AI-Driven Demand Forecasting

Trend Definition 

What is the essence of this trend? What is its impact? 

• Essence: Based on Machine Learning (ML) models, AI-Driven Demand Forecasting fosters the quality and accuracy of demand forecasts by combining historical data with predictive analytics, originating from internal and external sources, such as weather or market conditions 
• Impact: AI enables a more precise and constantly updated demand forecast by uncovering complex data patterns compared to traditional approaches such as trend projection

Trend Drivers 

Why is this trend emerging now? What’s changing? 

• Data Availability: E-commerce transactions, IoT in manufacturing, and market reports provide more data on customer behaviour, product demand cycles, and supply chain dynamics 
• Technological Advancements: ML algorithms enable companies to analyse a bigger amount of data and to obtain high-quality insights 
• Market Dynamics: Macro and market developments, for instance new regulations or supply chain shocks, require real-time adaptions of forecasts to these scenarios which traditional methods can hardly deliver

Use Cases 

How to apply this trend? 

• Inventory Management through AI Forecasting of Consumer Demands: Centralised data storage and utilisation to optimise procurement activities
  Example: Walmart’s “Data Cafe” enables the retailer to keep an optimal level of inventory and to reduce the need for last-minute adjustments to their procurement 
• AI-driven Direct Material Sourcing: Direct impact on material procurement activities 
  Example: Suite solutions such as GEP use AI engines to collect and cleanse data from different sources such as invoices and classify spend. With AI learning sourcing patterns, companies can save time in identifying and selecting suppliers

Procurement Relevance & Response Strategies 

How should Procurement adapt its Processes, Organisation, and Strategy?

• Data Foundation: Consolidated data hub to access different kinds of data utilising artificial intelligence and machine learning algorithms 
• Integration and Connection: Directly integrate predictive analytics to automate processes based on AI forecasts and connect the systems with the suppliers. This enables the integration of AI-driven insights in negotiations, for instance to reach dynamic pricing