Problemet som avhandlats i detta projekt är plockkvalitén på DHL Supply Chain i Örebro som opererar som ett distributionscenter för Ford och Volvo. Fokus i detta projekt har varit på plockprocessen på Fords avdelning. Med stigande reklamationskostnader utan vidare ökning av omsättning av varor, har detta problem varit i fokus i syfte att identifiera orsakerna samt presentera lönsamma och implementeringsbara förbättringsförslag. Problemet att avhandla har därmed varit: ”På vilket sätt kan orderprocessen effektiviseras för att minska reklamationskostnader?” De tre reklamationstyperna som varit aktuella på grund av att de är mer vanligt förekommande är: • Fel kvantitet levererat • Fel artikel levererad • Skadad artikel levererad Dessa reklamationstyper har behandlats med hjälp av olika verktyg, samt Lean-principer. Jidoka låg till grund för att bygga in kvalitet i processen genom Poka Yoke för att försvåra potentialen att plocka fel i lagret. Även Paretodiagram har varit av stor vikt för att identifiera reklamationstypen som hade störst inverkan på processen. Resultatet av detta arbete är bl.a. förbättringsförslag i form av implementering av streckkodsläsare eller pick to voice. Även förbättringsförslag i form av en modifiering av en algoritm för att försöka minska felen i val av emballage för plocket har presenterats. / The problem that was studied in this project is the pick quality in DHL Supply Chain in Örebro, which operates as a distribution center for Ford and Volvo. This project focuses entirely on the processes of the Ford department. With rising claim costs without further increase in turnover of goods, this problem has been the focus in order to identify the causes to this condition and present profitable and implementable improvement proposals to enhance the current state. The main research question that is analyzed in this thesis has thus been “How can the efficiency of the ordering process be enhanced to reduce the claim costs?”. The three types of claims that have been relevant because of their higher occurrence rate are: • Wrong quantity delivered • Wrong item delivered • Damaged item delivered These types of claims have been processed using various tools and Lean principles. Jidoka was the basis for building quality in the process through Poka Yoke, to raise the difficulty of potential picking errors in the warehouse. Even Paretodiagram have been of great importance to identify the type of claim that have the greatest impact on the process. The result of this project is, among other things, improvement suggestions that include the implementation of handscanners and pick to voice. Even improvements in the form of a modification of an algorithm to try to reduce the errors in the selection of packaging for the picking process have been presented.

Trygg-Hansa is a Swedish insurance company that specializes in business insurance, home insurance, vehicle insurance, and personal insurance. This work focuses on Trygg-Hansa’s House and Content portfolio, which insures customers’ homes, both the building itself and its contents. In the event of damage or accidents, the company compensates customers financially, but due to rising inflation, these expenses have become increasingly expensive.  Today, Trygg-Hansa has a model for predicting the future cost of compensate damages within the House and Content portfolio, but sees a great need to develop it further. The goal of this work is to find a better model for predicting future costs and to create an inflation index. This index can serve as a basis for the pricing department, as it can be used to adjust customers’ premiums to maintain a profitable business.  The data was collected from the company’s systems, and nine data sets were created, one for each type of damage. The models used to predict the future claim costs were Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Exponential Smoothing (ES). Each claim type was predicted two years ahead, and thereafter the Laspeyres Price Index was calculated. This was done for all three models, and then the results of the models were compared.  The models were trained for the years 2013-2021, while the years 2021-2023 were used to evaluate the models. All types of damage had rising costs between 2013- 2021, but at the beginning of 2021 and forwards, the trends changed to decreasing trends for almost all types of damage. This affected the results of the models, as they were only trained on rising trends, and therefore, the forecast evaluation (Root Mean Squared Error and Mean Average Percent Error) was not useful. The ARIMA and SARIMA models showed almost no trends in the predicted data. This may be due to too complex data with too much volatility and unclear trends for the implemented module.  The Exponential Smoothing model follows the historical data both trend-wise and with a likely seasonal pattern for all nine types of damage and for the historical LPI. The forecast made by the ES and SARIMA models also show similar seasonal patterns. Furthermore, the ES model has the best model fit according to the Box-Jenkins Diagnostic. The model may need to be corrected in a year when the declining trend has been included in the training data by setting more weight to the new data for the year 2021.