Artificial Intelligence (AI) can offer a huge benefit to supply chain managers, but only if it is based on solid fundamentals that take into account the diverse and dynamic nature of today’s modern supply chains.

WHAT IS ARTIFICIAL INTELLIGENCE?

“Artificial Intelligence AI can be about simulating human intelligence, incorporating traits such as reasoning, perception, problem-solving and forward planning. At its crux, though, AI is about the development and enactment of methods of transforming vast amounts of complex, often unstructured data into intelligent insights. The key elements of artificial intelligence – machine learning, cognitive computing, natural language processing, and sentiment analysis, combined with more effective real-time data management – make this possible.”

“Artificial Intelligence AI can be about simulating human intelligence, incorporating traits such as reasoning, perception, problem solving and forward planning. At its crux, though, AI is about the development and enactment of methods of transforming vast amounts of complex, often unstructured data into intelligent insights. The key elements of artificial intelligence – machine learning, cognitive computing, natural language processing, and sentiment analysis, combined with more effective real- time data management – make this possible.”

THE KEY REQUIREMENTS FOR AI IN SUPPLY CHAIN MANAGEMENT

For the AI solution to offer optimal value in supply chain, it’ important to ensure the following:

1. Access to real-time and community data

AI is only as intelligent as the data it receives. Many supply chain companies make the mistake of mining “near real-time” data that is days or weeks old and assuming it will serve the needs of its AI system. Instead, this stale data creates deficiencies in the decision-making process and the need for human intervention. Without correcting the issue of old data, a snowball effect of bad decisions will occur. Supply chain logistics happen quickly and from moment to moment. It therefore stands to reason that for an AI system to achieve optimal results and make the best decisions, it must be fueled by accurate, up-to-date information.

Secondly, the AI system needs access to data outside the supply chain platform. Without this key, downstream data, machine learning algorithms will be operating with a handicap and in a silo. This leads to costly course corrections that waste valuable time and consume additional resources.

2. Support for Network-Wide Objective Functions

The objective function, or primary goal, of the AI engine must be consumer service level at lowest possible cost. This is because the end-consumer is the only consumer of true finished goods products. Thus, AI solutions must support global consumer- driven objectives even when faced with constraints within the supply chain. Cost and efficiency simply can’t be the only two factors considered for AI. Consumer-driven objectives and customer expectations at all points across the supply chain must also be included as non-negotiable, foundational elements for AI algorithms.

3. Decision Process Must Be Incremental and Consider the Cost of Change

In the simplest terms, AI is intelligence exhibited by machines particularly in the context of reasoning, making decisions and taking ac on. Jonathan Kaftzan puts it well:t will serve the needs of its AI system. Instead, this stale data creates deficiencies in the decision-making process and the need for human intervention. Without correcting the issue of old data, a snowball effect of bad decisions will occur. Supply chain logistics happen quickly and from moment to moment. It, therefore, stands to reason that for an AI system to achieve optimal results and make the best decisions, it must be fueled by accurate, up-to-date information.

4. Decision Process Must Be Continuous, Self-Learning and Self-Monitoring

Data in a multi-party, real- me network is always changing. Variability and latency is a recurring problem, and execution efficiency varies constantly. The AI system must be looking at the problem continuously, not just periodically, and should learn as it goes on how to best set its own policies to tune its abilities. Part of the learning process is to measure the effectiveness “analytics,” then apply what it has learned.

5. AI Engines Must Be Autonomous Decision-Making Engines

Significant value can only be achieved if the algorithm can not only make intelligent decisions but can also execute them. Furthermore, they need to execute not just within the enterprise but where appropriate, across trading partners. While supply chain teams certainly need visibility on the status and outcomes of AI processes with the ability to override decisions, it should be the exception– not the rule. Instead of letting AI almost reach the finish line, supply chain organizations must have the courage to let it cross and execute crucial business decisions while they still have the opportunity to make an impact.

6. A scalable AI strategy

Supply chain networks are intricate webs, with many connected strands within the community of suppliers, consumers, and logistics providers. To be successful, organizations must be able to manipulate large quantities of data quickly to make on-the-spot decisions. Artificial Intelligence systems should not be constrained by data processing limitations. As supply organizations grow, they must implement AI processes and platforms that can grow with them and scale easily. Supply chain organizations must design machine learning algorithms to effortlessly accommodate times of explosive growth so that the right decisions are made without the need for re-programming or costly downtime.

The potential for AI to support supply chain management is exciting and limitless. Whether it’s warehouse management, inventory controls or circumstance monitoring, the sky is the limit in this brave new world of machine learning. By opening doors for machines to use vast amounts of crucial data in a way that humans simply can’t, supply chain organizations will reap the benefits of Artificial Intelligence as a vehicle for efficiency and higher profitability.

7. Must-Have a Way for Users to Engage with the System

AI should not operate in a “black box.” The UI must give users visibility to decision criteria, propagation impact, and enable them to understand issues that the AI system cannot solve. The users, regardless of type, must to be able to monitor and provide additional input to override AI decisions when necessary.