Motivation Mining is an industry with old traditions and historically high revenues; however, companies had to face several challenges in the previous years which can be rooted to inefficiency in their operational processes. Commodity prices dropped to record lows, but the slow industry could not keep up with the pace of its rapidly changing market, and their expenditures did not decrease the same way either. Easily accessible mines are depleting, and safety concerns are also in the center of attention. Mining companies have to react to these events; they have to alter their businesses to face the new challenges. Problem Statement A possible solution for decreasing operational costs is to implement new technologies that can improve their processes and leverage from big data that has been collected by sensors and parsed with advanced analytics techniques. The thesis’s research question is how can mining companies utilize machinegenerated big data in their operations? To find an extensive answer, several topics have to be introduced, such as machine-generated big data, Internet of Things, data analysis techniques and the operational processes of a mining company. Approach During the research a delimitation had to be taken: since maintenance costs are the largest contributions of operational expenditures, the paper focuses on this area. The research builds on interviews conducted with a mining company as a primary data source, complemented with definitions and theories, and it uses numerous sample cases to support the findings. Results The results of the interviews are used to create an optimal organization structure that incorporates connected devices and predictive maintenance solutions. The model is validated by examining current market trends and how other companies utilize these technologies.As mining enterprises have just started to move towards data analytics, they need to implement these solutions in their operations. The thesis provides a model of change management specifically for these cases. It is built on popular frameworks and methods, and it takes existing technologies, risks, and benefits, human behavior into consideration and it gives guidance for practical applications. The model is tested by another change framework that introduced business transformations in the mining industry. Conclusion The thesis concludes that companies recognized the need for improving their maintenance-related processes, and to dissolve limitation, further examples are presented to demonstrate other use cases of big data and data analytics in the mining industry.
|Educations||MSc in Business Administration and Information Systems, (Graduate Programme) Final Thesis|
|Number of pages||84|