AI in the Digital Supply Chain Network?

A multi-party business network to Gartner also known as a Control Tower in the market or what One Network calls the Digital Supply Chain Network? connects various organizations together to share information, collaborate on business processes, and conduct transactions. This network is commonly utilized in supply chain management to connect different companies and organizations involved in the movement of goods and materials.

The significance of the Digital Supply Chain Network? in supply chain management lies in their ability to promote collaboration and coordination among various organizations in the supply chain. By linking suppliers, manufacturers, distributors, and retailers, for instance, a multi-party business network can help streamline the flow of goods and materials, reduce costs and increase efficiency.

In addition to these operational benefits, the Digital Supply Chain Network? also provide enhanced visibility and control over the supply chain. By sharing information and collaborating on business processes, organizations can easily track and monitor the movement of goods and materials, and address any issues that may arise. This can help minimize risks and improve overall supply chain performance.

The integration of AI NEO in the Digital Supply Chain Network? is crucial in supply chain as it provides the necessary data and infrastructure for AI systems to make accurate and informed decisions. By connecting multiple organizations and sharing information, a Digital Supply Chain Network? can provide AI systems with a large and diverse dataset that they can use to learn and make predictions, particularly in supply chain management where AI can be utilized to optimize logistics, predict demand and enhance overall efficiency.

Despite numerous attempts by companies to integrate AI in supply chain management, the outcome has been underwhelming. This can be attributed to the current state of supply chain systems, which often involve high costs for numerous planners, complex processes at each step and network node, conflicts with other functions and partners, missed opportunities due to local sub-optimization, reliance on outdated data leading to poor decisions, and simplistic problem models that do not accurately reflect reality.

Artificial Intelligence (AI) has the potential to revolutionize supply chain management, but for it to truly deliver results, certain key factors must be taken into consideration. These eight key considerations that companies must keep in mind when implementing AI in their supply chain operations include:

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 (Information Lead-Time), but this results in poor decision-making that sub-optimizes 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. Every time an event occurs Ai NEO in a network will calculate the propagation impact holistically on all parties to decide if an action need to be taken. 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 the 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 optimizing service levels and cost to serve, which is obviously important as increased consumer sell-through drives value for everyone.
4. Decision Process Must Be Incremental and Consider the Cost of Change: Re-planning 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 Digital Supply Chain 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 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 optimized 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 UI must give users visibility to

The Digital Supply Chain Network? is vital for the optimal implementation of AI as it provides the necessary data and infrastructure for accurate and informed decision making. It also allows for autonomous decision making and execution of actions, which can help enhance the overall efficiency of the supply chain and reduce costs while simultaneously reducing lead time.