Designing Electronic Card Packages Against Shipping Shock

In optimizing packaging design, the product’s fragility is qualified by a protype undergoing quantitative and qualitative tests that rely heavily on past knowledge and experiments. By the addition of finite element analysis (FEA), the product’s fragility can be obtained in the initial stages of product design with material characterization and simulation. FEA can predict Gs on the product as well as examine the strains, which interpret product failure more easily in the design stage. To incorporate FEA, first the foam material was measured at various strain rates under compression. Next a shipping package containing an Al block with consistent density was dropped at different heights—610 mm (24”), 915 mm (36”), and 1067 mm (42”)—to confirm the methodology. An I/O book was packaged for the final demonstration incorporating FEA with an electronic card package. In an electronic card package, the electronic assemblies are sensitive to strains on the system board. If the strains on the board are high, the assemblies’ solder connections to the board could be damaged and result in a defect during shipment. The simulations’ predicted Gs and board strains were compared to experimental drop testing results at 610 mm (24”) and 915 mm (36”). The simulation results for each sensor location were within reasonable approximation of the experimental results, verifying that FEA could be used in the initial design stages to predict the accelerations and strains for packaging development in parallel to the product design.

Data-Driven Approaches to User Interface Design: A Case Study

The most common reason for software product failure is misunderstanding user needs. Analysing and validating user needs before developing a product can allow to prevent such failures. This paper investigates several data-driven techniques for user research and product design through prototyping, customer validation, and usability testing. The authors implemented a case study software product using the proposed techniques, and analyses how the application of UX/UI research techniques affected the development process. The case study results indicate that preliminary UX/UI research before the development reduces the cost of product changes. Moreover, the paper proposes a set of metrics for testing the effectiveness of UX/UI design.

Sorting It Out: Identifying and Addressing Conflicts and Business Ethics in Global Value Networks

Global value networks are often large, complex, and opaque. Understanding the relationships among stakeholders involved in these networks or organizations can be challenging. This card sort task provides an interactive way to engage participants in questioning the roles of stakeholders who are involved in a business ethics dilemma or an organizational product failure. This card sort task and discussion activity encourages participants to recognize that stakeholders may hold different knowledge, responsibility, or power; identify competing, conflicting, or complementary interests across stakeholders; articulate logical arguments; and engage in debate, compromise, and critical evaluation. This technique has been used successfully with undergraduate and postgraduate business, management, and social science students and is suitable for in-person and remote classes.

FORECASTING REPAIR AND MAINTENANCE SERVICES OF MEDICAL DEVICES USING SUPPORT VECTOR MACHINE

Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large dataset of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely random failure and physical damage. Frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the number of failures, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations.

Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine

Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large dataset of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely random failure and physical damage. A frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the number of failures, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations.

Consumers' preferences for intrinsic and extrinsic product attributes of plant-based eggs: an exploratory study in the United Kingdom and Italy

PurposePlant-based eggs have recently been developed to provide consumers with a healthier, animal-friendlier and more sustainable alternative to conventional eggs. The purpose of this paper is to investigate intrinsic and extrinsic attribute preferences for three prototypes of plant-based egg, namely the liquid, powder and egg-shaped.Design/methodology/approachNine focus groups in the United Kingdom and nine in Italy were conducted, with a total of 180 participants. A thematic analysis of results was conducted.FindingsIn terms of intrinsic product attributes, consumers' preferences for colour, shape, taste, ingredients, nutrients, method of production and shelf-life for plant-based eggs were revealed. Regarding the extrinsic attributes, preferences for price, packaging, country of origin and product naming emerged. Similarities and differences between consumers from the two countries are also discussed. Differences in preferences also emerged between vegan and non-vegan consumers.Research limitations/implicationsThis study adds to the existing knowledge on consumers' preferences for new plant-based food alternatives and identifies future quantitative approaches based on qualitative findings.Practical implicationsResults from this study can assist plant-based egg manufacturers in improving their products in line with consumers' expectations, which may help reducing risk of product failure.Originality/valueThis study is the first to investigate consumers' preferences, expectations and needs for new food products like plant-based eggs and provides information that can be practically applied by manufacturers, as well as suggestions for future research.