FREMONT, CA: End-to-end visibility is the key factor in the supply chain. And technologies such as artificial intelligence (AI), machine learning (ML), and cognitive analysis are proving to be crucial to gain visibility into the chain. A self-learning supply chain will drive the future of supply chain innovation and supply chain teams would use AI and ML infused systems to analyze their conventional strategies and the scope of improvement. Here are some of the key points that AI and ML solution providers should consider while developing AI-driven systems for the supply chain:
A Supply Chain System that can Read and Manage Massive Data
The system must be able to process a variety of unstructured data sets such as temperature, weather events, social trends, and others. For instance, weather forecasts and port congestion data will provide an insight into the freights in route and predict delays in delivery.
A Futuristic System
A system must be conceptualized in a way that it can provide reliable forecasts. The companies need to apply AI and ML technology to draw the right conclusions from the data they collect. Assessment based on the current market and where it’s heading can provide a lead to the future set of events. Against the traditional approach that was focused on a single dimension approach of demand and supply, AI and ML blend internal and external data to predict demand and the areas for growth. Thus having algorithms designed to leverage such insights will play a detrimental role in supply chain dynamics.
Technology vs. Human Bias
With the advancement in cognitive technologies, AI and ML powered systems will learn and respond to ensure that price points and orders are in line to keep the business stocked and efficient. But it is essential as humans to trust the system’s recommendations against their personal biases. Doing so will not only save their time but also allow them to focus on challenges that can be solved exclusively by humans.