Given the changes in the demand environment, delivery risks and extended supply chains of modern manufacturing enterprises, the previous model of sales and operations planning has shown several defects, such as scattered data, slow forecasting and departmental misalignment. Based on the EU enterprise digital technology application statistics in 2025, this paper proposes a multi-source big data-driven framework for collaborative sales and operations planning by means of secondary data analysis, standardised modelling and process design. The five kinds of models in this study are: demand perception, dynamic weight updates, inventory balancing, multi-objective cost optimisation and service-level risk constraints. The four levels of the implementation plan are data governance, collaborative planning, decision optimisation and closed-loop execution. Based on the statistics, large enterprises significantly outperform the average enterprise in terms of ERP, business intelligence, cloud computing, data analytics and artificial intelligence applications, and the adoption rates of data analytics vary substantially among different countries. Therefore, to optimise the supply chain, we do not need a single algorithm that replaces human labour; rather, we should establish a unified data semantics, cross-departmental constraint negotiation, and an interpretable rolling decision-making mechanism.

Qian Lin. Research on Multi-source Demand Perception and Inventory-Capacity Co-optimization of Sales and Operation Planning in Discrete Manufacturing Enterprises. International Journal of Business Management and Economics and Trade (2026), Vol. 7, Issue 3: 47-55. https://doi.org/10.38007/IJBMET.2026.070305.

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