A Research on Business Process Optimisation Model Integrating AI and Big Data Analytics

• URL: http://arxiv.org/abs/2511.08934v1
• Date: Thu, 13 Nov 2025 01:19:24 GMT
• Title: A Research on Business Process Optimisation Model Integrating AI and Big Data Analytics
• Authors: Di Liao, Ruijia Liang, Ziyi Ye,
• Abstract summary: 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.<n>The model adopts a three-layer architecture incorporating data processing, AI algorithms, and business logic to enable real-time process monitoring and optimization.<n> 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%.
• Score: 4.644589778994777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Demystifying Reinforcement Learning in Agentic Reasoning [90.3737088727791]
  We conduct a comprehensive and systematic investigation to demystify reinforcement learning in agentic reasoning.<n>We highlight our key insights: (i) replacing stitched synthetic trajectories with real end-to-end tool-use trajectories yields a far stronger SFT.<n> Exploration-friendly techniques are crucial for agentic RL, such as clip higher, overlong reward shaping, and maintaining adequate policy entropy could improve the training efficiency.
  arXiv  Detail & Related papers  (2025-10-13T17:57:15Z)
• ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research [56.961539386979354]
  We introduce ORMind, a cognitive-inspired framework that enhances optimization through counterfactual reasoning.<n>Our approach emulates human cognition, implementing an end-to-end workflow that transforms requirements into mathematical models and executable code.<n>It is currently being tested internally in Lenovo's AI Assistant, with plans to enhance optimization capabilities for both business and consumer customers.
  arXiv  Detail & Related papers  (2025-06-02T05:11:21Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring [3.4437362489150254]
  We present a novel framework called Fine-Tuned Offline Reinforcement Learning Augmented Process Sequence Optimization.<n>FORLAPS aims to identify optimal execution paths in business processes by leveraging reinforcement learning enhanced with a state-dependent reward shaping mechanism.<n>We show that FORLAPS achieves 31% savings in resource time spent and a 23% reduction in process time span.
  arXiv  Detail & Related papers  (2025-01-17T20:31:35Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• A Systematic Review of Business Process Improvement: Achievements and Potentials in Combining Concepts from Operations Research and Business Process Management [0.0]
  Business Process Management and Operations Research aim to enhance value creation in organizations. This systematic literature review identifies and analyzes work that uses combined concepts from both disciplines. Results indicate a strong focus on resource allocation and scheduling problems.
  arXiv  Detail & Related papers  (2024-09-02T14:13:14Z)
• Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning [15.615208767760663]
  This study focuses on automated picking systems in warehouses, utilizing deep learning and reinforcement learning technologies. We demonstrate the effectiveness of these technologies in improving robot picking performance and adaptability to complex environments.
  arXiv  Detail & Related papers  (2024-08-29T15:39:12Z)
• Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives [40.62773366902451]
  This paper advocates a specific viewpoint on the field of process mining. We first summarize the framework of process mining, common industrial applications, and the latest advances combined with artificial intelligence. This particular perspective aims to revolutionize process mining by leveraging artificial intelligence to offer sophisticated solutions for complex, multi-organizational data analysis.
  arXiv  Detail & Related papers  (2024-07-15T23:30:34Z)
• Sparse Attention-driven Quality Prediction for Production Process Optimization in Digital Twins [53.70191138561039]
  We propose to deploy a digital twin of the production line by encoding its operational logic in a data-driven approach. We adopt a quality prediction model for production process based on self-attention-enabled temporal convolutional neural networks. Our operation experiments on a specific tobacco shredding line demonstrate that the proposed digital twin-based production process optimization method fosters seamless integration between virtual and real production lines.
  arXiv  Detail & Related papers  (2024-05-20T09:28:23Z)
• Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics [0.8399688944263843]
  Data pre-processing has out-sized influence on the success of real-world artificial intelligence applications. We present practical considerations for pre-processing industrial time series data to inform the efficient development of reliable soft sensors.
  arXiv  Detail & Related papers  (2022-11-11T19:01:21Z)
• AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes [56.65379135797867]
  We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
  arXiv  Detail & Related papers  (2020-08-29T14:57:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.