Deep Learning-Based Risk Management in Product Development: Strategies for Uncertainty Mitigation

This paper aims to analyze the application of deep learning for risk management in new product development, particularly, to highlight the possible ways to manage risks associated with uncertainties. The research also works through CNNs, RNNs, and autoencoders and thereby identifies and forecasts risks that are at different stages of development. The model obtained by using an autoencoder achieved the best results in terms of accuracy, these being 89. 3%, precision 88. 7%, recall, 87. 6% and F1 Score of 88. 1%. Applying the framework based on the proposed deep learning experts has cut the cost aspect by a 20% while minimizing the human resource needed by 16%. Time savings include a reduction in overall project time by 20%, schedule delay by 7%, and calendar delay by 7%. These findings accentuate the importance of deep learning in the improvement of risk control and allocation of resources towards new products, respectively. Keywords: Data, Artificial-, Machine-, Advanced-, Deep-, Artificial Neural Networks, Credit-, Operational-, Business-, Financial-, Decision- Intelligence, Risk- Control, Mitigation- Management, New Product Development for Risk Reduction, Forecasting- Analytics.

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