Traditional supply chain software is written to assume the world operates exactly as planned. This means variability in supplier quality, production yield, lead times, demand, etc. are not considered and traditional planning applications inherently assume supply chains operate with perfect execution. This approach tends to put supply chain planners behind the eight ball: where they’re constantly reacting to gaps in supply and demand, often at the expense of excess inventory, poor fill rates and lost sales.

 

With a foundation of AI/ML based algorithms underpinning our suite of exception-driven planning applications, planit really stand apart from traditional planning applications. Planit takes the supply chain variability into account, mining for patterns in supply chain data and making prediction that are more aligned with reality. This approach yields:

 

• Probabilistic demand plans that are significantly more accurate

• Probabilistic purchasing, manufacturing and inventory decisions more closely aligned to demand

• Probabilistic risk predictions in S&OP allowing preemptive mitigation of supply/demand imbalances