Background Forecasts are used as a basis for decision making and they mainly affect decisions at strategic and tactical levels in a company or organization. There are two different methods to perform forecasts. The first one is a qualitative method where a n expert or group of experts tell about the future. The second one is a quantitative method where forecast are produced by mathematical and statistical models. This study used a quantitative method to build a forecast model and took into account external f actors in forecasting the sales volume of Bosch Rexroth’s hydraulic motors. There is a very wide range of external factors and only a limited selection had been analyzed in this study. The selection of the variables was based on the markets where Bosch Rexroth products are used, such as mining. Purpose This study aimed to develop five predictive models: one model for the global sales volume, one model each for sales volume in USA and China and one model each for sales volume of CA engine and Viking engine. By identifying external factors that showed significant relationship in various time lags with Bosch Rexroth’s sales volume, the forecasts 2016 and 2017 were produced. Methods The study used a combination of multiple linear regression and a Box - Jenkins AR MA errors to analyze the association of external factors and to produce forecasts. Externa l factors such as commodity prices, inflation and exchange rates between different currencies were taken into account. By using a cross - correlation function between external factors and the sales volume, significant external factors in different time lags were identified and then put into the model. The forecasting method used is a Causal forecasting model. Conclusions The global sales volume of Bosch Rexroth turned out to be affected by the historical price of copper in three different time lags , one, six and seven months . From 2010 to 2015, the copper price have been continuously dropping which explain s the downward trend of the sales volume. The sales volume in The U SA showed a significant association by the price of coal with three and four time lags. This means that the change of coal price takes three and four months before it affects the sales volume in the USA. The market in China showed to be affected by the development of the price of silver. The volume of sales is affected by the price of silver by four and six time lags. CA engine also displayed association with the price of copper at the same time lags as in the global sales volume. On the other hand, Viking engine showed no significant association at all with any of the external factors that were analyzed in this study. The forecast for global mean sales volume will be between 253 to 309 units a month for May 2016 – December 2017. Mean sales volume in USA projected to be in between 24 to 32 units per month. China's mean sales volume is expected to be in between 42 to 81 units a month. Mean sales volume of CA engine has a forecast of 175 to 212 units a month. While the mean s ales of Viking engine projected to stay in a constant volume of 25 units per month.

Demand forecasting is a well-established internal process at Ericsson, where employees from various departments within the company collaborate in order to predict future sales volumes of specific products over horizons ranging from months to a few years. This study aims to evaluate current predictions regarding radio unit products of Ericsson, draw insights from historical volume data, and finally develop a novel, statistical prediction approach. Specifically, a two-part statistical model with a decision tree followed by a neural network is trained on previous sales data of radio units, and then evaluated (also on historical data) regarding predictive accuracy. To test the hypothesis that mid-range volume predictions of a 1-3 year horizon made by data-driven statistical models can be more accurate, the two-part model makes predictions per individual radio unit product based on several predictive attributes, mainly historical volume data and information relating to geography, country and customer trends. The majority of wMAPEs per product from the predictive model were shown to be less than 5% for the three different prediction horizons, which can be compared to global wMAPEs from Ericsson's existing long range forecast process of 9% for 1 year, 13% for 2 years and 22% for 3 years. These results suggest the strength of the data-driven predictive model. However, care must be taken when comparing the two error measures and one must take into account the large variances of wMAPEs from the predictive model. / Ericsson har en väletablerad intern process för prognostisering av försäljningsvolymer, där produktnära samt kundnära roller samarbetar med inköpsorganisationen för att säkra noggranna uppskattningar angående framtidens efterfrågan. Syftet med denna studie är att evaluera tidigare prognoser, och sedan utveckla en ny prediktiv, statistisk modell som prognostiserar baserad på historisk data. Studien fokuserar på produktkategorin radio, och utvecklar en två-stegsmodell bestående av en trädmodell och ett neuralt nätverk. För att testa hypotesen att en 1-3 års prognos för en produkt kan göras mer noggran med en datadriven modell, tränas modellen på attribut kopplat till produkten, till exempel historiska volymer för produkten, och volymtrender inom produktens marknadsområden och kundgrupper. Detta resulterade i flera prognoser på olika tidshorisonter, nämligen 1-12 månader, 13-24 månader samt 25-36 månder. Majoriteten av wMAPE-felen för dess prognoser visades ligga under 5%, vilket kan jämföras med wMAPE på 9% för Ericssons befintliga 1-årsprognoser, 13% för 2-årsprognerna samt 22% för 3-årsprognoserna. Detta pekar på att datadrivna, statistiska metoder kan användas för att producera gedigna prognoser för framtida försäljningsvolymer, men hänsyn bör tas till jämförelsen mellan de kvalitativa uppskattningarna och de statistiska prognoserna, samt de höga varianserna i felen.

This study investigates the underlying factors driving OKQ8’s electric vehicle charging sales in kWh and aims to understand their customers better by identifying the factors that impact sales in the electric vehicle charging market. The study focuses on transactions from 17 OKQ8 stations in Sweden and Denmark, from July 1st, 2022, to February 28th, 2023, categorized into five distinct subgroups based on similar attributes. The report uses a multiple linear regression model in the programming language R and macroeconomic principles to identify the variables that lead to additional sales of electric vehicle charging.  The final regression model includes the variables Sales, Station, Month, Weekday, and Time, with an Adjusted R2 of over 68% for all five groups. The study reveals that price does not have a significant impact on the sales of charging. Moreover, the analysis highlights the significance of variables related to time, month, day of the week, and individual charging stations, which demonstrate varying impacts on charging sales across different groups.  The study provides valuable insights into the influence of price and consumer behavior on electric vehicle charging sales, emphasizing the importance of adopting strategic approaches to encourage a wider adoption of electric vehicles, enhance charging infrastructure, and consider government support such as subsidies or tax incentives. The study has some limitations, including a lack of data for March to May and only covering a single year, limiting the ability to identify recurring patterns. Nonetheless, the study’s findings provide insights into the factors affecting OKQ8’s sales of electrical vehicle charging and ongoing research is necessary to validate and expand upon these findings, considering the constantly evolving electric vehicle market. / Denna studie syftar till att undersöka de underliggande faktorer som påverkar försäljningen av laddning av elektriska fordon i kWh hos OKQ8 och strävar vidare efter att få en djupare förståelse för deras kundgrupp. Studien bygger på data som sträcker sig från den 1a juli 2022 till den 28e februari 2023 och är i form av transaktioner från 17 olika OKQ8-stationer i Sverige och Danmark. Dessa 17 stationer delas sedan sedan in i fem olika undergrupper där stationerna i varje undergrupp erhåller liknande egenskaper. Rapporten använder sig av en multipel linjär regressionsmodell i programmeringsspråket R, och makroekonomiska principer för att identifiera variabler som leder till ökad försäljning av laddning av elektriska fordon. Den slutliga regressionsmodellen inkluderar variablerna Försäljning, Station, Månad, Veckodag och Tid, där Adjusted R2 har ett värde över 68% för samtliga fem grupper. Studien avslöjar att priset inte har en signifikant inverkan på försäljningen av laddning och belyser vidare betydelsen av variabler relaterade till tid, månad, veckodag och individuella laddningsstationer, vilka visar en varierande signifikans på försäljningen av laddning inom de olika grupperna. Studien ger värdefulla insikter om konsumentbeteende kopplat till försäljning av laddning för elektriska fordon. Den betonar också vikten av att vidta strategiska åtgärder för att främja en mer omfattande spridning av elektriska fordon, förbättra laddningsinfrastrukturen och överväga regeringsstöd såsom subventioner eller skattelättnader. Studien har vissa begränsningar att ta hänsyn till, däribland avsaknad av data för perioden mars till maj och att den endast täcker ett år. Dessa begränsningar påverkar möjligheten att identifiera eventuella återkommande mönster. Trots detta bidrar studiens resultat med värdefulla insikter om de faktorer som påverkar försäljningen av laddning för elektriska fordon som OKQ8 kan dra nytta av i sin verksamhet. På grund av marknadens ständiga utveckling är det dock nödvändigt med fortsatt analys för att validera och vidareutveckla resultaten.