The interest of forecasting future demand has significantly increased throughout the years. This trend can in part be explained by the turbulent environment and the strongly increased uncertainty most business areas are experiencing. The industrial world was strongly affected by frequent technology changes and a significant increase in competition during the late 1980’s and early 1990’s. The improved transportation methods in conjunction with the accelerating implementations of sophisticated information technologies have taken the globalization to an almost intimidating dimension during the last years of the century and the first years of the 2100 century. This research project has been conducted at a company known as Driltech. Driltech was founded 1973 in Gainesville, Florida which also is the current location of their head office. Driltech is a part of Sandvik Mining and Construction’s product area, Driltech Mission LLC. The company has developed into one of the leading manufacturer of surface blast-hole drill rigs for the coal, gold, copper and diamond mining industries as well as drill rigs for residential water wells. Driltech has experienced a consistent decline in their sales since 1997 which can partly be explained by general economic factors in the mining industry but also by possible market share losses due to longer delivery times than leading competitors. The company is currently converting from being an order- based manufacturer to become a forecast-based assembly plant in order to overcome the problem with long delivery times. The objective with this research project was to develop a flexible forecasting system to predict future demand, with the highest possible degree of accuracy, in order to achieve decreased delivery times without significantly increasing the inventory levels on the assigning company’s behalf. In addition, the system had to be able to utilize the already existing forecasting program known as Sales Tools. Sales Tools is a database that uses a judgmental forecasting method more commonly known as judgmental assessments of subjective probabilities. The researcher of this project developed a forecasting system that incorporates several complementary indicators, in order to increase the forecasting accuracy and facilitate identification of possible changes in the established patterns or relationships. The selected indicators are originating from different forecasting approaches and are therefore considered to efficiently complement each others weak points. The aforementioned forecasting system incorporates two judgmental forecasting methods and three quantitative forecasting methods as well as two indicators that are also known as practical implications. The forecasting system’s full capacity has not been possible to measure within the time frame of this project. However the measured performance of the individual indicators that have been available for measurement exhibited excellent results. An additional increase of the accuracy is expected once the individual parts are accommodated into a complete system.