Measuring uncertainty to identify missing customer information relevant to the design process

The advances in customer information gathering techniques are constantly increasing. However, the tools used today to translate such information into product specifications provide lack of emphasis on communicating insights to engineering teams. In addition, little investigation on how the gathered customer information is helpful to product designers is rarely explored in the literature. At the end, this situation results in a still uncertain target setting process that increases the risk to set wrong product specifications due to the lack of customer information insightful to the designers. In order to quantify such, todays' risk assessment methodologies cannot be used. The reason is that they use a set of undesirable events as a starting point without ensuring that all possible undesirable events are considered. Thus, uncertainty cannot be estimated without knowing what customer information is relevant to designers. By means of the p-diagram and Analytical Hierarchy Process this paper proposes a novel way to identify what customer information is relevant to the design process and calculates uncertainty as the risk of designers' decisions to deviate from the customer picture due to the lack of relevant customer information. To do so, existing customer information from the company database is taken as basis. To show the validity of the proposed methodology, a case study regarding the balancing of electric consumption of an electric vehicle is proposed. Results show that the risk indicator helps the team members to identify what customer information is uncertain and therefore relevant to the design process as well as to establish a more customer-focused and context-specific information gathering strategy.