In the VUCA environment of global economy and deep integration of supply chain, enterprises need to achieve differentiated competition through S&OP process optimization. Research has found that traditional S&OP implementation faces multiple challenges: departmental communication barriers lead to insufficient information sharing, and cross departmental collaboration efficiency is low; The dynamic balance between supply and demand is difficult to achieve and is susceptible to the impact of the "bullwhip effect"; The synergy effect of the supply chain is limited, and there is insufficient collaboration between enterprises, customers, and suppliers in demand forecasting, production planning, and other aspects; The connection between the strategic and executive levels is broken, making it difficult to translate goals into executable plans. This study constructs a management decision support framework through integrated analysis of sales and operational data, and verifies its effectiveness in an empirical study of a certain enterprise. Optimization strategies include: using a combination of qualitative and quantitative methods for demand forecasting to reduce departmental workload and improve accuracy; Requirement management follows the principle of "order priority", promotes cross departmental joint forecasting, classifies products into ABC-XYZ categories, and formulates corresponding strategies; Customer classification is based on Pareto principle to identify key customers and implement agile S&OP strategy to shorten the cycle; Adopt more agile demand forecasting and supply planning responses for key projects; Establish complaint channels and assessment mechanisms for data management to stimulate the drive for improving data quality; Establish cross departmental teams for process management, optimize the RACI matrix to clarify responsibilities, build a unified performance evaluation system to improve overall performance, and establish an online and offline training system to enhance employee skills. The research contribution lies in enriching the theory of S&OP optimization at the theoretical level and expanding the application boundaries of data integration analysis; At the practical level, by breaking down departmental barriers, optimizing resource integration, and enhancing supply chain collaboration, inventory costs can be reduced, market response speed can be improved, and decision-making support capabilities can be strengthened. The optimization work basically does not increase additional costs and has certain feasibility, but there are limitations - the lack of overall industry comparative research may result in results that are not applicable to other enterprises. In the future, the scope of research will be expanded to different types of companies to enhance universality and applicability.

Niming Wang. Research on Integrated Analysis Method of Sales and Operations Data for Management Decision Support. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 87-94. https://doi.org/10.38007/SESR.2026.070110.

[1] Telha C, Carvalho M. Integration of Sales and Operations: A Dynamic Mixed-Integer Programming Game[J]. Dynamic Games and Applications, 2025, 15(1):306-328.

[2] Chen, M. (2026, March). Design of a Privacy-Oriented AI Compliance Hook System Based on Static Code Analysis. In 2026 IEEE Madhya Pradesh Section Conference (MPCON) (pp. 648-654). IEEE.

[3] Zhang, Q. (2025, July). Research on Improving the Effectiveness of Programmatic Advertising Using Multi-armed Bandit Algorithms. In International Conference on Frontier Computing (pp. 543-552). Singapore: Springer Nature Singapore.

[4] Wang, Z. (2026). Exploration of the Application of Resource Circular Economy in the Agricultural Industry Chain.

[5] Liu, X., & Yang, D. (2025, March). LLM Data Strategy: Improving Data Availability and Efficiency. In Doctoral Symposium on Computational Intelligence (pp. 425-437). Singapore: Springer Nature Singapore.

[6] Yin, J. (2026, March). An Efficient Iterative Algorithm for Calibrating Agency MBS Estimation Parameters Based on Bayesian Optimization, Implemented in Python. In 2026 IEEE Madhya Pradesh Section Conference (MPCON) (pp. 1462-1467). IEEE.

[7] Ding, J. (2025). Exploration of Process Improvement in Automotive Manufacturing Based on Intelligent Production. International Journal of Engineering Advances, 2(2), 17-23.

[8] Liu, H. (2025, December). Mlops Model Deployment System for Multi-Cloud Environments and Improvement of Commercial AI Service Availability. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 1047-1052). IEEE.

[9] Huang, J. (2025, August). Research on Multi-Model Fusion Machine Learning Demand Intelligent Forecasting System in Cloud Computing Environment. In 2025 2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-7). IEEE.

[10] Hong, Y. (2025, December). Chain Demand Mutation Identification and Emergency Response Decision-Making Integrated With Social Media Sentiment Analysis. In 2025 IEEE International Conference on Communication Networks and Computing (CNC) (pp. 425-432). IEEE.

[11] Lyu, N. (2026, April). Toward Robust AI Agents: A Closed-Loop Task Planning–Execution–Feedback Framework for Open Scenarios. In 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 1182-1188). IEEE.

[12] Zhang, C., Han, J., Zou, Y., Dong, K., Li, Y., Ding, J., & Han, X. (2024, April). Detecting the anomalies in LiDAR pointcloud. In WCX SAE World Congress Experience. SAE Technical Paper.

[13] Huang, J. (2025, September). Performance Evaluation Index System and Engineering Best Practice of Production-Level Time Series Machine Learning System. In 2025 International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT) (pp. 01-07). IEEE.

[14] Hou, Y. (2026). Research on Heterogeneous Server Upgrade Strategies and Resource Utilization Efficiency Oriented Toward Green Computing Objectives. Advances in Computer and Communication, 7(1).

[15] Wang, Y. (2025, July). Research on Federated Learning Algorithm Design and Privacy Protection Mechanism of Sports Rehabilitation Data Based on Multimodal Fusion. In International Conference on Frontier Computing (pp. 563-574). Singapore: Springer Nature Singapore.

[16] Zhang, K. (2025, December). Research on Cross-Selling Precision Marketing Strategy Based On Xgboost Model and SHAP Explanatory Framework. In 2025 IEEE 1st International Conference on Recent Trends in Computing and Smart Mobility (RCSM) (pp. 1-7). IEEE.

[17] Zheng, H. (2026, April). Research on Cloud-Edge Collaborative Elastic Computing and Cost Optimization for High-Concurrency Scenarios. In 2026 IEEE 15th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 1489-1496). IEEE.

[18] Wang, Y. (2025, December). Construction of an Intelligent Recommendation System for Personalized Training Plans for Sports Rehabilitation Patients Based on Graph Neural Networks. In 2025 IEEE 1st International Conference on Recent Trends in Computing and Smart Mobility (RCSM) (pp. 1-6). IEEE.

[19] Wu, W. (2025, June). Construction and optimization of intelligent gateway software management platform based on jenkins cluster management under cloud edge integration architecture in industrial internet of things. In International Conference on 6G Communications Networking and Signal Processing (pp. 633-645). Singapore: Springer Nature Singapore.

[20] Ding, J. (2025). Research On CODP Localization Decision Model Of Automotive Supply Chain Based On Delayed Manufacturing Strategy. arXiv preprint arXiv:2511.05899.