Supply Chain Analysis: AI-enabled vs Manual-based Planning

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Dispelling misconceptions and mistrust around AI-enabled forecasting and planning tools

With artificial intelligence capabilities automating more and more decisions and tasks in our personal lives, the use cases for supply chain analysis and planning are ever expanding.   Trusting automation platforms to make decisions for your business increasingly saves time, reduces error, and frees planners to focus on strategic decisions. The issue of trust, however, often leaves companies concerned that automated features may make incorrect choices. Lets review the benefits of AI forecasting for better supply chain analysis.

What AI-enabled planning looks like

A proper AI solution automates routine and non-routine decisions within its own parameters, and flags planners for human resolution when needed.  The alerts and notifications that planners see are backed by as much knowledge as the system can provide, enabling well-informed human decision-making when decisions are out of scope for the system.

Autonomous (lights out) planning is not equivalent to planning in the dark – or having no control over your plans.  Effectively, an AI-enabled tool performs work under human management. By freeing up time for human resources, organizations can jump from a reactive to a proactive approach instantaneously.

What AI-enabled planning doesn’t look like

No, there is no AI monster that is going to run amuck with your business plans.

The fact is AI & ML applications are not going to scale to the height of human ability – but they can often perform a breadth of tasks with higher accuracy. Processes, where time consumption is a significant opportunity cost to planners, can be confidently automated, and alert humans when higher level decisions need to be made.

AI vs Manual for Supply Chain Analysis

AI decision-making is not a replacement for forward-thinking leadership and strategic planning intelligence.  But there are a few advantages (other than resource reduction) to using AI-enabled decision-making rather than human-led.  Because machine learning algorithms can consider all possibilities in seconds, and self-correct when new information comes in, they are a more practical and accurate choice for many decisions.

Forecast method selection: each time new data enters the system, an AI-led tool will automatically self-check its forecast method selection to ensure the highest level of forecast accuracy. Time and time again, the machine learning algorithms have proven to produce more accurate forecasts for customers of the Predictive Planning Platform.

To learn more about our AI-enabled planning solutions for supply chain analysis, request a demo.