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.

Box compression strength (BCS) is a critical parameter for assessing the performance of shipping containers during transportation. Traditionally, BCS evaluation relies heavily on physical testing, which is both time-consuming and costly. These limitations have prompted industry to seek more efficient and cost-effective alternatives. This study explores the application of artificial neural networks (ANNs) to estimate BCS at an industry-applicable level. A real-world dataset—covering approximately 90% of the box dimensions commonly used in the industry—was utilized to train a generalized ANN model for BCS prediction. The model achieved a prediction error of approximately 10%. When validated against experimentally measured data from laboratory testing, with single-wall B-flute as a representative, the prediction error was at a much lower level, further demonstrating the model’s reliability. This study offers a novel approach to BCS prediction, providing a cost-effective and scalable alternative to traditional physical testing methods in the packaging industry.