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El blog de Mikel Niño
Industria 4.0, Big Data Analytics, emprendimiento digital y nuevos modelos de negocio

Our paper on using Big Data Analytics to drive the servitization of a capital equipment manufacturer

Back from my short stay in the United States, it is time to write some blog entries with the main information and the most relevant conclusions after my presence at the 2015 IEEE International Conference on Big Data. I will begin summarizing the main keys about the work I am developing with my colleagues at the Interoperable Databases Group in the Faculty of Computer Science in San Sebastián, which I presented at the conference in a special session focusing on Smart Manufacturing and the application of Big Data in manufacturing industries.

The title of the presented paper is “Business Understanding, Challenges and Issues of Big Data Analytics for the Servitization of a Capital Equipment Manufacturer”, and is derived from our collaboration with a capital equipment manufacturer and their IT provider. In this project we use Big Data Analytics as the cornerstone for this manufacturer’s servitization strategy. This manufacturer is developing a new business model based on offering value-added services to their customers (larger manufacturing companies using the infrastructure and machinery provided by this capital equipment manufacturer in their production processes), helping them to optimize the result of such processes and the final product quality.

Presenting our work at the 2015 IEEE International Conference on Big Data

In our paper, apart from describing the manufacturing process analyzed in this case study and the deployed architecture for data capture, processing and analysis, we detail the business context where the project is developed and the map of stakeholders. The main contribution of our work is to disseminate our conclusions about the main challenges we have faced when understanding business goals and needs, and their impact in the definition of the technical goals of the project. The paper presents our findings and observations (our “lessons learned”) so that they can be leveraged in future similar projects.

Therefore, our goal is to provide valuable information that could be leveraged by research and development teams in this field. The motivation of this focus is two-fold: on one hand, we consider these business understanding aspects as the part of these projects that might consume more effort and resources from research teams, given the usual gap between business needs and data mining goals; on the other hand, a servitization context will present specific additional complexities that influence the project requirements for these research teams, because they won’t interact with a company whose goal is to build an internal tool just for internal use, but with a company who wants to build a product to be later marketed and sold to a customer segment (which research teams won’t have direct access to).

Link to the paper on the IEEE Xplore website (DOI: 10.1109/BigData.2015.7363897)

[Haz clic aquí para la versión en español de esta entrada]

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