AI and Supply Chain Problem Solving

AI and Supply Chain Problem Solving

What’s really required is real-time “always on” planning and execution capabilities that eliminate the information lead times between unplanned shifts in consumer demand or supply capability.

Solving Real World Problems in our Supply Chain Networks

The big joke among us mathematicians in today’s competitive landscape is that “We market around AI, recruit around ML, and apply regression when problem solving!”

In reality, regression still makes up a big part of our analytical activities. Further, we still apply quite a mix of mathematical approaches to solve for real world problems in our supply networks including:

• Rule-based engines to make decisions around alternate sourcing or substitute parts
• Heuristics, the strategies derived from previous experiences with similar problems, for use in supply and demand netting
• Algorithms for use in optimizing to objectives like revenue, cost, or profit
• Now expanding into machine learning, which we can apply when extending the data model to include new vectors such as weather and traffic patterns
• Finally, deep learning for true pattern recognition

Let’s think about what is really happening in our supply network. The ecosystem is rewarded when an end consumer purchases a product or service. Let’s call this time zero or the “moment of truth”. Now let’s travel backward in time from this moment of truth through our supply network - hours prior to purchase, days, weeks, and months.

Yesterday’s systems will certainly enable monthly, weekly, or even daily planning and execution but this is no longer enough. Real-time “always on” planning and execution capabilities that eliminate the information lead times between unplanned shifts in consumer demand or supply capability are required.

Download this informative and educational "AI and Supply Chain Problem Solving" white paper and discover:

• So How Do We Make It All Happen?
• Delivering the Targeted Outcome
• Consumer Packaged Goods Case Study
• Correlation-Based and Causal-Based AI/ML
• Dramatic Results with AI/ML