Application of decision tree analysis and expected monetary value technique in quantitative risk management : Evaluation of less risky investment strategy. - Metsänen, Tuomas (My University, 2022)
The objective of this thesis is to produce information for decision making for a case company that is in the industry of truck spare parts and accessories. The company intends to expand its operations in the coming years. For the past couple of years, the company has been pilot-ing a business where used truck brake calipers are collected from the customer, recondi-tioned, and sold to the market as good as new with full warranties. In this thesis investment strategies have been named Option 1 and Option 2. The first option has higher capital in-vestment costs due to larger production facilities, more workforce and equipment as well as larger brake caliper stock. The circular economy is strongly present in this thesis because the brake caliper business is truly the heart of the circular economy. The growing world popula-tion, the strong growth of the middle class, and the increased consumption of natural re-sources are making climate change reality. This is driving companies towards net-zero emis-sions and sustainable development. Two different investment strategies were examined through decision tree analysis (DTA) and the expected monetary value (EMV) technique. These investment strategies were carefully planned and quantified. The investment strategy with higher or lowest negative EMV value was selected and evaluated by using financial anal-ysis calculations. The financial analysis calculations were used to calculate the profitability of the selected investment and thus needed information was generated for the case company´s decision making. Risk management is an important area when considering new investments and this needs to be adapted in day-to-day operations.
Keywords: risk management; circular economy; fi=Tuotantotalous (tekniikka)|en=Industrial Management and Engineering|; Master's Programme in Industrial Systems Analytics