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The thesis work develops an automated, data-driven bottleneck detection procedure based on real-world data. Following a seven-step process it is possible to determine the average as well as the shifting bottleneck by automatically applying the active period method. A detailed explanation of how to pre-process the extracted data is presented which is a good guideline for other analysists to customize the available code according to their needs. The obtained results show a deviation between the expected bottleneck and the bottleneck calculated based on production data collected in one week of full production. The expected bottleneck is currently determined by the case company by measuring cycle times physically at the machine, but this procedure does not represent the whole picture of the production line and is therefore recommended to be replaced by the developed automated analysis. Based on the analysis results, different optimization potentials are elaborated and explained to improve the data quality as well as the overall production capacity of the investigated production line. Especially, the installed gantry systems need further analysis to decrease their impact on the overall capacity. As for data quality, especially, the improvement of the machines data itself as well as the standardization of timestamps should be focused to enable better analysis in the future. Finally, future recommendations mainly suggest to run the analysis several times with new data sets to validate the results and improve the overall understanding of the production lines behavior. / Detta examensarbete utvecklar en process för en automatiserad, datadriven flaskhalsidentifiering baserad på verkliga data. Följt av en sjustegsprocess ges det möjlighet att bestämma den genomsnittliga och den varierande flaskhalsen genom en automatisk implementering av ”the active period method”. En detaljerad förklaring av hur man förbehandlar informationen som extraherats är presenterat vilket är en god riktlinje för andra analytiker för att anpassa den tillgängliga koden utifrån deras behov. Det samlade resultatet illustrerar en avvikelse mellan den förväntade flaskhalsen och den flaskhalsen som utgår ifrån beräkningar av tillverkningsdata ansamlat i en vecka av full produktion. Den förväntade flaskhalsen är för nuvarande bestämt av fallets företag genom en fysisk mätning av cykeltiderna på maskinen, däremot är denna process inte representativ för helhetsbilden på tillverkningslinjen och det är därvid rekommenderat att ersätta den föregående flaskhalsidentifieringen med den utvecklade automatiserade analysen. Baserat på analysens resultat framkom det olika optimiseringsmöjligheter som är utvecklade och klargjorda för att förbättra kvaliteten på data samt den övergripande produktionskapaciteten av den undersökta produktionslinjen. Speciellt när det gällerde installerade portalsystemen så behövs det en fördjupande analys för att minimera dess verkan på den översiktliga kapaciteten. När det gäller datakvalitet, speciellt förbättringen av maskindata, behövs det en standardiserad tidsstämpling för att utföra enbättre analys i framtiden. De framtida rekommendationerna föreslår huvudsakligen att köra analysen ett flertal gånger med nya datauppsättningar för att validera resultaten och förbättra den övergripliga uppfattningen av produktionslinjens beteende.

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Assuring production forms a crucial part of mining business profitability. Factors related to various mine operations, activities and business processes can threaten required/planned mine production. To address problems and ensure production level in mining, it is necessary to implement a mine production assurance program (MPA). Since such a guideline does not exist for mining as a process industry, this study started by reviewing four such techniques used in similar industries. These methods include: total productive maintenance, six sigma, a method prescribed by European foundation of quality management, and production assurance program (PAP) used in the oil and gas industry.These methods and techniques were reviewed according to their objectives and applications. Their implementation and achieved success was determined through a literature review and field participation/study. Comparing the tools, techniques and focus with mining productivity and production factors, it was observed that applicability of these methods for mining is limited due to a lack of tools for specific analysis or a lack of consideration of the requirements of mining. However, given certain similarities in objective and methods, PAP from the oil and gas industry may provide some guidance for MPA.As a basis of MPA, an index is required to create a clear relationship between different situations which can occur in mining operation and production loss. A literature review on mining productivity improvement methods shows availability, utilisation and production performance of equipment are the key factors in determining overall production. A single index applicable for chain operation in mining is needed. Overall equipment effectiveness (OEE) which includes these three elements has some limitations for application in mining. A Mine Production index (MPi) is thus proposed. This index involves all three parameters for equipment productivity mentioned above. It also consists of weights for each parameter. The weights in this study are determined through expert opinions/judgements using fuzzy analytical hierarchy process (FAHP). Equipment with low MPi can be labelled as bottlenecks. Weights associated with MPi calculation for bottleneck equipment can point out critical factors in equipment operation. Once bottleneck equipment and relevant critical factors are known, further analysis can be carried out to determine the exact cause of production loss.By using MPi for machine operations, it is possible to rank machines in terms of production effectiveness. When the study applied MPi to chain operations in a mining case study, a crusher was determined as bottleneck equipment. Further root cause analysis and uncertainty detection for bottleneck equipment is also possible, and this forms the basis for MPA. / CAMM - Lean mining

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