Gen AI & Machine Learning in ERP

How artificial intelligence is transforming enterprise resource planning. This guide covers overview of ai/ml capabilities in predictiv, neuralprophet sales forecasting, gl anomaly detection algorithms, and more.

Overview of AI/ML capabilities in Predictiv

Syntegra’s DRIVE Framework (Data, Robotics, Intelligence, Value, and Efficiency) consolidates best-in-class methodologies (Lean Management, Six Sigma) and hyper automation capabilities (RPA, AI) to systematically enhance process maturity. - Intelligence: Embeds AI-driven analytics and predictive models to proactively address inefficiencies. These benefits typically arise from cost savings, productivity gains, and improved decision-making:

Organisation SizeIndicative ImprovementPrimary Drivers USD 10m – 50m5–10% cost savings; 15–25% productivity improvementAutomated invoice processing, streamlined procurement, reduced manual data entry USD 50m – 250m10–15% cost savings; 20–30% productivity improvementCentralised data management, integrated RPA for repeatable tasks, AI-driven reporting USD 250m – 1bn10–20% cost savings; 25–40% productivity improvementEnterprise-wide process optimisation, predictive analytics, advanced supply chain planning

These improvements typically arise from:

1.

NeuralProphet sales forecasting

NeuralProphet sales forecasting is a core capability within Predictiv, designed to streamline operations and improve visibility. The implementation follows best practices while remaining configurable to meet your organization's specific needs.

GL anomaly detection algorithms

GL Anomaly Detection

DRIVE Operational Efficiency Framework

**1.

Predictive analytics dashboards

• Intelligence: Embeds AI-driven analytics and predictive models to proactively address inefficiencies. 1 Rationale**

By tying together data analytics, automation (RPA), AI, and lean principles, DRIVE helps organisations realise continuous improvements. These benefits typically arise from cost savings, productivity gains, and improved decision-making:

Organisation SizeIndicative ImprovementPrimary Drivers USD 10m – 50m5–10% cost savings; 15–25% productivity improvementAutomated invoice processing, streamlined procurement, reduced manual data entry USD 50m – 250m10–15% cost savings; 20–30% productivity improvementCentralised data management, integrated RPA for repeatable tasks, AI-driven reporting USD 250m – 1bn10–20% cost savings; 25–40% productivity improvementEnterprise-wide process optimisation, predictive analytics, advanced supply chain planning

These improvements typically arise from:

1. Enhancing Data Quality and Insights (through AI and analytics).

Natural language query generation

In such environments, a DRIVE implementation may face delays, inco

AI Query Generation.

Getting Started

To implement gen ai & machine learning in erp in your Predictiv environment:

1. Assess your current state - Review existing processes and identify improvement opportunities

2. Configure the module - Work with your implementation team to set up the required components

3. Train your team - Ensure users understand the new capabilities and workflows

4. Monitor and optimize - Track key metrics and continuously improve

Related Resources

For more information on related topics, explore our other guides in this collection.

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