A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics

Martijn Koot; Martijn R.K. Mes; Maria E. Iacob

doi:10.1016/j.cie.2020.107076

Koot, Martijn;Mes, Martijn R.K.;Iacob, Maria E.;

2021-04-01 00:00:00

Computers & Industrial Engineering 154 (2021) 107076 Contents lists available at ScienceDirect Computers & Industrial Engineering journal homepage: www.elsevier.com/locate/caie A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics Martijn Koot , Martijn R.K. Mes , Maria E. Iacob Department Industrial Engineering & Business Information Systems (IEBIS), University of Twente, Drienerlolaan 5, 7522 NB Enschede, the Netherlands ARTICLE INFO ABSTRACT Keywords: The willingness to invest in Internet of Things (IoT) and Big Data Analytics (BDA) seems not to depend on supply Internet of Things nor demand of technological innovations. The required sensing and communication technologies have already Big Data Analytics matured and became affordable for most organizations. Businesses on the other hand require more operational Supply chain management data to address the dynamic and stochastic nature of supply chains. So why should we wait for the actual Decision making implementation of tracking and monitoring devices within the supply chain itself? This paper provides an Systematic literature review objective overview of state-of-the-art IoT developments in today’s supply chain and logistics research. The main aim is to find examples of academic literature that explain how organizations can incorporate real-time data of physically operating objects into their decision making. A systematic literature review is conducted to gain insight into the IoT’s analytical capabilities, resulting into a list of 79 cross-disciplinary publications. Most re- searchers integrate the newly developed measuring devices with more traditional ICT infrastructures to either visualize the current way of operating, or to better predict the system’s future state. The resulting health/con- dition monitoring systems seem to benefit production environments in terms of dependability and quality, while logistics operations are becoming more flexible and faster due to the stronger emphasis on prescriptive analytics (e.g., association and clustering). Further research should extend the IoT’s perception layer with more context- aware devices to promote autonomous decision making, invest in wireless communication networks to stimulate distributed data processing, bridge the gap in between predictive and prescriptive analytics by enriching the spectrum of pattern recognition models used, and validate the benefits of the monitoring systems developed. 1. Introduction decision complexity even further, resulting into a wide variety of deci- sion support tools originating from management sciences with limited Supply Chain Management (SCM) heavily relies on the use of well value (Riddals, Bennett, & Tipi, 2000). Recent SCM trends like e-com- analyzed data, simply because data driven decisions lead to better re- merce, lean operations, and increasing customer requirements have sults in complex business environments (Speranza, 2018). Gathering the made the supply chain even more vulnerable to both internal and necessary data sources is far from trivial however, mainly due to the external disruptions (Ponomarov & Holcomb, 2009; Stank, Autry, dynamic and stochastic nature of real-world logistics networks (Pillac, Daugherty, & Closs, 2015), suggesting that online modifications of the Gendreau, Gueret, & Medaglia, 2013). Modern-day decision support initial planning are required to achieve optimal outcomes (Koot, 2019). tools should incorporate the data source’s uncertainty to provide a One way to address the dynamic and stochastic nature of supply sound representation of the problem context, while simultaneously chains is to implement multiple identification and monitoring devices maintaining the models’ simplicity for the application of analytical re- during key logistics activities, or decision milestones. The idea to sults (Bianchi, Dorigo, Gambardella, & Gutjahr, 2009). This trade-off remotely monitor products and their surroundings is commonly used in between uncertainty and simplicity makes it difficult for decision SCM for several years already (Lee & Lee, 2015). For example, Radio makers to derive a reliable description of the system’s current and future Frequency Identification (RFID) became popular during the 1980s to state, since the models’ assumptions are often not valid in reality (Sar- automatically trace and monitor products without the need to be in line- imveis, Patrinos, Tarantilis, & Kiranoudis, 2008). The occurrence of of-sight (Atzori, Iera, & Morabito, 2010; Xu, He, & Li, 2014). In the unforeseen events and changing parameter values aggravates the 1990s, Wireless Sensor Networks (WSN) extended the RFID’s * Corresponding author. E-mail addresses: [email protected] (M. Koot), [email protected] (M.R.K. Mes), [email protected] (M.E. Iacob). https://doi.org/10.1016/j.cie.2020.107076 Received 9 June 2020; Received in revised form 17 September 2020; Accepted 16 December 2020 Available online 19 December 2020 0360-8352/© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 monitoring capabilities with the installation of spatially distributed We conduct a systematic literature review (SLR) to explore how the real- sensors (Lee & Lee, 2015; Li, Xu, & Zhao, 2015). Nowadays, the concept time data of physically operating objects is applied into SCM and lo- of remote business monitoring is extended even further towards re- gistics research. The contribution of this research is twofold. First, we sources that are operating physically within the supply chain itself (e.g., explain how, where, and why organizations could apply IoT devices into machinery, vehicles, containers, etc.). More and more physical objects their SCM and logistics operations by conducting an integrated review are empowered with wireless sensors and communication devices, towards the gathering, processing, and application of real-time data. resulting into an interconnected network of uniquely addressable ob- Second, by using a proper classification of the state-of-the-art IoT de- jects that is better known as the “Internet of Things” (IoT) (Atzori et al., velopments, we validate the theoretical benefits and/or limitations of 2010). The IoT paradigm is one of the most recent advancements of emerging tracking and monitoring techniques, which in turn allows Information and Communication Technologies (ICT), combining sen- business practitioners to make well-informed investments (or not). The sory, communication, networking, and information processing tech- SLR is based on the systematic review methodology proposed by Denyer nologies throughout an inter-connected network (Li et al., 2015). and Tranfield (2009). Therefore, the remainder of this paper is struc- Both industry practitioners and scientists are highly interested into tured as follows. First, we explain how our SLR will extend the current the usage of IoT devices within SCM activities. The increasing volume of body of knowledge by elaborating on the theoretical background related IoT data is essential to improve our understanding of today’s complex to IoT networks, data analytics, and SCM in Section 2. In Section 3, we supply chains. The real-time monitoring of physical assets will improve introduce the research methodology applied, including a description of the transparency, traceability, and reliability of logistics operations by the search strategy, selection criteria, and data extraction forms. Next, mapping the real world into the virtual world (Atzori et al., 2010; we summarize the SLR results in Section 4. In Section 5, we discuss the Chung, Gesing, Chaturvedi, & Bodenbenner, 2018; Speranza, 2018). observations made, compare our SLR results with other relevant publi- Decision makers can even move from descriptive statistics towards cations, summarize our findings and give some pointers to future IoT- structural improvements by the application of analytical models (e.g., driven SCM research. We end with our conclusions and recommenda- combinatorial optimization algorithms, data mining, machine learning, tions in Section 6. etc.) that transform IoT data into predictions, and optimization out- comes (Barton & Court, 2012). Therefore, the real potential of IoT ap- 2. Theoretical background plications lies into the capability to mine for original insights and optimization opportunities that support decision making (Chung, Ges- Answering our research question would encompass several concepts ing, Chaturvedi, & Bodenbenner, 2018; Macaulay, Buckalew, & Chung, from three different academic disciplines: 2015; Xu et al., 2014). For example, intelligent data analytics may stimulate organizations to proactively act in a more resilient way once a (1) IoT networks: concerns the gathering of data by empowering disturbance is observed, or even predicted, in real time (Atzori et al., physical objects with sensing, processing and communication 2010; Barton & Court, 2012; Chung, Gesing, Chaturvedi, & Bod- devices (Section 2.1); enbenner, 2018; Stank et al., 2015). (2) Data analytics: addresses the analysis of the data generated by The adoption and proliferation of IoT devices satisfies the supply IoT networks to mine for original insights and optimization op- chain’s demand for collecting and processing data on changeable busi- portunities (Section 2.2); ness environments (Stank et al., 2015). However, it remains unknown (3) SCM: relates to the application of real-time data to support supply how organizations can directly use the IoT generated data into their chain and logistics decision making (Section 2.3). decision making. Modern-day SCM activities such as transportation, warehousing or maintenance are resource intensive, resulting into a lot In this section, we summarize the results of our scoping study into a of physical objects empowered by primitive or no data handling capacity brief description of the theoretical background for each topic separately. at all (Atzori et al., 2010; Macaulay, Buckalew, & Chung, 2015). Sci- The initial scoping study allows us to define multiple sub-questions for entists expect that a slight increase of the objects’ autonomy would our SLR to extend the current body of knowledge (Denyer & Tranfield, already provide new business insights that may drive innovations 2009). The research gap will be discussed in Section 2.4, while the (Atzori et al., 2010; Macaulay, Buckalew, & Chung, 2015), and the ob- theoretical background itself will be used to predefine relevant key- ject’s functionality may be enhanced even further once connected to words for our search strategy in Section 3. other related products (Wortmann & Flüchter, 2015). Even though real- life applications of IoT in supply chain decision making should exist, as 2.1. Internet of Things reflected by IoT’s position on the peak of inflated expectations on Gartner’s Hype Cycle methodology (Gartner, 2018; O’Leary, 2008), the The main concept of the Internet of Things (IoT) is to sense the number of validated IoT implementations remains limited within sci- physical world by connecting physical objects to each other (Li et al., entific community, since the IoT paradigm is not fully mature yet. 2015; Macaulay, Buckalew, & Chung, 2015). The IoT’s perception ca- This paper aims at delivering an objective overview of the state-of- pabilities build upon a variety of identification and tracking technolo- the-art IoT developments in today’s SCM and logistics research. The gies that enable remote monitoring of physical objects without the need main goal is to search for academic literature that explains how orga- to be in line-of-sight (Atzori et al., 2010; Xu et al., 2014). Nowadays, nizations can incorporate real-time data of physically operating objects more and more physical objects are equipped with remote sensing and into their decision making. Better understanding of the IoT’s analytical controlling devices (e.g., embedded sensors and/or actuators, RFID tags, capabilities stimulates future SCM research to customize information WSN, bar codes, GPS signal, etc.) to either observe the object’s status or systems by proactively acting on the dynamic and stochastic nature of its surroundings continuously (Macaulay, Buckalew, & Chung, 2015; supply chains. Therefore, we have to map which type of IoT devices and Madakam, Ramaswamy, & Tripathi, 2015; Xu et al., 2014). Each indi- analytical models are prescribed by scientists to improve supply chain vidual sensing device is uniquely addressable and inherits standardized performances. We summarize our intentions by proposing the following communication protocols (Atzori et al., 2010), which allows the devices research question: to autonomously gather, process, and share data in a global infrastruc- ture of interconnected physical objects (Xu et al., 2014). Therefore, the Research question: To what extent do IoT technologies support supply chain decision making by the acquisition, analysis, and application of real-time data from cyber- IoT’s wireless sensor capabilities extend the concept of physical moni- physical objects? toring with ambient intelligence and autonomous control (Li et al., 2015). The employment of a Service-Oriented Architecture (SOA), as 2 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Fig. 1. Service-Oriented Architecture for IoT applications (Patel, Patel, & Scholar, 2016). Fig. 2. The data mining process proposed by Chen et al. (2015). visualized in Fig. 1, is commonly proposed to decompose the IoT Court, 2012; Waller & Fawcett, 2013; Wang, Angappa, Ngai, & Papa- network into smaller, re-usable and well-defined components (Al- dopoulos, 2016). The rise of relational database technologies (e.g., Fuqaha, Guizani, Mohammadi, Aledhari, & Ayyash, 2015; Atzori et al., DBMS, data warehouses, data marts, OLAP, etc.) allowed humans to 2010; Li et al., 2015). The network layer is equipped with internet-based gather, manipulate, and query through structured datasets to obtain new technologies, which allows IoT devices to communicate with each other insights (Chen, Chiang, & Storey, 2012; Turban, Sharda, Delen, King, & in close proximity (e.g., RFID, NFC, Bluetooth, ZigBee), but also to share Aronson, 2011; Vercellis, 2009). The descriptive analytics gradually data among networks for distributed data processing through wider area evolved into the capability to mine for valid, novel, and potentially networks (Chiang & Zhang, 2016; Colakovi´ c & Hadˇ ziali´ c, 2018; Gubbi, useful patterns that were previously hidden within the structured da- Buyya, Marusic, & Palaniswami, 2013). It is expected that the IoT tabases (Fayyad, Piatetsky-Shapiro, & Smyth, 1996; Lee & Siau, 2001). paradigm will revolutionize our way of communication by extending the Therefore, application of the data mining process (Fig. 2) extended the ICT infrastructure with more machine-to-machine (M2M) connections, analytical toolbox with new mathematical models designed for pattern resulting into a more system-oriented approach towards remote moni- recognition, resulting into new functionalities like classification, clus- toring (Wortmann & Flüchter, 2015), and a better alignment of the tering, association, time series analysis, and outlier detection. (Chen physical world and computer-based systems (Atzori et al., 2010; Sper- et al., 2015; Liao, Chu, & Hsiao, 2012; Turban et al., 2011; Vercellis, anza, 2018). A recent description of the IoT paradigm’s challenges and 2009). Nowadays, algorithms can even search for patterns by them- ´ ˇ ´ open research issues is given by Colakovic and Hadzialic (2018). selves due to advances in machine learning and Artificial Intelligence (AI), without any human intervention at all (Bishop, 2006; Gesing, Peterson, & Michelsen, 2018). 2.2. Data analytics The continuous growth of the world’s data volume provides oppor- tunities for organizations to identify new value-adding patterns that Raw data can be transformed into valuable predictions, and opti- were previously hidden (Addo-Tenkorang & Helo, 2016). IoT networks mization outcomes by the application of analytical models (Barton & 3 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Fig. 3. A visualization of the systematic review approach followed in this research. All activities are classified into five sections as proposed by (Denyer & Tran- field, 2009). will amplify data sharing among objects even further with larger vol- initiatives are also reflecting on the organizational benefits enabled by umes and more varieties of sensing objects that did not gather data BDA implementations (Dubey, Gunasekaran, & Childe, 2019; Dubey, traditionally (Hashem et al., 2015; Macaulay, Buckalew, & Chung, 2015; Gunasekaran, Childe, Blome, & Papadopoulos, 2019; Gunasekaran Speranza, 2018). Therefore, the world’s annual volume of data gener- et al., 2017; Gunasekaran, Yusuf, Adeleye, & Papadopoulos, 2018; ated, captured or replicated is expected to accelerate exponentially, a Matthias, Fouweather, Gregory, & Vernon, 2017; Papadopoulos et al., growing pace that traditional relational databases cannot process effi - 2017). ciently anymore (Addo-Tenkorang & Helo, 2016; Chen, Mao, & Liu, 2014; Reinsel, Gantz, & Rydning, 2018). The term ‘Big Data’ is used to 2.4. Research gap describe these enormous datasets that are growing at an accelerated pace, while ‘Big Data Analytics’ (BDA) refers to the extraction of useful Both scientists and logistics managers expect that the IoT and BDA information from these massive datasets that could be valuable for or- developments are closely intertwined with each other, since IoT net- ganizations (Chen et al., 2014). A recent description of the BDA para- works will amplify data sharing among objects in terms of larger vol- digm’s challenges and open research issues is given by Mikalef, Pappas, umes, increased speed, and more varieties (Cai, Xu, Jiang, & Vasilakos, Krogstie, and Giannakos (2018). 2017; Chen et al., 2015; Hashem et al., 2015; Macaulay, Buckalew, & Chung, 2015; Marjani et al., 2017; Mourtzis, Vlachou, & Milas, 2016; Riggins & Wamba, 2015; Speranza, 2018). Therefore, we expected to see 2.3. Supply chain management an increasing number of publications reflecting on the IoT’s analytical capabilities within SCM and logistics operations. However, our initial Modern-day supply chain decision making heavily relies on well scoping study resulted in a handful of studies addressing an integrated analyzed data to support predictions and optimization outcomes (Barton approach towards the IoT, BDA, and SCM research disciplines (Addo- & Court, 2012; Speranza, 2018). Therefore, business practitioners are Tenkorang & Helo, 2016; Hopkins & Hawking, 2018; Kusiak, 2018; highly interested into the recent IoT advances to retrieve up-to-date data Rathore et al., 2018). All four papers include an extensive description of of physical objects and their surroundings (Chung, Gesing, Chaturvedi, several case studies, which highlight some potential benefits that we & Bodenbenner, 2018; Gartner, 2018; Macaulay, Buckalew, & Chung, might expect from combining IoT networks with intelligent data ana- 2015). The real-time monitoring capabilities may improve the trans- lytics (e.g., higher resource utilization, enhanced safety, lower costs, parency, traceability, and reliability of logistics operations (Atzori et al., etc.). Multiple general architectures are proposed to guide the imple- 2010; Chung, Gesing, Chaturvedi, & Bodenbenner, 2018; Speranza, mentation of the IoT’s analytical capabilities as well, but we envision a 2018). Firms and supply chains can achieve higher efficiency levels by a more detailed assessment of the interrelated technologies required for faster response to the internal and external disruptions observed (Ben- gathering, communicating, and analyzing real-time data. We would also Daya, Hassini, & Bahroun, 2019). Higher payoffs are even expected once appreciate more insights into the altered decisions themselves, including the connected objects are empowered with ambient intelligence and a description of the corresponding efficiency improvements. Therefore, autonomous control (Li et al., 2015). As a result, more research initia- a more detailed combination of keywords is required that explicitly tives have been proposed to apply IoT concepts into SCM and logistics searches for the application of both IoT and BDA techniques in the SCM operations (Ben-Daya et al., 2019; Lee & Lee, 2015; Liu & Gao, 2014; domain. Lou, Liu, Zhou, & Wang, 2011; Sun, 2012; Tan, 2008; Tu, 2018; Xu et al., It is our aim to extend the academic body of knowledge with a 2014). literature overview that addresses the extant research work found at the Supply chain managers are also inspired by the innovative BDA ca- intersection of the IoT, BDA, and SCM disciplines. We will use the results pabilities to improve their decision making (Chung, Gesing, Chaturvedi, of our scoping study to refine our initial hypothesis of Section 1 into a & Bodenbenner, 2018; Gesing, Peterson, & Michelsen, 2018; Jeske, more detailed set of sub-questions: Grüner, & Weiß, 2013; Reinsel, Gantz, & Rydning, 2018), resulting into more academic publications that combine BDA and SCM as well. The (1) Sub-question A: Which combinations of IoT devices and larger volumes and more varieties of data sources stimulate decision analytical models are commonly applied during SCM and logis- makers to make better predictions of the supply chain’s future state, tics operations? allowing firms to become more flexible and remain competitive in a (2) Sub-question B: How do the IoT’s analytical capabilities affect business environment that is highly dynamic and stochastic. Most BDA supply chain decision making? research efforts are discussing the techniques and architectures required (3) Sub-question C: What type of supply chain improvements result for pattern recognition and predictive analytics (Baryannis, Validi, Dani, from IoT-driven decision making? & Antoniou, 2019; Chen et al., 2012; Nemati & Barko, 2001; Nguyen, Zhou, Spiegler, Ieromonachou, & Lin, 2018; Provost & Fawcett, 2013; Tiwari, Wee, & Daryanto, 2018; Waller & Fawcett, 2013; Wang et al., 2016; Zhong, Newman, Huang, & Lan, 2016), but some research 4 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Fig. 4. Bibliometric network including the top 25 most frequent co-occurring keywords related to the Internet of Things (IoT). The keywords originate from a total of 563 review articles found in ‘Scopus’ by searching for the keyword “Internet of Thing” (search date: 9th of April 2019). 3. Systematic review methodology three times for the IoT, BDA, and SCM disciplines separately. All three bibliometric studies are structured in three main steps: In this paper, we conduct a SLR to deliver an objective state-of-the- art overview of the emerging IoT’s analytical capabilities in today’s (1) First, we search for review articles summarizing published studies SCM and logistics research. The SLR should support our search for related to either the IoT, BDA, or SCM discipline. Both the author- publications that explicitly investigate the linkages between the IoT, and index keywords of all selected review articles are exported BDA, and SCM research disciplines simultaneously. Therefore, we apply (RIS file) for further assessment; the systematic review methodology proposed by (Denyer & Tranfield, (2) Second, we visualize the co-occurrences of keywords for each 2009) to answer all (sub-) questions addressed in Section 2.4. In this separate discipline in VOS viewer by constructing a so-called section, we explain how the SLR was conducted by defining the search bibliometric network (e.g., see Fig. 4), including the top 25 strategy (Section 3.1), the selection criteria (Section 3.2), and the keywords most frequently used by all exported review articles; criteria for analysis and synthesis (Section 3.3). The search results, the (3) Third, the VOS viewer tool automatically emphasizes the most resulting articles, and the filled-in data extraction forms are discussed in frequent keyword sets and search for appropriate clusters based Section 4. The sequence of research activities is depicted in Fig. 3. on the keywords’ association strength. 3.1. Search strategy General keywords referring to document types and scientific char- acteristics are removed from the bibliometric network (e.g., survey, The main aim of our systematic approach is to locate, select and review, future recommendations, etc.). A thesaurus file is created to assess relevant literature by using search strings, grouping keywords, ensure that synonyms are not double counted. Finally, all three biblio- and applying search conventions within a citation database (Denyer & metric networks are compared to select the most common grouping of Tranfield, 2009). In our case, the research question addresses the IoT-, BDA-, and SCM-related keywords. intersection of the IoT, BDA, and SCM disciplines, meaning that we have The results of our bibliometric study show that the IoT, BDA, and to search for those keywords commonly shared by all three disciplines. SCM research disciplines are closely intertwined with each other, see Appendix A. For example, the IoT related bibliometric network (Fig. 4) Therefore, we have initiated our SLR with a bibliometric study to find relevant keywords for the IoT, BDA, and SCM research disciplines includes several terms that are also shared by the BDA paradigm (e.g., big data, information systems, artificial intelligence), while these data- simultaneously, see Appendix A. A brief overview of the most common grouping of keywords is visualized for each separate discipline by using driven techniques support supply chain decision making in return. The ‘VOS viewer’, a software tool for constructing, analyzing, and visualizing inclusion of the most frequently used keywords of the IoT, BDA, and bibliometric networks (https://www.vosviewer.com). The critical SCM disciplines ensures that we can locate the multidisciplinary type of comparison of all three bibliometric networks enables us to select those publications searched for. Therefore, the bibliometric networks are used IoT-, BDA-, and SCM-related keywords that co-occur at the intersection to define relevant keywords for all three research disciplines separately. of multiple disciplines. The results of our bibliometric study are summarized in Table 1, The bibliometric study forms the first step in the third phase of our including a list of synonyms, related concepts/technologies, and real-life systematic review approach depicted in Fig. 3. The study is executed applications within the second, third and fourth column respectively. 5 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 The keywords in Table 1 are grouped together by using truncation Economics). The Multidisciplinary category is also included, characters (e.g., ‘*’, and ‘?’), Boolean Logic Operators (e.g., AND, OR), since we are explicitly looking for linkages in between the IoT, phrase searching, and parentheses. The final search string is constructed BDA, and SCM research areas; iteratively by using the following steps. First, relevant articles are (3) Only academic document types are included to obtain validated searched for the IoT, BDA, and SCM discipline separately. We explicitly concepts only (e.g., articles, books, book chapters, conference look for relevant studies that include the discipline’s synonyms (related papers, and review articles). terms) or the enabling technologies (narrower terms) into the publica- tion’s the title, abstract or keywords. Second, a fourth keyword category Second, the abstracts of the remaining articles are screened to eval- is defined consisting of the SCM application fields only (broader terms). uate the usefulness of the content itself. Four additional inclusion This additional category is required to actively search for the academic criteria are defined for the abstract screening as well: improvements enabled by the IoT’s analytical capabilities in SCM and logistics operations. Both the IoT’s and BDA’s application fields are (4) The data acquisition should at least include two interconnected ignored, because of the interdisciplinary relationships observed within data gathering devices within the physical domain; our bibliometric study. Finally, all four keyword groupings are com- (5) The raw data should be (pre-) processed in order to identify useful bined into one search string to find the multidisciplinary type of publi- patterns for organizational decision making; cations searched for. A simplified version of the resulting search profile (6) The proposed technologies are applied to supply chain and lo- is given below, while Appendix B includes a more detailed search profile gistics decision making, including the allocation and movement by referring to the selected keywords of Table 1. of resources in order to produce valuable products, services and/ or information; TITLE-ABS-KEY[ IoT (Synonyms OR Enabling technologies) ] AND (7) The technology’s benefits should be stated explicitly by either: TITLE-ABS-KEY[ Big Data Analytics (Synonyms OR Enabling technologies ] (a) indicating which performance indicators are improved; AND (b) proposing an architectural design; TITLE-ABS-KEY[ SCM (Synonyms OR Enabling technologies) ] (c) referring to a specific use case. AND TITLE-ABS-KEY[ SCM (Application fields) ] The third layer consist of exclusion criteria only. These criteria are implemented to assess the articles’ uniqueness: Our SLR covers three interrelated academic disciplines (IoT, BDA, and (8) Remove the article if the proposed technology itself is improved SCM). Therefore, a generic citation database like ‘Scopus’ is required. only, without specifying any application at all; The Scopus database is selected only, because it is one of “the world’s (9) Remove all duplicated articles that consider the same case study, largest abstract and citation database of peer-reviewed research literature” only the most recent version is saved for further reading; (see https://www.elsevier.com/solutions/scopus). (10) Remove all articles referring to decisions that civil supply chain organizations will rarely make (e.g., military operations, space 3.2. Selection criteria exploration, homeostatic mechanisms, etc.). The search profile of Section 3.1 will provide a list of potentially The remaining articles are downloaded for full text reading. How- useful articles, but not every article will contribute to answering the ever, it is possible that the article is not publicly available, or that the research question. Therefore, four layers of inclusion/exclusion criteria content is of insufficient quality for further analysis. Therefore, the final are defined to assess the relevance of each publication found (Denyer & layer includes three additional criteria to increase the opportunity that Tranfield, 2009). First, the resulting articles should fulfil three inclusion the selected articles are actually found: criteria related to the document type itself: (11) The article should be available online (either open access or (1) The articles should be fully published and written in English; through subscription); (2) The articles’ subject areas have to align the academic fields taken (12) If the article is not publicly available, the following procedure is into consideration (e.g., Computer Science, Engineering, Mathe- activated: matics, Decision Sciences, Business Management, and Table 1 A selection of relevant keywords the IoT, BDA, and SCM research disciplines, including a list of synonyms, related concepts/technologies, and real-life applications. Discipline Synonyms (related terms) Enabling technologies (narrower terms) Application fields (broader terms) IoT Internet of Things; Internet-of-Things; Cyber-Physical Systems (CPS); Sensors; Internet; Mobile Industry 4.0; Artificial Intelligence; Big Data; Internet-of-Things (IoT); Internet of Telecommunication Systems; Wireless Sensor Networks Automation; Information Management; Smart Industry; Everything; Industrial Internet; Web of (WSN); Wireless Sensor and Actuator Networks (WSAN) Smart Planet; Smart Cities; Smart Homes; Smart Things; things; Web-of-Things (WoT) Smart Objects; Smart Devices; Intelligent Things BDA Big Data Analytics; Big Analytics; Massive Data Mining; Process Mining; Pattern recognition; Data- Artificial Intelligence; Intelligent Systems; Learning Data Analytics; Mass Data Analytics; Large driven Knowledge Discovery; Machine Learning; Neural Systems; Decision Making; Decision Support; Data Data Analytics; Enormous Data Analytics Networks; Reinforcement Learning; Deep Learning; Genetic visualization algorithms; Classification; Association; Clustering; Regression SCM Supply Chain Management; Logistics Decision Making; Industrial Management; Industrial Product Development; Research and Development Management Engineering; Industrial Economics; Management Science; (R&D); Purchasing; Procurement; Project Management; Optimization; Optimization; Planning; Scheduling; Production; Manufacturing; Warehousing; Inventory Loading; Sequencing; Monitoring; Algorithm; Heuristic Management; Order fulfilment; Transportation; Logistics; Physical Distribution; Distribution Management; Marketing; Sales; Maintenance; Aftersales; Returns Management; Service Logistics; Reverse Logistics; Demand Management; Customer- Relationship; Supplier-Relationship; Customer-Service; Finance 6 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Table 2 Data extraction forms for the IoT, BDA, and SCM research disciplines, plus a fourth category to gain insight into the supply chain performances enabled by the IoT’s analytical capabilities within SCM and logistics operations. Category Variable Classifications Source IoT Perception layer (1) Sensors and/or actuators; (2) Tags; (3) Mobile devices; (4) Satellites; (5) Transaction (Al-Fuqaha et al., 2015; Atzori et al., 2010; ´ ´ Processing Systems; (6) Data warehouses; (7) External sources; (8) Autonomous agents; Colakovic & Hadˇ zialic, 2018; Li et al., 2015; Xu (9) User input, and; (10) Location receiver. et al., 2014) Stimuli (1) Acoustic; (2) Biological; (3) Chemical; (4) Electric; (5) Magnetic; (6); Mechanical; (7) (White, 1987) Optical (8) Radiation; (9) Thermal; and (10) Event. Network layer (1) Radio Frequency Identification (RFID); (2) Near Field Communication (NFC); (3) (Al-Fuqaha et al., 2015; Chiang & Zhang, 2016; ´ ˇ ´ Radio navigation; (4) Internet; (5) Low-power WAN; (6) Wireless LAN (IEEE 802.11); (7) Colakovic & Hadzialic, 2018; Gubbi et al., 2013) Wireless PAN (IEEE 802.15); (8) Wired connection; and (9) Middleware technology. BDA Data management (1) Acquisition & integration; (2) Cleaning; (3) Transformation & feature extraction; (4) (Chen et al., 2015; Turban et al., 2011; Vercellis, Reduction & feature selection; (5) Aggregation & storage; (6) Modelling & analysis; and 2009) (7) Interpretation & application. Pattern (1) Characterization & discrimination; (2) Classification; (3) Regression; (4) Association (Chen et al., 2015; Liao et al., 2012; Turban et al., recognition rules; (5) Clustering; (6) Time series analysis; (7) Visualization; (8) Rule induction. 2011; Vercellis, 2009) Algorithm type (1) Decision trees; (2) Statistical methods; (3) Neural networks; (4) K-nearest thernet; (5) (Bishop, 2006; Laudon & Laudon, 2017) Support vector machines; (6) Linear regression; (7) Non-linear regression; (8) Expert systems; (9) Genetic algorithm; (10) Principal component analysis; (11) Automata learning; (12) Fuzzy logic; (13) Markov model; (14) Linear discriminant analysis; (15) Ontology; (16) Computer vision; and (17) Finite element method. SCM Analytics type (1) descriptive analytics; (2) diagnostic and/or explanatory analytics; (3) predictive (Pawar & Attar, 2016; Sun, Zou, & Strang, 2015; analytics; and (4) prescriptive analytics. Vercellis, 2009) Decision type (1) Loading; (2) Sequencing; (3) Scheduling; and (4) Monitoring. (Slack, Chambers, & Johnston, 2010) Decision hierarchy (1) Strategic; (2) Tactical; (3) Operational offline; and (4) operational online. (Hans, Herroelen, Leus, & Wullink, 2007) Application Supply chain (1) Research & development; (2) Purchasing; (3) Production; (4) Logistics; (5) Marketing (Lambert, 2008) activity & sales; (6) Finance; (7) Customer relationship management; (8) Supplier relationship management; (9) Customer service management; (10) Demand management; (11) Order fulfilment; (12) Manufacturing flow management; (13) Product commercialization; and (14) Returns management. Technology (1) TRL01: Basic principles; (2) TRL02: Technology concept and/or application (Mankins, 2009) readiness level formulated; (3) TRL03: Analytical and experimental critical function; (4) TRL04: Component validation in laboratory; (5) TRL05: Component validation in relevant environment; (6) TRL06: System/sub-system model or prototype demonstration in relevant environment; (7) TRL07: System prototype demonstration in operational environment; (8) TRL08: Actual system completed and “qualified ” through test and demonstration; and (9) TRL09: Actual system “flight proven” through successful mission. Key performance (1) Costs; (2) Dependability; (3) Flexibility; (4) Quality; and (5) Speed. (Slack et al., 2010) indicator (a) Request full-text permission from the article’s first author; performances/improvements as well, including three additional vari- (b) In case of negative response, search for another article of the ables. The resulting twelve assessment criteria are summarized in same author(s) that covers the same topic (the article should Table 2, including a list of possible classification types and corre- also comply with the other layers of inclusion/exclusion sponding sources in the third and fourth column respectively. We will criteria); use the three-layered IoT architecture for the evaluation of the inter- (c) In case of no results, remove the article for full-text reading. connected data gathering devices (see Fig. 1). The BDA extraction form (13) The article’s content should be of sufficient quality. The following addresses the type of patterns searched for, including the data pre- procedure is applied in order to assess the article’s quality: processing steps and corresponding algorithms and/or modeling tech- (a) The article’s publisher should be trustworthy and include niques. The third assessment category refers to the application of peer-reviewed papers only (e.g., ACM, Elsevier, IEEE, recognized patterns into supply chain decision making, especially by Springer, etc.). The article is accepted immediately if (and evaluating how the analytical techniques are used to match supply and only if) this requirement is fully met. demand in terms of volumes, timing, and quality. The fourth assessment (b) If criterion (13a) is not true, then the article is accepted if category reflects the theoretical improvements obtained from the IoT’s (and only if) the number of citations is nonzero; analytical capabilities. This last assessment category will help us to fully (c) If criterion (13b) is not true, then the article is accepted if answer the main research question by analyzing the techniques’ (and only if) the source’s scientific journal Rankings (SJR) is commercialization progress for multiple intra- and inter-organizational greater than 0.2 (see https://www.scimagojr.com/); activities. (d) If criterion (13c) is not true, the article is removed from All variables consist of either categorical or ordinal data types, further analysis. allowing us to classify the SLR results faster and more consistent. Each article may include one or more classifications for each variable, only the 11th variable “Technology readiness level” is restricted to one 3.3. Analysis & synthesis criteria classification per article only. We will also enrich our discussion with a bivariate correlation test among all SLR classification types. Therefore, The major output of our SLR is a comprehensive listing of relevant we need to transform the nominal/ordinal classifications of our data research contributions to address our research question. However, each extraction forms into multiple Boolean variables. Each Boolean variable individual publication should still be analyzed once we have applied the is equal to one, if (and only if) an article includes the corresponding search profile from Section 3.1 and the selection criteria from Section variable’s classification, otherwise the value is equal to zero. For 3.2. Therefore, we have predefined four data extraction forms that example, the IoT category includes a variable called “Perception Layer”, reflect on the (sub-) questions in Section 2.4.We have constructed three which in turn includes ten possible classifications. We can transform the assessment criteria for all three research disciplines included (IoT, BDA, first class “sensors and/or actuators” into a Boolean variable by checking SCM). A fourth category is added to assess the supply chain’s 7 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 which articles include sensors and/or actuators (or not). This procedure is repeated for all twelve variables’ classification types, resulting into a total of 101 Boolean variables. The Boolean variables are imported into the statistical software package “IBM SPSS Statistics 25”, allowing us to run the bivariate correlation test to empirically search for those classi- fication types that co-occur frequently. The Pearson correlation coeffi - cient is used for further assessment, while we verbally describe the correlation’s strength as well (Evans, 1996). We only highlight the moderate, strong, and very strong correlations with a two-tailed sig- nificance level of α = 0.01 in Section 5, including a minimum threshold of 5 classifications for each Boolean variable to avoid false correlations in between coincidental SLR results. 4. Results This section provides a comprehensive overview of the publications Fig. 5. The number of new publications in Scopus per year, separated for each resulting from the search strategy from Section 3. First, some key figures keyword category. The resulting number of publications are modified, based on are defined regarding the number and type of publications for all four the inclusion criteria defined for all permitted document types in this research. keyword categories separately (Section 4.1). Second, the implementa- tion of our search strategy is visualized by a so-called search roadmap (Section 4.2). The output of the SLR is listed in Section 4.3, while the corresponding descriptive statistics are discussed in Section 4.4. The filled-in data extraction forms are discussed in Section 5. 4.1. Search results – Research disciplines The first step of our search strategy is to locate relevant publications for each keyword category separately (e.g., IoT, BDA, SCM, and appli- cations). The number of publications found in Scopus differs for each category (Fig. 5). Most publications are related to SCM decision making every year, but the number of submissions related to BDA is growing with increasing pace since 2015. The total number of IoT publications also continues to grow since 2015. The grow rate of academic publica- tions related to SCM applications seems to be stagnated since 2005. Fig. 6 includes the relative frequencies of the scientific disciplines from which the articles originate for each keyword category separately. Most articles are related to both ‘Engineering’ and ‘Computer Science’. The Fig. 6. The relative frequency of relevant academic disciplines found in Scopus, proportion of business related articles is relatively low, even the SCM separated for each keyword category. The data originates from the period 1980–2019. application category includes no more than 20% articles related to the fields of ‘Business Management and Accounting’, ’Decision Sciences’ and ‘Economics, Econometrics and Finance’. The low proportion of business 4.4. Search results – Descriptive SLR statistics related articles provides the hypothesis that most academic articles are related to technology development instead of real-life implementations. More than 50% of all SLR publications originate from either the IEEE, Elsevier, or Springer publishers (Fig. 8), while approximately a quarter of all articles includes a single/unique publisher that is not shared with 4.2. Search results – Roadmap other publications. The high number of ‘other’ publishers may be caused by the relatively large proportion of conference proceedings (Fig. 9). A search roadmap is constructed to visualize the search strategy Only 31 out of 79 articles are documented as a journal article, workshop executed (Fig. 7). The roadmap consists of two types of activities rep- paper, book, or book section, all other publications originate from resented by the blue rectangles: (1) inserting the search profile into the conference proceedings. The number of studies that include an inte- Scopus database and (2) evaluating the article’s content based on the grated approach towards the IoT, BDA, and SCM techniques is growing inclusion/exclusion criteria defined. The number of articles added and/ with an acceleration pace for the last five years (Fig. 10). or removed is visualized for each step separately, and the remaining number of articles is shown between the steps. Finally, 79 articles are 5. Discussion selected for further assessment. The execution of our search strategy resulted into 228 potentially 4.3. Search results – Selected articles useful articles. Only 79 articles were actually selected after screening all the articles’ titles, keywords, and abstracts (see Fig. 7). The articles are Table 3 illustrates the existing literature found by applying the evaluated by using the twelve variables included in our pre-defined data search roadmap visualized in Fig. 7. For each article, we have summa- extraction forms (see Section 3.3). A full description of the filled-in data rized some essential reference information as well as the number of ci- extraction forms is given in the Appendices C, D, E, and F, including the tations in Scopus, which is included to understand which publications descriptive statistics for each individual variable. In this section, we first are accepted by fellow scholars. Note that some articles were not discuss our observations by reflecting on the four data extraction forms downloaded from Scopus (see exclusion criterion 12 in Section 3.2); the separately (Section 5.1 till Section 5.4), after which we address the citations of those articles are based on the available metrics released by cross-disciplinary SLR results (Section 5.5) to compose a research the publisher. 8 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Fig. 7. Roadmap corresponding to the SLR strategy described in Section 3. The selection criteria are represented by the blue rectangular objects (IC = Inclusion criteria; EC = Exclusion criteria; QC = Quality criteria), while the research output is visualized by the file-shaped objects. agenda for IoT, BDA, and SCM. Therefore, Section 5.5 will be used to 5.1. Discussion – IoT answer the three sub-questions previously raised in Section 2.4 by reflecting on the SLR results and relevant academic literature simulta- Most research articles empower their physical objects with multiple neously, while Fig. 11 summarizes all our recommendations. The dis- types of sensors and/or actuators (66 out of 79 articles), which can cussion is based on the data extraction forms in Appendix C till F, plus communicate with traditional data warehouses and mobile devices via the bivariate correlation test with a two-tailed significance level of α = the internet, wireless LAN or wired connections (see Appendix C). The 0.01. majority of those sensors are used to capture modifications of the environmental conditions at hand (e.g., mechanical, thermal, and opti- cal). Some sensor types seem to co-occur quite often. For example, a 9 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Table 3 A comprehensive overview of the SLR output, including 79 academic publications that address the IoT’s analytical capabilities within SCM and logistics operations. ID APA reference (authors, year) Title Publisher #Citations 01 (Anderson, 1997) Future directions of R & D in the process industries Elsevier 1 02 (Athani, Tejeshwar, Patil, Patil, & Soil moisture monitoring using IoT enabled Arduino sensors with neural networks for IEEE 16 Kulkarni, 2017) improving soil management for farmers and predict seasonal rainfall for planning future harvest in North Karnataka — India 03 (Ayaz et al., 2018) Wireless Sensor’s Civil Applications, Prototypes, and Future Integration Possibilities: A Review IEEE 23 04 (Aziz et al., 2017) Leveraging BIM and Big Data to deliver well maintained highways Emerald 6 05 (Bacon, et al., 2011) Using Real-Time Road Traffic Data to Evaluate Congestion Springer 28 06 (Bagheri & Movahed, 2016) The Effect of the Internet of Things (IoT) on Education Business Model IEEE 24 07 (Beal & Flynn, 2015) Toward the digital water age: Survey and case studies of Australian water utility smart-metering Elsevier 29 programs 08 (Belkaroui, Bertaux, Labbani, Hugol- Towards events ontology based on data sensors network for viticulture domain ACM Press 1 Gential, & Christophe, 2018) 09 (Bellini et al., 2017) Wi-Fi based city users’ behavior analysis for smart city Elsevier 9 10 (Bibri, 2018) The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big Elsevier 73 data applications for environmental sustainability 11 (Birken, Schirner, & Wang, 2012) VOTERS: Design of a Mobile Multi-Modal Multi-Sensor System ACM Press 11 12 (Carfagni, Daou, & Furferi, 2008) Real-time estimation of olive oil quality parameters: a combined approach based on ANNs and ACM Press 0 machine vision 13 (Chakurkar, Shikalgar, & An Internet of Things (IOT) based monitoring system for efficient milk distribution IEEE 0 Mukhopadhyay, 2018) 14 (Chan, Lau, & Fan, 2018) IoT data acquisition in fashion retail application: Fuzzy logic approach IEEE 4 15 (Chaudhary, Singh, Sandhya, Chauhan, Machine Learning Based Adaptive Framework for Logistic Planning in Industry 4.0 Springer 1 & Srivastava, 2018) 16 (Cherkasova, Ozonat, Mi, Symons, & Anomaly? Application change? Or workload change? Towards automated detection of IEEE 53 Smirni, 2008) application performance anomaly and change 17 (Chien et al., 2017) An empirical study for smart production for TFT-LCD to empower Industry 3.5 Taylor & Francis 18 18 (Chiu, Chang, & Chang, 2008) A Forecasting Model for Deciding Annual Vaccine Demand IEEE 2 19 (Cho, et al., 2018) A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Springer 6 Future 20 (Clayton, et al., 2006) Off-the-shelf modal analysis: Structural health monitoring with Motes SEM 6 21 (Darrah, Rubenstein, Sorton, & DeRoos, On-board Health-state Awareness to Detect Degradation in Multirotor Systems IEEE 1 2018) 22 (Dixon et al., 2007) Experience with data mining for the anaerobic wastewater treatment process Elsevier 22 23 (Dragan, Dziendzikowski, Kurnyta, Active structural integrity monitoring of the aircraft based on the PZT sensor network-the SAGE 1 Leski, & Uhl, 2013) symost project 24 (Dragone et al., 2015) A cognitive robotic ecology approach to self-configuring and evolving AAL systems Elsevier 16 25 (ElMoaqet, Ismael, Patzolt, & Ryalat, Design and Integration of an IoT Device for Training Purposes of Industry 4.0 ACM Press 1 2018) 26 (Enshaeifar et al., 2018) The Internet of Things for Dementia Care IEEE 10 27 (Ernest, Fattic, Chang, Chitrapu, & WRmt case study: GIS with rule-based expert system for optimal planning of sensor network in ASEE 0 Davenport, 2010) drinking water systems 28 (Faizul, et al., 2017) Modelling of Application-Centric IoT Solution for Guard Touring Communication Network Springer 0 29 (Fathy & Mohammadi, 2018) A method to predict travel time in large-scale urban areas using Vehicular Networks ACM Press 0 30 (Gaˇ sov´ a et al., 2017) Advanced Industrial Tools of Ergonomics Based on Industry 4.0 Concept Elsevier 17 31 (Gat, Subramanian, Barhen, & Spectral imaging applications: remote sensing, environmental monitoring, medicine, military SPIE 13 Toomarian, 1997) operations, factory automation, and manufacturing 32 (Ghiani, et al., 2018) VIRTUALENERGY: A project for testing ICT for virtual energy management IEEE 0 33 (Großwindhager, et al., 2017) Dependable internet of things for networked cars Elsevier 16 34 (Gu & Liu, 2013) Research on the application of the internet of things in reverse logistics information Omnia-Science 15 management 35 (Howell, Rezgui, & Yuce, 2014) Knowledge-Based Holistic Energy Management of Public Buildings ASCE 6 36 (Huang, 2018) Infrastructural development for farm-scale remote sensing big data service SPIE 0 37 (Iyyver, et al., 2009) Architecture for dynamic component life tracking in an advanced HUMS, RFID, and direct load AHS 0 sensor environment 38 (Kadar, Covaciu, Jardim-Gonçalves, & Intelligent Defect Management System For Porcelain Industry Through Cyber-Physical Systems IEEE 0 Bullon, 2017) 39 (Kolodziejczyk, et al., 2008) A methodological approach ball bearing damage prediction under fretting wear conditions. IEEE 4 40 (Kuhl, Wiener, & Krauß, 2013) Multisensorial Self-learning Systems for Quality Monitoring of Carbon Fiber Composites in Elsevier 3 Aircraft Production 41 (Kuo, 1993) Intelligent robotic die polishing system through fuzzy neural networks and multi-sensor fusion IEEE 7 42 (Kviesis & Zacepins, 2016) Application of neural networks for honey bee colony state identification IEEE 5 43 (Latinovic et al., 2019) Big Data as the basis for the innovative development strategy of the Industry 4.0 IoP 3 44 (Lee, Funk II, Feuerbacher, & Hsiao, Development of an emergency C-section facilitator using a human–machine systems IISE 0 2007) engineering approach 45 (Liu et al., 2015) Study on real-time construction quality monitoring of storehouse surfaces for RCC dams Elsevier 21 46 (Lujan, et al., 2019) Cloud Computing for Smart Energy Management (CC-SEM Project) Springer 4 47 (Matarazzo, D’Addona, Caramiello, Di Cognitive Decision-making Systems for Scraps Control in Aerospace Turbine Blade Casting Elsevier 1 Foggia, & Teti, 2015) 48 (Mehdiyev, Emrich, Stahmer, Fettke, & iPRODICT – Intelligent process prediction based on big data analytics Springer 2 Loos, 2017) 49 (Moi & Rodehutskors, 2016) Design of an ontology for the use of social media in emergency management IADIS Press 3 50 (Morales & Haas, 2004) Adaptive Sensors for Aircraft Operational Monitoring AIAA 8 51 (Morales-Menendez et al., 2007) Low-cost cutting tool diagnosis based on sensor-fusion Elsevier 1 52 (Moreno, Skarmeta, & Jara, 2015) How to intelligently make sense of real data of smart cities IEEE 5 (continued on next page) 10 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Table 3 (continued ) ID APA reference (authors, year) Title Publisher #Citations 53 (Niggemann, et al., 2015) Data-driven monitoring of cyber-physical systems leveraging on big data and the internet-of- CEUR-WS 25 things for diagnosis and control 54 (Papaefthimiou, et al., 2017) OLEA Framework for non refined olive oil traceability and quality assurance CEUR-WS 1 55 (Papas, Estibals, Ecrepont, & Alonso, Energy Consumption Optimization through Dynamic Simulations for an Intelligent Energy IEEE 5 2018) Management of a BIPV Building 56 (Pasic, Martinez-Salio, Zarzosa, & Diaz, ZONESEC: built-in cyber-security for wide area surveillance system ACM Press 0 2017) 57 (Pickard, Linn, Awojana, & Lunsford, Designing a converged plant-wide thernet/IP lab for hands-on distance learning: An ASEE 1 2018) interdisciplinary graduate project 58 (Pilgerstorfer & Pournaras, 2017) Self-Adaptive Learning in Decentralized Combinatorial Optimization – A Design Paradigm for IEEE 14 Sharing Economies 59 (Ray, et al., 2018) Optimizing routine collection efficiency in IoT based garbage collection monitoring systems IEEE 1 60 (Richardson, Keairns, & White, 2018) The role of sensors and controls in transforming the energy landscape SPIE 0 61 (Rodríguez et al., 2017) A System for the Monitoring and Predicting of Data in Precision Agriculture in a Rose Elsevier 12 Greenhouse Based on Wireless Sensor Networks 62 (Rouet & Foucher, 2011) Smart Monitoring System for Aircraft Structures SAE 0 63 (Rymarczyk et al., 2017) Practical Implementation of Electrical Tomography in a Distributed System to Examine the IEEE 36 Condition of Objects 64 (Sabeur, et al., 2017) Large Scale Surveillance, Detection and Alerts Information Management System for Critical Springer 0 Infrastructure 65 (Sallis, Jarur, Trujillo, & Ghobakhlou, Frost prediction using a combinational model of supervised and unsupervised neural networks MSSANZ 1 2009) for crop management in vineyards 66 (Schatzinger & Lim, 2017) Taxi of the Future: Big Data Analysis as a Framework for Future Urban Fleets in Smart Cities Springer 10 67 (Schneider, 2017) The industrial Internet of Things (IioT) Wiley & Sons 6 68 (Senthilkumar, Kumar, Ozturk, & Lee, An ANFIS Based Sensor Network for a Residential Energy Management System ISCA 3 2010) 69 (Sosunova, et al., 2013) SWM-PnR: Ontology-Based Context-Driven Knowledge Representation for IoT-Enabled Waste Springer 1 Management 70 (Spanias, 2017) Solar energy management as an Internet of Things (IoT) application IEEE 15 71 (Sramota & Skavhaug, 2018) RailCheck: A WSN-Based System for Condition Monitoring of Railway Infrastructure IEEE 1 72 (Talamo & Atta, 2019) FM Services Procurement and Management: Scenarios of Innovation Springer 0 73 (Taylor et al., 1999) Adaptive Fusion Devices for Condition Monitoring: Local Fusion Systems of the NEURAL- Trans Tech 5 MAINE Project Publications 74 (Verhoosel & Spek, 2016) Applying ontologies in the dairy farming domain for big data analysis CEUR-WS 0 75 (Wang, Birken, & Shamsabadi, 2014) Framework and implementation of a continuous network-wide health monitoring system for SPIE 6 roadways 76 (Wang, Zhang, Zhang, & Lim, 2012) Smart Traffic Cloud: An Infrastructure for Traffic Applications IEEE 17 77 (Whittle, Allen, Preis, & Iqbal, 2012) Sensor Networks for Monitoring and Control of Water Distribution Systems ISHMII 38 78 (Won, Zhang, Jin, & Eun, 2018) WiParkFind: Finding Empty Parking Slots Using WiFi IEEE 1 79 (Yang, et al., 2017) Domestic water consumption monitoring and behavior intervention by employing the internet Elsevier 3 of things technologies strong positive correlation is found between the application of thermal 5.2. Discussion – BDA and chemical sensors (ρ = 0.623), while mechanical and acoustic sen- sors are moderately correlated (ρ = 0.407). The presence of business The results in Appendix D show that almost all SLR publications events does also trigger data registration by more traditional informa- contain a proper explanation of the data acquisition processes involved tion systems, especially the application of Transaction Processing Sys- (76 out of 79 articles). However, data acquisition forms the first initi- tems (TPS) seems to co-exist with the registration of business events in ating step of the data mining process only (Fig. 2); the remaining the IoT’s perception layer (ρ = 0.454). Another moderate positive cor- sequential activities receive less attention by the SLR results. Re- relation can be found between the application of mobile devices and searchers seem to provide more insight into the data management pro- location receivers (ρ = 0.400), resulting into a significant use of radio cess once the pattern searching algorithm is explained in more detail (58 navigation techniques like GPS to locate objects as well (ρ = 0.493). out of 79 articles), because a moderate positive correlation is found All 79 SLR publications adequately explained which type of mea- between the data transformation and modelling activities (ρ = 0.573). surement devices to install into the IoT’s perception layer, but the However, other intermediate data management activities are not network layer’s design receives less interest. Some researchers did not explained in enough detail to replicate the research findings adequately. even mention how the data is communicated throughout the network at For example, activities like data cleaning, data reduction, feature se- all (12 out of 79 articles). We did expect to see quite some RFID appli- lection, pattern interpretation, and real-life application are rarely cations in nowadays SCM and logistics operations, since IoT networks described in combination. Only a few articles include a full description originate from the RFID developments in the early 1980s’ (Atzori et al., of all essential steps in between the data gathering and modelling ac- 2010; Xu et al., 2014). However, the proportion of articles including tivities (Chaudhary, Singh, Sandhya, Chauhan, & Srivastava, 2018; RFID technology is relatively low; only 11 out of 79 articles did imple- Darrah, Rubenstein, Sorton, & DeRoos, 2018; ElMoaqet, Ismael, Patzolt, ment RFID tags into the perception layer. Most of these RFID applica- & Ryalat, 2018; Matarazzo, D’Addona, Caramiello, Di Foggia, & Teti, tions were only considering wireless data transmissions to support the 2015; Whittle, Allen, Preis, & Iqbal, 2012). The lack of insight into the system’s health monitoring capabilities; only two articles did gather data management process obstructs other researchers and business business event data by using a RFID reader (Enshaeifar et al., 2018; practitioners to reuse the BDA techniques, while many SCM stakeholders Rouet & Foucher, 2011). The share of NFC technologies is even lower; have limited capacity to analyze large sums of data in modern-day op- only 5 out of 79 articles mentioned the usage of NFC tags into their IoT erations already (Tiwari et al., 2018). infrastructure. The Low-Power WAN technologies are also not Characterization, classification, and regression are the more popular commonly applied yet (only 7 out of 79 articles), while these techniques patterns searched for in data-intensive environments. This observation is are specially designed for IoT applications (Ben-Daya et al., 2019). also reflected by the type of algorithms used, most articles rely on either 11 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Yuce, 2014; Lee, Funk, Feuerbacher, & Hsiao, 2007; Papas, Estibals, Ecrepont, & Alonso, 2018; Ray, Tapadar, Chatterjee, Karlose, Saha, & Saha, 2018). A moderate negative correlation is found between the loading and monitoring decision types (ρ = 0.450), meaning that most researchers use the IoT’s analytical capabilities to support only one of those two decision types; (2) Sequencing: 6 out of 79 articles use the derived knowledge to prioritize the system’s task at hand (Birken, Schirner, & Wang, 2012; Chakurkar, Shikalgar, & Mukhopadhyay, 2018; Faizul, Rashid, Hamid, Sarijari, Mohd, & Abdullah, 2017; Fathy & Mohammadi, 2018; Papaefthimiou, Ventouris, Tabakis, Valsa- midis, Kazanidis, & Kontogiannis, 2017; Wang, Birken, & Fig. 8. The number of SLR publications for each publisher. Shamsabadi, 2014); (3) Scheduling: 5 out of 79 articles allocate the prioritized workload over time (Chien, Hong, & Guo, 2017; Dragone et al., 2015; Pil- gerstorfer & Pournaras, 2017; Ray, et al., 2018; Senthilkumar, Kumar, Ozturk, & Lee, 2010). A moderate positive correlation is found in between the scheduling decisions and prescriptive analytical capabilities (ρ = 0.446), meaning that the corre- sponding decision makers are frequently supported with explicit future actions. The significant presence of operational monitoring activities is quite remarkable, since multiple authors hypothesize that the combination of IoT and BDA implementations will evolve from track-and-trace appli- Fig. 9. The number of SLR publications for each document type. cations towards self-steering and event-driven logistics (Ben-Daya et al., 2019; Chung, Gesing, Chaturvedi, & Bodenbenner, 2018; Li et al., 2015; descriptive statistical techniques (32 out of 79 articles) or neural net- Macaulay, Buckalew, & Chung, 2015; Xu et al., 2014). Since 2017 works (30 out of 79 articles). A moderate positive correlation exists for however, more research initiatives moved beyond predictions only and the application of neural networks into classification problems (ρ = used the BDA results to prescribe the decision makers what to do next 0.496), while those neural networks are often supported by the principal (20 out of 79 articles), a trend paving the way for AI algorithms to component analysis to reduce the dataset’s dimensionality as well (ρ = autonomously learn and intervene within SCM and logistics operations 0.429). On the other hand, regression patterns seem to depend on the (Gesing, Peterson, & Michelsen, 2018). We also expect to see more more traditional multivariate regressive analysis (ρ = 0.546), and the k- research initiatives addressing tactical and strategic decision support to nearest neighbors algorithm is frequently used for clustering tasks (ρ = increase the return on investments of those data-intensive projects in the 0.496). Visualization techniques originating from Business Intelligence near future. (BI) also remain popular, since 37 out of 79 articles explicitly describe how the model’s output is visualized to support human pattern recog- 5.4. Discussion – applications nition. Association rules, clustering, and rule induction techniques are less often implemented however (17, 11, and 7 out of 79 articles The results in Appendix F show that the IoT’s analytical capabilities respectively), while it is our conjecture that these techniques are are most commonly applied in production and logistics environments essential to support prescriptive decision making. It will help if more (38 and 28 out of 79 articles respectively). Nearly all publications refer authors explain how the data is (pre-) processed exactly, allowing other to either one of those two SCM activities, because of the negative investigators to mine through the datasets with other pattern recogni- moderate correlation found in between the production and logistics tion algorithms. disciplines (ρ = 0.501). A wide range of inter-organizational processes is supported as well (e.g., order fulfilment, reverse logistics, customer 5.3. Discussion – SCM services, manufacturing flows, and balancing demand); only the activ- ities related to customer and supplier relationship management seem to Most articles apply the patterns emerging from the BDA techniques be less popular in production and logistics environments. Production to either describe or predict the system’s conditions at hand; a total of 25 related investigations are moderately focusing on the efficient man- out of 79 articles were found for both type of analytical applications agement of manufacturing flows (ρ = 0.488), while logistics publica- separately (see Appendix E). The relatively high number of descriptive tions have a moderate emphasis on order fulfilment (ρ = 0.438). A and predictive applications can be explained by the large proportion of relative high number of R&D publications proposed a newly developed monitoring research efforts (63 out of 79 articles). The vast majority of decision support system (38 out of 79 articles), but the number of arti- those monitoring publications act on the newly developed data streams cles supporting supply chain activities like Purchasing and Marketing & in real-time, while 11 out of those 63 monitoring articles apply the Sales are scarce (4 and 2 out of 79 articles respectively), while there derived knowledge offline. Therefore, we can conclude that most re- were no articles found related to Finance at all. The lack of financial searchers combine IoT and BDA techniques to enhance supply chain publications in our SLR study forms an interesting observation for future resilience by either detecting or predicting deviations from the opera- research, but this may be caused by our focus on the allocation and tional planning, allowing decision makers to respond in a timely manner movement of physical resources equipped with sensing and communi- and restore the system’s conditions preferred. Only a few research ini- cating devices (see Section 3.1). tiatives were used to support other planning capabilities: Most SLR outcomes were related to dependability (50 out of 79 ar- ticles) and costs (48 out of 79 articles) performances. Quality (32 out of (1) Loading: 6 out of 79 articles apply the IoT’s analytical capacities 79 articles) and speed (29 out of 79 articles) are also present, but flex - to allocate the system’s workload properly (Bellini, Cenni, Nesi, ibility (17 out of 79 articles) is not addressed that often. The absence of & Paoli, 2017; Chiu, Chang, & Chang, 2008; Howell, Rezgui, & flexibility improvements is quite remarkable, because both researchers 12 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Fig. 10. The annual number of SLR publications, including a fitted polynomial function y = 0,046x3 – 0,8397x2 + 4,6038x – 4,8382, with × the number of years since 2005, and y as the yearly publication frequency. Fig. 11. A visualization of our recommendations for further interdisciplinary research towards the IoT’s analytical capabilities in the SCM domain (Blue rectangles = technical and/or managerial recommendations; Orange clouds = potential new areas for future interdisciplinary research). 13 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 and business practitioners expect that the combination of IoT and BDA expert system and external databases, visualizes the working conditions will increase supply chain resilience towards disturbances and change- and initiates an ergonomic assessment. The visualizations and use cases able markets (Atzori et al., 2010; Barton & Court, 2012; Chung, Gesing, seem to be promising, but more insights into the analytical capacities Chaturvedi, & Bodenbenner, 2018; Macaulay, Buckalew, & Chung, and the actual benefits in comparison with traditional methods are 2015). A moderate negative correlation is found between the SLR arti- currently missing. cles focusing on dependability and quality (ρ = 0.442), meaning that most researchers focus on one of these two performance indicators only. 5.5. Addressing the research gaps The maturity level of the IoT’s analytical capabilities in today’s SCM and logistics research also seem to differ significantly among all SLR The IoT and BDA paradigms have become increasingly popular publications: among scientists since the last decade (Fig. 5). The IoT’s analytical ca- pabilities seem to be quite promising for SCM and logistics operations as (1) Conceptual level (TRL 1 þ 2): A relatively large proportion of well, because more research efforts are applying an integrated approach the SLR results introduces the expected benefits that IoT and BDA towards data gathering, pattern recognition, and decision making (see may bring to SCM and logistics operations (18 out of 79 articles); Fig. 10 and Appendix A). Especially computer scientists and engineers (2) Component level (TRL 3 þ 4 þ 5): A total of 45 out of 79 ar- seem to appreciate the expected benefits that real-time condition ticles relate to component validation activities in a laboratory monitoring may offer (Fig. 6), resulting into a sharp rise of conference setting or related environment (e.g., measurement devices, proceedings and journal papers (Fig. 9). However, the discussion of the communication networks, analytical models, etc.). While these SLR results from Section 5.1 until Section 5.4 revealed multiple gaps at articles do not validate the IoT’s analytical capabilities in real- the intersection of the IoT, BDA, and SCM research disciplines already. life, they do provide a strong evidence of the technology’s2 In the following subsections, we introduce a research agenda for future feasibility and developmental requirements (Mankins, 2009); interdisciplinary research towards the IoT’s analytical capabilities in (3) Prototype level (TRL 6 þ 7): The number of prototypes pub- SCM and logistics operations by answering the three sub-questions lished is relatively small (12 out of 79 articles), but still prom- raised in Section 2.4. Fig. 11 summarizes all our recommendations ising. The technological developments seem similar for different made. SCM activities, since there are prototypes found in multiple en- vironments like production (Chien et al., 2017; Liu, Zhong, Cui, 5.5.1. Sub-question A: IoT + BDA Zhong, & Wei, 2015; Pickard, Linn, Awojana, & Lunsford, 2018; We will first answer sub-question A by reflecting on the combina- Rodríguez, Gualotuna, ˜ & Grilo, 2017; Sabeur, Zlatev, Melas, tions of IoT and BDA techniques found in this SLR study. There are no Veres, Arbab-Zavar, Middleton, & Museux, 2017), logistics op- significant correlations found between the type of data gathering de- erations (Bellini et al., 2017; Lee, Funk II, Feuerbacher, & Hsiao, vices and the analytical capacities proposed by the researchers; the IoT 2007; Pilgerstorfer & Pournaras, 2017; Won, Zhang, Jin, & Eun, and BDA techniques seem to be customized to the problem context at 2018), customer services (Enshaeifar et al., 2018; Yang, Yang, hand. Most researchers integrate the newly developed measuring de- Magiera, Froelich, Jach, & Laspidou, 2017) and maintenance vices with more traditional ICT infrastructures (e.g., data warehouses, activities (Rymarczyk et al., 2017); mobile devices, external databases) to either visualize the current way of (4) System level (TRL 8 þ 9): A small portion of the SLR results operating, or to better predict the system’s future state. The strong includes an IoT system that is fully tested or even operational to emphasis on classification, regression and visualization explains the support supply chain decision making in real-life (4 out of 79 relatively high proportion of artificial neural networks, statistics, and BI articles). An overview of all four articles is given in Table 4, each techniques used. Therefore, we conclude that today’s SCM researchers publication includes sensors and/or actuators to monitor SCM use the IoT’s analytical capabilities to stimulate a more system-oriented and logistics operations with either descriptive or predictive approach towards remote monitoring of physical objects (Atzori et al., analytical capabilities. However, the type of sensors and BDA 2010; Wortmann & Flüchter, 2015; Xu et al., 2014). However, capa- techniques strongly depend on the decision/problem at hand. bilities regarding ambient intelligence and autonomous control are receiving limited attention within our SLR results, while the IoT’s We conclude this section by evaluating the actual benefits proposed wireless communication among physical objects could provide a strong by the four system publications in Table 4. Carfagni, Daou, and Furferi foundation for the real-time processing of information (Li et al., 2015). (2008) have installed a wired camera into an olive oil extraction mill to It is remarkable that the IoT’s perception layers reported in our SLR estimate the oil’s quality parameters with the aid of neural networks and strongly rely on the integration of wired sensor networks and legacy machine vision. The prototype gives promising and validated quality systems, since other researchers hypothesized a more frequent use of metrics, but the benefits in terms of cheaper and faster quality control both identification tags and location receivers (Atzori et al., 2010; Ben- are not addressed yet. Wang, Birken, & Shamsabadi (2014) have created Daya et al., 2019; Xu et al., 2014). The focus on sensor networks and an innovative dynamic health monitoring system by equipping a van business events explains the application of BDA techniques to provide with GPS receivers, video cameras, radar technology, tire pressure early warnings of potential disruptions. However, the lack of identifi - sensors, and an axle accelerometer. The combination of real-time data cation tags and location receivers obstructs SCM operators to fully gathering, regression techniques and flexible resource allocations harness the spatiotemporal information available. Nowadays, people enabled the researchers to speed up road inspection tasks with almost movements are tracked solely due to the common integration of mobile 95% in comparison with traditional methods, allowing decision makers devices and location receivers into the IoT’s perception layer (ρ = to prioritize road maintenance activities. Moreno, Skarmeta, and Jara 0.400), but other resources are scarcely tracked. Diversification of the (2015) explain how cities can become smarter by combining real-time available data streams with the objects’ movements stimulates SCM system monitoring with data analytics and optimization capabilities. researchers to anticipate on the changing environment by directly For example, city planners can apply neural networks to predict traffic controlling those objects monitored (Addo-Tenkorang & Helo, 2016). jams 15 min in advance by using temperature and traffic sensors only, Therefore, we argue that future SCM research must diversify the IoT’s while a building’s energy demand can be reduced with 29% by sensor networks with more context-aware devices to fully capture the combining infrared sensors and RFID tags to locate people movements. dynamic and stochastic behavior of the objects themselves, because ˇ ´ ˇ ´ Finally, Gasova, Gaso, and Stefanik (2017) enabled organizations to appropriate identity management is considered as one of the most speed up the design of an ergonomic workspace by using camera images ´ ˇ ´ critical success factors for IoT implementations (Colakovic & Hadzialic, of simple mobile devices. A newly developed application, including an 2018). 14 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Table 4 System evaluations (TRL8 or TRL 9). ˇ ´ Category Variable (Carfagni, Daou, & Furferi, (Wang, Birken, & Shamsabadi, 2014) (Moreno, Skarmeta, & Jara, 2015) (Gasova et al., 2017) 2008) IoT Perception Sensors and/or actuators; Data Sensors and/or actuators; Sensors and/or actuators; Tags; Sensors and/or actuators; layer warehouses Mobile devices; External sources Mobile devices; External sources Stimulus Biological; Chemical; Acoustic; Electric; Magnetic; Acoustic; Chemical; Electrical; Optical Mechanical; Optical; Thermal; Mechanical; Optical Mechanical; Thermal; Event Event Network layer Wired Wired; Internet; Radio navigation; RFID; Internet; W-PAN Internet; W-LAN BDA Data Acquisition; Transformation; Acquisition; Transformation; Acquisition; Transformation; Acquisition; Aggregation; management Modelling Reduction; Aggregation; Modelling; Reduction; Modelling; Application Modelling; Application Application Pattern Characterization; Characterization; Regression; Time Characterization; Classification; Visualization recognition Classification; Visualization series analysis; Visualization Time series analysis; Association rules Algorithm type Statistics; Neural networks; Statistics; Linear regression; Non- Statistics; Neural networks; Linear Expert system Computer vision linear regression regression; Fuzzy logic SCM Analytics type Predictive Predictive Predictive Descriptive Decision Operational (online) Operational (online) Operational (online) Tactical hierarchy Decision type Monitoring Sequencing; Monitoring Monitoring Monitoring Application Supply chain Production; Manufacturing R&D; Logistics; Customer services; Logistics; Order fulfilment R&D; Customer services; activity flow Return management Readiness level TRL08 TRL09 TRL09 TRL09 KPI Cost; Quality; Speed Flexibility; Speed Cost; Quality; Speed Quality; Speed The SLR results indicate that most SCM researchers apply a static monitoring decision types and the application of sensors and actuators cloud infrastructure, consisting of centralized master-server imple- (ρ = 0.456). mentations only. We expected more research initiatives into distributed computing systems (e.g., edge and fog computing), because the internet- 5.5.2. Sub-question B: BDA + SCM based technologies empower IoT devices with both machine-to-machine The majority of all BDA techniques support supply chain decision (M2M) communication and cloud-based processing capabilities (Chiang making with monitoring capabilities at an operational level. Newly & Zhang, 2016; Colakovi´ c & Hadˇ ziali´ c, 2018; Gubbi et al., 2013). Our developed decision support tools integrate the heterogeneous data bibliometric study revealed that the IoT technologies and distributed sources with popular techniques like data visualizations, statistics, computer systems are closely intertwined (see Fig. 4 and Appendix A), expert systems, and ontologies to support human decision makers with but only one SLR paper highlighted the importance of using fog and edge either descriptive or explanatory analytical capabilities. More data- computing to minimize the amount of data transferred across the IoT’s intensive techniques like neural networks and regression analyses are cloud infrastructure (Bibri, 2018). The absence of distributed computing commonly applied to predict the system’s conditions at a future state. in SCM research may be caused by the lack of wireless communication Therefore, we answer sub-question B by stating that most scientists networks implemented nowadays, since these technologies speed up combine IoT and BDA techniques to timely inform human decision collaborative communication among context-aware objects to build up makers about observed or predicted disturbances. Especially neural ambient intelligence and autonomous control (Atzori et al., 2010; Li networks seem to be used for prediction purposes, because of the et al., 2015) For example, neural networks rarely use input from flexible moderate negative correlation found in between neural networks and resources like mobile devices for their classification tasks (ρ = 0.434), descriptive analytics (ρ = 0.420). However, it is our hypothesis that but instead rely on hard-wired sensor networks. This means that one of the IoT’s prescriptive capabilities are most beneficial for the SCM and the most popular classification techniques in AI research does not logistics operations, because human decision makers have limited consume the full potential of the IoT’s wireless sensing and communi- cognitive capacity to efficiently transform the ever increasing flow of cation technologies yet (e.g., RFID, NFC, W-PAN, W-LAN, LP-WAN, additional data sources into effective actions (Chen et al., 2015). etc.), preventing researchers to fully monitor and control the inter- Future SCM research into the IoT’s analytical capabilities should connected physical objects in real-time. Even most RFID applications are evolve from track-and-tracing towards self-steering and event-driven used for data transmitting purposes only, instead of supporting the self- logistics (Ben-Daya et al., 2019; Chung, Gesing, Chaturvedi, & Bod- steering systems or automated identification envisioned in the 1980 s enbenner, 2018; Li et al., 2015; Macaulay, Buckalew, & Chung, 2015; Xu (Atzori et al., 2010; Xu et al., 2014). Therefore, future IoT systems et al., 2014). Innovative BDA techniques can stimulate those AI de- require more wireless communication among objects to create parallel velopments by transforming the derived knowledge into parameters for and distributed data processing capacities in order to handle the improved decision making and continuous learning (Zhong et al., 2016). increasing volumes of dynamic data in a flexible way (Cai et al., 2017). Therefore, scientists should expand their analytical toolbox with asso- It is interesting to note that not all BDA applications require new ciation, clustering, and rule induction techniques to autonomously measuring and communication devices; more traditional ICT in- transform the perception of dynamic and stochastic disturbances into frastructures and combinatorial optimization procedures could be used real-time interventions. Our SLR results indicate that only a few studies to enable data-driven decision making in modern day SCM and logistics apply the BDA techniques to automatically prescribe decision makers environments (Chien et al., 2017; Pilgerstorfer & Pournaras, 2017). what to do next in terms of loading, sequencing, and scheduling. By Therefore, it may be interesting to investigate the minimum amount of summarizing the prescriptive studies found in this research, it is our perception devices to support the corresponding decision efficiently in hope that future SCM research efforts will move beyond descriptive and terms of investment costs, processing time, and reliability. Especially the predictive analytics only. For example, a smart allocation of free Wi-Fi critical usage of sensors and actuators should be investigated in more access points and GPS receivers allows city planners to re-allocate detail, because of the moderate positive correlation found between the traffic flows throughout the city (Bellini et al., 2017). Clustering 15 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 customer locations by using the product’s and/or client’s real-time applied into resource intensive inter-organizational SCM activities like conditions enables vehicles to dynamically re-optimize their routes demand management, manufacturing flow management, order thernet, (Chakurkar, Shikalgar, & Mukhopadhyay, 2018; Ray, et al., 2018), and return management. However, the absence of procurement related while the shortest route can also be found by combining regression and studies seems remarkable due to the supply chain’s interest into IoT and real-time traffic monitoring (Fathy & Mohammadi, 2018). A simulation- BDA to improve transparency and reduce supply chain risks. The major based scheduling system can enhance manufacturing intelligence by focus of the SLR results onto operational monitoring decisions is also using the data of the facility’s manufacturing execution system (Chien noteworthy, since other researchers published several IoT and/or BDA et al., 2017). The combination of neural networks, fuzzy logic, and rule implementations to support tactical and strategic decision making inductive techniques may stimulate facility managers to automatically (Addo-Tenkorang & Helo, 2016; Ben-Daya et al., 2019; Hopkins & reduce the energy consumption of their buildings (Howell, Rezgui, & Hawking, 2018; Lou et al., 2011; Marjani et al., 2017; Zhong et al., Yuce, 2014; Senthilkumar, Kumar, Ozturk, & Lee, 2010). Finally, a 2016). Our SLR study indicates that most SCM researchers use the IoT’s cognitive robotic ecology empowered with neural networks can assist analytical capabilities to maintain reliable and efficient operations in the elderly people with scheduling their daily tasks (Dragone et al., 2015). presence of potential disruptions, while the same patterns can improve Nowadays, classification and regression algorithms are used for overall business models as well. Techniques for pattern recognition and machine learning and AI developments predominantly (e.g., neural mathematical optimization have to be combined more often to networks, support vector machines, linear regression, time series anal- adequately incorporate disturbances and changing parameter values ysis, principal components analysis, etc.). The predictive power of those into operational, tactical, and strategic decision making simultaneously. algorithms enable SCM operators to adopt a proactive approach in For example, production environments can reshape the factory layout by response to supply chain risks monitored (Tiwari et al., 2018), but the reflecting on the machines’ utilization rates; logistics operations can use autonomous construction of interventions also requires recognition of the objects’ track-and-trace data to reallocate facilities; and better the workload ahead. Therefore, we strongly recommend future research insight into customer buying patterns enable purchasing managers to projects to broaden their scope with prescriptive algorithms like k- adapt the product portfolio in near real-time. Future monitoring systems nearest neighbors, genetic algorithms, or fuzzy logic. The extension of should explicitly highlight why and how the IoT’s additional data the IoT’s perception layer with autonomous operating agents including streams are required for better decision making at all decision levels, prescriptive analytical capabilities seems to be quite promising for since object monitoring is just a mean to improve supply chain perfor- future AI developments in SCM research (Dragone et al., 2015; Fathy & mances, not the objective itself. A comprehensive modeling approach Mohammadi, 2018; Ghiani, Mocci, Franceschelli, Anedda, Desogus, & may help decision makers to gain insight into the added value of their Fadda, 2018; Pilgerstorfer & Pournaras, 2017). We also belief that ad- IoT and BDA implementations (e.g., Enterprise Architectures, Unified vances in reinforcement learning may bridge the gap between predictive Modeling Language, Business Process Modeling, etc.). and prescriptive analytics, as described by two of the SLR articles as well The current research gaps depicted in the heatmap of Table 5 can (Dragone et al., 2015; Pilgerstorfer & Pournaras, 2017). help future researchers to publish more real-life demonstrations of the IoT’s analytical capabilities into SCM and logistics operations. Some 5.5.3. Sub-question C: SCM + Application interesting correlations were found between the TRLs and the pattern The third and last sub-question relates to the improvements that searching algorithms as well. For example, neural networks are often supply chain decision makers may expect from the IoT’s analytical ca- applied in laboratory settings only (TRL4; ρ = 0.414), and support vector pacities. Production environments include the highest proportion of machines are moderately included into fully developed system pro- condition monitoring systems consisting of wired sensor and/or actuator totypes (TRL6; ρ = 0.414). The number of TRL9 systems is relatively low networks, resulting in a strong focus on higher reliability outcomes and (4 out of 79), but it is still interesting to note that these systems often better quality standards. Logistics operations seem to benefit from depend on linear regression techniques (ρ = 0.443). However, most flexible resources (e.g., tags, mobile devices, location receivers, wireless research efforts are still not completely mature yet; the majority of the communication devices, etc.) to support prescriptive analytics. There- SLR articles highlight the system’s analytical (sub-) components only (e. fore, logistics planners use the real-time data of physically operating g., by reflecting on the prediction’s mean squared error). Researchers objects to move beyond track-and-trace applications towards loading, have to critically think when the measuring system becomes beneficial sequencing, and scheduling decisions, which explains the stronger for business practitioners, because validation of the IoT’s analytical emphasis on flexibility and speed. Those SLR outcomes are quite capacities should not depend on the system’s descriptive or predictive consistent with the IoT’s and BDA’s expectations (Tiwari et al., 2018). output measures only. There is a need for a more general set of KPIs to Especially logistics operations seem to fully exploit the IoT’s dynamic measure the efficiency and effectivity of IoT and BDA implementations monitoring capabilities, probably because the logistics sector is used to consistently (Zhong et al., 2016). Multiple case studies including fully track-and-trace technologies for some decades already (Atzori et al., developed IoT systems and modern pattern searching algorithms are 2010; Ben-Daya et al., 2019; Xu et al., 2014). For example, the logistics’ required to validate the benefits envisioned too. A simulation study may perception layer is relatively often equipped with location receivers (ρ form a reasonable alternative if the IoT system is highly complex, = 0.418), and the corresponding communication networks consist of including both stochastic and dynamic input components (Law, 2015). wireless LAN (ρ = 0.453). However, other SCM disciplines do not fully Future research should have an explicit focus on how the data is gath- benefit from the dynamic information released by the object’s sensing ered, communicated, and processed by the IoT devices themselves to and communication devices, while flexibility is one of the major benefits speed up the validation of the IoT’s analytical capabilities. Proper envisioned by both the IoT and BDA research paradigms (Atzori et al., administration of the data management activities may also support re- 2010; Barton & Court, 2012; Chung, Gesing, Chaturvedi, & Bod- searchers to apply a wider spectrum of pattern recognition capabilities enbenner, 2018; Macaulay, Buckalew, & Chung, 2015). The monoto- across the supply chain at different hierarchical levels. nous focus on operational decisions in the production and logistics environments also limits prototyping of the IoT’s analytical capabilities 6. Conclusions & further research across the supply chain (e.g., Purchasing, Marketing & Sales, and Finance). This paper presented the results of state-of-the-art IoT developments The large scale implementations of IoT and BDA techniques into proposed in academic literature to improve supply chain decision production and logistics operations is consistent with other research making by gathering, analyzing, and applying real-time data of physical observations (Ben-Daya et al., 2019; Nguyen et al., 2018; Tiwari et al., objects. For this, we followed the systematic review methodology pro- 2018; Wang et al., 2016). The IoT’s analytical capabilities are commonly posed by Denyer and Tranfield (2009), including an initial bibliographic 16 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Table 5 Heatmap of the IoT’s technology readiness levels (columns) across multiple SCM activities (rows). The heatmap visualizes the research gaps found in the SLR literature by reflecting on the SLR publication frequency for each TRL and SCM combination. Note that one article may cover multiple SCM activities, which explains why the sum of combinations exceeds the total number of SLR results (79 articles). study to search for the most relevant keywords of the IoT, BDA, and SCM RFID, NFC, W-PAN, W-LAN, LP-WAN, etc.). Innovative BDA techniques disciplines separately. The assessment of all 79 articles enables us to should bridge the gap between predictive and prescriptive analytics by construct an overview of IoT’s analytical applications found in SCM and the construction of real-time interventions once a disturbance is logistics research. observed and/or predicted. Therefore, scientists should focus more on We can conclude that typically measuring devices are integrated flexibility improvements by expending their analytical toolbox with with more traditional ICT infrastructures to either visualize the current association, clustering, and rule induction techniques. We also recom- way of operating, or to better predict the system’s future state. Neural mend future research initiatives to explicitly report how the data is networks, statistics, and BI techniques are the most popular techniques gathered, communicated, and processed by the IoT devices themselves applied within IoT networks, which empowers supply chain decision to support a wider spectrum of pattern recognizing capabilities. Finally, makers with real-time monitoring capabilities at an operational level. more real industry case studies, going beyond toy-examples, are Production managers apply the IoT’s analytical capabilities to monitor required to validate the expected benefits enabled by the IoT’s analytical the condition of their physical products and/or equipment to obtain capabilities. higher reliability outcomes and better quality standards. Logistics op- erations have a stronger emphasis on flexibility and speed improve- CRediT authorship contribution statement ments, which explains that these operations rely on other planning activities instead of operational monitoring only (e.g., loading, Martijn Koot: Conceptualization, Methodology, Software, Valida- sequencing, and scheduling). Therefore, the construction of resilient tion, Formal analysis, Investigation, Resources, Writing – original draft, supply chains seems to be the driving force in today’s SCM research, Visualization. Martijn R.K. Mes: Conceptualization, Methodology, resulting into the integration of IoT and BDA techniques to either detect Writing – review & editing, Supervision. Maria E. Iacob: Conceptuali- or predict deviations from the operational planning. However, the real zation, Methodology, Writing – review & editing, Supervision. potential of equipping physical objects with sensing, communication, and processing capacities remains open for future research by extending Declaration of Competing Interest the concept of physical monitoring with ambient intelligence and autonomous control. The authors declare that they have no known competing financial One of the main findings that emerged from this study is a new interests or personal relationships that could have appeared to influence research direction to be pursued in the SCM discipline, which concerns the work reported in this paper. the empowerment of physical objects with more context-aware data gathering devices to keep track of their variable status in real-time (e.g., Acknowledgements identification tags, location receivers, multi-agent systems). Investments into distributed computer systems are also required to embrace the This work was supported by the Netherlands Organization for Sci- increasing data volumes of future IoT systems, especially by extending entific Research (NWO) [grant number 628.009.015]. The authors the wireless communication networks among physical objects (e.g., would also like to thank all DataRel project partners. 17 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Appendix A. Keywords A simplistic literature search is made in Scopus to find relevant keywords for the IoT, BDA, and SCM research discipline separately. The co- occurrences of keywords are analyzed by ‘VOS viewer’, a software tool for constructing and visualizing bibliometric networks. Several search con- ditions are installed to provide a brief overview of the most common grouping of keywords: (1) the keyword search will include review articles only to provide a generic overview of academic discipline and its developments; (2) the review articles should be fully published and written in English; (3) the review articles’ subject areas have to align the academic fields taken into consideration (e.g., Computer Science, Engineering, Mathematics, Decision Sciences, Business Management, Economics). The Multidisciplinary category is also included, since we are explicitly looking for linkages in between the IoT, BDA, and SCM research areas. The top 25 most frequently co-occurring keywords are visualized into a bibliometric network for all three academic fields separately. The VOS viewer tool will automatically emphasize the most frequent keyword sets and search for appropriate clusters based on the keywords’ association strength. General keywords referring to document types and scientific characteristics are removed from the bibliometric network (e.g., survey, review, future recommendations, etc.). A thesaurus file is created in order to make sure that synonyms are not counted separately. The bibliometric networks are used to define relevant keywords for our SLR search strategy. The resulting keyword selection is visualized in Table 1 (see Section 3.1). A.1. IoT keywords A total of 563 review articles are found in ‘Scopus’ by searching for the keyword “Internet of Thing” (search date: 9th of April 2019). The bib- liometric network of the 25 most frequently used keywords is visualized in Fig. 12. Note that each cluster should include a minimum number of five keywords at least, otherwise too many fragmented clusters were obtained. The three clusters in Fig. 12 can be classified based on the theoretical background given in Section 2.1: Fig. 12. Bibliometric network including the top 25 most frequent co-occurring keywords related to the Internet of Things (IoT). 18 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 (1) Cluster 1 ( ) – Wireless Sensor Networks: the concept of IoT networks strongly relies on wireless interconnected sensing devices that will gather high volumes of heterogeneous data; (1) Cluster 2 ( ) – Distributed computing: the new network architectures required for distributed computations enabled organizations to gather, process, store and distribute information at larger scale; (2) Cluster 3 ( ) – IoT applications: The combination of WSN and distributed computing allows organizations to analyze data for innovative applications (e.g., smart cities, industry 4.0, artificial intelligence, etc.). The Internet of Things is clearly supported by two key technologies: (1) Wireless Sensor Networks (WSN) and (2) Cloud Computing. Only the WSN related keywords are included into the search strategy ( cluster), since the main aim of this article is to support supply chain decision making by looking into the large volumes of real-time data gathered. Cloud computing provides the necessary backbone architectures for efficient data pro- cessing, but these systems will not search for the hidden patterns required for decision making. Therefore, the marked keywords fall outside our research scope. The clustered keywords are also not included within the IoT keyword selection, we will address the required data analytics in Section A.3. A.2. BDA keywords A total of 221 review articles are found in ‘Scopus’ by searching for the keywords “Big Data” AND “Analy*” (search date: 9th of April 2019). The asterisk symbol is added to allow multiple analysis synonyms (e.g., analysis, analyses, analyze, analyze, etc.). The bibliometric network of the 25 most frequently used keywords is visualized in Fig. 13 There is no minimum cluster size specified. The four clusters in Fig. 13 can be classified by using the theoretical background given in Section 2.2: (1) Cluster 1 ( ) – Data acquisition: the analysis of large heterogenous datasets strongly depends on the data sources available; (1) Cluster 2 ( ) – Data Analytics: the Big Data Analytics are mainly supported by a wide variety of data mining and analytics techniques; (2) Cluster 3 ( ) – Artificial Intelligence: the large datasets are ideal for the application of machine learning techniques to empower algorithms with learning capabilities; (3) Cluster 4 ( ) – Human decision making: the recognition of hidden patterns within large datasets will also support human decision making, which is comparable to the application of preceding BI technologies. Our main focus is on the data acquisition made available by IoT networks. Therefore, all other data sources mentioned within the cluster fall outside our research scope. Most of the and clustered keywords are related to data handling activities to support both human and artificial decision making (e.g., storage, processing, handling, learning etc.). The transformation of large volumes heterogeneous data into data-driven insights is strongly related to the application of pattern recognition techniques originating from the Data Science discipline. Therefore, the keywords related to ‘Big Data Analytics’ will mostly consist of data-driven techniques for pattern recognition. The marked keywords are not included within the BDA keyword selection, since they relate to decision making itself (see Section A.3.). A.3. SCM keywords A total of 1,589 review articles are found in ‘Scopus’ by searching for the keywords related to supply chain decision making (search date: 9th of April 2019). The resulting review articles should include two combinations of keywords at least: (“Supply chain”’ OR “Logistic?”) AND Fig. 13. Bibliometric network including the top 25 most used keywords related to Big Data Analytics. 19 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Fig. 14. Bibliometric network including the top 25 most used keywords related to SCM. (“Management” OR “Decision”’). The question mark is included to allow the term “Logistics” notation as well. The bibliometric network of the 25 most frequently used keywords is visualized in Fig. 14. There is no minimum cluster size specified. It is more difficult to find proper SCM labels for the four clusters identified in Fig. 14, but an attempt can still be made by applying logical reasoning based on the theoretical background of Section 2.3. For example, the cluster is largely dedicated to decision support tools, which provides a link to the “Big Data Analytics” keywords included in section A.2. However, the keyword types themselves seem to be more interesting instead of the clusters formed. The bibliometric network in Fig. 14 reveals four types of SCM keywords: (1) decision types; (2) decision support tools; (3) supply chain activities; and (4) key performance indicators (KPIs). The search strategy will include all keywords related to decision types and supply chain activities to find relevant IoT developments in academic literature, while the KPI keywords are used to search for the potential benefits of the new techniques proposed. A wide variety of tools exists to support SCM decision making. In this research, the data-driven techniques discussed in Section A.2 are included only. There is not specifically searched for the model-driven techniques originating from Industrial Engineering and Operations Research. Appendix B. Search string TITLE-ABS-KEY[“Internet of Thing” OR “Internet of Everything” OR “Industrial Internet” OR “Web of Thing” OR “Cyber Physical System” OR “Wireless Sensor Network” OR (Sensor AND (Network OR Connected OR Internet)) OR ((Wireless OR Mobile) AND (“Communication System”))] AND TITLE-ABS-KEY[((“Big Data” OR “Mass* Data” OR “Large Data” OR “Enormous Data”) AND Analy*) OR “Data? Driven” OR “Pattern Rec- ogni*” OR “Data Mining” OR “Process Mining” OR “Machine Learning” OR “Neural Network” OR “Deep Learning” OR “Genetic Algorithm” OR Classification OR Association OR Clustering OR Regression] AND TITLE-ABS-KEY[“Supply chain Management” OR SCM OR “Decision Making” OR “Industrial Management” OR “Industrial Engineering” OR “Industrial Economic” OR “Management Science” OR Optimi?ation OR Planning OR Scheduling OR Loading OR Sequencing OR Monitoring] AND TITLE-ABS-KEY[“Product Development” OR “Research and Development” OR R&D OR Purchasing OR Procurement OR Project Management OR Production OR Manufactur* OR Warehous* OR Inventor* OR Transport OR Logistic? OR “Physical distribution” OR “Distribution Management” OR Marketing OR Sales OR Maintenance OR Aftersales OR “After-sales” OR “Returns Management” OR “Service Logistic” OR “Reverse Logistic” OR “Demand Management” OR “Customer-relationship” OR “Supplier-relationship” OR “Customer-service” OR Financ*] 20 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Appendix C. IoT data extraction form 21 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Appendix D. BDA data extraction form 22 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Appendix E. SCM data extraction form 23 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Appendix F. Application data extraction form 24 M. Koot et al. Computers & Industrial Engineering 154 (2021) 107076 Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015). Data mining References for the internet of things: Literature review and challenges. International Journal of Distributed Sensor Networks, 11(8), 1–14. https://doi.org/10.1155/2015/431047 Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply- Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From chain management: A literature review. 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A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics

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