High failure rates of new products have been an ongoing topic in the scientific and industry domains. One of the highest failure rates of new products has been recorded in the food industry. Launching new food products and maintaining their success requires a timely flow of information on product performance among the three main functions, i.e. marketing experts, food technologists and consumer researchers. Furthermore, performance needs to be weighed against the firm’s strategic objectives and the environment in which the firm operates. However, interconnectedness of the multitude of factors that influence new product performance leads to a high level of complexity and difficulty in successfully managing new products. To unfold this complexity, systems approach can be used to analyse the separate functions in a firm in terms of information flows, and to synthesize these findings to improve learning about the problem of high failure of new food products. Once the problem of new food product success has been systematically analysed, computer simulation and modelling, such as system dynamics, can be employed to define optimal strategies for improving the success of new products on the market.
The project aims at identifying if system dynamics approach is able to lead to new insights in improving new food product success throughout the product life cycle, by developing an integrated framework and by undertaking modelling and simulation to support new product development decision making.
Systems approach is used to simplify and manage complex problems. It emphasizes the interdependence of factors affecting a certain problem. Employing a systems approach includes the analysis of smaller components of a system, after which the study of interactions among all components is necessary.
System dynamics is one of the most formalised systems approaches, which includes computer modelling and simulation of the analysed system. System dynamics models are descriptive aggregate models. This approach enables the experimentation within a model in a safe virtual environment on a computer, instead of conducting experiments on the system in the real world. To design and simulate a model, the flow of particular interest (e.g. information, goods) has to be traced throughout the whole system. Once the system is modelled and quantitative historical data from the real world is inputted, the computer gives time charts of information of interest (e.g. volume sales of a product over time), which express the emergent behaviour of the modelled system. Interpretation of results is qualitative, due to a non-predictive nature of system dynamics models.
Group model building is a method of building system dynamics models with a group of people. It is especially beneficial when opposing viewpoints of the origin of a problem, and its solution, exist. Group model building includes development of a system dynamics model with the use of scripts, together with a group of managers. It allows structuring the problem, such as high failure of a food product, collection of data necessary for developing a quantitative system dynamics model, and testing various strategies to improve the problem under study.