In order to satisfy heterogeneous and unstable consumer demands, firms increasingly leverage product development efficiencies by adopting a platform approach, based on cross-sharing of resources, for developing and introducing product variants, constituting a product family. Although the benefits and costs of utilising platform-based product development to increase product variety have been addressed by previous research, there has been little empirical work focusing on the managerial factors that enable firms to successfully develop new products that extend the product family. The current study addressesth e gap in our understandingo f the relationships between a firm's product variety strategy, new product development (NPD) proficiencies and structural features, and product family performance. The current study's findings are based on data collected from a sample of one hundred South-Korean manufacturers in a wide range of assembling industries. When firms expand platform-based product variety, superior predevelopment planning proficiencies in platform projects are essential for securing all dimensions of product family performance (i. e., operational/technical performance, profitability, and market share/sales)P. roduct family successi s also conditional upon highly proficient execution of marketing activities (business and market opportunity analysis and planning, and commercialisation) in both platform and derivative projects. The findings of this research stress the primacy of predevelopment planning and marketing capabilities. In addition, the findings of this research stress specific structural mechanisms (e. g., spatial proximity, formalisation, and organisational modularity), as drivers of product family performance. This study contributes to the understanding of inter-relationship between platform-based product variety, NPD proficiencies and structural features, and product family performance. This study can act as a guide to further studies of platform-based product development, as well as being useful to practitioners who develop product families.

In this thesis, three methods are presented to model the decision process of whether or not to bid for a tender in offshore petroleum platform fabrication. A sample data and the assessment based on this data are gathered from an offshore petroleum platform fabrication company and this information is analyzed to understand the significant parameters in the industry. The alternative methods, &ldquo / Regression Analysis&rdquo / , &ldquo / Neural Network Method&rdquo / and &ldquo / Fuzzy Neural Network Method&rdquo / , are used for modeling of the bidding decision process. The regression analysis examines the data statistically where the neural network method and fuzzy neural network method are based on artificial intelligence. The models are developed using the bidding data compiled from the offshore petroleum platform fabrication projects. In order to compare the prediction performance of these methods &ldquo / Cross Validation Method&rdquo / is utilized. The models developed in this study are compared with the bidding decision method used by the company. The results of the analyses show that regression analysis and neural network method manage to have a prediction performance of 80% and fuzzy neural network has a prediction performance of 77,5% whereas the method used by the company has a prediction performance of 47,5%. The results reveal that the suggested models achieve significant improvement over the existing method for making the correct bidding decision.