This paper introduces the Web-Based Intelligent Packaging Evaluation (WIPE) platform, a novel approach to assess the performance of product and packaging systems within the e-commerce distribution sector. Unlike traditional methods that primarily rely on laboratory evaluations under controlled conditions, WIPE addresses the unique challenges posed by e-commerce distribution, such as increased handling points and unforeseen hazards that standard physical tests may not capture. Leveraging advanced machine learning algorithms and association rule mining, WIPE extracts insights about packaging defects directly from customer reviews on e-commerce platforms. Analyzing both images and text from these reviews establishes connections between frequently used words and the predicted damages, causes, and effects. This innovative approach was exemplified in two case studies involving laundry detergent liquid bottles and pods sold on the Amazon. The findings from these studies demonstrate WIPE’s capability to extract pertinent information from customer feedback and identify specific packaging defects and predict their potential causes. This integration of sentiment analysis and association rule mining into the packaging evaluation process marks a significant advancement in the field. The introduction of WIPE represents a transformative step in packaging evaluation, offering a more dynamic, real-world analysis that can significantly enhance product and packaging design, ultimately leading to improved customer satisfaction in the rapidly evolving e-commerce landscape.

Food waste is a barrier to the development of sustainable food systems, and a large portion of it occurs at the household level. Household food waste can be decreased by using appropriate packaging. Despite the high rate of food waste in US households, little is known about how packaging affects this. This study assessed US consumers’ awareness of how structural packaging designs and technologies affect food freshness and their willingness to purchase and to pay extra for packaging designed to reduce household food waste. To gather data, 1000 US consumers were surveyed online. Responses were analyzed overall and by population segments. The impacts of only 3 out of 15 structural packaging designs on maintaining food freshness were known by >50% of consumers. Regarding packaging technologies, while 78% of consumers knew about the impact of vacuum packaging on maintaining food freshness, just 27.6, 23, and 16% knew how modified atmosphere packaging, active packaging, and aseptic packaging affected food freshness. Only 32% of consumers knew that intelligent packaging provides information on food freshness. Just 9% of consumers recognized that foods in plastic pouches and cans possess the same food freshness. Approximately 91% of consumers will always/sometimes buy food in most of the above packaging technologies after learning about them. Half were willing to pay more for food in packages that reduce household food waste, and 40% may. Differences (p ≤ 0.05) and two-way interactions were observed between population segments. This study’s findings can help develop new packaging, education campaigns, and policies to reduce household food waste in the US.

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.

Packaging has an important role in protecting products during the distribution process. Therefore, it is essential for the packaging designer to evaluate the packaging even after the design implementation. While every new packaging design needs to go through a series of physical tests to ensure it delivers the product to the customer safely, it is not uncommon to encounter packaging failures after the design implementation. Redesigning a completely new package and redoing these tests is very expensive and a time‐consuming process. Therefore, an alternative evaluation method is needed to enhance efficiency while maintaining evaluation quality. The main purpose of this paper is the evaluation of package performance during distribution using customer reviews. Sentiment analysis (SA) is implemented to identify the positive or negative sentiment from customer reviews. Moreover, the proposed method utilizes an in‐house library containing packaging related words (Pack‐List) to identify the reviews concerning the packaging. As a result, this method provides a systematic approach to detect problems by analysing customer reviews as feedback to a defective packaging product. By using the results of SA with Pack‐List, a percentage of negative and positive reviews are calculated to examine the packaging performance during distribution. Then, the percentage of failure over various months and years is examined to identify how time affects the packaging failure. Next, a word cloud of negative sentences is created to show the most mentioned issue. With the proposed method, packaging designers can identify packaging problems in the early stages and keep track of the packaging performance.

Class learning outcomes (CLOs) supporting the interdisciplinary curriculum of the School of Packaging (SoP) at Michigan State University (MSU) were mapped to competency‐based, programmatic learning outcomes (CPLOs) undergirded by broad learning goals of the University (MSU‐LGs). Six CPLOs related to the core curriculum, including the ability to design and evaluate packaging systems, as well as students' professionalism and teamwork skills, were developed using consensus building with all teaching faculty. Relationships, from specific to broad (i.e., CLOs to CPLOs to MSU‐LGs), were mapped with the SoP faculty. This mapping scheme (class‐specific CLOs supporting broader program CPLOs and, ultimately, MSU‐LGs) was developed to guarantee alignment of expectations for learning from the course to the packaging program to the MSU‐LGs. From Fall 2018 until Fall 2019, assessment tools, including rubrics and assignments intended to evaluate learning, were developed to assess core and elective courses offered by the SoP. Data collection of each student's performance was conducted utilizing Watermark's VIA software from Fall 2018 until Fall 2021 and continuous. Assessment of student performance provided evidence of learning across the SoP curriculum as well as how CLOs, delivered and assessed at the individual student level, translate into competence achieved at the programmatic and university levels.