Development of a guideline for introducing and using big data in SMEs of the manufacturing industry
As a term to describe the constantly growing presence of data, and the corresponding technological advances, “big data” has long established itself as a potential driver of innovation across numerous industries. Even in the metals and electronics industry (M+E), technological developments based on large volumes of highly-available data are gaining in importance. Potential opportunities and visions based on data can be found in areas such as the reduction of production rejects, the prediction of required maintenance activities (predictive maintenance) or the connection of the entire supply chain. Given the economic, engineering and organizational challenges however, the cost and effort associated with realizing such potential can be extremely high, especially for small-to-medium enterprises (SMEs). Companies often lack the know-how and experience to manage such projects on their own, plus the investment in personnel, time and other resources must be weighed against the risks at an early stage.
In order to address this challenge and provide the SME sector a suitable tool, the project team at fortiss set out to develop a methodology designed to help SMEs in the M+E industry identify the potential of big data and carry out corresponding projects on their own. The “guideline” that was created out of the project is designed on the one hand to give companies a structured approach for managing the entire project, and on the other to provide support through the targeted availability of empirical data drawn from practical experience during all phases of the project.
For the development of the guideline, the project team collaborated with four SMEs in the Bavarian M+E industry to assist them in carrying out their own big data projects. Over a period of 15 months the companies were guided and supported during various phases of their individual projects. This involved activities such as workshop formats for identifying individual big data potential or for creating strategic designs and plans for specific big data application scenarios and executing them within the company. Collaboration was also carried out in the area of exploratory data analysis and the development of model prototypes to create solutions and then transfer the acquired know-how and experience back into the companies. In all phases of the collaboration, the focus was to gain knowledge from the case studies for development of the methodology and process it in generalized form for use by other companies
The guideline developed by fortiss during this research project contains a detailed phase model designed to help companies structure and implement big data projects on their own (compare to the overview in figure 1).
Figure 1: The guideline leads SMEs through various phases until they learn to utilize big data on their own.
The guideline provides special assistance for each phase, from the design of application scenarios and the initial exploratory analysis of the company’s own data, to the implementation of prototypes. Apart from the detailed description of the implementation, the guideline also features workshop templates for the company’s own use, as well as practical tips, information and recommendations, plus checklists that streamline the transition between the project phases. The guideline is designed specifically for companies that have little or no experience with the systematic analysis of (large) data repositories. The idea is to make it easier for companies to get started with the topic of big data and to support a long-term internal learning process. In collaboration with the Kompetenzzentrum Mittelstand GmbH (KME), the guideline will be made available to the members of bayme and vbm (metals and electronic industry associations in Bavaria) as an independent part of the research report.
01.05.2018 - 31.01.2020