• Written by Sonali Subhadarsini, Product Manager

A shift supervisor at a distribution centre is likely to have experienced the frustration of dealing with carrier performance variance, where the time it takes for a shipment to arrive can vary significantly from one day to another, affecting the entire logistics operation. This is just one of the challenges that logistics teams face on a daily basis, and it is an area where AI productivity in the enterprise can potentially make a significant impact.

Logistics challenges

Logistics makes it harder for organisations to achieve productivity gains due to the inherent complexity of the networks involved. Carrier performance variance, dwell and detention, and exception handling at scale are just a few examples of the issues that logistics teams have to contend with. The category manager, for instance, has to compare the performance of different carriers and negotiate contracts that balance cost and service levels, all while ensuring that the organisation's service level agreements are met.

The impact of these challenges can be seen in the way that logistics teams spend their time. A significant amount of time is spent on exception handling, such as resolving issues with missed deliveries or damaged goods, which can be time-consuming and take away from more strategic activities. The use of AI productivity tools in logistics can help to alleviate some of these issues, but it is not a silver bullet, and organisations need to be aware of the limitations and potential pitfalls.

Time saved vs time redeployed

One of the key differences between time saved and time redeployed is that the former is often easier to measure, but the latter is where the real productivity gains are made. When logistics teams use AI productivity tools to automate certain tasks, such as data entry or reporting, they may save time in the short term, but if that time is not redeployed to more strategic activities, then the overall impact on productivity is limited. The supply chain manager, for example, may use the time saved to focus on more strategic activities, such as analysing supply chain data to identify areas for improvement.

However, in many cases, the time saved is simply absorbed into other tasks, or is used to deal with the increased volume of work that comes with growth. This is why it is so important for organisations to have a clear understanding of how they want to use AI productivity tools in logistics, and to have a plan in place for how to redeploy the time saved to drive real productivity gains.

Self-reported time savings

Self-reported time savings are often overstated, and this can be a problem when trying to evaluate the effectiveness of AI productivity tools in logistics. When teams are asked to estimate the amount of time they have saved, they may be inclined to overestimate, either because they are enthusiastic about the new technology, or because they want to demonstrate the value of their work. The warehouse manager, for instance, may report that a new automated system has saved a significant amount of time, but when the data is analysed, it may turn out that the actual time saved is much less.

This is why it is so important to have objective measures of productivity, such as data on throughput, inventory levels, or customer satisfaction. By using data to measure the impact of AI productivity tools, organisations can get a more accurate picture of the benefits, and make informed decisions about how to invest in these technologies. For more information on how to get started with AI productivity tools, visit our website.

Tasks worth removing

When it comes to using AI productivity tools in logistics, it is often the case that the tasks that are worth removing are those that are repetitive, mundane, or prone to error. Data entry, for example, is a task that can be easily automated, freeing up staff to focus on more strategic activities. The transport manager, for instance, may use AI productivity tools to automate the process of tracking shipments, allowing them to focus on more complex tasks, such as route optimisation.

In some cases, the tasks that are worth removing may be those that are not core to the business, such as reporting or compliance activities. By automating these tasks, organisations can free up staff to focus on more value-added activities, such as customer service or supply chain optimisation. To find out more about how AsscherAi can help with this, contact us.

Limitations of AI productivity tools

While AI productivity tools can be a powerful way to drive productivity gains in logistics, they are not a solution to every problem. In some cases, the complexity of the logistics network, or the need for human judgment and decision-making, may make it difficult to automate certain tasks. The logistics manager, for instance, may need to use their expertise and experience to resolve complex issues, such as supply chain disruptions or unexpected changes in demand.

In these cases, AI productivity tools may not be the best solution, and organisations may need to look to other technologies, such as data analytics or simulation tools, to drive productivity gains. By understanding the limitations of AI productivity tools, organisations can make informed decisions about how to invest in these technologies, and how to use them to drive real productivity gains in logistics.