Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies

Abstract Today, we are undoubtedly in the era of data. Big Data Analytics (BDA) is no longer a perspective for all level of the organization. This is of special interest in the manufacturing process with their high capital intensity, time constraints and given the huge amount of data already captured. However, there is a paucity in past literature on BDA to develop better understanding of the capabilities and strategic implications to extract value from BDA. In that vein, the central aim of this paper is to develop a novel model that summarizes the main capabilities of BDA in the context of manufacturing process. This is carried out by relying on the findings of a review of the ongoing research along with a multiple case studies within a leading phosphate derivatives manufacturer to point out the capabilities of BDA in manufacturing processes and outline recommendations to advance research in the field. The findings will help companies to understand the big data analytics capabilities and its potential implications for their manufacturing processes and support them seeking to design more effective BDA-enabler infrastructure.

[1]  Saurabh Tewari,et al.  Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs , 2019, Comput. Ind. Eng..

[2]  Pavel A. Smirnov,et al.  Quality-based Workload Scaling for Real-time Streaming Systems , 2016 .

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

[4]  Roman N. Zhuchkov,et al.  Application of predictive control approach in stabilizing control design of networked plants , 2015, Autom. Remote. Control..

[5]  Hacene Smadi,et al.  Bayesian Belief Network Used in the Chemical and Process Industry: A Review and Application , 2017, Journal of Failure Analysis and Prevention.

[6]  Amine Belhadi,et al.  Lean production in SMEs: literature review and reflection on future challenges , 2018, Journal of Industrial and Production Engineering.

[7]  Amine Belhadi,et al.  Benefits of adopting lean production on green performance of SMEs: a case study , 2018, Production Planning & Control.

[8]  Angappa Gunasekaran,et al.  Agile manufacturing practices: the role of big data and business analytics with multiple case studies , 2018, Int. J. Prod. Res..

[9]  Ray Y. Zhong,et al.  Big Data Analytics for Processing Time Analysis in an IoT-enabled manufacturing Shop Floor , 2018 .

[10]  Wen-De Zhong,et al.  Energy efficient cloud service pricing: A two-timescale optimization approach , 2016, J. Netw. Comput. Appl..

[11]  Rhoda C. Joseph,et al.  Big Data and Transformational Government , 2013, IT Professional.

[12]  John Lindström,et al.  Towards Intelligent and Sustainable Production: Combining and Integrating Online Predictive Maintenance and Continuous Quality Control , 2017 .

[13]  Thomas Y. Choi,et al.  Renaissance of case research as a scientific method , 2014 .

[14]  Chen-Fu Chien,et al.  Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement , 2017, Int. J. Prod. Res..

[15]  Fabio Favoino,et al.  Optimal control and performance of photovoltachromic switchable glazing for building integration in temperate climates , 2016 .

[16]  Sven Jacobi,et al.  Big Data Analytics for Predictive Manufacturing Control - A Case Study from Process Industry , 2014, 2014 IEEE International Congress on Big Data.

[17]  Richard D. Braatz,et al.  Perspectives on Process Monitoring of Industrial Systems , 2015 .

[18]  Nicolas Hidalgo,et al.  Measuring stream processing systems adaptability under dynamic workloads , 2018, Future Gener. Comput. Syst..

[19]  Jay H. Lee,et al.  Machine learning: Overview of the recent progresses and implications for the process systems engineering field , 2017, Comput. Chem. Eng..

[20]  Fei Tao,et al.  Data and knowledge mining with big data towards smart production , 2017, J. Ind. Inf. Integr..

[21]  Ian K. Craig,et al.  Perspective for equipment automation in process industries , 2017 .

[22]  Niraj Kumar,et al.  Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice , 2017, Transportation Research Part E: Logistics and Transportation Review.

[23]  Amine Belhadi,et al.  A framework for effective implementation of lean production in small and medium-sized enterprises , 2016 .

[24]  Arindam Banerjee,et al.  Data Analytics: Hyped Up Aspirations or True Potential? , 2013 .

[25]  G. M. Stanley,et al.  Big Data Approximating Control (BDAC)—A new model-free estimation and control paradigm based on pattern matching and approximation , 2017, Journal of Process Control.

[26]  Defang Li,et al.  Perspective for smart factory in petrochemical industry , 2016, Comput. Chem. Eng..

[27]  Christian Ramsauer,et al.  Profit Per Hour as a Target Process Control Parameter for Manufacturing Systems Enabled by Big Data Analytics and Industry 4.0 Infrastructure , 2017 .

[28]  Ying-Jen Chen,et al.  Manufacturing intelligence for reducing false alarm of defect classification by integrating similarity matching approach in CMOS image sensor manufacturing , 2016, Comput. Ind. Eng..

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

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

[31]  F. M’Sahli,et al.  A neural network model based predictive control approach: application to a semi-batch reactor , 2005 .

[32]  Yun Li,et al.  High-order Volterra Model Predictive Control and its application to a nonlinear polymerisation process , 2005, Int. J. Autom. Comput..

[33]  George Chryssolouris,et al.  Cloud-based Control of Thermal Based Manufacturing Processes , 2016 .

[34]  Bo Lu,et al.  Big Data Analytics in Chemical Engineering. , 2017, Annual review of chemical and biomolecular engineering.

[35]  Zakarya Chiremsel,et al.  Probabilistic Fault Diagnosis of Safety Instrumented Systems based on Fault Tree Analysis and Bayesian Network , 2016, Journal of Failure Analysis and Prevention.

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

[37]  Jin Wang,et al.  Statistical process monitoring as a big data analytics tool for smart manufacturing , 2017, Journal of Process Control.

[38]  Ignacio E. Grossmann,et al.  Challenges in the application of mathematical programming in the enterprise-wide optimization of process industries , 2014, Theoretical Foundations of Chemical Engineering.

[39]  Ying-Jen Chen,et al.  Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0 , 2017, Appl. Soft Comput..

[40]  Tsan-Ming Choi,et al.  Big Data Analytics in Operations Management , 2018 .

[41]  Yingfeng Zhang,et al.  A big data driven analytical framework for energy-intensive manufacturing industries , 2018, Journal of Cleaner Production.

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

[43]  Jongsawas Chongwatpol,et al.  Prognostic analysis of defects in manufacturing , 2015, Ind. Manag. Data Syst..

[44]  Parikshit Mehta,et al.  Smart manufacturing analytics application for semi-continuous manufacturing process – a use case , 2018 .

[45]  Chih-Hsuan Wang,et al.  Using Bayesian belief network and time-series model to conduct prescriptive and predictive analytics for computer industries , 2018, Comput. Ind. Eng..

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

[47]  Sudarsan Rachuri,et al.  Predictive Analytics Model for Power Consumption in Manufacturing , 2014 .

[48]  María Carmen Carnero,et al.  Statistical quality control through process self-induced vibration spectrum analysis , 2012 .

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

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

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

[52]  Sirish L. Shah,et al.  Framework for a smart data analytics platform towards process monitoring and alarm management , 2017, Comput. Chem. Eng..

[53]  Jongsawas Chongwatpol,et al.  Managing big data in coal-fired power plants: a business intelligence framework , 2016, Ind. Manag. Data Syst..

[54]  Stefan Haeussler,et al.  Successful implementation of an order release mechanism based on workload control: a case study of a make-to-stock manufacturer , 2018, Int. J. Prod. Res..

[55]  Shing I. Chang,et al.  MLCPM: A process monitoring framework for 3D metal printing in industrial scale , 2018, Comput. Ind. Eng..

[56]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[57]  Lihui Wang,et al.  Big data analytics based fault prediction for shop floor scheduling , 2017 .

[58]  Manish Shukla,et al.  Next generation smart sustainable auditing systems using Big Data Analytics: Understanding the interaction of critical barriers , 2019, Comput. Ind. Eng..

[59]  Shahriar Akter,et al.  Big data analytics and firm performance: Effects of dynamic capabilities , 2017 .

[60]  Ju Yeon Lee,et al.  A big data analytics platform for smart factories in small and medium-sized manufacturing enterprises: An empirical case study of a die casting factory , 2017 .

[61]  Xu Ji,et al.  Cloud manufacturing model in polymer material industry , 2016 .

[62]  Fadel M. Megahed,et al.  Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective , 2016 .

[63]  S. Joe Qin,et al.  Process data analytics in the era of big data , 2014 .

[64]  Jing Zhang,et al.  Efficient jobs scheduling approach for big data applications , 2018, Comput. Ind. Eng..

[65]  Joaquín B. Ordieres Meré,et al.  Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm , 2014, 2014 IEEE International Conference on Industrial Engineering and Engineering Management.

[66]  Fengqi You,et al.  Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods , 2018, Comput. Chem. Eng..

[67]  Michael Dellnitz,et al.  A Comparison of two Predictive Approaches to Control the Longitudinal Dynamics of Electric Vehicles , 2016 .

[68]  Minsu Cho,et al.  A system architecture for manufacturing process analysis based on big data and process mining techniques , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[69]  Nenad Stojanovic,et al.  Big data process analytics for continuous process improvement in manufacturing , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[70]  Zhiqiang Ge,et al.  Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.

[71]  Yubao Chen,et al.  Integrated and Intelligent Manufacturing: Perspectives and Enablers , 2017 .

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

[73]  Markus Hammer.  Digitization Perspective: Impact of Digital Technologies in Manufacturing , 2019 .

[74]  Iiro Harjunkoski,et al.  The impact of digitalization on the future of control and operations , 2017, Comput. Chem. Eng..

[75]  Shahriar Akter,et al.  Quality dominant logic in big data analytics and firm performance , 2018, Bus. Process. Manag. J..

[76]  Ratna Babu Chinnam,et al.  Observational data-driven modeling and optimization of manufacturing processes , 2017, Expert Syst. Appl..

[77]  Jennifer Rowley,et al.  Conducting a literature review , 2004 .

[78]  Jie Li,et al.  Progress in Aluminum Electrolysis Control and Future Direction for Smart Aluminum Electrolysis Plant , 2017 .

[79]  Zheng Liu,et al.  Latent Variable Models and Big Data in the Process Industries , 2015 .

[80]  Luca Benini,et al.  Continuous learning of HPC infrastructure models using big data analytics and in-memory processing tools , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

[81]  Peter Butala,et al.  Big data analytics for operations management in engineer-to-order manufacturing , 2018 .

[82]  Nima Khakzad,et al.  Application of Bayesian network and multi-criteria decision analysis to risk-based design of chemical plants , 2016 .

[83]  Yong Qin,et al.  A fuzzy Fine-Kinney-based risk evaluation approach with extended MULTIMOORA method based on Choquet integral , 2018, Comput. Ind. Eng..

[84]  Z. Irani,et al.  Critical analysis of Big Data challenges and analytical methods , 2017 .