A QUANTITATIVE FRAMEWORK FOR SELECTING RISK RESPONSE STRATEGIES IN STARTUPS (CASE STUDY: A NANOTECHNOLOGY)

The failure probability and establishment risk of startups is high due to the required high technical knowledge and added value, new product developments, lack of financial sources and need for specialists. By the use of risk management approaches, one can systematically evaluate the risks related to the decisions and improve the success probability by studying the corresponding opportunities and treats. In this paper, we consider the development phase of Nanotechnology startups as a project and propose a quantitative framework for analyzing the risk response strategies based on the PMBOK standard and a combination of FMEA, TOPSIS, and optimization model. Using FMEA, we can study the effect of risks and risk responses on the startup goals. TOPSIS helps to analyze the impact of risk responses on all the risk factors. The bi-objective optimization model is formulated subject to the prevailing constraints such as budget, time, production capacity and incompatibility of strategies. The proposed optimization model is for determining the Pareto frontier of risk and profits that as a decision tool may help managers to select the appropriate risk response strategies. The validation and sensitivity of proposed framework is analyzed applying it for a Nanotechnology startup in Yazd science & technology Park. Although the complete enumeration can be used to solve the small and medium instances, we propose the Epsilon constraint method successfully in order to solve the large-sized problems. The validation of framework confirm that it can apply to identify, evaluate and select of optimal risk response strategies. Also, the sensitivity analysis results show that the estimation quality of the model parameters particularly costs has a great impact of the outcomes. Therefore, developing the quantitative methods such artificial neural network to estimate the key parameters such as costs, revenues and risk levels is as a direction for the future study.