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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.
Manufacturing companies are facing an increasingly turbulent market – a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.
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.