases within the scope and area of the research.In each of the databases, four keyword groups were used to cover all relevant literature: "Supply Chain 4.0"; "Supply Chain" AND "Industry 4.0"; "Digital Supply Chain" and; "Supply Chain" AND ("CPS" OR "big data" OR "IoT" OR "smart objects").
Then the work was analyzed using three steps, as presented in Figure 2: (a) title, abstract and keywords; (b) duplicate papers; and (c) complete reading.In addition, it was considered a snowballing process to identify new papers which were not within the databases analyzed.The second step, named Processing, is composed of six substeps, namely (a) knowing: identify the publications and extract their essential information; (b) understanding: understand the importance of the study performed and the result presented; (c) applying: identify the important concepts and categorize them accordingly; (d) analyzing: identify the relevance of the presented information; (e) synthesizing: integrate results to understand the concept in its entirety; and (f) evaluating: distinguish between opinions, theories, and instituted facts in an empirical way.
Finally, in the third step, Outputs, the results obtained through the method are presented.

Results and discussion
Through the use of the keywords presented in the previous section, 3,737 articles were identified in the databases, as presented in Table 2.The articles were analyzed according to the inclusion and exclusion criteria, resulting in a total of 88 selected papers.The quantities of articles filtered in each criterion are shown in Table 3.When analyzing the 88 papers identified, it is observed that studies related to the challenges within the SC4.0's context have been growing and gaining more and more attention over the years.Moreover, studies done between 2016 and 2018 have concentrated the largest quantity of publications, as presented in Table 4.These numbers demonstrate the growing concern and search for solutions to minimize these challenges and maximize the benefits of this concept.A preliminary analysis of the challenges makes it possible to classify them into four distinct macrogroups based on the similarities that they imply on their occurrence.Figure 3 shows the macrogroups and their respective challenges: (a) technological challenges; (b) financial, environmental, and legal challenges; (c) sociocultural challenges; and (d) technical challenges.Table 5 shows the percentage of articles and proceedings published that discuss the challenges of each macrogroup.Table 6 summarizes the challenges that companies face and/or will face when implementing the technologies of Industry 4.0 in the SC.Regarding the technological challenges, a first barrier is the need to develop technologies leveraged by Industry 4.0, which are still at an early stage and require great strides to create solidity and generate greater benefits (Kynast & Marjanovic, 2016;Wu et al., 2016).Haddud et al. (2017) identified the different operations models between members of the SC, and Ivanov et al. (2016) recognized the different dynamics and temporal structures of manufacturing processes and equipment that can compromise the data's collection, analysis, and sharing as well as the programming of the entire production structure.
The various formats and structures of the data add problems to the analysis and use of the data.Nevertheless, questions related to the data and their sharing are still reasons for further studies and inquiries.As noted in Pearsall (2016), the kind of data needed to share and the real purpose of doing this along the entire SC are not clear.
In addition, the technical feasibility for implementing technologies such as big data the knowledge to use these data well were pointed out by Jeske et al. (2013), Kynast &Marjanovic (2016), andHallman et al. (2014).Arya et al. (2017) emphasized the lack of data specialists for the development of potential technology.Moreover, Queiroz & Telles (2018) also identified the lack of knowledge of the potential of this technology within Brazilian territory.Among those covering the studies that present other technologies, Bienhaus & Haddud (2018) highlighted the lack of specialists to leverage the digitization in companies.Already, Wu et al. (2016) pointed out that among barriers to developing intelligent applications in SC are the lack of initiative, skills, and knowledge in technologies as well as the immaturity of the SC4.0 concept and its initial stage of development.
Regarding the legislative policies, laws are needed to ensure the safety and integrity of people within this new environment in which humans and robots share spaces and work collaboratively both inside (Tjahjono et al., 2017) and outside the corporate environment (Büyüközkan & Göçer, 2018;Casey & Wong, 2017).In addition to that, international legislation is required to ensure security and privacy (Weber, 2010), to solve problems related to personal injury and product liability in case of failure (Mohr & Khan, 2015), and to share responsibility for the information (Alotaibi & Mehmood, 2017).The balance of legal obligations between infrastructure's providers and customers (Urquhart & McAuley, 2018) within the digital universe is also important to consider.Authors discussing big data and RFID technologies (Isasi et al., 2015) and IoT (Khanna & Sharma, 2017) addressed the problems of global standards aiming the compatibility between systems, where the lack of these standards can lead to difficulties in data's collection and use.Pishdar et al. (2018) highlighted the importance of the laws as a way to form the basis for the technologies' development: As noted in their study, the lack of legislation is a challenge that can bring on many others.
Miao & Zhang (2014) raised the environmental challenges' issue of Industry 4.0 technologies, such as greater energy consumption due to the increased use of electronic equipment and integrated systems in SC.Pishdar et al. (2018) also added their concern about the number of electronic components that will become obsolete and require replacement over time.
The lack of capacity to combine data and obtain quality data is one of the challenges in the macrogroup sociocultural challenges and should be taken into account by companies due to (Jeske et al., 2013;Pishdar et al., 2018): The various existing communication patterns and formats (Kumar et al., 2013;Lee & Lee, 2015;Wang et al., 2016); The different ways of collecting, storing, and combining the data (Ong et al., 2016); The lack of IT professionals with knowledge of matching and using data appropriately (Kache & Seuring, 2017); and The problems of synchronizing data and systems (Haddud et al., 2017) as well as their quality (Song et al., 2018;Spanaki et al., 2018).Arya et al. (2017) and Büyüközkan & Göçer (2018) explained that these factors hinder the use of the data in its totality, which brings about a low return from the use of technologies.
Cooperating with the SC's participants implies difficulties that start from mutual adoption and investments in the technologies of Industry 4.0 (Büyüközkan & Göçer, 2018;Clancy, 2017;Kache & Seuring, 2017) and extend to the difficulty in sharing responsibilities for errors within the digital context (Pishdar et al., 2018).Additionally, authors cited the diversity of operational models and the lack of integration between functions (Haddud et al., 2017), the lack of SC's governance structure (Kache & Seuring, 2017), the lack of a strategy for the use of the technologies (Bienhaus & Haddud, 2018;Kynast & Marjanovic, 2016;Pishdar et al., 2018), and the lack of ability to adapt and integrate firms to this new digital business model (Haddud et al., 2017;Pishdar et al., 2018).Khanna & Sharma (2017) discussed the difficulties faced by companies in finding technology solutions that meet their specific needs.Isasi et al. (2015) emphasized that the nonimplementation of technologies at all stages of the supply chain implies a loss of integration and visibility.
Finally, the resistance to accepting and learning to use new technologies, the ethics and safety issues of dividing the workspace with machines, the replacement of the workforce by technologies, and the fear of making use of intelligent applications in SC were also considered (Büyüközkan & Göçer, 2018;Haddud et al., 2017;Khanna & Sharma, 2017;Tjahjono et al., 2017;Wu et al., 2016).
Regarding the technical challenges, studies that highlighted the big data technology discussed in greater amount the challenges related to the data generation.These challenges begin with the search, fragmentation, and visualization of data (Mikavicaa et al., 2015) and extend to the need to interpret and obtain information with quality and to access and make this data visible to other members in the SC (Kynast & Marjanovic, 2016).
Authors such as Alotaibi & Mehmood (2017), Amudhavel et al. (2015), Isasi et al. (2015), and Wolfert et al. (2017) explained that challenges like storing, analyzing, and processing data using traditional methods are due to the volume, variety, and heterogeneity of the data.
In addition, the creation of a system capable of interpreting and presenting value information from the data is complex (Arya et al., 2017;Kumar et al., 2013) and requires strategy (Choi et al., 2018), a trained team (Kumar et al., 2013;Kache & Seuring, 2017), and infrastructure capable of supporting all computing needs (Kache & Seuring, 2017).Furthermore, Kache & Seuring (2017) elucidated that IT systems with different levels of maturity can bring efficiency and visibility challenges to the global network.
System scalability and robustness challenges related to the collection, analysis, and simultaneous transmission of data were cited by Arya et al. (2017) and Zhong et al. (2016).These authors also highlighted the stability and vulnerability in transmitting data to systems, especially when done via wireless network.
As addressed with big data technology, IoT and other digital applications present challenges in storing, discovering, and sharing data (Szozda, 2017;Xu et al., 2013) and problems with scalability and interoperability (Büyüközkan & Göçer, 2017;Pishdar et al., 2018).Lee & Lee (2015) and Xu et al. (2013) addressed compatibility challenges when connecting all systems in SC.Khanna & Sharma (2017) reinforced this idea, noting the difficulty of predicting failures and exemplifying a simple electrical failure as a cause of chain reactions to all interconnected firms.
Finally, CPS and other automation systems studied imply challenges such as integration, efficient information exchange (Chen et al., 2017), and validation of solutions within the SC-a theme that still needs to be explored (Harrison et al., 2016).
Within this macrogroup, a concern arises with the challenge of security and privacy, addressed in approximately 72% of papers.Security and privacy in the use of technologies (big data, RFID, Internet of Things, cyber-physical systems, among others) are challenges mentioned in a great number of publications and show the extent of this challenge that SC members will face when implementing technologies driven by Industry 4.0.National Cyber Security Centre (2015) supported this idea by explaining that different companies have different levels of security in their systems, and the lower level of security allows the exposure of all other companies connected through attacks and intrusions.

Conclusion
Industry 4.0 introduces a range of innovations that allow companies to increase the performance of their internal and external activities and thus gain competitive advantage.In the SC, a sector that presents difficulties in obtaining efficiency due to the complexity in managing its internal and external operations, technologies from this context allow greater integration between systems and company resources to generate global value-that means for the whole chain.
However, there are numerous challenges when analyzing the concept of SC4.0.Although coordinating internal activities, systems, and other resources of a company with its customers and suppliers allows obtaining benefits in performance, quality, and costs, it can also bring to the companies great challenges, especially with regard to security and privacy.For example, there are several studies that presented models, tools, and systems that seek to minimize the risks of improper data acquisition, leakage and unauthorized disclosure of information, and insertion of malicious software, among other challenges.However, none of these studies eliminated the risks altogether.In addition, while different systems and equipment are integrated within the SC, greater potential for failures and risks is observed.
Moreover, the full integration of the internal activities in SC functions and the activities of customers and suppliers requires greater synergy among companies and researchers regarding investments, studies, and further technology development.At present, studies report partial integrations of functions and activities.While the theme is still at an early stage of development, obtaining practical data for analysis and comparison with the theory is difficult.
Investments and efforts by the government are also needed to overcome challenges regarding legislation.Some challenges exist due to other causes.In this sense, the lack of regulations creates security problems and harms users, as well as leading to the development of other challenges.
This study contributed to identifying the challenges related to SC4.0.In all, 20 technical, sociocultural, technological and financial, environmental, and legal challenges require further studies to minimize them and even eliminate them when aiming to achieve full SC efficiency through the use of available technologies.For future work, companies should be consulted on how these challenges are perceived by them as a concern and decision factor in relation to the implementation process.

Table 2 .
Number of articles identified in each search.

Table 3 .
Results of the systematic literature review process' steps.

Table 4 .
Number of papers published per year.

Table 5 .
Percentage of articles addressing the challenges according to classification.

Table 6 .
Challenges of Industry 4.0 Technologies in Supply Chain.

Table 6 . Continued… Challenges High degree of computerization / Computing requirements and strategies Compatibility Complexity Reliability, robustness and interoperability of systems Challenges in storing, discovering and sharing data Scalability Security / privacy Environmental Challenges Financial investment Standardization / Legislative policies Different dynamics and time structures of manufacturing processes Lack of initiative, skills and / or insufficient knowledge Immature Technologies Adapting to new business models Strategic alignment between functions, companies and governance SC's participants cooperation Lack of ability to combine data / obtain quality data Fear of change Man-technology relation Human Resource Replacement / Dismissal Authors
Source: Authors.