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Comparative Analysis of Traditional Excel and AI-Powered Business Intelligence Tools for Manufacturing Cash Flow Forecasting: An Evaluation of Accuracy, Usability, and Cost-Effectiveness

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  • Ge, Liya

Abstract

Manufacturing enterprises face mounting pressure to enhance cash flow forecasting accuracy amid increasingly volatile market conditions. This study presents a systematic comparative evaluation of traditional Excel-based methods against AI-powered business intelligence platforms, specifically Power BI and Tableau, for cash flow forecasting in manufacturing contexts. Through empirical analysis of 18 months of transaction data from a mid-sized manufacturing enterprise processing $750,000 weekly cash flows, the research quantifies performance differences across three critical dimensions: forecasting accuracy, operational usability, and cost-effectiveness. Results demonstrate that AI-enabled tools improve forecast accuracy by up to ~33% (Excel 12.5% → Power BI 8.3%) and ~27% (Tableau 9.1%), as measured by Mean Absolute Percentage Error, reduce ongoing analytical time requirements by 57-66%, and deliver a positive return on investment within 14-16 months despite higher initial implementation costs. The findings establish an evidence-based decision framework for manufacturing financial managers evaluating the adoption of business intelligence tools.

Suggested Citation

  • Ge, Liya, 2026. "Comparative Analysis of Traditional Excel and AI-Powered Business Intelligence Tools for Manufacturing Cash Flow Forecasting: An Evaluation of Accuracy, Usability, and Cost-Effectiveness," Journal of Science, Innovation & Social Impact, Pinnacle Academic Press, vol. 2(1), pages 96-110.
    • Handle: RePEc:dba:jsisia:v:2:y:2026:i:1:p:96-110
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