Compatibility modeling to support opportunity analysis in new product development

The general context of this Ph.D. dissertation is the new product development (NPD) which is the process for transforming an opportunity that is a business or technology gap in the current situation into a product available for sale. This Ph.D. dissertation focuses on the opportunity analysis in NPD. Therefore, a framework to support opportunity analyses is developed. The framework is structured in two parts: the information search and the modeling of compatibility. Opportunity analyses require a systematic approach to obtain all the information related to the identified opportunity, as well as an approach to determine whether or not this opportunity is compatible with respect to the different attributes/criteria representing the product development context. The state of the art review reveals that such approaches are not available in the published literature. Therefore, the objective of this research is to develop a comprehensive framework comprising an information search approach and a compatibility modeling approach to respond to these needs. The early phases of NPD are characterized by uncertainty that corresponds to the difference between the amount of information required to perform a task and the available amount of information. Uncertainty may have serious consequences on NPD, resulting in the worst case in a project failure. Therefore, information is crucial for the product development process. The information search approach developed in this Ph.D. dissertation allows the reduction of this uncertainty. This approach comprises four levels. The first level comprises the activities involved in NPD, such as market analysis and manufacturing. The second level proposes a classification of the different types of information required during the product development process: product-, process-, resource- and context-related. The third level represents the different sources where each type of information can be found. Two main classes are identified: the first one comprises the company departments such as marketing, and manufacturing, while the second class contains the sources from the external environment of the company, such as suppliers, customers, competitors, research communities, and governmental and non governmental organizations. The fourth level contains different methods of collecting information. Two main classes are distinguished: meetings- and documents-related. The links between the different levels are established on the basis of a thorough review of the related literature. Once the NPD activities associated with the identified opportunity are determined, the related information types, the sources where they can be found and the methods for their collections can be easily deduced from the information search approach. The compatibility modeling approach aims at evaluating the compatibility relationships for pairs of alternatives. Two types of compatibility can be distinguished: hard compatibility and soft compatibility. Hard compatibility corresponds to the notion of matching (i.e. the possibility that two alternatives coexist in a specific product development context) and is evaluated with respect to hard attributes/criteria related to the basic laws of nature or to the technical possibilities. Soft compatibility corresponds to the notion of non-degradation (i.e. the shift from a situation where the two alternatives are considered individually to a situation where they are considered together does not induce a degradation in a specific product development context) and is evaluated with respect to soft attributes/criteria such as development cost, and lead time. The core concept of the compatibility modeling approach is the compatibility structure. The crisp compatibility structure is a quadruplet of crisp relations M, C, I, and J, called mutual compatibility, strict incompatibility, mutual incompatibility and mutual incomparability respectively that satisfy a certain number of conditions. The relations M, C, C-1 and I correspond to four different situations: no degradation – no degradation, no degradation – degradation, degradation – no degradation, and degradation – degradation respectively. J corresponds to the situation where the alternatives cannot be compared using one of the relations M, C, C-1 or I. For each attribute/criterion, partial compatibility structures without incomparability are constructed from the impact that an alternative exerts on another and vice-versa. Then, relations characterizing the partial structures are aggregated into a global relation that takes into account all the attributes/criteria. By their nature, crisp compatibility structures cannot express degrees of mutual compatibility, strict incompatibility, mutual incompatibility or mutual incomparability, and are consequently too constraining in practice. Therefore, the crisp compatibility structures are meticulously extended to the case of fuzzy binary relations. A case study from the Swiss company Laurastar illustrates the framework that supports the opportunity analysis. This case study investigates the possibility to collaborate with new suppliers located in Bucharest and Hong-Kong. The compatibility between alternatives related to the supply chain activity is evaluated with respect to the various attributes/criteria.