AI Ushers Supply Chains Into Data-Driven Future

Tags: Logistics I.T., Logistics, Technology , Supply Chain, Visibility

Ram Krishnan, VP Product Marketing, Aera Technology

Over the years, organizations have deployed an array of transactional and analytic systems for supply chain operations. While gains have been made, supply chains remain far from the ideal of real-time visibility and data-driven decisions that define the supply chain of the future. 

Supply chains have grown infinitely more complex in today’s faster digital environments. Soaring data volumes and diversity are overwhelming the relatively static and simplistic business rules of legacy transactional and analytic applications. More partners, products, and geographies complicate the dilemma. 

As a result, too many supply chains run on guesswork decisions based on information that’s often outdated and contradictory. Companies risk delays, needless costs, and revenue loss by continuing to rely on status quo processes and decade-old software. 

Enter artificial intelligence. AI is poised to transform supply chains with breakthrough capabilities to process huge amounts of real-time data and make intelligent recommendations to usher supply chains into a truly data-driven future.

A Smarter Supply Chain

How will this intelligent supply chain actually work? Rather than the limiting rules of traditional software, AI relies on machine learning algorithms to learn and refine in real time as it crawls internal and external data sets. That could include inventory data, supplier performance, demand fluctuations, and even weather or road conditions. 

AI combines this disparate knowledge to make recommendations or decisions on optimal actions. Think of it almost as a self-driving business based on cognitive automation—the ability to learn, think, and take actions. 

Take the available-to-promise function, which responds to customer order inquiries based on resource availability. In traditional software, ATP is fundamentally a rules-based calculation based on theoretical lead times and allocation rules that are incredibly variable and volatile. Using those data points in ATP calculations can result in wrong ATP dates.

In contrast, AI can automatically generate a “supply chain map,” showing everything about an order, including allocated quantity and expected delivery date. It delivers highly accurate recommendations and predictions based on machine learning and data science, not simple rules-based ATP calculations. 

ATP is just one example. AI’s powerful cognitive automation capabilities can be applied to all supply chain processes, from demand and supply forecasting to inventory optimization, manufacturing performance, procurement automation, and supplier reliability assessments. 

Getting Started With Cognitive Automation

The AI-driven supply chain is a journey—and like any journey, it needs a roadmap and a driver. Supply chain managers at innovative companies are today moving through five key levels on their way to intelligent, data-driven operations: 

  • Understanding. Leverage AI to fully understand the true operative states of your supply chain.
  • Recommendations. Utilize the AI system for recommendations on key risks and opportunities.
  • Predictions. Gain insights with predictions and probabilities based on AI’s continuously evolving machine learning.
  • Augmented decisions. AI suggests optimal decisions that require human review and approval.
  • Autonomous decisions. AI makes decisions autonomously without human intervention. 

Moving through various levels of understanding and decision-making, supply chains can ultimately reach completely autonomous decision-making, leaving room for experts and managers to focus on strategy and growth. So where should you start?

First, think through specific use cases for applying AI in your supply chain, whether it’s demand forecasting, inventory planning, manufacturing, or ATP / CTP (capable to promise) optimization. From there, assess each of the business rules for those disciplines and how to make them more algorithmic with data science. 

Once you’ve mastered the basics, it’s important to operationalize AI into your business. AI cannot be a side project—when done right, it should be embedded within your processes.

Despite the hype around AI, it’s already delivering the insights and optimizations that many supply chains desperately need. By embracing it vigorously, we enter a brave new world with boundless possibilities for supply chains to run as efficiently as proverbial clockwork.






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