Abstract
Today, Big Data Analytics (BDA) are definitely the key basis of competitiveness for enterprises in their Supply Chains. The outburst of data captured, accumulated and analyzed is impacting the value-added-chain at all levels from manufacturers to customers. In this paper, we develop a structured methodology to provide a deep analysis of Big Data Analytics methods across the Supply-Chain Operations Reference (SCOR) model processes. An exhaustive literature review is illustrated to afford a comprehensive Mind-Map cartography with a BDA-SCOR matching matrix. The proposed approach points to a number of research concerns that need to be addressed by research community. Outcomes of this study may highlight relevant guidelines for upcoming works of both academics and industrials. It highlights the need for collaborative Big Data to manage SCM more intelligently. Our objective is to provide an effective analysis to understand how Big Data Analytics become even more valuable for better Supply Chain Management.
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1 Introduction
There is no doubt; Big Data Analytics (BDA) is progressively becoming inevitable practice to extract more and more business values [1]. The main motive of Supply Chain (SC) managers is certainly to possess meaningful insights that allow them to better forecast, predict, reveal hidden patterns, and then to gain wider edge of competitiveness [2]. Henceforth, academics and industrials are aware that conventional databases are not capable to process such Big Data [3]. Analytics are reforming the way Supply Chain Management (SCM) handle acquired data internally; such as transactional information systems, and externally such as social networks, mobile devices, and IoT (Internet of Things) sensors [4].
Beyond considerate what Big Data earns, companies now are focusing on how analytics might be used to obtain business meaningful insights [5]. Given the variety and complexity of very large unstructured and high-dynamic datasets, this task is not trivial and may be very challenging [6].
Accordingly, we propose this paper with multifold purposes: (a) Foremost, it carried out a profound literature review across past and present patterns in academic publications. The bibliographical analysis is conducted by observing contributions, synthetizing knowledge, drawing tendencies, and making causality relationships by classifying intakes and highlighting further research opportunities. (b) Secondly, it provides a Mind-Map cartography of using BDA methods in Supply-Chain Operations Reference (SCOR) model as a clear synopsis of current research trends. (c) Lastly, this study draws future research trajectories and sheds light on the gaps that need to be overcome. Scholars can leverage this paper as a reference for future research opportunities, and SC specialists can use it as a methodology to benchmark BDA methods and measure impact in valuable information for SC.
This paper is structured as follows. We begin by introducing BDA methods and SCOR model reminder. We then develop the proposed methodology of bibliography analysis. Finally, we conclude by underlining the current trends of SCM-BDA researches, the untaken breaks, and the expected potential in the near future.
2 Related Works and Research Gaps
Analytics are ranged into several collections of methods, sub-methods and techniques that diverge in their objectives, prerequisites and applicability [7]. Hence, prior to hastily study applicability of these methods in SCM, there is a prerequisite to first understand their settings and features. The next sub-section offers a brief overview.
2.1 Big Data Analytics Methods and Techniques
The word ‘value’ in the context of BD denotes extraction of worthy information by investigating the accumulated data through transformation and processing. Aligned with [8], BDA are emerging as the ‘next big thing’ and is defined as a collection of technologies that handle, incorporate, and report massive data by transforming it into new insights not attainable with traditional technologies.
In one hand, academics are aware that decision makers progressively view in BDA an imperative driver of modernization and a substantial source of value creation for competitive benefit [9]. In fact, since 2014, there is an increasing emerging of research works: case studies ([10,11,12], etc.), original works ([13,14,15], etc.), surveys ([16, 17], etc.), and a variety of literature papers ([18,19,20], etc.). Likewise, industrials are attentive to the potential of analytics in management of SC. Indeed, the worldwide leader in research and advisory Gartner’s® has mentioned in [21] that BDA appear among the Top 25 SC research topics for 2016. Forbes®, also, argued in [22] that no single research topic in the last decade has as much effect on incumbent investments for industrials.
Approaches of BDA are numerous and may be alienated into several methods and sub-methods; to be employed solely or combined jointly [17]. Each method/sub-method uses one or a mixture of statistical or mathematical techniques, up on the expected business value. According our literature review, the main root methods that concerned by SC are: Exploratory Data Analysis (EDA) [8], Confirmatory Data Analysis (CDA) [11], Qualitative Data Analysis (QDA) [23], and Real-Time Analysis (RTA) [24]. Based on the underlying methods, sub-methods and techniques can also be categorized into several groups, but given the breadth of these techniques, an exhaustive list exceeds the extent of a single paper. Though, a relevant sub-list which has already been used in the field of SC covers mainly: regression [25], optimization [26], induction [27], fuzzy logic [28], classification [10], aggregation [12], and so on.
2.2 Supply Chain Operations Reference (SCOR) Model
In order to expand in competiveness of companies, SCOR model was developed by Supply Chain Council (SCC) for designing and enhancing SCs. It offers standard frame for deciding, organizing and fulfilling SC processes [29]. Nevertheless, it is traditionally used in benchmarking studies [30]. SCOR model governs five processes; they are consecutively named and briefly described in Table 1.
Based on pre-defined processes, SCOR can detect where action is required. It can also help to arrange more accurate projections. Accordingly, we base our study through SCOR processes. The next section describes step-by-step our methodology.
3 Proposed Methodology
3.1 Data Collection
By using Harzing’s Publish or Purish V.5® [31], we queried out the more relevant and recent publications in the topic of BDA and SCM (from 2014 to 2017). Parameters of Harzing’s search request are itemized in Table 2.
Once the information were sorted, we then cleansed and filtered the academic database by deleting unwanted columns (ranking, types, ISSN) and rows (programs, books, technical reports, white papers, patents, literature reviews, unrelated researches, etc.). We kept only used columns (cites, authors, title, year, source, publisher, URL, countries) and rows (journals).
3.2 Analysis and Findings
By using the sorted list of last step, we reviewed each contribution and recorded a matrix that matched information with respect to BDA methods and SCOR processes. Discussion of our relevant findings and empirical results are in following sections.
Finding1: SCOR-BDA matching matrix.
For each row (reference), we analyzed BDA methods, sub-methods and techniques (Sect. 2.1), and then recorded matching cells with regard to SCOR processes according to Table 1. Table 3 below illustrates the SCOR-BDA matrix as applied to the collected bibliographical dataset.
Finding 2: Mind-Map Cartography of SCOR-BDA.
We constructed cartography by using Mind-Mapping technique [32] with Coggle® tool [33]. The branching out in (Appendix, Fig. 3) illustrates that all defined methods are employed in all SCOR processes but not with the same techniques. For instance, the FUzzy Logic technique (FUL) is used in Data Statistical-inference Methods (DSM) in ‘Make’ process but not in ‘Source’ process. Another ascertainment; the process ‘Deliver’ is the most prolific branch regard to methods and techniques already addressed in literature.
Finding 3: Overview of SCOR-BDA evolution.
Bars diagrams in Fig. 1; summarize the total number of publications for each process in SCOR, in the current three years (2014–2017).
As seen in Fig. 1, there is an exponential increase in the number of contributions for all SCOR processes with an overall exponential variation of +0.064 (R2 = ~0,90). A few publications was issued in early 2014 (equal and less than 11%), and at the end of 2015 (~11%–~23%). However, in just two consecutive years later (2016 and 2017), publication rates have almost doubled for each year and each process (for instance; in plan process, the rate jumped from 11% to 26%, and then to 53%). This demonstrates that BDA tools have emerged as one of the fastest growing fields in SCM in recent years. On the other hand, as we observe an unequal growing interest for processes, we then broke down the total numbers by processes.
Finding 4: Detailed view of SCOR-BDA evolution.
Figure 2 sorts the number of contributions for each process in the matrix BDA-SCOR of the selected dataset.
As we observe in Fig. 2, all SCOR processes have adopted BDA, however with mixed paces. The evolution rate is sorted to make processes in a consecutive order: (1) Plan (+0,238), (2) Deliver (+0,100), (3) Return (+0,092), (4) Make (+0,056) and lastly (5) Source (+0,052).
Subsequently, we assume that: the more operations are oriented inside the company (manufacturer), the less analytics are exploited. In other words, researchers need valuation of the data that are positioned at company’s connectivity with the outside (mainly Deliver, Return and Plan). This indicates a lack of collaboration between companies in the process of valuing their data. We can explain this by the fact that these companies compete to extract as much value from data, but unfortunately each company acts separately. This point is very significant as it raises the need for collaborative Big Data to exploit it intelligently in SCM.
Furthermore, from a rational point of view, BDA are applied to processes differently because of the nature of these processes themselves. In instance, it seems normal to use analytics in Plan process as it is the most uncertain activity and then which necessities advanced techniques to obtain pertinent results. While for Make process, which is less unstable and better setup and maintained, analytics are less pertinent.
4 Conclusion: Discussion and Future Trends
It is true that the proposed approach may have some limits such as restriction in dataset collection. Indeed, we have collected data by the way of Harzing’s Publish or Purish® tool which is itself limited in search queries and cannot combine searching engines. Despite this, the study allowed us to have a deep overview of the BDAs and their positioning in the SC processes.
The proposed approach offers a matching matrix and a Mind-Map cartography for BDA-SCOR benchmarking. Both tools can be largely taken up by academics and SC specialists to intuitively integrate them in orienting understanding. Furthermore, the empirical results presented in this paper argue that future researches should embrace more largely BDA, to continue redefining the focus of the contemporary management of SC. However, as the relevant literature shows, using BDA must not be hastily due to a fashion effect. SC specialists must establish a comprehensive schema to identify the objective of using analytics, the reason to expand volumes of exploited data and expressly the expected business value by using analytics.
Otherwise, the usage of BDA methods and techniques in the field of SC can still be significantly improved in at least three aspects: (a) more attention needs to be focused to BDA in manufacturing (Make process) and procurement (Source process). (b) A faultless and feasible deployment of BDA should be set up between all SC actors, thus, valuation of massive data would be conducted more accurately in terms of collaboration with decreasing of underlying costs, and timely truthful information can be provided for decision-makers (c) Upstream of the data processing step, this data should be collected, arranged, grouped, and summarized jointly by all SC actors. Rather than just accumulating and processing data, managers need to structure, and link their efforts to create a coherent picture of valuable data across overall the chain. This leads us to scrutinize more collaborative and intelligent Big Data utilization.
References
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., Akter, S.: Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res. 70, 308–317 (2017)
Bendoly, E.: Fit, bias, and enacted sensemaking in data visualization: frameworks for continuous development in operations and supply chain management analytics. J. Bus. Log. 37(1), 6–17 (2016)
Hazen, B.T., Boone, C.A., Ezell, J.D., Jones-Farmer, L.A.: Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. Int. J. Prod. Econ. 154, 72–80 (2014)
Satpute, P., Tembhurne, O.: A review of: cloud centric IoT based framework for supply chain management in precision agriculture. Int. J. Adv. Res. Comput. Sci. Manage. Stud. 2(11), 175–180 (2014)
Ketter, W., Peters, M., Collins, J., Gupta, A.: Competitive benchmarking: an IS research approach to address wicked problems with big data and analytics. MIS Q. ERS-2015-01 (2015)
Hazen, B.T., Skipper, J.B., Boone, C.A., Hill, R.R.: Back in business: Operations research in support of big data analytics for operations and supply chain management. Ann. Oper. Res. 1–11 (2016)
Bi, Z., Cochran, D.: Big data analytics with applications. J. Manage. Anal. 1(4), 249–265 (2014)
Zhao, R., Liu, Y., Zhang, N., Huang, T.: An optimization model for green supply chain management by using a big data analytic approach. J. Clean. Prod. 142, 1085–1097 (2017)
Cao, G., Duan, Y., Li, G.: Linking business analytics to decision making effectiveness: A path model analysis. IEEE Trans. Eng. Manage. 62(3), 384–395 (2015)
Chong, A.Y.L., Liu, M.J., Luo, J., Keng-Boon, O.: Predicting RFID adoption in healthcare supply chain from the perspectives of users. Int. J. Prod. Econ. 159, 66–75 (2015)
Dubey, R., Gunasekaran, A., Childe, S.J., Wamba, S.F., Papadopoulos, T.: The impact of big data on world-class sustainable manufacturing. Int. J. Adv. Manufact. Technol. 84(1–4), 631–645 (2016)
Wu, K.J., Liao, C.J., Tseng, M.L., Lim, M.K., Hu, J., Tan, K.: Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties. J. Clean. Prod. 142, 663–676 (2017)
Zhang, Y., Zhang, G., Du, W., Wang, J., Ali, E., Sun, S.: An optimization method for shopfloor material handling based on real-time and multi-source manufacturing data. Int. J. Prod. Econ. 165, 282–292 (2015)
Rehman, U.M.H., Chang, V., Batool, A., Wah, T.Y.: Big data reduction framework for value creation in sustainable enterprises. Int. J. Inf. Manage. 36(6), 917–928 (2016)
Giannakis, M., Louis, M.: A multi-agent based system with big data processing for enhanced supply chain agility. J. Enterprise Inf. Manage. 29(5), 706–727 (2016)
Emani, C.K., Cullot, N., Nicolle, C.: Understandable big data: a survey. Comput. Sci. Rev. 17, 70–81 (2015)
Chen, C.P., Zhang, C.Y.: Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf. Sci. 275, 314–347 (2014)
Akter, S., Wamba, S.F.: Big data analytics in E-commerce: a systematic review and agenda for future research. Electron. Mark. 26(2), 173–194 (2016)
Schoenherr, T., Speier-Pero, C.: Data science, predictive analytics, and big data in supply chain management: Current state and future potential. J. Bus. Log. 36(1), 120–132 (2015)
Duan, L., Xiong, Y.: Big data analytics and business analytics. J. Manage. Anal. 2(1), 1–21 (2015)
Gartner: Survey Analysis: What Does Increased Adoption of Big Data Mean to Supply Chain Strategists? Research Analysis, Gartner, Inc., California (2016)
Forbes: 51% of Enterprises Intend to Invest More in Big Data. Research Press Report, Forbes Inc., California (2016)
Luthra, S., Garg, D., Haleem, A.: Critical success factors of green supply chain management for achieving sustainability in Indian automobile industry. Prod. Plann. Control 26(5), 339–362 (2015)
Vera-Baquero, A., Colomo-Palacios, R., Molloy, O.: Real-time business activity monitoring and analysis of process performance on big-data domains. Telematics Inform. 33(3), 793–807 (2016)
Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S.J., Fosso-Wamba, S.: The role of Big Data in explaining disaster resilience in supply chains for sustainability. J. Clean. Prod. 142, 1108–1118 (2017)
Dutta, D., Bose, I.: Managing a big data project: the case of ramco cements limited. Int. J. Prod. Econ. 165, 293–306 (2015)
Tan, K.H., Zhan, Y., Ji, G., Ye, F., Chang, C.: Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. Int. J. Prod. Econ. 165, 223–233 (2015)
Chang, H.-T., Mishra, N., Lin, C.C.: IoT big-data centred knowledge granule analytic and cluster framework for BI applications: a case base analysis. PLoS ONE 10(11), e0141980 (2015)
Supply Chain Council (SCC): Supply Chain Operations Reference Model: Release 11.0. Supply Chain Council (SCC), New York (2012)
Jamehshooran, G.B., Shaharoun, M., Haron, N.H.: Assessing supply chain performance through applying the SCOR model. Int. J. Supply Chain Manage. 4(1) (2015)
Harzing. Harzing’s Publish Or Perish, 19 February 2017. http://www.harzing.com/. Accessed 22 Mar 2017
mindmapping.com. Mind Mapping. 31 December 2016. http://www.mindmapping.com/fr/. accès le 25 Mar 2017
Coggle.it. Coggle Mind-Map, 31 December 2016. https://coggle.it. accès le 25 Mar 2017
Song, M.L., Fisher, R., Wang, J.L., Cui, L.B.: Environmental performance evaluation with big data: theories and methods. Ann. Oper. Res. 1–14 (2016)
Wedel, M., Kannan, P.K.: Marketing analytics for data-rich environments. J. Mark. 80(6), 97–121 (2016)
Li, J., Tao, F., Cheng, Y., Zhao, L.: Big data in product lifecycle management. Int. J. Adv. Manufact. Technol. 81(1–4), 667–684 (2015)
Miklos, A., Vasarhelyi, A.K., Tuttle, B.M.: Big Data in accounting: an overview. Account. Horiz. 29(2), 381–396 (2015)
Xu, J., Huang, E., Chen, C.H., Lee, L.H.: Simulation optimization: a review and exploration in the new era of cloud computing and big data. Asia-Pacific J. Oper. Res. 32(03), 34 (2015)
Alles, M.G.: Drivers of the use and facilitators and obstacles of the evolution of big data by the audit profession. Account. Horiz. 29(2), 439–449 (2015)
Phillips-Wren, G., Hoskisson, A.: An analytical journey towards big data. J. Decis. Syst. 24(1), 87–102 (2015)
Lee, J., Ardakani, H.D., Yang, S., Bagheri, B.: Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP 38, 3–7 (2015)
Vera-Baquero, A., Colomo-Palacios, R., Molloy, O.: Measuring and querying process performance in supply chains: an approach for mining big-data cloud storages. Procedia Comput. Sci. 64, 1026–1034 (2015)
Zhong, R.Y., Xu, C., Chen, C., Huang, G.Q.: Big data analytics for physical internet-based intelligent manufacturing shop floors. Int. J. Prod. Res. 1–12 (2015)
Xu, M., Cai, H., Liang, S.: Big data and industrial ecology. J. Ind. Ecol. 19(2), 205–210 (2015)
Chae, B.K.: Big data and IT-enabled services: ecosystem and coevolution. IT Prof. 17(2), 20–25 (2015)
Hsu, C.Y., Yang, C.S., Yu, L.C., Lin, C.F., Yao, H.H., Chen, D.Y., Robert, L.K., Chang, P.C.: Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system. Int. J. Prod. Econ. 164, 454–461 (2015)
Zakir, J., Seymour, T., Berg, K.: Big data analytics. Issues Inf. Syst. 16(2), 81–90 (2015)
Donnelly, C., Simmons, G., Armstrong, G., Fearne, A.: Digital loyalty card ‘big data’ and small business marketing: Formal versus informal or complementary? Int. Small Bus. J. 33(4), 422–442 (2015)
Gölzer, P., Simon, L., Cato, P., Amberg, M.: Designing global manufacturing networks using Big Data. Procedia CIRP 33, 191–196 (2015)
Vera-Baquero, A., Colomo-Palacios, R., Molloy, O., Elbattah, M.: Business process improvement by means of big data based decision support systems: a case study on call centers. Int. J. Inf. Syst. Project Manage. 2182–7788 (2015)
O’Donovan, P., Leahy, K., Bruton, K., O’Sullivan, D.T.J.: An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities. J. Big Data 2(1), 25 (2015)
Lee, J., Kao, H.A., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16, 3–8 (2014)
Govindan, K., Kaliyan, M., Kannan, D., Haq, A.N.: Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. Int. J. Prod. Econ. 147, 555–568 (2014)
Milliken, A.L.: Transforming big data into supply chain analytics. J. Bus. Forecast. 33(4), 23 (2014)
Schlegel, G.L.: Utilizing big data and predictive analytics to manage supply chain risk. J. Bus. Forecast. 33(4), 11 (2014)
Chae, B., Olson, D., Sheu, C.: The impact of supply chain analytics on operational performance: a resource-based view. Int. J. Prod. Res. 52(16), 4695–4710 (2014)
Lasi, H., Fettke, P., Kemper, H., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)
Baars, H., Felden, C., Gluchowski, P., Hilbert, A., Kemper, H.G., Olbrich, S.: Shaping the next incarnation of business intelligence. Bus. Inf. Syst. Eng. 6(1), 11–16 (2014)
Vera-Baquero, A., Colomo-Palacios, R., Molloy, O.: Towards a process to guide Big data based decision support systems for business processes. Procedia Technol. 16, 11–21 (2014)
Chae, B.K., Yang, C., Olson, D., Sheu, C.: The impact of advanced analytics and data accuracy on operational performance: a contingent resource based theory (RBT) perspective. Decis. Support Syst. 59, 119–126 (2014)
Stefanovic, N.: Proactive supply chain performance management with predictive analytics. Sci. World J. 17 (2014)
Bhimani, A., Willcocks, L.: Digitisation, ‘Big Data’ and the transformation of accounting information. Account. Bus. Res. 44(4), 469–490 (2014)
Chae, B.K.A.: Complexity theory approach to IT-enabled services (IESs) and service innovation: Business analytics as an illustration of IES. Decis. Support Syst. 57, 1–10 (2014)
Yesudas, M., Menon, G., Ramamurthy, V.: Intelligent operational dashboards for smarter commerce using big data. IBM J. Res. Dev. 58(5/6), 13 (2014)
Bärenfänger, R., Otto, B., Österle, H.: Business value of in-memory technology–multiple-case study insights. Ind. Manage. Data Syst. 114(9), 1396–1414 (2014)
Zhu, J., Huang, Z.: Banks’ micro enterprises loan credit risk decision-making model innovation in the era of big data and internet finance. J. Manage. Strateg. 5(2), 63 (2014)
samiagamoura.com. SCOR-BDA Mind-Map. Vers. 1.0, 20 February 2017. http://www.samiagamoura.com/mes-outils-my-coggle-mind-maps.html. Accessed 22 Mar 2017
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Figure 3 bellow illustrates the Mind-Map cartography according the collected dataset.
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Chehbi-Gamoura, S., Derrouiche, R. (2017). Big Valuable Data in Supply Chain: Deep Analysis of Current Trends and Coming Potential. In: Camarinha-Matos, L., Afsarmanesh, H., Fornasiero, R. (eds) Collaboration in a Data-Rich World. PRO-VE 2017. IFIP Advances in Information and Communication Technology, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-65151-4_22
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