• Written by Muhammed Abdulla NC, CEO

Supply chain managers expect AI to streamline their operations, but in practice, many deployments fail to deliver because they automate a process nobody fully understands. This mistake can lead to automated inefficiencies, where the AI system perpetuates existing flaws, rather than improving the overall process. A shift supervisor, for example, may use an AI-powered system to manage inventory, but if the underlying process is not well-mapped, the system may end up recommending stock levels that are consistently too high or too low.

Automating a process nobody mapped

Most supply chain deployments start with a desire to automate a specific process, such as demand forecasting or inventory management. However, many teams dive into automation without taking the time to thoroughly map out the existing process. This can lead to a situation where the AI system is automating a process that is already inefficient or ineffective. To avoid this mistake, teams should take the time to thoroughly map out their existing processes, identifying areas of inefficiency and opportunities for improvement. This can involve working with stakeholders from across the organisation, including those in operations and logistics.

A well-mapped process is essential for successful AI deployment, as it allows teams to identify areas where automation can add the most value. By taking the time to understand the existing process, teams can ensure that their AI system is automating a process that is efficient, effective, and aligned with the organisation's overall goals.

Treating purchase and delivery history as complete when it is not

Many AI systems rely on historical data to make predictions and recommendations. However, in supply chain management, this data is often incomplete or inaccurate. For example, a company may have changed its purchasing processes over time, or may have experienced disruptions to its delivery schedule. If this data is not properly cleaned and prepared, the AI system may make recommendations that are based on flawed assumptions. Most people get wrong the idea that historical data is always reliable, but this is wrong, as it can be incomplete, inaccurate, or outdated.

A category manager, for instance, may use an AI system to compare the performance of different suppliers, but if the historical data is incomplete, the system may recommend a supplier that is not actually the best choice. To avoid this mistake, teams should take the time to thoroughly clean and prepare their historical data, ensuring that it is accurate, complete, and relevant to the task at hand.

Giving the capability only to the analyst who already had it

One common mistake in AI deployment is giving the capability only to the analyst who already had it. This can limit the potential benefits of the AI system, as it may not be used by the people who need it most. For example, a company may deploy an AI-powered demand forecasting system, but only make it available to the demand forecasting team. This can mean that other teams, such as sales and marketing, do not have access to the insights and recommendations generated by the system.

This can lead to a situation where the AI system is not being used to its full potential, and where the benefits of the system are not being realised across the organisation. To avoid this mistake, teams should consider making the AI system available to a wider range of users, including those in sales, marketing, and other areas of the business.

Declaring success against a metric with no baseline

Finally, many teams declare success with their AI deployment without establishing a clear baseline for measurement. This can make it difficult to determine whether the AI system is actually having a positive impact, or whether any improvements are due to other factors. For example, a company may deploy an AI-powered inventory management system, and then claim that it has reduced inventory levels by a certain amount. However, if there is no baseline for comparison, it is impossible to know whether this reduction is actually due to the AI system, or whether it would have happened anyway.

To avoid this mistake, teams should establish clear metrics and baselines before deploying their AI system. This can involve tracking key performance indicators, such as inventory levels or supply chain costs, and comparing them to historical data. By establishing a clear baseline, teams can determine whether their AI system is actually having a positive impact, and make adjustments as needed.

When AI is not the right choice

While AI can be a powerful tool for supply chain management, there are certain situations where it may not be the right choice. For example, in situations where the data is limited or of poor quality, an AI system may not be able to generate accurate insights or recommendations. In these cases, teams may need to consider alternative approaches, such as manual analysis or process improvement initiatives. If you are considering deploying an AI system for your supply chain, but are unsure whether it is the right choice, you can contact us to discuss your options and determine the best approach for your organisation.