Data Analysis of R&D Partnerships to Design a Decision-Making Support System for Use in Alliance Formation

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The main objective of this research is to contribute to applied statistics and optimization by developing mathematical and statistical methodology for the creation of a decision support system that could be used by Canadian semiconductor companies in research and development (R&D) alliance decisions. For this objective, using regression analysis, Phase 1 of the proposed research project will reveal determinants of innovation success and leverage these determinants in a supervised learning environment to create a mathematical model for evaluating the uncertainty of innovation success in the Canadian semiconductor industry. In the model's development, the Bayesian information criterion and the Akaike information criterion will be employed for variable selection, and the potential benefits of using specialized forms of regression, such as LASSO or ridge, will be examined. Using the results of Phase 1, the aim of Phase 2 will be to develop an optimization programming model, of which the decision variables are the statistically significant determinants found in Phase 1; and of which the objective function returns the probability of innovation success for R&D partnerships within the semiconductor industry. The proposed research project has the following three major contributions to the intended research fields as well as Canada: (1) Unlike many previous R&D partner selection methods, the methodology that I intend to develop is a more fully data-driven approach with attention to understanding the predictive accuracy; (2) The proposed study explicitly takes into account the recent challenges and changing trends faced by the Canadian semiconductor industry; and (3) The proposed study also seeks to create an optimization model for R&D partner selection along with an efficient algorithm to solve the corresponding optimization problem. Data and information from the Government of Canada indicates that the Canadian semiconductor industry has recently faced multiple challenges, including rapid innovation and foreign competition. The information also indicates that the industry has experienced substantial changes to its operations as more companies are expanding in "chipless" manners, in which the firms are focused on creating only the intellectual property for semiconductors (and not in manufacturing the semiconductors themselves). The recent trend of the Internet of Things (IoT) spurs the expectation for further innovation in the industry. Aiding firms in this transition to a heavier focus on R&D, the anticipated outcome of the proposed work will be the development of a decision support system to assist the industry within Canada in its R&D partner selection choices. By guiding Canadian semiconductor companies in making a smooth transition to a more research-oriented environment, the proposed work can help the industry respond effectively to its recent challenges.