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Direct material procurement - What to expect from machine learning

Machine learning creates new opportunities for commodity, energy and raw material buyers. This three-page datasheet describes the main benefits that Chief Procurement Officers can expect from machine learning.

• Unified view of the market
• Price forecasting
• Better understanding of price risk
• Better alignement with operations
• Effective long-term planning
• Broader hedging opportunities

Direct material procurement optimization - What's required?

Datapred for Procurement can shave 3-5% off your commodity, energy and raw material spend, year over year. This six-page datasheet discusses the main requirements for using Datapred:

• Price and contextual data
• Costs and constraints
• Wiggle room
• Basic technical stack

Datapred and sustainability

The Datapred buyer checklist

This 4-page datasheet summarizes our answers to Gartner's top 12 questions corporate buyers should ask to AI solution providers.

Key strengths of the Datapred modeling engine

DDatapred includes a powerful modeling engine for continuous intelligence applications. This three-page datasheet discusses the key strengths of that proprietary technology:

• Handling streaming data
• Aggregating multiple models
• Providing explainability
• Accelerating deployment

Bring your own model

Datapred lets you leverage your experience and data science competencies, and bring your own predictive or optimization models to the software. This three-page datasheet discusses how.

Vital checklist for time series modeling

Applying machine learning to time series is tricky. Don't dive into your time series modeling project without checking this three-page life-saving list first.