• Written by Sonali Subhadarsini, Product Manager

The shift supervisor at a large distribution centre is staring at a bank of screens displaying real-time data on carrier performance, trying to make sense of the constant fluctuations in delivery times and costs, which is where AI process automation in logistics can help. As they scan the data, they are aware that even small changes in carrier performance can have a significant impact on the entire logistics operation, and that automating the steps around decision-making can help mitigate some of these issues. For instance, a category manager comparing two suppliers might use AsscherAi to query the organisation's own data on supplier performance, allowing them to make more informed decisions about which suppliers to use.

Logistics challenges

Logistics operations are inherently complex, with multiple variables interacting in intricate ways, making it harder to automate processes. Carrier performance variance, dwell and detention, and exception handling at scale are just a few of the challenges that logistics teams face on a daily basis. These challenges can lead to delays, increased costs, and decreased customer satisfaction, which is why AsscherAi can be a useful tool in helping logistics teams to automate some of these processes and make more informed decisions.

For example, a logistics team might use AsscherAi to query their organisation's own data on carrier performance, allowing them to identify trends and patterns that can inform their decision-making. By automating the steps around decision-making, logistics teams can free up more time to focus on higher-level strategic decisions, rather than getting bogged down in the details of day-to-day operations.

Mapping the current process

Before automating any part of the logistics process, it is essential to map out the current process in detail, including all the handoffs, decision points, and exception handling procedures. This involves observing and documenting the daily activities of logistics teams, from the shift supervisor to the category manager, to understand how they currently make decisions and handle exceptions. By doing so, logistics teams can identify areas where automation can add the most value and streamline processes.

This mapping process can be time-consuming, but it is crucial in identifying opportunities for automation and ensuring that any automated processes are aligned with the overall goals of the logistics operation. It also helps to identify potential roadblocks and areas where automation may not be feasible, allowing logistics teams to develop strategies to address these challenges.

Handoffs and delays

Handoffs between different teams and systems are often the source of significant delays in logistics operations. When information is not communicated effectively or in a timely manner, it can lead to misunderstandings, misallocations, and mistakes, which can have a ripple effect throughout the entire operation. By automating the handoffs between teams and systems, logistics teams can reduce the likelihood of errors and delays, and improve overall efficiency.

For instance, automating the handoff between the logistics team and the transportation team can ensure that shipments are properly routed and tracked, reducing the risk of lost or delayed shipments. This can be achieved through the use of AI process automation in logistics, which can help to streamline communication and reduce the risk of human error.

Exception handling

Exception handling is a critical component of logistics operations, as it involves dealing with unexpected events or disruptions that can impact the entire operation. While automation can help with routine tasks, exception handling often requires human intervention and judgment, making it a challenging area to automate. However, by using AI process automation in logistics, logistics teams can develop strategies to handle exceptions more effectively, such as by identifying patterns and trends that can inform decision-making.

Exception handling is often the majority of the real work in logistics, as it requires logistics teams to think on their feet and come up with creative solutions to complex problems. By automating some of the routine tasks and providing logistics teams with better data and insights, AI process automation in logistics can help teams to focus on the exceptions and develop more effective strategies for handling them.

Limitations of AI process automation in logistics

While AI process automation in logistics can be a powerful tool in streamlining logistics operations, it is not a silver bullet, and there are limitations to its use. For example, AI process automation in logistics may not be suitable for operations with highly variable or unpredictable workflows, or where the cost of automation outweighs the potential benefits. In such cases, logistics teams may need to explore alternative solutions, such as process re-engineering or manual workarounds, and can contact AsscherAi support for more information on how to get started with AI process automation in logistics.

Ultimately, the decision to implement AI process automation in logistics depends on the specific needs and goals of the logistics operation, and logistics teams should carefully weigh the potential benefits against the potential limitations and challenges. By doing so, logistics teams can make informed decisions about how to use AI process automation in logistics to improve their operations and achieve their goals.