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Managing a Big Data project: The case of Ramco Cements Limited

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  • Dutta, Debprotim
  • Bose, Indranil

Abstract

Currently many organizations are in the process of implementing Big Data related projects in order to extract meaningful insights from their data for better decision making. Though there are various frameworks postulating the best practices which should be adopted while implementing analytics projects, they do not cater to the complexities associated with a Big Data project. In this paper our goal is two-fold: to develop a new framework that can provide organizations a holistic roadmap in conceptualizing, planning and successfully implementing Big Data projects and to validate this framework through our observation of a descriptive case study of an organization that has implemented such a project. Although the manufacturing sector has been slow in incorporating analytics in their strategic decision making, the situation is changing with increasing use of analytics for product development, operations and logistics. We explore the Big Data project at a manufacturing company, Ramco Cements Limited, India, describe the system developed by them and highlight the benefits accrued from it. We investigate the entire process by which the project is implemented using the lens of our proposed framework. Our results reveal that a clear understanding of the business problem, a detailed and well planned step-by-step project map, a cross functional project team, adoption of innovative visualization techniques, patronage and active involvement of top management and a culture of data driven decision making are essential for the success of a Big Data project.

Suggested Citation

  • Dutta, Debprotim & Bose, Indranil, 2015. "Managing a Big Data project: The case of Ramco Cements Limited," International Journal of Production Economics, Elsevier, vol. 165(C), pages 293-306.
    • Handle: RePEc:eee:proeco:v:165:y:2015:i:c:p:293-306
    DOI: 10.1016/j.ijpe.2014.12.032
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    1. Ting, S.L. & Tse, Y.K. & Ho, G.T.S. & Chung, S.H. & Pang, G., 2014. "Mining logistics data to assure the quality in a sustainable food supply chain: A case in the red wine industry," International Journal of Production Economics, Elsevier, vol. 152(C), pages 200-209.
    2. Hsu, Shao-Chung & Chien, Chen-Fu, 2007. "Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing," International Journal of Production Economics, Elsevier, vol. 107(1), pages 88-103, May.
    3. Mandal, Purnendu & Gunasekaran, A., 2003. "Issues in implementing ERP: A case study," European Journal of Operational Research, Elsevier, vol. 146(2), pages 274-283, April.
    4. Shereen Mekawie & Ahmed Elragal, 2013. "ERP and SCM Integration: The Impact on Measuring Business Performance," International Journal of Enterprise Information Systems (IJEIS), IGI Global, vol. 9(2), pages 106-124, April.
    5. Bent Flyvbjerg, 2013. "Quality Control and Due Diligence in Project Management: Getting Decisions Right by Taking the Outside View," Papers 1302.2544, arXiv.org.
    6. Chica, Manuel & Cordón, Óscar & Damas, Sergio & Bautista, Joaquín, 2013. "A robustness information and visualization model for time and space assembly line balancing under uncertain demand," International Journal of Production Economics, Elsevier, vol. 145(2), pages 761-772.
    7. Schikora, Paul F. & Godfrey, Michael R., 2003. "Efficacy of end-user neural network and data mining software for predicting complex system performance," International Journal of Production Economics, Elsevier, vol. 84(3), pages 231-253, June.
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