8 keys to achieving success with AI in supply chain

How supply chain managers can ensure optimal success when implementing AI in supply chain…


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. More importantly, the efficacy of AI is heavily dependent on the availability of the timely and accurate data that is needed to make smart decisions. There are eight criteria that are required for a successful AI implementation. Miss one of these and you’ll be lucky to achieve mediocre outcomes, but when you meet them all, you can indeed achieve world-class results. For the AI solution to offer optimal value in supply chain, it is important to ensure the following:


1. Access to real-time data

To improve on traditional enterprise systems with older batch planning systems, new AI systems must eliminate the stale data problem. Most supply chains today attempt to execute plans using data that is days old, but this results in poor decision making that sub-optimises the supply chain or requires manual user intervention to address. Without real-time information, an AI tool is just making bad decisions faster.


2. Access to community (multi-party) data

The ability to access data outside of the enterprise or, more importantly, receive permission to see the data that is relevant to your trading community must be made available to any type of AI, Deep Learning or Machine Learning algorithms. Unless the AI tool can see the forward-most demand and downstream supply, and all relevant constraints and capacities in the supply chain, the results will be no better than that of a traditional planning system. Unfortunately, this lack of visibility and access to real-time, community data is the norm in over 99 percent of all supply chains. Needless to say, this must change for an AI tool to be successful.


3. 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. If we ignore this fact, trading partners will not get the full value that comes from optimising service levels and cost to serve, which is obviously important as increased consumer sell-through drives value for everyone. A further enrichment of the decision algorithm should support enterprise level cross-customer allocation to address product scarcity issues and individual enterprise business policies. Thus, AI solutions must support global consumer-driven objectives even when faced with constraints within the supply chain.


4. Decision process must be incremental and consider the cost of change

Replanning and changing execution plans across a networked community in real time can create nervousness in the community. Constant change without weighing the cost of the change creates more costs than savings and reduces the ability to effectively execute. An AI tool must consider trade-offs in terms of cost of change against incremental benefits when making decisions.


5. Decision process must be continuous, self-learning and self-monitoring

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


6. 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. This requires your AI system and the underlying execution system to support multi-party execution workflows.


7. AI engines must be highly scalable

For the supply chain to be optimised across an entire networked community of consumers to suppliers, the system must be able to process huge volumes of data very quickly. Large community supply chains can have millions, if not hundreds of millions, of stocking locations. AI solutions must be able to make smart decisions, fast, and on a massive scale.


8. Must have a way for users to engage with the system

AI should not operate in a ‘black box’. The user interface (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. However, the AI system must drive the system itself and only engage the user on an exception basis, or allow the user to add new information the AI may not know at the request of the user. 


Article Courtesy One Network Enterprises


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