S. Alaswad and Y. Xiang, A review on condition-based maintenance optimization models for stochastically deteriorating system, Reliability Engineering & System Safety, vol.157, pp.54-63, 2017.

J. Amankwah-amoah and S. Adomako, Big data analytics and business failures in data-rich environments: An organizing framework, Computers in Industry, vol.105, pp.204-212, 2019.

D. An, J. H. Choi, and N. H. Kim, Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab, Reliability Engineering and System Safety, vol.115, pp.161-169, 2013.

M. Antikainen, T. Uusitalo, and P. Kivikytö-reponen, Digitalisation as an enabler of circular economy, Procedia CIRP, vol.73, pp.45-49, 2018.

O. O. Aremu, A. Salvador-palau, D. Hyland-wood, A. K. Parlikad, and P. R. Mcaree, Structuring data for intelligent predictive maintenance in asset management, 16th IFAC Symposium on Information Control Problems in Manufacturing, 2018.

M. Bahrepour, The forgotten step in CRISP-DM and ASUM-DM methodologies, pp.2019-2022

F. Blomsma and G. Brennan, The emergence of circular economy: A new framing around prolonging resource productivity, Journal of Industrial Ecology, vol.21, issue.3, pp.603-614, 2017.

N. M. Bocken and S. Short, Towards a sufficiency-driven business model: Experiences and opportunities, Environmental Innovation and Societal Transitions, vol.18, pp.41-61, 2016.

G. Bressanelli, F. Adrodegari, M. Perona, and N. Saccani, The role of digital technologies to overcome circular economy challenges in pss business models: an exploratory case study, Procedia CIRP, vol.73, pp.216-221, 2018.

P. Chapman, J. Clinton, R. Kerber, H. Khabaza, T. Reinartz et al., Step-by-step data mining guide, Tech. rep, 2000.

D. Cielen, A. Meysman, and M. Ali, Introducing data science: big data, machine learning, and more, using Python tools, p.12, 2016.

V. Dhar, Data science and prediction, 2012.

, Domino Data Lab: Key factors on the journey to become model-driven -a survey report, 2018.

, Ellen MacArthur Foundation: Towards a circular economy and business rationale for an accelerated transition, 2013.

, Ellen MacArthur Foundation: Delivering the circular economy: A toolkit for policymakers, 2015.

, Ellen MacArthur Foundation: Growth within: a circular economy vision for a competitive europe, Ellen MacArthur Foundation Cowes, 2015.

, Ellen MacArthur Foundation: Towards a circular economy: business rationale for an accelerated transition, 2015.

, Ellen MacArthur Foundation: Intelligent assets. unlocking the circular economy potential, 2016.

, Ellen MacArthur Foundation: Artificial intelligence and the circular economy, 2019.

R. Elshawi, S. Sakr, D. Talia, and P. Trunfio, Big data systems meet machine learning challenges: Towards big data science as a service, Big data research, 2018.

U. Fayyad, G. Piatetsky-shapiro, and P. Smyth, From Data Mining to Knowledge Discovery in Databases, 1996.

B. Flyvbjerg and A. Budzier, Why your it project may be riskier than you think, 2011.

Y. Geng and B. Doberstein, Developing the circular economy in china: Challenges and opportunities for achieving'leapfrog development, The International Journal of Sustainable Development & World Ecology, vol.15, issue.3, pp.231-239, 2008.

P. Ghisellini, C. Cialani, and S. Ulgiati, A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems, Journal of Cleaner production, vol.114, pp.11-32, 2016.

W. Haas, F. Krausmann, D. Wiedenhofer, and M. Heinz, How circular is the global economy: An assessment of material flows, waste production, and recycling in the european union and the world in 2005, Journal of Industrial Ecology, vol.19, issue.5, pp.765-777, 2015.

N. Haddar, M. Tmar, and F. Gargouri, A framework for data-driven workflow management: modeling, verification and execution, International Conference on Database and Expert Systems Applications, pp.239-253, 2013.

G. T. Henry, Practical sampling, vol.21, 1990.

T. C. Ho, S. C. Mat, and L. H. San, A prediction model for co2 emission from manufacturing industry and construction in malaysia, 2015 International Conference on Space Science and Communication (IconSpace), pp.469-472, 2015.

, IBM: Analytics solutions unified method -implementations with agile principles, 2016.

C. J. Jabbour, A. B. De-sousa-jabbour, J. Sarkis, and M. Godinho-filho, Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda, Technological Forecasting and Social Change, 2017.

G. James, D. Witten, T. Hastie, and R. Tibshirani, An introduction to statistical learning, vol.112, 2013.

M. Janssen, H. Van-der-voort, and A. Wahyudi, Factors influencing big data decisionmaking quality, Journal of Business Research, vol.70, pp.338-345, 2017.

U. S. Kameswari and I. R. Babu, Sensor data analysis and anomaly detection using predictive analytics for process industries, 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), pp.1-8, 2015.

J. Kirchherr, D. Reike, and M. Hekkert, Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation and Recycling, vol.127, pp.221-232, 2017.

D. Kiron and R. Shockley, Creating business value with analytics, MIT Sloan Management Review, vol.53, issue.1, p.57, 2011.

W. Kun, L. Tong, and X. Xiaodan, Application of big data technology in scientific research data management of military enterprises, Procedia Computer Science, vol.147, pp.556-561, 2019.

D. Larson and V. Chang, A review and future direction of agile, business intelligence, analytics and data science, International Journal of Information Management, vol.36, issue.5, pp.700-710, 2016.

Y. Lei, N. Li, L. Guo, N. Li, T. Yan et al., Machinery health prognostics: A systematic review from data acquisition to rul prediction, Mechanical Systems and Signal Processing, vol.104, pp.799-834, 2018.

Z. Li, Y. Wang, and K. Wang, A data-driven method based on deep belief networks for backlash error prediction in machining centers, Journal of Intelligent Manufacturing, pp.1-13, 2017.

M. Lieder and A. Rashid, Towards circular economy implementation: a comprehensive review in context of manufacturing industry, Journal of Cleaner production, vol.115, pp.36-51, 2016.

J. Lin, E. Keogh, L. Wei, and S. Lonardi, Experiencing sax: a novel symbolic representation of time series, Data Mining and knowledge discovery, vol.15, issue.2, pp.107-144, 2007.

B. Liu, Z. Liang, A. K. Parlikad, M. Xie, and W. Kuo, Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model, Reliability Engineering & System Safety, vol.168, pp.200-209, 2017.

A. Mcafee, E. Brynjolfsson, T. H. Davenport, D. Patil, and D. Barton, Big data: the management revolution, Harvard business review, vol.90, issue.10, pp.60-68, 2012.

J. Meierhofer and K. Meier, From data science to value creation, International Conference on Exploring Services Science, pp.173-181, 2017.

M. Molina-solana, M. Ros, M. D. Ruiz, J. Gómez-romero, and M. J. Martín-bautista, Data science for building energy management: A review, Renewable and Sustainable Energy Reviews, vol.70, pp.598-609, 2017.

P. Nath, S. Nachiappan, and R. Ramanathan, The impact of marketing capability, operations capability and diversification strategy on performance: A resource-based view, Industrial Marketing Management, vol.39, issue.2, pp.317-329, 2010.

R. Newman, V. Chang, R. J. Walters, and G. B. Wills, Model and experimental development for business data science, International Journal of Information Management, vol.36, issue.4, pp.607-617, 2016.

G. C. Nobre and E. Tavares, Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study, Scientometrics, vol.111, issue.1, pp.463-492, 2017.

K. J. Ottenbacher, J. E. Graham, and S. R. Fisher, Data science in physical medicine and rehabilitation: Opportunities and challenges. Physical Medicine and Rehabilitation, Clinics of North America, 2019.

A. Pagoropoulos, D. C. Pigosso, and T. C. Mcaloone, The emergent role of digital technologies in the circular economy: A review, Procedia CIRP, vol.64, pp.19-24, 2017.

Y. Peng, M. Dong, and M. J. Zuo, Current status of machine prognostics in conditionbased maintenance: A review, International Journal of Advanced Manufacturing Technology, vol.50, issue.1-4, pp.297-313, 2010.

G. Piatetsky, Crisp-dm, still the top methodology for analytics, data mining, or data science projects, KDD News, 2014.

P. Planing, Business model innovation in a circular economy reasons for nonacceptance of circular business models, Open journal of business model innovation, vol.1, issue.11, 2015.

C. Ponsard, M. Touzani, and A. Majchrowski, Combining process guidance and industrial feedback for successfully deploying big data projects, Open Journal of Big Data (OJBD), vol.3, issue.1, pp.26-41, 2017.

M. E. Porter and J. E. Heppelmann, How smart, connected products are transforming competition, Harvard business review, vol.92, issue.11, pp.64-88, 2014.

F. Provost and T. Fawcett, Data Science for Business: What you need to know about data mining and data-analytic thinking, 2013.

D. Romero and O. Noran, Towards green sensing virtual enterprises: Interconnected sensing enterprises, intelligent assets and smart products in the cyber-physical circular economy, IFAC-PapersOnLine, vol.50, issue.1, pp.11719-11724, 2017.

. Sas:-semma, , pp.2019-2023

B. Schmarzo, Big Data MBA: Driving Business Strategies with Data Science, 2015.

T. T. Sousa-zomer, L. Magalhães, E. Zancul, and P. A. Cauchick-miguel, Exploring the challenges for circular business implementation in manufacturing companies: An empirical investigation of a pay-per-use service provider. Resources, Conservation and Recycling, vol.135, pp.3-13, 2018.

G. A. Susto, A. Schirru, S. Pampuri, S. Mcloone, and A. Beghi, Machine learning for predictive maintenance: A multiple classifier approach, IEEE Transactions on Industrial Informatics, vol.11, issue.3, pp.812-820, 2015.

S. Viaene, Data scientists aren't domain experts, IT Professional, vol.15, issue.6, pp.12-17, 2013.

R. Vidgen, S. Shaw, and D. B. Grant, Management challenges in creating value from business analytics, European Journal of Operational Research, vol.261, issue.2, pp.626-639, 2017.

C. Voss, Case research in operations management, Researching operations management, pp.176-209, 2010.

M. A. Waller and S. E. Fawcett, Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management, Journal of Business Logistics, vol.34, issue.2, pp.77-84, 2013.

R. Wirth and J. Hipp, Crisp-dm: Towards a standard process model for data mining, Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, pp.29-39, 2000.

D. Wood, M. Zaidman, L. Ruth, and M. Hausenblas, , 2014.

R. K. Yin, Applied social research methods series case study research: Design and methods, 1984.