Prisförändringar vid förändrad försörjningskedja för livsmedel

Sammanfattning: Global food prices are currently rising at a rapid pace. The current supply chain involves a number of different steps, where each step involves a price surcharge that is ultimately paid by the consumer. Modern technology, such as machine learning and smart logistics, enables alternative supply chains. This report examines the possibility of designing a model that, with the help of scenarios of change based on previous studies and the taskmaster’s vision, can make predictions for future food prices. The report was based on the supply chain and current prices for potatoes. The models used are ARIMA, SVR with different cores, linear regression, Ridge regression and Lasso regression. The models are evaluated with the error measurements Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and R2. The best-performing models, with which the prediction was then performed, were ARIMA and SVR with a linear core. The predictions and calculations showed drastically reduced food prices and a large reduction in unnecessary food waste, especially in the scenario that involves an overall change of the supply chain. This has major macroeconomic effects, as food prices affect inflation. The analysis also shows the importance of the industry’s players working with analysis and strategy to handle a future shift that entails higher uncertainty in the market. There are uncertainties about the effect on other supply chains, as well as the net effect of a shift as the costs for this are unknown.