Analysing Production Flow of Discrete Manufacturing Systems Using Simple Node-based Data
Sipilä, Atte (2019)
Sipilä, Atte
2019
Automaatiotekniikka
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2019-04-26
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201903081303
https://urn.fi/URN:NBN:fi:tty-201903081303
Tiivistelmä
Optimizing, developing and improving production process of manufacturing companies needs lot of data about the process to support the decision making. The data can be collected with monitoring systems. This thesis aims to justify the need for a new monitoring system which monitors material and production item flow in the manufacturing process using simple formatted data collected from the detection nodes. To justify the need, market situation is explored, and two possible competitors are presented. Based on evaluation of these, there is room for new simple commissioned monitoring system which can be used side by side with possible control system.
Theories ja practices behind key performance indicators and traceability are discussed to get deeper understating of key performance indicator development and flow monitoring. Using the standard for manufacturing key performance indicators, indicators for flow monitoring are presented. The challenge is that the collected data must be kept as simple as possible.
Visualization for key performance indicators are discussed and developed. Choosing the visualization platform happens by comparing ready business intelligence tool and own implementation. The data is generated by simulator which implements the designed interfaces. Analyzing the visualization making process, styles of visualization and costs of different solutions, using the own implementation is selected.
The traceability data is visualized with directed graphs. The examples of the possible visualizations are provided. The realistic looking data is generated with simulator which is using the designed interface to the data collecting module. Visualization challenges of flow of multiple items is solved by making the edges of directed graphs thicker if there are more transactions between nodes.
The decisions are made keeping mind that the flow monitoring system is added to Inspector application of InSolution. The requirements like scalability, commission easiness and clearness and informativeness of visualizations are coming from InSolution. The target is to switch the view point of Inspector monitoring from production devices to production items.
Theories ja practices behind key performance indicators and traceability are discussed to get deeper understating of key performance indicator development and flow monitoring. Using the standard for manufacturing key performance indicators, indicators for flow monitoring are presented. The challenge is that the collected data must be kept as simple as possible.
Visualization for key performance indicators are discussed and developed. Choosing the visualization platform happens by comparing ready business intelligence tool and own implementation. The data is generated by simulator which implements the designed interfaces. Analyzing the visualization making process, styles of visualization and costs of different solutions, using the own implementation is selected.
The traceability data is visualized with directed graphs. The examples of the possible visualizations are provided. The realistic looking data is generated with simulator which is using the designed interface to the data collecting module. Visualization challenges of flow of multiple items is solved by making the edges of directed graphs thicker if there are more transactions between nodes.
The decisions are made keeping mind that the flow monitoring system is added to Inspector application of InSolution. The requirements like scalability, commission easiness and clearness and informativeness of visualizations are coming from InSolution. The target is to switch the view point of Inspector monitoring from production devices to production items.