The Role of Big Data Analytics and Artificial Intelligence in Strengthening Integrated Business Planning: A Sales and Operations Planning Case Study

Abstract

Integrated business planning (IBP) depends upon timely information, reliable forecasting and agreement among different functions. However, many sales and operations planning (S&OP) processes continue to operate through fragmented information and periodic managerial judgement. This study aims to investigate how big data analytics (BDA), artificial intelligence (AI) and related decision-support capabilities can strengthen the IBP process through a PRISMA-informed systematic review that was conducted by using a manually developed dataset for the period 2016–2026. Twenty-four structured searches through the Crossref DOI registry were followed by verification using exact titles, DOIs, Semantic Scholar and publisher pages. From 1,440 returned records, 932 unique records were screened and 120 candidates were coded, producing 70 primary studies, 16 background reviews and 34 excluded records. Descriptive mapping, multi-label coding and thematic synthesis were applied through a 47-field extraction matrix, and the findings show that machine learning and deep learning represented the largest primary category with 24 studies, whereas S&OP and demand planning each contained 23 studies. Analytics has a significant role in forecast accuracy, planning speed, evaluation of scenarios, integration and resilience. However, its value depends upon data quality, organisational capability, cross-functional coordination and the trust of managers. Only three studies explicitly considered explainability and ten discussed human judgement, which reflects an important governance gap. Therefore, an evidence-based S&OP case synthesis and an augmented IBP control wheel are proposed. The framework considers AI as a governed planning partner rather than an autonomous replacement of planners. Since full texts were not systematically retrieved for all records, the findings support systematic mapping and cautious thematic interpretation instead of statistical meta-analysis.