Where to apply machine learning for supply chain optimization in Microsoft Dynamics 365
Artificial intelligence, specifically machine learning (ML), is quickly becoming essential for running smarter business operations. One of the greatest features of Dynamics 365 is its ability to incorporate ML capabilities within business applications, which provides predictive insights and helps businesses execute operations in a more effective manner.
According to a recent study by the Mckinsey Global Institute, advanced AI technologies have the potential to unlock a global economic impact of 10 to 15 trillion dollars across all industry segments.
Below are a few candidate scenarios for AI-enabled optimization for the retail and CPG verticals in particular. Later in the article, one use case is explained in detail using Microsoft business applications.
- ML based demand and sales forecasting
- Personalized product recommendations
- Price and promotion recommendations to optimize markups and margins
- Inventory optimization with correct stock levels
- Logistics planning workbench and warehouse throughput optimization
- Build a 360° view of consumers
- Consumer insights (sentiment analysis/preferences/social listening) using Cognitive Services
- Shop-floor yield optimization
- Predictive equipment maintenance in factories
- Predictive lead scoring to improve lead qualification, prioritization, and acquisition
According to Forbes, 61 percent of organizations picked ML as their company's most significant upcoming initiative.
Dynamics 365 Operations and Azure Machine Learning Studio
Demand forecasting use case
Dynamics 365 for Finance and Operations allows you to integrate Azure Machine Learning into your Dynamics environment to predict demand more accurately by infusing more demand planning parameters and considering new statistical models.
1) Historical data: The first and most important step of the process is gathering and preparing the transactional data from Dynamics 365 and providing it to Azure Machine Learning Studio for training the mode.
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