In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

This study investigates the escalating complexity and unpredictability of global supply chains, with a particular emphasis on resilience in the agricultural sector of Antioquia, Colombia. The aim of the study is to identify and analyze the dynamic capabilities, specifically flexibility and adaptability that significantly enhance resilience within agri-food supply chains. Given the sector’s vulnerability to external disruptions, such as climate change and economic volatility, a thorough understanding of these capabilities is imperative for the formulation of effective risk management strategies. This research is essential to provide empirical insights that can inform stakeholders on fortifying their supply chains, thereby contributing to enhanced competitiveness and sustainability. By presenting a comprehensive framework for evaluating dynamic capabilities, this study not only addresses existing gaps in the literature but also offers practical recommendations aimed at bolstering resilience in the agricultural sector.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

The study examined the mediating role of supply chain security performance on the relationship between supply chain security practices and supply chain disruptions occurrences in the manufacturing industry in Ghana. Drawing on a survey of 336 manufacturing firms, dynamic capability, and contingency theories were applied using structural equation modeling (SEM) to test the conceptual model. It was discovered that both direct and indirect hypotheses supported the findings of the study. The results indicate that Ghanaian manufacturing firms have made progress in implementing supply chain security measures. The findings revealed that the adoption of comprehensive supply chain security practices is positively associated with improved performance metrics, including reduced inventory losses and damages, faster order fulfillment and delivery times, lower costs related to security incidents, and enhanced brand reputation and customer trust. Policymakers can leverage these insights to develop support programs aimed at strengthening the security capabilities of manufacturing firms, ensuring they are equipped to compete effectively in both local and global markets, improving security performance, and reducing the likelihood and impact of supply chain disruptions. In the quest of bridging the gap between theory and practice, this research contributes valuable knowledge to the discourse on supply chain security in developing countries, offering a roadmap for enhancing resilience and performance in the manufacturing sector.

All sectors have an increasing interest in smart phone applications based on their many advantages that support business, especially the medical sector, which is constantly competing to develop the medical services provided, and accordingly in this research study we industrialized a mobile medical supplies and equipment ordering application (mobile medical app) classic and make an effort to authenticate it factually. When clients (hospitals doctors) create consumptions on the application, three dimensions can be identified: platform emotion stage, fear effect, and familiarity with product. This research designed to reinforce and brighten the most important magnitudes that improve a physician’s judgment of mobile medical app and the purpose to usage. Furthermore, this study inspected the availability of the model between hospital physicians in UAE. The classic ideal was observed by means of a model of 340 UAE clinic physicians and their personal assistant who utilize mobiles facilities in overall. The review technique, a calculable method, was applied; the fractional smallest cubes organizational calculation exhibiting systems was owned to inspect the planned agenda. The platform emotion dimension, especially fear and resistance to change, and the familiarity with the products were evaluated, and it was discovered that these factors positively influenced the objective to use the application. And the other side, the first dimension of emotion, fear, manifested as “apparent threat”, had no outcome on the purpose to using. These discoveries recommended that scholars should emphasis more on the facilities, merchandises, and the key task of the mobile medical app to control their inspirations on clients’ ordering purpose. This will progress the purchasing ways associated to acquiring medicinal materials utilizing mobile medical app and/or on other operational stages in unambiguously in UAE and the Central East at great.