Journal
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
Volume 165, Issue -, Pages 293-306Publisher
ELSEVIER
DOI: 10.1016/j.ijpe.2014.12.032
Keywords
Big Data analytics; Cement industry; Data driven decision making; Manufacturing; Outbound logistics; Visualization
Categories
Funding
- Indian Institute of Management Calcutta [3557/RP: ATBOCOMSUDM]
- Indian Institute of Management Calcutta Case Research Center (IIMCCRC)
- Ramco Cements Limited
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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. (C) 2015 Elsevier B.V. All rights reserved.
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