Artificial neural networks as a cost engineering methods in collaborative manufacturing environment

Twenty-first century manufacturing is increasingly dependent on dynamic networks of companies working in just-in-time synchrony. The calculation of cost within these company networks is vastly more complex than in traditional manufacturing. To support the complexity of the modern manufacturing environment it is vital that cost modeling under a collaborating network of companies is greatly developed. A cost model development process is described and a novel cost modeling technology Artificial Neural Networks is developed. The artificial neural networks have the ability to learn and respond in producing cost estimates for manufacturing processes and also seek to find new patterns within existing cost data for forecasting and ranking make intelligent computing a very viable option in moving the modeling process forward. A series of experiments were undertaken to select an appropriate network structure for estimate the cost within the production network and the model is validated through a case study. Trial and error cost estimating would possibly be made easier within a linguistic and intuitive framework.