Harnessing customized AI to create voice of customer via GPT3.5

Shahin, Mohammad; Chen, F. Frank; Hosseinzadeh, Ali

doi:10.1016/j.aei.2024.102462

Cited by 6 publications

(2 citation statements)

References 103 publications

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“…Finally, articles on the Product Development Process (PDP) [81][82][83][84][85][86][87][88][89][90][91][92][93][94][95] present a wide variety of approaches and techniques to optimize product development and improve market performance. From using advanced machine learning techniques to integrating sustainable practices into the PDP, these studies highlight the importance of innovation and efficiency in the Product Development Process.…”

Section: Discussionmentioning

confidence: 99%

A Systemic Approach to the Product Life Cycle for the Product Development Process in Agriculture

Lourenço,

Gonçalves,

Canciglieri Júnior

et al. 2024

Sustainability

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For a long time, a company’s Product Development Process (PDP) was seen as supporting the operations department, although PDP decisions and mistakes have a considerable impact on market performance. This is critical even in agriculture where bad habits and practices in the PDP can lead rural producers to great losses. Therefore, this research investigates the effect of the PDP on the market performance of rural products (bananas) in the southern region of Brazil, based on two analyses: (i) how sustainability practices support the PDP phases and (ii) how the phases of the Product Life Cycle Assessment (LCA) mediate sustainability practices and PDP phases. This study presents a quantitative analysis using Confirmatory Factor Analysis (CFA) and hierarchical ordinary least squares (OLS) regression of data obtained from a survey of 110 rural producers who directly participate in the banana production and planning process in southern Brazil. Our results show that sustainability practices support the PDP, and we confirm that the product development and post-development phase has an effect on market performance. In addition, we identify that in the pre-development phase of the PDP, dealing with rural products (bananas), the maturity stage of the LCA mediates sustainability. In the PDP development phase, we conclude that rural families who develop economic and environmental practices with their products, which are in the market growth phase may have reduced results. As for the post-development phase of the PDP, we conclude that when companies invest in environmental and social practices, there is a complete mediation of the effect, where these practices lose strength if the product is in the introductory and maturity phases in the market. In an original matter, our study contributes to demonstrating the value of the product life cycle for the Product Development Process in agriculture using sustainability practices through a systemic approach, filling the gap in the literature due to a lack of integrated research on these areas seen.

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Section: Discussionmentioning

confidence: 99%

A Systemic Approach to the Product Life Cycle for the Product Development Process in Agriculture

Lourenço,

Gonçalves,

Canciglieri Júnior

et al. 2024

Sustainability

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“…ViT demonstrates that transformers can achieve remarkable performance on image recognition tasks, challenging the supremacy of CNNs in computer vision and thus representing a novel approach to image classification [103,104]. The core idea behind ViT is to treat an image as a sequence of patches, akin to how a sentence is viewed as a sequence of words in NLP [105]. This methodology enables the application of transformer models directly to patches of images, allowing the model to capture complex dependencies and relationships between different parts of an image.…”

Section: Visual Transformer (Vit)mentioning

confidence: 99%

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

Self Cite

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Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

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Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network

Shahin,

Maghanaki,

Hosseinzadeh

et al. 2024

Int J Adv Manuf Technol

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Harnessing customized AI to create voice of customer via GPT3.5

Cited by 6 publications

References 103 publications

A Systemic Approach to the Product Life Cycle for the Product Development Process in Agriculture

A Systemic Approach to the Product Life Cycle for the Product Development Process in Agriculture

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network

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