Articles

Conventional SCADA-based water utility control relies on hydraulic models recalibrated through discrete campaigns spaced months to years apart, leaving controllers to act on models that degrade silently between updates. This paper specifies and evaluates a framework that couples a gradient-calibrated hydraulic digital twin to a hierarchically decomposed reinforcement learning controller, coordinating production, transmission, and distribution decisions through Lagrangian relaxation of inter-tier coupling constraints. A differentiable hydraulic solver updates pipe roughness, demand allocation, and valve loss parameters through exact gradients of the prediction residual within a rolling 24-hour window, while a temporal fusion transformer propagates quantile demand forecasts into a risk-sensitive reward term. Evaluated on a 2,400-node benchmark network against a rule-based SCADA baseline across thirty replicated simulation runs, the framework reduced specific energy consumption from 0.58 to 0.40–0.42 kWh/m³, demand forecast error from 14.7% to 4.0–5.0% MAPE, anomaly detection latency from 4.3 hours to 0.3–0.5 hours, treatment dosing error from ±15% to ±4%, and non-revenue water from 32.1% to 21–22%. Lagrangian decomposition reduced joint action-space cardinality from a multiplicative to an additive function of per-tier size, yielding an approximately two-orders-of-magnitude reduction in effective search space for the tested topology. Parameter identifiability under sensor coverage sparser than the benchmark configuration remains untested and bounds the framework's applicability to deployments matching the assumed instrumentation density.

Managing supply chains efficiently has become a major concern for organizations. One of the important factors to optimize in supply chain management is logistics. The advent of technology and the increase in data availability allow for the enhancement of the efficiency of logistics in a supply chain. This discussion focuses on the blending of analytics with innovation in logistics to improve the operations of a supply chain. An approach is presented on how predictive analytics can be used to improve logistics operations. In order to analyze big data in logistics effectively, an artificial intelligence computational technique, specifically deep learning, is employed. Two case studies are illustrated to demonstrate the practical employability of the proposed technique. This reveals the power and potential of using predictive analytics in logistics to project various KPI values ahead in the future based on the contemporary data from the logistics operations; sheds light on the innovative technique of employing deep learning through deep learning-based predictive analytics in logistics; suggests incorporating innovative techniques like deep learning with predictive analytics to develop an accurate forecasting technique in logistics and optimize operations and prevent disruption in the supply chain. The network of supply chains has become more complex, necessitating the need for the latest technological advancements. The sectors that have gained a fair amount of attention for the application of technology to optimize their operations are manufacturing, healthcare, aerospace, and the automotive industry. A little attention has been diverted to the logistics sector; many describe how analytics and artificial intelligence can be used in the logistics sector to achieve higher optimization. Currently, significant research has been done in optimizing logistics operations. Nevertheless, with the explosive volume of historical data being produced by the logistics operations of an organization, there is a great opportunity to learn valuable insights from the data accumulated over time for more long-term strategic planning. To develop the logistics operations in an organization, the use of historical data is essential to understand the trends in the operations. For example, regular maintenance planning and resource allocation based on trends are long-term activities that will not affect logistics operations immediately but can affect the business’s strategic planning in the long run. A predictive analysis technique employed on historical data of logistics can narrow down conclusions based on the future trends of logistics operations. Thus, the technique can be used to prevent the disruption of the supply chain.

The growing complexity of the operating environment urges pharmaceutical innovation. This essay addresses the need for the integration of advanced technologies in the pharmaceutical supply chain. It justifies the value proposition and presents a concrete use case for the integration of deep learning insights to make data-driven decisions. The supply chain has always been a priority for the pharmaceutical industry; research and development recognizes companies' increasing investment in big data strategies, with plans for a CAGR in big data tool adoption. The work presented herein has a preliminary explorative character to recuperate and integrate evidence from partly overlooked practical experience and know-how. The practical relevance of the essay is directed toward practitioners in pharmaceutical production, supply chain management, logistics, and regulatory agencies. The literature has shown a long-term concern for enhanced performance in the pharmaceutical supply chain network. This essay demonstrates the application of deep learning-driven insights to reveal non-evident flow dependencies. The main aim is to present a comprehensive insight into deep learning-driven decision support. The supply chain is portrayed in a holistic manner, seeking end-to-end visibility. Implications for public policy are discussed, such as data equity: many countries are protecting their populations and economic growth by building resilience and efficiency to ensure the capacity to move goods across supply chains. The implementation strategy is covered. The combined reduction of variability, efficiency as matured richness, reliability (on stochastic flows and their understanding through deep learning and data), and system noise (increased dampening through the inclusiveness of all stakeholders) results in increased responsiveness of supply chains for pharmaceutical products. Future work involves the integration of external data, closing the loop between planning and its application in reality.