Developing sales forecasting by utilizing business intelligence : A Single Case Study

Abstract

In a highly volatile business environment, companies must utilize technological tools to make decisions effectively and efficiently. Therefore, companies use business intelligence to aid in decision-making. Sales forecasting is a key component of decision-making in companies, as it lays the groundwork for a vast number of decisions. However, only some companies have harnessed the full potential of business intelligence and sales forecasting.

This study examines how business intelligence can be utilized in sales forecasting. More precisely, this study examines the significance of capabilities in improving sales forecasting accuracy. The role of capabilities is vital as capabilities are intangible assets, in contrast to technological tools that are easily imitable and mobile.

This study's literature review focuses on business intelligence and sales forecasting literature. Previous studies of business intelligence and sales forecasting capabilities are examined to identify the key capabilities. Previous studies that synthesize business intelligence and sales forecasting are lacking. Moreover, there is a gap in the previous literature as the key business intelligence capabilities for sales forecasting have not been identified.

This thesis adopts a qualitative approach to answering the research questions. The case study method is used to examine how business intelligence can be used to develop sales forecasting. More precisely, the study is a single case study focusing on a company operating in the rental industry. The data is gathered for the study through six semi-structured interviews. The interviewees are chosen based on their business intelligence and sales forecasting expertise.

The findings of this study provide insight into how business intelligence can be utilized to develop sales forecasting. The theoretical framework developed for this study presents the key business intelligence and sales forecasting capabilities that improve sales forecasting accuracy. More precisely, the key capabilities are analyzed to get more detailed information on how capabilities can be developed to match the needs of the sales forecasting process. This study concludes by addressing its limitations and suggesting future research to extend the research in business intelligence and sales forecasting.