Total quality management in sawmills

Detta är en avhandling från Luleå : Luleå tekniska universitet

Sammanfattning: This work was initiated in order to show the potential for Swedish sawmills to achieve higher productivity by implementation of improved process control tools in breakdown and production flows. An effective and profitable sawmill must utilize its raw material and the skill of its staff effectively. Sawmill production could usefully adopt appropriate areas of process thinking and optimizing methods from the mechanical process industry. Breakdown simulations performed during this study show a potential to improve volume yield by treating logs as single individuals during the breakdown procedure rather than treating logs as parts in a batch. Increased volume yield can be achieved from every log by applying optimal sawing pattern, log rotation and lateral offsets in first and second saw. Results achieved during surveys and simulations reveal a large potential to improve equipment availability and effectiveness on the sawline. Appropriate methods, tools and a higher awareness are required in order to achieve improvements. A toolbox containing methods, process-monitoring or decision-support tools such as Total Quality Management (TQM), process measurements, visualization and benchmarking methods, analysis tools and simulation software creates a solid base for implementation of process control, knowledge and improved productivity. Overall Equipment Effectiveness (OEE) is a benchmarking method addressing product quality and equipment availability and performance issues. These factors can be monitored over a period of time. Achieved metrics can be compared to earlier performed measurement and furthermore preferably be used in order to visualize improvement for the staff. A successful deployment of the TQM concept would gain from deployment of fact-based decisions, cross-functional teamwork and a clearly defined focal point, actively supported by management and resources. OEE can serve this purpose, if properly adapted to sawmill prerequisites. Accessible and reliable production data are crucial to providing information concerning the area of process control. Flexible diagnostic systems can, besides providing a sawmill with diagnosis and process knowledge, provide process data required for simulation models. These tools enable decisions to be based on facts.Simulation software provides tools with which log-breakdown or production scenarios can be evaluated repeatedly without causing disturbances in the sawmill. The resulting model shows that Discrete Event Simulation models can be credible and quite exact if correct stoppage data are available. The final model is, however, only valid under the prerequisites included in the model. The dynamics of independently occurring events occurring on a sawmill log yard serves an excellent example where Discrete Event simulation is adequate. A logging module added to a GPS support system simplified activity monitoring and data acquisition and serves as an example of invaluable modern technology implemented in sawmills.

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