Big data analytics in supply chain management: A state-of-the-art literature review

The rapid growing interest from both academics and practitioners towards the application of Big Data Analytics (BDA) in Supply Chain Management (SCM) has urged the need of review up-to-date research development in order to develop new agenda. This review responds to this call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structured based on the content analysis method of Mayring (2008), addressing four research questions on (1) what areas of SCM that BDA is being applied, (2) what level of analytics is BDA used in these application areas, (3) what types of BDA models are used, and finally (4) what BDA techniques are employed to develop these models. The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.

[1]  Davy Janssens,et al.  Identifying mismatch between urban travel demand and transport network services using GPS data: A case study in the fast growing Chinese city of Harbin , 2016, Neurocomputing.

[2]  Ray Y. Zhong,et al.  A big data approach for logistics trajectory discovery from RFID-enabled production data , 2015 .

[3]  A. K. Das,et al.  Optimization models with economic and game theoretic applications , 2016, Ann. Oper. Res..

[4]  Buyue Qian,et al.  Improving rail network velocity: A machine learning approach to predictive maintenance , 2014 .

[5]  Ichiro Sakata,et al.  Machine learning approach for finding business partners and building reciprocal relationships , 2012, Expert Syst. Appl..

[6]  Namchul Do Application of OLAP to a PDM database for interactive performance evaluation of in-progress product development , 2014, Comput. Ind..

[7]  Justin Zhijun Zhan,et al.  Sentiment analysis using product review data , 2015, Journal of Big Data.

[8]  Lianbiao Cui,et al.  Environmental performance evaluation with big data: theories and methods , 2016, Annals of Operations Research.

[9]  Richard F. Hartl,et al.  Supply chain dynamics, control and disruption management , 2016 .

[10]  Rashid Mehmood,et al.  Exploring the influence of big data on city transport operations: a Markovian approach , 2017 .

[11]  S. Fawcett,et al.  Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management , 2013 .

[12]  Charles W. Chase Next Generation Demand Management: People, Process, Analytics, and Technology , 2016 .

[13]  Angappa Gunasekaran,et al.  Big Data and supply chain management: a review and bibliometric analysis , 2018, Ann. Oper. Res..

[14]  M. Tseng,et al.  Toward Sustainability : Using Big Data to Explore Decisive Attributes of Supply Chain Risks and Uncertainties , 2017 .

[15]  Yossi Sheffi,et al.  Preparing for disruptions through early detection , 2015 .

[16]  C.K.H. Lee,et al.  A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0 , 2017, Int. J. Prod. Res..

[17]  Robert B. Handfield,et al.  Measuring the benefits of ERP on supply management maturity model: a “big data” method , 2015 .

[18]  Benjamin T. Hazen,et al.  Back in business: operations research in support of big data analytics for operations and supply chain management , 2016, Annals of Operations Research.

[19]  Ray Y. Zhong,et al.  A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing , 2015, Adv. Eng. Informatics.

[20]  Qi Shi,et al.  Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways , 2015 .

[21]  Stefan Seuring,et al.  From a literature review to a conceptual framework for sustainable supply chain management , 2008 .

[22]  Chun Ling Ho,et al.  Applying Data Ming to Develop a Warning System of Procurement in Construction , 2014 .

[23]  Francisco Ballestín,et al.  Static and dynamic policies with RFID for the scheduling of retrieval and storage warehouse operations , 2013, Comput. Ind. Eng..

[24]  P. Mayring Qualitative Content Analysis , 2000 .

[25]  Shahriar Akter,et al.  Guest editorial: information technology-enabled supply chain management , 2015 .

[26]  David L. Olson,et al.  A review of supply chain data mining publications , 2015, Journal of Supply Chain Management Science.

[27]  Robert Gordon,et al.  DEALING WITH CONSTRUCTION COST OVERRUNS USING DATA MINING , 2015 .

[28]  Petri T. Helo,et al.  Big data applications in operations/supply-chain management: A literature review , 2016, Comput. Ind. Eng..

[29]  Mohamed Abdel-Aty,et al.  Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data , 2014 .

[30]  Milovanović Miloš,et al.  Semantic technologies on the mission: Preventing corruption in public procurement , 2014 .

[31]  Jose Berengueres,et al.  Airline new customer tier level forecasting for real-time resource allocation of a miles program , 2013, Journal Of Big Data.

[32]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[33]  Shimon Y. Nof,et al.  Dynamic storage assignment with product affinity and ABC classification—a case study , 2016 .

[34]  Erik Hofmann,et al.  Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect , 2017, Int. J. Prod. Res..

[35]  Nicolas Saunier,et al.  Large-scale automated proactive road safety analysis using video data , 2015 .

[36]  Mu-Chen Chen,et al.  The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres , 2011, Enterp. Inf. Syst..

[37]  Saumyadipta Pyne,et al.  Big Data Analytics: Methods and Applications , 2016 .

[38]  Jie Zhang,et al.  Big data analytics for forecasting cycle time in semiconductor wafer fabrication system , 2016 .

[39]  Mu-Chen Chen,et al.  Data mining based storage assignment heuristics for travel distance reduction , 2014, Expert Syst. J. Knowl. Eng..

[40]  T. Schoenherr,et al.  Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential , 2015 .

[41]  J. Wong,et al.  Enhancing Order-picking Efficiency through Data Mining and Assignment Approaches , 2014 .

[42]  Christophe Nicolle,et al.  Understandable Big Data: A survey , 2015, Comput. Sci. Rev..

[43]  Rajkumar Buyya,et al.  Big Data computing and clouds: Trends and future directions , 2013, J. Parallel Distributed Comput..

[44]  George Q. Huang,et al.  Radio frequency identification-enabled real-time manufacturing execution system: a case study in an automotive part manufacturer , 2012, Int. J. Comput. Integr. Manuf..

[45]  Nadia Nedjah,et al.  Soft computing in big data intelligent transportation systems , 2016, Appl. Soft Comput..

[46]  Yingfeng Zhang,et al.  A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products , 2017 .

[47]  Indranil Bose,et al.  Managing a Big Data project: The case of Ramco Cements Limited , 2015 .

[48]  Xiao Song,et al.  Improving the predictability of business failure of supply chain finance clients by using external big dataset , 2015, Ind. Manag. Data Syst..

[49]  Benjamin Schmidt,et al.  Real-Time Predictive Analytics, Big Data & Energy Market Efficiency: Key to Efficient Markets and Lower Prices for Consumers , 2014 .

[50]  Jae Kwon Bae,et al.  Product development with data mining techniques: A case on design of digital camera , 2011, Expert Syst. Appl..

[51]  M. White,et al.  Digital workplaces , 2012 .

[52]  Wu He,et al.  A novel social media competitive analytics framework with sentiment benchmarks , 2015, Inf. Manag..

[53]  Chieh-Yuan Tsai,et al.  A data mining approach to optimise shelf space allocation in consideration of customer purchase and moving behaviours , 2015 .

[54]  Liang Ming,et al.  Abnormal situation management: Challenges and opportunities in the big data era , 2016, Comput. Chem. Eng..

[55]  Nicolas Saunier,et al.  Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application , 2015 .

[56]  Nezih Altay,et al.  Big data in humanitarian supply chain networks: a resource dependence perspective , 2016, Annals of Operations Research.

[57]  Tao Huang,et al.  Promises and Challenges of Big Data Computing in Health Sciences , 2015, Big Data Res..

[58]  John Gantz,et al.  The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East , 2012 .

[59]  S. Fawcett,et al.  Supply Chain Game Changers — Mega, Nano, and Virtual Trends — And Forces that Impede Supply Chain Design (i.e., Building a Winning Team) , 2014 .

[60]  Chen-Fu Chien,et al.  A back-propagation neural network with a distributed lag model for semiconductor vendor-managed inventory , 2015 .

[61]  Keith Gordon,et al.  What is Big Data , 2013 .

[62]  Kannan Govindan,et al.  Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future , 2015, Eur. J. Oper. Res..

[63]  N. Subramanian,et al.  Role of social media in retail network operations and marketing to enhance customer satisfaction , 2017 .

[64]  Ray Y. Zhong,et al.  Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors , 2017, Int. J. Prod. Res..

[65]  Do-Hyung Park,et al.  The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference , 2014 .

[66]  Alain Yee-Loong Chong,et al.  Predicting online e-marketplace sales performances: A big data approach , 2016, Comput. Ind. Eng..

[67]  Qian Wang,et al.  Application and integration of an RFID-enabled warehousing management system - a feasibility study , 2016, J. Ind. Inf. Integr..

[68]  Shahriar Akter,et al.  Guest editorial: transforming operations and production management using big data and business analytics: future research directions , 2017 .

[69]  Lakshman S. Thakur,et al.  A big data MapReduce framework for fault diagnosis in cloud-based manufacturing , 2016 .

[70]  Ciprian Dobre,et al.  Intelligent services for Big Data science , 2014, Future Gener. Comput. Syst..

[71]  Ying Liu,et al.  Understanding big consumer opinion data for market-driven product design , 2016 .

[72]  Aa Alshehri Ay Ghazwani Ra Darwesh Sa Alzahrani Alotaibi,et al.  Big Data for the Enterprise , 2018 .

[73]  Célia Ghedini Ralha,et al.  A multi-agent data mining system for cartel detection in Brazilian government procurement , 2012, Expert Syst. Appl..

[74]  George T. S. Ho,et al.  A cloud‐based responsive replenishment system in a franchise business model using a fuzzy logic approach , 2016, Expert Syst. J. Knowl. Eng..

[75]  Chen-Fu Chien,et al.  A data mining approach for analyzing semiconductor MES and FDC data to enhance overall usage effectiveness (OUE) , 2014, Int. J. Comput. Intell. Syst..

[76]  Khairy A.H. Kobbacy,et al.  Intelligent systems in manufacturing: current developments and future prospects , 2000 .

[77]  Seung Ki Moon,et al.  A Decision Support System for market-driven product positioning and design , 2015, Decis. Support Syst..

[78]  Qingquan Li,et al.  Optimizing the Locations of Electric Taxi Charging Stations: a Spatial-temporal Demand Coverage Approach , 2016 .

[79]  Daqiang Zhang,et al.  Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination , 2016, Comput. Networks.

[80]  Wang Hao,et al.  Optimization for service supply network base on the user's delivery time under the background of big data , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[81]  Alain Yee-Loong Chong,et al.  Predicting online product sales via online reviews, sentiments, and promotion strategies , 2016 .

[82]  Benny Tjahjono,et al.  Big data analytics in supply chain management: trends and related research , 2014 .

[83]  Manoj Kumar Tiwari,et al.  Big data and predictive analytics applications in supply chain management , 2016, Comput. Ind. Eng..

[84]  Marta C. González,et al.  The path most traveled: Travel demand estimation using big data resources , 2015, Transportation Research Part C: Emerging Technologies.

[85]  Estela Marine-Roig,et al.  Tourism analytics with massive user-generated content: a case study of Barcelona. , 2015 .

[86]  Biqing Huang,et al.  A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics , 2016 .

[87]  Qi Li,et al.  Big Data Driven Supply Chain Management , 2019, Procedia CIRP.

[88]  R. J. Kuo,et al.  The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation , 2015, Appl. Math. Comput..

[89]  Xifan Yao,et al.  Abnormal Condition Monitoring of Workpieces Based on RFID for Wisdom Manufacturing Workshops , 2015, Sensors.

[90]  Gang Wang,et al.  Efficient vehicles path planning algorithm based on taxi GPS big data , 2016 .

[91]  L Y Ding,et al.  A Big-Data-based platform of workers' behavior: Observations from the field. , 2016, Accident; analysis and prevention.

[92]  C. L. Philip Chen,et al.  Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..

[93]  Qianqian Zhu,et al.  Camera location for real-time traffic state estimation in urban road network using big GPS data , 2015, Neurocomputing.

[94]  Zahir Irani,et al.  Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector , 2016, Annals of Operations Research.

[95]  Shahriar Akter,et al.  How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study , 2015 .

[96]  Zili Zhang,et al.  A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting , 2016, Neurocomputing.

[97]  Angappa Gunasekaran,et al.  The impact of big data on world-class sustainable manufacturing , 2015, The International Journal of Advanced Manufacturing Technology.

[98]  Mihalis Giannakis,et al.  A multi-agent based system with big data processing for enhanced supply chain agility , 2016, J. Enterp. Inf. Manag..

[99]  Madhav Erraguntla,et al.  Better management of blood supply-chain with GIS-based analytics , 2011, Ann. Oper. Res..

[100]  A. Gunasekaran,et al.  The role of Big Data in explaining disaster resilience in supply chains for sustainability , 2017 .

[101]  Mohammad Salehan,et al.  Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics , 2014, Decis. Support Syst..

[102]  Petri Helo,et al.  Cloud manufacturing system for sheet metal processing , 2017 .

[103]  Rick Siow Mong Goh,et al.  Understanding Natural Disasters as Risks in Supply Chain Management through Web Data Analysis , 2022 .

[104]  Nenad Stefanovic,et al.  Collaborative predictive business intelligence model for spare parts inventory replenishment , 2015, Comput. Sci. Inf. Syst..

[105]  G Koren,et al.  Adherence and tolerability of iron-containing prenatal multivitamins in pregnant women with pre-existing gastrointestinal conditions , 2009, Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology.

[106]  George T. S. Ho,et al.  Mining logistics data to assure the quality in a sustainable food supply chain: A case in the red wine industry , 2014 .

[107]  Wee Leong Lee,et al.  Evaluation and Improvement of Procurement Process with Data Analytics , 2015 .

[108]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[109]  Athanasios V. Vasilakos,et al.  Big data analytics: a survey , 2015, Journal of Big Data.

[110]  Wei Du,et al.  An optimization method for shopfloor material handling based on real-time and multi-source manufacturing data , 2015 .

[111]  A. Gunasekaran,et al.  Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .

[112]  M. Taisch,et al.  The value of Big Data in servitization , 2015 .

[113]  Stefan Seuring,et al.  A review of modeling approaches for sustainable supply chain management , 2013, Decis. Support Syst..

[114]  Benjamin T. Hazen,et al.  Big data and predictive analytics for supply chain and organizational performance , 2017 .

[115]  Ray Y. Zhong,et al.  Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing , 2015, The International Journal of Advanced Manufacturing Technology.

[116]  Jan A. Van Mieghem,et al.  Clickstream Data and Inventory Management: Model and Empirical Analysis , 2014 .

[117]  Lian Duan,et al.  Big data analytics and business analytics , 2015 .

[118]  Guy Walker,et al.  Big data and ergonomics methods: A new paradigm for tackling strategic transport safety risks. , 2016, Applied ergonomics.

[119]  Ying Wah Teh,et al.  Big data reduction framework for value creation in sustainable enterprises , 2016, Int. J. Inf. Manag..

[120]  Robert X. Gao,et al.  A new paradigm of cloud-based predictive maintenance for intelligent manufacturing , 2015, Journal of Intelligent Manufacturing.

[121]  D. Tranfield,et al.  Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review , 2003 .

[122]  Athanasios V. Vasilakos,et al.  Big data: From beginning to future , 2016, Int. J. Inf. Manag..

[123]  Harrison Hyung Min Kim,et al.  Demand trend mining for predictive life cycle design , 2014 .

[124]  Zhenhua Wang,et al.  Analysis of user behaviors by mining large network data sets , 2014, Future Gener. Comput. Syst..

[125]  Jan Fabian Ehmke,et al.  Data-driven approaches for emissions-minimized paths in urban areas , 2016, Comput. Oper. Res..

[126]  Sachchidanand Singh,et al.  Big Data analytics , 2012 .

[127]  Remzi Seker,et al.  Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook , 2016, Comput. Ind..

[128]  P. O'Donovan,et al.  Big data in manufacturing: a systematic mapping study , 2015, Journal of Big Data.

[129]  Ning Zhang,et al.  An optimization model for green supply chain management by using a big data analytic approach , 2017 .

[130]  Basilis Boutsinas,et al.  A method for improving the accuracy of data mining classification algorithms , 2009, Comput. Oper. Res..

[131]  Kim Hua,et al.  Harvesting Big Data to Enhance Supply Chain Innovation Capabilities : An Analytic Infrastructure Based on Deduction Graph , 2016 .

[132]  Dursun Delen,et al.  Data, information and analytics as services , 2013, Decis. Support Syst..

[133]  Peter Loos,et al.  Prescriptive Control of Business Processes , 2015, Business & Information Systems Engineering.

[134]  Yongyun Cho,et al.  A Study on Intelligent User-Centric Logistics Service Model Using Ontology , 2014, J. Appl. Math..

[135]  Rajeev Jain,et al.  Using data mining synergies for evaluating criteria at pre-qualification stage of supplier selection , 2014, J. Intell. Manuf..

[136]  Li Li,et al.  Robust causal dependence mining in big data network and its application to traffic flow predictions , 2015 .

[137]  Pei-Chann Chang,et al.  Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system , 2015 .