A readiness checklist for supply chain AI
Implementing AI in the supply chain requires a thorough readiness checklist to ensure connected data, clear definitions, and proper permissions, as a category manager comparing suppliers needs accurate and consistent data to make informed decisions, and a shift supervisor checking the maintenance log needs real-time data to focus on critical tasks.
A meeting to discuss implementing AI in the supply chain can quickly go off track if the organisation is not properly prepared. I have seen it happen: the IT team is excited to integrate AsscherAi with their existing systems, but the supply chain team is still arguing over what constitutes an "in-stock" item. Without a clear understanding of the data and definitions, the project stalls. The business consequence is a delayed rollout, which means the organisation misses out on the benefits of having its supply chain data queried in real time.
Connected data
The first step in preparing for supply chain AI is to ensure that all relevant data is connected and accessible. This includes data from various sources such as inventory management systems, logistics providers, and manufacturing plants. If this data is not connected, the AI system will not be able to provide accurate answers to queries. For example, a shift supervisor checks the maintenance log to see when the next scheduled maintenance is due, but the system does not have real-time data from the manufacturing plant, so the supervisor has to manually check with the plant manager.
This breaks the entire process, as the supervisor has to spend more time gathering information instead of focusing on more critical tasks. In a large organisation, this can lead to significant inefficiencies and delays. To avoid this, it is crucial to have all relevant data connected and accessible before implementing AI in the supply chain.
Definitions
Most people get wrong the idea that definitions are not crucial in implementing AI in the supply chain. They are wrong. Having clear and agreed-upon definitions is essential to ensure that everyone is on the same page. For instance, what constitutes an "in-stock" item? Is it when the item is available in the warehouse, or when it is available to be shipped? Without clear definitions, the AI system may provide inaccurate answers, leading to confusion and mistakes.
A category manager compares two suppliers, but the system uses different definitions for "lead time" and "delivery time", resulting in incorrect comparisons. To avoid this, it is vital to agree on definitions in writing before implementing AI in the supply chain. This ensures that everyone understands what the data means and can make informed decisions.
Permissions
Deciding on permissions before anyone asks is critical to ensuring the smooth implementation of AI in the supply chain. Who has access to the data? Who can query the system? Without clear permissions, the organisation may face security risks or data breaches. For example, a new employee is given access to the system without proper clearance, and they accidentally query sensitive data.
To avoid this, permissions should be decided and implemented before the AI system is rolled out. This includes determining who has access to the system, what data they can access, and what actions they can perform. By doing so, the organisation can ensure the security and integrity of its data. For more information on implementing permissions, visit our operations page.
A first question worth answering
A good first question to answer with supply chain AI is one that the supply chain team already waits on. For example, "what is the current inventory level of product X?" or "when will the next shipment of product Y arrive?" Answering these questions can provide significant value to the organisation and help to build trust in the AI system. A supply chain manager can use AsscherAi to query the system and get real-time answers, allowing them to make informed decisions and respond quickly to changes in the supply chain.
By starting with a question that is already relevant to the supply chain team, the organisation can demonstrate the value of AI and build momentum for further implementation. This can also help to identify areas where the AI system can be improved and expanded to provide even more value to the organisation.
Limitations
This AI checklist for supply chain does not guarantee the success of the implementation. It is just a starting point to ensure that the organisation is properly prepared. There are many other factors that can affect the success of the project, such as the quality of the data, the complexity of the supply chain, and the level of user adoption. If the organisation is not willing to invest time and resources into implementing and maintaining the AI system, it may not be the right choice.
In some cases, the organisation may not have the necessary infrastructure or expertise to support an AI system. In these cases, it may be better to focus on building the necessary foundations before implementing AI. For more information on how to get started with AsscherAi, visit our contact page.
Frequently asked questions
What are the key steps to prepare for supply chain AI implementation?
The key steps include connecting relevant data, agreeing on clear definitions, and deciding on permissions to ensure a smooth rollout.
Why are clear definitions crucial in supply chain AI implementation?
Clear definitions ensure everyone is on the same page, preventing confusion and mistakes, such as incorrect comparisons between suppliers.
What is the risk of not deciding on permissions before AI implementation?
Not deciding on permissions can lead to security risks or data breaches, such as a new employee accessing sensitive data without proper clearance.
What is a good first question to answer with supply chain AI?
A good first question is one that the supply chain team already waits on, such as current inventory levels or shipment arrival times, to provide value and build trust in the AI system.