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The article provides a brief overview of group-wise inspection methods that enable several products to be simultaneously inspected in terms of one or more parameters by means of one tool. In order to reduce the batch inspection time compared to its piece-by-piece inspection, a search method for group-wise inspection was developed and studied. This method relies on an approach used to find hidden faults in radio equipment by dividing the circuit into parts and then inspecting them. The requirements to be met by inspected products are defined. The effect of defect rate, batch size, and distribution of defective items throughout the batch on its inspection time is analyzed. It was proved that with uniform distribution of defective products throughout the batch, the batch inspection time is the longest and that the time of batch inspection via this method decreases significantly with decreasing defect rate. Taking the size and defect rate of the batch into account, the author determines when applying this method provides an advantage over the methods of piece-by-piece inspection. A device used to implement the method is described. The performance of the search method for group-wise batch inspection is evaluated for application under specific conditions. The obtained results can be used by technologists at various production facilities in the development of batch inspection processes.

Introduction Sunflower seeds are prone to producing defective products during planting, production, and preservation due to issues such as foreign matter mixing, heat damage, mildew, and insect infestation. These defects ultimately affect the overall quality of the products. With increasing consumer demand for high-quality sunflower seeds, efficient sorting has become a crucial part of quality assurance. However, traditional hand-sorting methods (usually 0.005kg/s) are inefficient and insufficient to meet current demands. Objective This study aims to address the challenges of sorting cooked sunflower seeds, which differ in color from raw seeds due to skin abrasion and soup water impregnation during preparation. We developed a new algorithm in collaboration with Meiya Company to introduce color-sorting technology specifically designed for ripe seeds, thereby improving consumer satisfaction and product quality. Main Results Compared to manual selection, the new color-sorting technology achieved an average removal rate of defective products of approximately 99.4%, a significant improvement from the previous rate of around 66%. It can also achieve a 100% removal rate for certain foreign elements, such as insects, animal feces, and yellow seeds. The by-product rate of color selection is about 0.02%, comparable to the 0.01% for manual selection. Additionally, the implementation of this technology can save enterprises more than 5 million yuan per year. Conclusion The introduction of the ripe seed color-sorting technology significantly enhances the removal rate of defective products while maintaining a low by-product rate. This technology not only improves the overall quality of sunflower seed products but also provides substantial economic benefits for companies, demonstrating significant practical implications for the industry.

Reducing carbon emissions is of immense interest to most modern organizations striving for sustainability. Effective inventory management is crucial for achieving resource optimization and minimizing environmental impact. Very little work has been conducted up to this point on slowly declining, low-quality products with multi-level trade credit rules under the influence of carbon emissions. In this study, an inventory model is tailored specifically for imperfect and gradually deteriorating products with a multi-level trade credit policy. Further, the impact of carbon emissions on the retailer’s ordering strategies is also considered. To determine the optimal policy for supply chain partners, three trade credit instances with seven subcases are taken into consideration. To choose the best scenario out of ten cases, an algorithm is also developed. The model’s validity is illustrated through a numerical experiment and sensitivity analysis. This study is an innovative approach to balancing economic trade credit policy in sustainable supply chain management.

In recent years, reducing energy consumption has become a key issue in the industrial world. Therefore, industrial corporations must develop methods of pre-evaluation and production management for reducing their energy consumption while maintaining productivity. Moreover, production lines occasionally generate defective products, reducing the productivity and wasting energy, which affects the energy consumption per unit of production. These production lines require inspection machines to exclude defective products. The layout and configuration of inspection machines change when defective products are excluded, which affects the energy consumption per product. However, no methods have been developed for evaluating the energy consumption per product by considering the number of defective products and the layout and configuration of the inspection machines. In this study, we formulated the energy consumption rate of a production line that generates defective products as the production planning and management method. Specifically, we developed a formula for the energy consumption rate of a production line by considering the defect rate of its production machines and the layout and configuration of the inspection machines. A simulation involving a semiconductor manufacturing line was conducted to validate the proposed theory.

A product-harm crisis is a discrete event in which products are found to be defective and therefore dangerous to at least part of the product’s customer base. Product-harm crises are not only dangerous for consumers; they also represent a major threat to the reputation and equity of brands or companies, which often struggle with how to best respond. The marketing literature has witnessed a surge in interest on the consequences of product-harm crises for a variety of stakeholders, including consumers, the brand or company itself, its investors, as well as competitors. This article offers a systematic review of research on product-harm crises in the marketing literature. We discuss the antecedents and consequences of product-harm crises, their moderators and mediators, and the theories and methodologies used. We identify commonalities and differences between the studies, as well as gaps in the literature and avenues for future research. Finally, we synthesize the managerial implications across studies.

Xiuyan Li and K. Lu discuss a strategy, alternative to alloying, to tailor the mechanical properties of metals. By engineering defects, metals with bespoke performance might be obtained while reducing the materials' compositional complexity.

The constructs in solid boxes are observed in our data. The constructs in the boxes with dashed lines are modeled as latent. [Display omitted] •Overly positive review valence induces more returns, in addition to more purchases.•Products returns must be considered when examining total OCR effects.•Overly positive reviews may hinder a retailer's financial performance.•The effect of review valence on returns is weaker for experienced customers. While many studies examined the effects of online customer reviews (OCRs) on product sales, a clear understanding of the effects of OCRs on product returns is lacking. This study examines the impact of OCRs characteristics (valence, volume, and variance) on return decisions with a rich multi-year dataset from a major online retailer covering the electronics and furniture category. The main finding is that overly positive review valence (i.e., higher than the long-term product average), induces more purchases, but also more returns. An explanation for these findings is that OCRs help to form product expectations at the moment of purchase. Therefore, the purchase probability increases but the high expectations due to overly positive reviews may not be met, which results in negative expectation disconfirmation and consequently increases return probability as well. The effect of review valence on returns is stronger for novice buyers and for cheaper products. We further find that review volume and variance mainly affect purchase decisions, and have little to no effect on product returns. This study thus demonstrates that products returns should be considered when examining OCR effects, especially because overly positive reviews may hinder a retailer's financial performance, due to large reverse logistics costs associated with product returns.

By applying a deep neural network to selective laser melting, we studied a classification model of melt-pool images with respect to 6 laser power labels. Laser power influenced to form pores or cracks determining the part quality and was positively-linearly dependent to the density of the part. Using the neural network of which the number of nodes is dropped with increasing the layer number achieved satisfactory inference when melt-pool images had blurred edges. The proposed neural network showed the classification failure rate under 1.1% for 13,200 test images and was more effective to monitor melt-pool images because it simultaneously handled various shapes, comparing with a simple calculation such as the sum of pixel intensity in melt-pool images. The classification model could be utilized to infer the location to cause the unexpected alteration of microstructures or separate the defective products non-destructively.

This research suggests a simple parameter for monitoring a manufacturing plants performance to be used in the pharmaceutical, ceramic, powder metallurgical and other industries that use powder as the raw material and make solid components such as tablets as the product. It is the fraction or percentage of super blends among all the powder blends used in a year. A super powder blend is one that gives a product yield of above 90%. Yield is defined as the weight of quality cleared solid product as a percentage of the weight of the starting powder. The advantage of plots of proportion of super blends over normal yield plots is that the trend of quality performance stands amplified. This enables early detection and corrective actions by employees, management, and other stake holders. Eight case studies from the author's own experience in nuclear fuel manufacturing are presented to illustrate the scope of the parameter.

The integration of artificial intelligence (AI) into industrial processes is a promising method for enhancing operational efficiency and quality control. In particular, contact lens manufacturing requires specialized artificial intelligence technologies owing to stringent safety requirements. This study introduces a novel approach that employs polar coordinate transformation and a customized deep learning model, the Polar Sequence-guided Attention Network (PSgANet), to improve the accuracy of defect detection in the rim-connected zone (RCZ) of contact lenses. PSgANet is specifically designed to process polar coordinate-transformed image data by integrating sequence learning and attention mechanisms to maximise the capability for detecting and classifying defective patterns. This model converts irregularities along the edges of contact lenses into linear arrays via polar coordinate transformation, enabling a clearer and more consistent identification of defective regions. To achieve this, we applied sequence learning architectures such as GRU, LSTM, and Transformer within PSgANet and compared their performances with those of conventional models, including GoogleNetv4, EfficientNet, and Vision Transformer. The experimental results demonstrated that the PSgANet models outperformed the existing CNN-based models. In particular, the LSTM-based PSgANet achieved the highest accuracy and balanced precision and recall metrics, showing up to a 7.75% improvement in accuracy compared with the traditional GoogleNetv4 model. These results suggest that the proposed method is an effective tool for detecting and classifying defects within the RCZ during contact lens manufacturing processes.