Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN)

Packaging design plays a crucial role in ensuring the protective performance of packages. Various factors must be considered to ensure package strength during the packaging design process. Understanding the relative importance of each influencing factor or design feature provides valuable insights for optimizing packaging designs. However, traditional methods such as testing and numerical analysis have limitations in evaluating the relative significance of these parameters. To address this challenge, this work applied four distinct Artificial Neural Network (ANN)-based approaches, including connection weights, gradient-based, permutation, and Shapley additive explanations (SHAP) values, to explore the relative importance of different packaging design features on a given packaging property. In this study, box compression strength (BCS) was used as a representative packaging property, and the relative importance of up to six BCS features (edge crush test (ECT), perimeter, thickness, depth, and flexural stiffness in both the machine and cross-machine directions) were evaluated. The findings of this study demonstrate the effectiveness of artificial neural network (ANN)-based approaches in evaluating the relative importance of packaging design features. This approach can be readily applied to assess other packaging design features’ importance beyond the studied features with significant potential for cost savings and material reduction in packaging design.