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Institute
With big data-technologies on the rise, new fields of application appear in terms of analyzing data to find new relationships for improving process under-standing and stability. Manufacturing companies oftentimes cope with a high number of deviations but struggle to solve them with less effort. The research project BigPro aims to develop a methodology for implementing counter measures to disturbances and deviations derived from big data. This paper proposes a methodology for practitioners to assess predefined counter measures. It consists of a morphology with several criterions that can have a certain characteristic. Those are then combined with a weighting factor to assess the feasibility of the counter measure for prioritization.
Failure management in the production area has been intensely analyzed in the research community. Although several efficient methods have been developed and partially successfully implemented, producing companies still face a lot of challenges. The resulting main question is how manufacturers can be assisted by a sustainable approach enabling them to proactively detect and prevent failures before they occur. A high-resolution production system based on analyzed real-time data enables manufacturers to find an answer to the main question. In this context, Big Data technologies have gained importance since the critical success factor is not only to collect real-time data in the production but also to structure the data. Therefore, we present in this paper the implementation of Big Data technologies in the production area using the example of an actual research project. After the literature review, we describe a Big Data based approach to prevent failures in the production area. This approach mainly includes a real-time capable platform including complex event processing algorithms to define appropriate improvement measures.
Towards the Generation of Setup Matrices from Route Sheets and Feedback Data with Data Analytics
(2018)
The function or department of production control in manufacturing companies deals with short-term scheduling of orders and the management of deviations during order execution. Depending on the equipment and characteristics of orders, sequence dependent setup times might occur. In these cases for companies that focus on high utilization of their assets due to long phases of ramp up and high energy costs, it might be optimal to choose sequences with minimal setup time times between orders. Identifying such sequences requires detailed and correct information regarding the specific setup times. With increasing product variety and shorter lot sizes, it becomes more difficult and rather time intense to determine these values manually. One approach is to analyse the relevant features of the orders described in the route sheets or recipes to find similarities in materials and required tools. This paper presents a methodology, which supports setup optimized sequencing for sequence dependent setup times through constructing the setup matrix from such route sheets with the use of data analytics.
Due to Digital Transformation, also called Industry 4.0 or the Industrial Internet of Things, the barrier for implementing data collecting technology on the shop floor has decreased dramatically in the past years – leading to an increasingly growing amount of data from a multitude of IT systems in production companies worldwide. Despite that, the production controller still relies heavily on intrinsic knowledge and intuition for the management of disruptions in production. Thanks to advances in the fields of production control and artificial intelligence, potentials for the collected data for disruption management arise. However, in order to transform data into usable information and allow drawing conclusions for disruption management in production, the relevant data-objects, disturbances and alternative actions must be known. Thus, the decision-making can be supported, reducing the decision latency and increasing benefit of alternative actions. Therefore, the goal of this paper is to discuss the prerequisites necessary to perform a data based disruption management and the methodology itself, serving as an approach to allow companies to build a data basis, classify disruptions and alternative actions in order to improve decision making in the future. [https://link.springer.com/chapter/10.1007/978-3-030-28464-0_13]