• Written by Muhammed Abdulla NC, CEO

While a stable average lead time may suggest a well-oiled supply chain, it can often mask underlying variance that can have significant business consequences, such as increased inventory costs and reduced customer satisfaction. In reality, a stable average can be hiding a multitude of sins, including late deliveries, stockouts, and overstocking. This is where AI can help, by analyzing the data to identify patterns and trends that can inform decision-making.

What has to be in the data for this to be possible

To reduce lead time variance hidden behind a stable average, the data must include purchase and delivery history, as well as lead times. This data should be granular, with detailed information on each transaction, including the date, time, and quantity of goods purchased or delivered. The data should also be comprehensive, covering all aspects of the supply chain, from procurement to delivery. With this data, AI can begin to identify patterns and trends that may not be immediately apparent.

A shift supervisor, for example, may check the maintenance log to identify any issues that may have caused delays in the delivery of goods. By analyzing this data, the supervisor can identify areas for improvement and take corrective action to reduce lead time variance. This is particularly important in industries where timely delivery is critical, such as in the operations of a manufacturing plant.

The mechanism, stated plainly

The mechanism by which AI reduces lead time variance is straightforward. By analyzing the data, AI can identify patterns and trends that may be contributing to variance. For example, AI may identify that deliveries from a particular supplier are consistently late, or that a particular transportation route is prone to delays. With this information, decision-makers can take corrective action, such as renegotiating with the supplier or exploring alternative transportation routes.

This is not a complex process, but it does require a significant amount of data and computational power. Most people get wrong that AI is a magic bullet that can solve all supply chain problems, but in reality, it is simply a tool that can help inform decision-making. By providing insights into lead time variance, AI can help decision-makers make more informed decisions, but it is not a replacement for human judgment and expertise.

What it will not fix

AI will not fix underlying issues with the supply chain, such as inadequate inventory management or poor supplier relationships. These issues require human intervention and expertise to resolve. AI can only provide insights and recommendations, it is up to the decision-makers to take action. Furthermore, AI is not a substitute for good supply chain management practices, such as regularly reviewing and updating inventory levels and supplier contracts.

Many people are wrong to assume that AI can solve all supply chain problems, but in reality, it is just one tool that can be used to inform decision-making. By understanding the limitations of AI, decision-makers can use it more effectively to reduce lead time variance and improve overall supply chain performance.

How you would know it worked

The success of AI in reducing lead time variance can be measured by tracking lead time variance over time. If the variance decreases, it is likely that the AI is having a positive impact. Additionally, decision-makers can track other metrics, such as inventory levels and customer satisfaction, to see if the AI is having a broader impact on the supply chain. By regularly reviewing these metrics, decision-makers can refine their approach and make adjustments as needed.

For example, a category manager may compare the lead time variance of different suppliers to identify areas for improvement. By using AI to analyze the data, the manager can identify patterns and trends that may not be immediately apparent, and take corrective action to reduce lead time variance. This can have a significant impact on the overall performance of the supply chain, and can help to improve customer satisfaction and reduce costs.

What this does not do, or when it is the wrong choice

This approach does not provide a complete overhaul of the supply chain, but rather a targeted solution to a specific problem. If the underlying issues with the supply chain are more fundamental, such as a lack of visibility or inadequate inventory management, then a more comprehensive approach may be needed. In these cases, AI may not be the right choice, and a more traditional approach to supply chain management may be more effective. If you are unsure about how to proceed, you can contact us to discuss your options.