Real-time Analytics
Caterpillar's finance organization relied on decades-old forecasting processes, hundreds of data sources and countless spreadsheets, leaving teams with limited time for strategic insight and making it difficult to improve forecast accuracy and scenario planning.
Reported impact
Caterpillar established a practical AI-driven forecasting foundation that improved the speed and flexibility of finance planning, created a reusable scenario process and gave the finance organization a scalable method for expanding analytics-based forecasting into additional business areas.
Caterpillar's Global Finance Services Division worked with EY to prove how AI- and ML-enabled forecasting could replace labor-intensive financial planning. EY began with a proof of concept that used a shadow copy of Caterpillar data plus selected external sources to build modeling tools and demonstrate better speed, accuracy and scenario analysis without disrupting live operations. The architecture combined EY Data Fabric and EY Intelligent Forecasting and Scenario Modeling with newly developed statistical models, scenario-planning workflows and a custom user interface. The new interface created a global repository for critical planning data and allowed finance teams to run more scenarios, inspect model outputs and override predictions when fresh information arrived. After the proof of concept, the team piloted the solution alongside Caterpillar's current process in one business unit, then used the results to expand advanced analytics capabilities for future finance forecasting work.
Caterpillar finance data and selected external sources were copied into an EY modeling environment, where Data Fabric and Intelligent Forecasting and Scenario Modeling supported statistical forecast generation. Results flowed into a custom interface and scenario-planning process so teams could compare outcomes, apply overrides and reuse a central planning-data repository.
"A newly designed custom user interface allowed the team to execute more planning scenarios and establish a global repository capturing the critical data."
"Big differentiators for these tools included the EY Data Fabric solution and EY Intelligent Forecasting and Scenario Modeling."
"The goal was to equip our teams with better tools and technologies to reduce team time, waste, and improve accuracy."
"The pilot ran in parallel with Caterpillar's current process, allowing the team to test the new technology without disrupting current operations."
"These modeling tools used a shadow copy of Caterpillar's own data, along with selected external sources, to run forecasting models."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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