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The most costly and labor-intensive activity for almost every warehouse is the order picking process and a key challenge for manufacturing companies is to store parts in an efficient way. Therefore, to minimize the order retrieval time when picking from a storage, the need of a sufficient storage categorization strategy becomes vital. One of the logistics centers at Scania in Södertälje stores parts that will be transported to the chassis assembly and the assembly of gearboxes and axles when needed in the production. In one of the storage areas at the logistics center, namely the PS storage, the forklift drivers picking from the storage have experienced congestion in the storage aisles and that it might be possible to reduce the order fulfillment time when picking the orders. This master thesis aims to investigate the possibility of optimizing the picking process in the PS storage, with respect to the order fulfillment time for the forklift drivers, with categorization of the goods. This has been analyzed with a heuristic optimization approach and with the use of a discrete event simulation model, where different categorization strategies have been applied on the storage and compared to the current state. By categorizing the goods in the PS storage, a reduction of the order fulfillment time can be done of around 4% - 5% compared to the current state with all tested categorization strategies. The strategy which has been shown to give the largest improvement is by categorizing the parts in the storage according to their final delivery address at the production line, which would reduce the order fulfillment time by 5.03% compared to the current state. With this categorization method, parts that are picked on the same route are located close to each other.

Today, inventory management at Marabou is organised in such way that articles are stored based on which production line they belong to and are sent to storage locations close to their production line. However, some storage locations are not optimised, insofar articles are stored out of pure habit and follow what is considered most convenient. This means that the storage locations are not based on any fixed instructions or standard. In this report, we propose optimal storage locations with respect to transportation cost by modelling the problem mathematically as a minimal cost matching problem, which we solve using the so-called Hungarian algorithm. To be able to implement the Hungarian algorithm, we collected data regarding the stock levels of articles in the factory throughout 2016. We adjusted the collected data by turning the articles into units of pallets. We considered three different implementations of the Hungarian algorithm. The results from the different approaches are presented together with several suggestions regarding pallet optimisation. In addition to the theoretical background, our work is based on an empirical study through participant observations as well as qualitative interviews with factory employees. In addition to our modelling work, we thus offer several further suggestions for efficiency savings or improvements at the factory, as well as for further work building on this report. / Idag är lagerhanteringen i Marabou fabriken ordnat på sådant sätt att artiklarna är lagrade utifrån vilken linje den tillhör och därmed står i ett lager nära den specifika linjen. Dock finns det lagerplatser idag som inte är optimerade, i den mån att det endast är lagrade från vana och vad som anses enklast. Därmed är lagerplatserna inte ordnade utifrån någon standard.I detta examensarbete föreslår vi därför de mest optimala lagerplatserna med hänsyn till totala transportkostnaderna. Det här problemet kan modelleras som ett matchningsproblem som kan lösas av en så kallad Ungersk algoritm. Denna ska resultera i den optimala matchningen mellan produktionslinjens behov mot lagerplatserna i fabriken med tillhörande kostnad. För att använda Ungerska algoritmen samlade vi in data av den totala mängd artiklar som fanns i fabriken för 2016, vilket togs fram genom datasystemet SAP som Marabou använder sig av. Därefter justerade vi datat genom att dela upp alla artiklarna i antalet pallar samt vilken linje den tillhör. Denna information kompletterades med empiriska undersökningar genom egna observationer samt kvalitativa intervjuer med de anställda i fabriken. I metoden använder vi tre olika implementeringar av den Ungerska algoritmen. I resultatet presenteras resultaten från de olika tillvägagångsätten tillsammans med flera palloptimeringsförslag. I slutet sammanställs flera förbättringsförslag och idéer om vidareutveckling i rapporten.