This simulation study was carried out at a company in the automotive industry. In their factory, the company manufactures gearboxes, where the gearbox assembly is one step in the manufacturing process. In the main assembly line, many different gearbox variants are manufactured, each with its own cycle time in the different assembly stations. Due to this variation in cycle times the throughput in the main assembly line can fluctuate depending on which gearbox variants are produced. To maintain a desired throughput a tool for forecasting the throughput would be of great value to the company. Due to the large number of gearbox variants, the company also experiences difficulties understanding if there are stations in the assembly which are recurring bottlenecks. The purpose of this project is to create a tool that can forecast the daily throughput of the main assembly line and identify bottlenecks in the main assembly line. The purpose is also to investigate whether some stations often are bottleneck stations during a certain period. The model evaluation was based on validation of input-output-transformation. A bottleneck analysis based on the active times of the stations was carried out with the best-performing model. The best-performing model version resulted in 68 percent valid forecasts for the 57 historical production days. The forecasts were valid for all historical production days which were Mondays, Tuesdays, Wednesdays, and Thursdays. For Fridays and Sundays, 25 and 0 percent were valid, respectively. A bottleneck analysis was carried out for 37 historical production days where the result showed that, based on the share of active time, a single station was identified as the bottleneck station for all days. The difference between the bottleneck station’s share of active time and other stations’ share of active time was deemed small enough to be within the error margin. Additionally, the queues in the main assembly line indicated that the bottleneck was located within another group of stations. Due to the ambiguity of the results, the conclusion drawn was that the main assembly line did not experience any clear bottleneck station during the 37 historical production days. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

Generating traffic to characterize large capacity network links with high accuracy in transport backbones is important for Internet service providers. Using a network application (Iperf) to measure throughput (and indirectly congestion) on links within a network can be used to perform traffic engineering. Using short bursts at high utilisation can ascertain whether a connection can support capacity sensitive applications, such as video streaming. Repeating the process to capture day-night effects can categorise links for management decisions. CheesePi is an Internet quality monitoring system that aims to characterise the services obtained from the home Internet connections of the users and establish an open measurement infrastructure in Sweden. The thesis evaluates a study of performance and characterization of network links within the CheesePi network at high data rates (Gbits/sec) and will extend the work done in the CheesePi software base. / Att kunna generera trafik för karakterisering av stora nätförbindensler med hög noggrannhet är en mycket viktigt faktor för internetleverantörer. Med hjälp av en nätverksapplikation (Iperf) så kan man mäta bithastigheten samt den indirekta överbelastningen på länkar i ett nätverk. Man kan även mäta performansen av nätet för ytterligare analys. Genom att ha intensiv trafik i korta perioder så kan man få ett hum om en uppkoppling kan stödja kapacitets känsliga applikationer så som videouppspelning. I och med att internet användningen varierar så kan man även kategorisera länkarna ytterligare så som dag och natt performansen. CheesePi är ett övervakningssystem som försäkrar kvaliteten och karakteriserarde tjänster som erhållits för hemanslutningar samt infrastrukturs mätningar i Sverige. Examensarbetet utvärderar prestandan och karakteriseringen av nätförbindelser med hög datahastighet (Gigabit/sec) igenom att använda CheesePi programvaran.