Modeling and Simulating Vicious Circles Problems

Time and cost overruns in new product development processes are often attributed to low quality information communication in early stages of the process when key decisions are made by individuals who have limited understanding of how micro-level decision-making can significantly affect macro-level system performance, that eventually leads to the phenomenon of “vicious circles” in new product development processes. Agent-based simulation is a promising approach that can be applied to understand complex systems, such as new product development processes. This paper reports an application of agent-based simulation to time-related aspects of vicious circles in a new product development process case study from a large UK-based manufacturing company. A simulation model was developed following an experimental method established in the research. In the model, work teams and their activities and contributions to the process are represented by autonomous agents. Initial simulation results showed agent-based simulation is an effective and efficient approach for understanding and studying vicious circles in new product development processes

Agent-Based Simulation New Product Development Processes

New Product Development Processes (NPDP) involve a series of functional teams who work in a complex information communication network. Agent-Based Simulation (ABS) comprises a set of autonomous agents that act and interact complying with simulation specifications within a simulation world. This paper analyzes key features of both new product development processes and agent-based simulation. A simulation mapping is introduced to highlight mirror relationships between key elements of new product development processes and key concepts that underpin agent-based simulation. We introduce a new product development process case study arising from a large UK-based manufacturing company, and an agent-based simulation model to represent the new product development process. Simulation results show that agent-based simulation is a promising method to study and explore complex socio-technical systems such as new product development processes

Simulating vicious circles in new product introduction systems

New product introduction systems are complex socio-technical systems that are used to design, develop, and deliver products and services to users. Lack of design information within such systems results in uncertainties that have an adverse effect on the performance of the whole system by creating a need for rework. Typical performance measurements for new product introduction systems are time, cost, and quality. Rework has a significant influence on time-related aspects of system performance because it consumes additional time resource that could otherwise be dedicated to other activities such as the development of new products. Rework reduces time resource available for information communication which in turn leads to more rework in the future. This results in vicious circles where limited time leads to more rework which further detracts from time to devote to other tasks in the future. Vicious circles have previously been reported in societal systems. The goal of this research was to apply modelling and simulation techniques to understand time-related aspects of the vicious circles phenomenon in new product introduction systems and explore potential management interventions to mitigate the consequences of vicious circles. A case study from an international manufacturing organisation was used to inform the development of a simulation mapping between key elements of the new product introduction system and key concepts that underpin agent-based simulation methods. A simulation model was developed to represent vicious circles in the case study, based on the simulation mapping. The simulation model was verified and validated through a series of seven experiments. Four further simulation experiments were then carried out. The first two experiments explored the impact of different prioritisations of responding to information requests on time-related aspects of the system performance. Results highlighted the importance of prioritising responses to information requests which significantly reduced rework volumes in the model. The final two experiments explored the balancing of time taken for individual product development activities and resources used. In simulations with low response rates, one means to avoid system collapse was to extend the time allowed for product development. Given the need to deliver products to market as quickly as possible, a final experiment explored ways to speed up product development to eliminate adverse effects on product development cycle time. By reducing the time taken to respond to requests, which in a real world system could be achieved in a number of ways, e.g. improving team size or design capability, the product development cycle could be shortened