This report aims to present the results of a master's thesis in the field of mechanical engineering with a focus on production technology. The thesis was carried out at Scania Ferruform AB in Luleå, targeting the side beam flow, specifically the sequence warehouse in the side beam painting process. The side beam flow is complex, with each side beam treated as an individual and deliveries occurring in sequence.  The capacity of the side beam flow is being increased through various investment projects. These improvements may cause the bottleneck in the flow to shift. The purpose of the thesis is to investigate how the sequence warehouse manages the capacity increase. This will be examined using a simulation model built in the program ExtendSim 10. The results of this work are intended to serve as a basis for upcoming investment projects.  With the help of the simulation model, two questions will be answered: How does the Stacker Crane handle an increased kit fill rate? and How is the warehouse capacity affected by the increased kit fill rate? To answer these, the simulation model is first built, function-tested, and verified. Once the simulation model is considered representative, a number of different experiments are conducted, varying input data and capacity to observe how the model behaves.  From the analysis of the experiments, it is evident that the Stacker Crane may encounter problems with increased capacity, with significant increases in crane utilization and the buffer zone for warehouse loading. Both of these results indicate how the crane will manage with increased capacity. The utilization rate increased from 78.7 percent to 91.0 percent. The buffer zone saw an even larger percentage increase. This substantial increase may mean that the crane will occasionally be unable to keep up.  An analysis of warehouse capacity revealed that the warehouse's capacity itself is not an issue even with the increased capacity. However, the current warehouse configuration would not be able to handle it. This is due to the current warehouse setup consisting of compartments with different length categories. Some compartments are filled to capacity while others are significantly under capacity. For the warehouse to handle the increased capacity, it needs to become more flexible to fully utilize its capacity.

Den fjärde industriella revolutionen, även kallad Industri 4.0, drivs av ett antal teknologier som medför digitalisering och automatisering av industriella processer. Konceptet innebär en applicering av dataanalys med avancerade analytiska verktyg på stora mängder data, vilka påstås ge stora möjligheter för kvalitetsförbättringar. För att en sådan övergång ska ske är förmågan att hantera data avgörande. Trots det uppvisar många företag idag bristande användning av data för att ta beslut. Frågan är hur företag kan göra för att hantera data och utföra en transformation till Industri 4.0. För att studera det här ämnet har det här examensarbetet utförts som en fallstudie på en stansprocess hos Scania Ferruform. Genom en litteraturstudie, kvantitativ datainsamling samt observationer och intervjuer undersökte examensarbetet den nuvarande användning av data i processen. Därefter undersöktes data med statistiska verktyg för att visa på hur data kan hanteras i en process för att erhålla större kunskap om orsaker till avvikelser. Examensarbetet utredde till sist hur fortsatt arbete med datahantering kan utföras för att uppnå målet Industri 4.0.Analysverktyg har använts för att analysera över 39 000 datapunkter. Resultatet visar på att det finns utvecklingsmöjligheter vad gäller insamling, kvalitet och användning av data. Ett ramverk presenteras för hur företaget bör hantera data för att kunna utvinna ny kunskap från deras processer samt hur Ferruform fortsatt kan arbeta mot Industri 4.0.Slutligen ges rekommendationer om fortsatta studier. Resultatet av examensarbetet blir ett stöd för Ferruform i deras arbete mot mer dugliga processer och den tekniska utveckling företaget eftersträvar. / The fourth industrial revolution, also called Industry 4.0 is powered by several technologies which result in digitalization and automatization of industrial processes. The concept includes the application of big data and advanced analytics, which are said to provide great opportunities for quality improvements. For such a transition to take place, the ability to handle data is crucial. Despite this, many companies today show a lack of use of data to drive decision-making. The question is how companies can manage data and ultimately transition towards Industry 4.0. To research this topic this thesis has been carried out as a case study of a punching process at Scania Ferruform. Through a literature review, quantitative data collection, as well as observations and interviews, the thesis examined the current use of data in the process. Subsequently, data were examined with statistical tools to illustrate how data can be managed in a process to attain increased knowledge about causes of deviations. Lastly, the thesis explored future work towards Industry 4.0. Analysis tools have been used to analyse over 39 000 data points. The result of the study shows that there are opportunities for development in terms of collection, quality and use of data. A framework of how Ferruform should manage data in order to extract new knowledge from its processes is presented. Furthermore, an action plan is presented for a transition towards Industry 4.0. Finally, recommendations are given for further studies. The result of the thesis will be helpful for Ferruform in its transition towards more efficient processes and the technical development of which the company strives towards.