With the deepening of digital transformation, business process optimisation has become the key to improve the competitiveness of enterprises. This study constructs a business process optimisation model integrating artificial intelligence and big data to achieve intelligent management of the whole life cycle of processes. The model adopts a three-layer architecture incorporating data processing, AI algorithms, and business logic to enable real-time process monitoring and optimization. Through distributed computing and deep learning techniques, the system can handle complex business scenarios while maintaining high performance and reliability. Experimental validation across multiple enterprise scenarios shows that the model shortens process processing time by 42%, improves resource utilisation by 28%, and reduces operating costs by 35%. The system maintained 99.9% availability under high concurrent loads. The research results have important theoretical and practical value for promoting the digital transformation of enterprises, and provide new ideas for improving the operational efficiency of enterprises.

--Mining process optimization, particularly truck dispatch scheduling, is a critical factor in enhancing the efficiency of open-pit mining operations. However, the dynamic and stochastic nature of mining environments--characterized by uncertainties such as equipment failures, truck maintenance, and variable haul cycle times--poses significant challenges for traditional optimization methods. While Reinforcement Learning (RL) has demonstrated promise in adaptive decision-making for mining logistics, its practical deployment requires rigorous evaluation in realistic and customizable simulation environments. T o address this challenge, we introduce Mining-Gym, a configurable, open-source benchmarking environment designed for training, testing, and comparing RL algorithms in mining process optimization. Built on Discrete Event Simulation (DES) and seamlessly integrated with the OpenAI Gym interface, Mining-Gym offers a structured testbed that enables the direct application of advanced RL algorithms from Stable Baselines. The framework models key mining-specific uncertainties, such as equipment failures, queue congestion, and stochasticity of mining processes, ensuring a realistic and adaptive learning environment. Additionally, a graphic user interface (GUI) for easy parameter selection for mine-site configuration, comprehensive data logging system, a built-in KPI dashboard and real-time representative visualization of mine-site enables in-depth performance analysis, facilitating standardized, reproducible evaluation across multiple RL strategies and baseline heuristics. INING process optimization aims to enhance efficiency and productivity by improving resource allocation, equipment scheduling, and material handling. However, these operations are highly complex, influenced by dynamic factors such as equipment failures, fluctuating ore quality, and unpredictable environmental conditions. Traditional optimization methods, such as linear programming and heuristics, struggle to adapt in real time, leading to inefficiencies and increased costs.

The crucial field of Optical Chemical Structure Recognition (OCSR) aims to transform chemical structure photographs into machine-readable formats so that chemical databases may be efficiently stored and queried. Although a number of OCSR technologies have been created, little is known about how well they work in different picture deterioration scenarios. In this work, a new dataset of chemically structured images that have been systematically harmed graphically by compression, noise, distortion, and black overlays is presented. On these subsets, publicly accessible OCSR tools were thoroughly tested to determine how resilient they were to unfavorable circumstances. The outcomes show notable performance variation, underscoring each tool's advantages and disadvantages. Interestingly, MolScribe performed best under heavy compression (55.8% at 99%) and had the highest identification rate on undamaged photos (94.6%). MolVec performed exceptionally well against noise and black overlay (86.8% at 40%), although it declined under extreme distortion (<70%). With recognition rates below 30%, Decimer demonstrated strong sensitivity to noise and black overlay, but Imago had the lowest baseline accuracy (73.6%). The creative assessment of this study offers important new information about how well the OCSR tool performs when images deteriorate, as well as useful standards for tool development in the future.

As the manufacturing industry shifts from mass production to mass customization, there is a growing emphasis on adopting agile, resilient, and human-centric methodologies in line with the directives of Industry 5.0. Central to this transformation is the deployment of digital twins, a technology that digitally replicates manufacturing assets to enable enhanced process optimization, predictive maintenance, synthetic data generation, and accelerated customization and prototyping. This chapter delves into the technologies underpinning the creation of digital twins specifically tailored to agile manufacturing scenarios within the realm of robotic automation. It explores the transfer of trained policies and process optimizations from simulated settings to real-world applications through advanced techniques such as domain randomization, domain adaptation, curriculum learning, and model-based system identification. The chapter also examines various industrial manufacturing automation scenarios, including bin-picking, part inspection, and product assembly, under Sim2Real conditions. The performance of digital twin technologies in these scenarios is evaluated using practical metrics including data latency, adaptation rate, simulation fidelity among others reported, providing a comprehensive assessment of their efficacy and potential impact on modern manufacturing processes.

Automation has changed the course of data entry mechanisms in the manufacturing industry. Modern technology tools and techniques have brought a whole world of revolution, providing extensive support to the manufacturing sector in their daily needs of recording entries and building data sets. Automation offers the ability to streamline process workflows and create an error-free and user-friendly database. Manufacturers rely on automation to drive accuracy, precision, and process enhancements while maximizing operational efficiency. Real-time data helps manufacturers analyze lead times while providing precise and timely estimates.

As a digital manufacturing method, additive manufacturing has already managed to establish itself in a wide variety of industries. Whether in medicine, the automotive sector or the consumer goods industry, there is hardly any sector that does not benefit from the strengths of 3D printing. Among other things, the technology innovates production processes by making components both more flexible and more sustainable. Nevertheless, 3D printing has not yet been able to realize its full potential in terms of productivity. Could artificial intelligence be the key?

Most industrial organizations don't need to be sold on AI. They know its benefits, its value for being competitive, and that it's critical to their existence as a business. Consider some of the findings from a 2019 Accenture study, where 84% of C-suite executives said AI was essential to achieving their growth objectives, and 75% added that failing to scale AI across their organization will lead to them going out of business in five years. So, the will to adopt AI is there; nobody still needs to hear the sales pitch. What's needed, though, is guidance on just how and where to get started.

The objective of the response optimization algorithm is to exploit the mathematical model to look for optimal operating conditions. Indeed, the predictive model allows us to simulate different operating scenarios and adjust the control variables to improve efficiency. For a given set of states, determine the controls that minimize or maximize the performance variables. The next figure illustrates the response optimization process. As we can see, for a given state value, s, the control value, c*, minimizes the performance value.

How can manufacturers put artificial intelligence to work in the industry? In this article, you will find five possible applications of Machine learning and Deep learning to industrial processes optimization. Successful manufacturers prevent equipment failures before they come up. Rather than relying on routine inspections, the ML approach uses time-series data to detect failure patterns and predict future issues. Equipment failure can be caused by various factors.

So far, the main areas of AI use in insurance include customer experience, process optimization and product innovation. Insurers already collect heaps of data; with artificial intelligence, they can use it to its full potential and improve at every level, from automating call center request processing to helping make accurate assessments and executive-level decisions. Through its power to recognize patterns and anticipate actions, AI can provide a predictive environment where risks are anticipated and hedged. So far, it seems that the main areas of AI application in insurance include customer experience (58%), process optimization (43%) and product innovation (19%), according to a 2018 study by Everest Global. AI could also be applied to fraud, which a report by the FBI shows costs more than $40 billion per year.