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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Published: 2026-02-13
Keywords:
cash flow forecasting, business intelligence tools, manufacturing financial analysis, predictive analytics

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Liya Ge
Master of Science in Finance, Washington University, MO, USA

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.

Article Details

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Vol. 2 No. 1 (2026)

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Articles

How to Cite

Comparative Analysis of Traditional Excel and AI-Powered Business Intelligence Tools for Manufacturing Cash Flow Forecasting: An Evaluation of Accuracy, Usability, and Cost-Effectiveness. (2026). Journal of Science, Innovation & Social Impact, 2(1), 96-110. https://sagespress.com/index.php/JSISI/article/view/84

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