This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI’s functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research.

Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation

This paper considers two pertinent research inquiries: ‘Can an AI-based predictive framework be utilised for the optimisation of solar energy management?’ and ‘What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?’ The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and $ R^2 $ R2. A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency.

AI-based forecasting for optimised solar energy management and smart grid efficiency

Modern manufacturing is the world's largest and most automated industrial sector. The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning has enabled a better connection with machines and factory systems. Data harvesting allowed for a more seamless and comprehensive implementation of the knowledge-based decision-making process. New models that provide a competitive edge must be created by combining the Lean paradigm with the new technologies of Industry 4.0. This paper presents novel computer-based vision models for automated detection and classification of damaged packages from intact packages. In high-volume production environments, the package manual inspection process through the human eye consumes inordinate amounts of time poring over physical packages. Our proposed three different computer-based vision approaches detect damaged packages to prevent them from moving to shipping operations that would otherwise incur waste in the form of wasted operating hours, wasted resources and lost customer satisfaction. The proposed approaches were carried out on a data set consisting of package images and achieved high precision, accuracy, and recall values during the training and validation stage, with the resultant trained YOLO v7 model. 

Waste reduction via image classification algorithms: beyond the human eye with an AI-based vision

Gathering expert consensus to inform a proposed trial in chronic nonbacterial osteomyelitis (CNO).

Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives. 

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

In make-to-order manufacturing workshops, accurate prediction value of production progress (PP) is a significant reference index for dynamic optimisation of production process and on-time delivery of production orders. The implementation of big data and Industrial Internet of Things (IIoT) in manufacturing workshops makes it possible to obtain large amounts of production data which can affect PP. However, the particularities of massive historical order data are not fully excavated and the amount of target order data is insufficient to support the training of high-precision prediction model, which will result in bad training approximation and generalisation. To overcome these shortcomings, a PP prediction approach consisting of two models with transfer learning (TL) is proposed. TL can avoid the training of PP prediction model from scratch every time. Consequently, computational efficiency can be greatly improved. A convolutional neural network (CNN) model with TL is devised to excavate the comprehensive features from historical and current orders. Additionally, a long short-term memory network (LSTM) model with TL is constructed to ﬁt the nonlinear relation of the features provided by CNN-TL model for PP prediction. In order to validate the performance of the proposed PP prediction approach, comparative experiments of eight algorithms are conducted in an IIoT-enabled manufacturing workshop.

A transfer learning CNN-LSTM network-based production progress prediction approach in IIoT-enabled manufacturing

<h3>Aims</h3> Chronic Nonbacterial Osteomyelitis (CNO) is an autoinflammatory bone disease primarily affecting children. It can cause pain, hyperostosis and fractures, thereby affecting quality-of-life and psychomotor development. While increased inflammasome activation has been reported, the exact molecular pathophysiology of CNO is unknown, and no disease biomarkers exist. <h3>Aim</h3> To identify disease mechanisms in CNO to allow patient stratification and individualized treatment. <h3>Methods</h3> Whole exome sequencing in families with CNO, and target sequencing of P2X7R in a large German CNO cohort. Findings were integrated with demographic and clinical datasets. Genetically modified THP-1 cells were used to investigate altered gene expression, inflammasome assembly (ASC specks), cytokine release and pyroptosis. <h3>Results</h3> In 8 families with a history of CNO in 2 generations, damaging mutations in P2X7R, a regulator of inflammasome assembly, were identified. Targeted sequencing in 196 unrelated patients identified rare damaging heterozygous variants in 5. In the remaining cohort, common variants were over-represented when compared to the healthy population. Patients with rare variants were younger, more frequently exhibited multifocal disease, and required more aggressive treatment with 2<sup>nd</sup>-line agents when compared to the remaining cohort. THP-1 cells expressing variant P2X7 exhibited altered inflammasome assembly, IL-1 and IL-18 release, and pyroptosis. <h3>Conclusion</h3> Rare damaging variants in P2X7R account for approximately 2.5% of CNO cases. Common P2X7R variants were present in all remaining (97.5%) patients and may represent a risk allele underscoring the key role of inflammasome dysregulation in CNO. Observations will aid in future patient stratification and individualized care. 

/pdf/678-damaging-variants-in-p2x7r-associate-with-chronic-3q3w8829.pdf

678 Damaging variants in P2X7R associate with Chronic Nonbacterial Osteomyelitis (CNO)

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A transfer learning CNN-LSTM network-based production progress prediction approach in IIoT-enabled manufacturing

678 Damaging variants in P2X7R associate with Chronic Nonbacterial Osteomyelitis (CNO)