• Written by Muhammed Abdulla NC, CEO

How can we get a better handle on logistics when the performance of our carriers is all over the map, and every delay or detention seems to trigger a fresh exception that needs handling? This is a question that comes up time and again in meetings, but it's hard to answer because the variables are so numerous and the trade-offs between cost and service are constant.

Logistics challenges

Logistics makes it harder to adopt new systems because of the inherent variability in carrier performance, dwell and detention times, and the need for exception handling at scale. A capable system like AsscherAi can help answer questions about an organisation's own data, but it's only as good as the data it's given, and logistics data is notoriously messy. The performance of different carriers can vary wildly, and even the best carriers can have bad days, which makes it hard to plan and predict.

And then there are the exceptions - the shipments that get delayed, the orders that get lost, the customers who change their minds at the last minute. These exceptions can be a major headache, and they require a lot of manual intervention to resolve, which can be time-consuming and expensive. A system that can help automate some of this exception handling, or at least provide better visibility into what's going on, could be a major boon.

Attaching new capability to existing routines

One way to make new systems more effective is to attach them to routines that already exist. For example, a shift supervisor might already be checking the maintenance log every morning to see what needs to be done that day. If a new system like AsscherAi can be integrated into that existing routine, it's more likely to get used, because it's not requiring the supervisor to do something entirely new. This can help build trust and familiarity with the system, and make it more likely that it will be used to inform decisions.

This approach can also help to identify areas where the new system can add the most value. By looking at where the existing routines are falling short, or where they're requiring a lot of manual intervention, it's possible to target the new system's capabilities at those specific pain points. This can help to ensure that the new system is having a real impact, and that it's not just being used as a fancy toy.

Who needs to be able to ask

It's not just a question of who needs the answer, but who needs to be able to ask the question in the first place. In many organisations, there are a lot of people who could benefit from being able to ask questions about logistics data, but who don't have the technical expertise to do so. This can create a bottleneck, where only a few people are able to get the information they need, and everyone else has to go through them. A system like AsscherAi, which allows users to ask questions in natural language, can help to break down this bottleneck, and make it possible for more people to get the information they need.

For example, a category manager might need to compare the performance of different suppliers, but not have the technical expertise to pull the relevant data. With a system like AsscherAi, they can simply ask the question, and get the answer they need. This can help to empower more people within the organisation, and make it possible for them to make better decisions. You can read more about how AsscherAi can help with this.

The first wrong answer and rebuilding trust

The first time a new system gives a wrong answer, it can be a major setback. It's natural to wonder if the system is trustworthy, and if it's worth continuing to use. But the truth is, all systems make mistakes sometimes, and it's how they recover from those mistakes that really matters. A system like AsscherAi, which is designed to learn and improve over time, can help to rebuild trust by providing more accurate answers as it goes along.

It's also important to have a clear understanding of how the system works, and what its limitations are. This can help to set expectations, and prevent disappointment when the system doesn't perform as hoped. By being transparent about how the system works, and what it can and can't do, it's possible to build trust with users, and make it more likely that they will continue to use the system. If you have any questions about how AsscherAi works, you can get in touch with us.

When it's the wrong choice

There are some cases where a system like AsscherAi might not be the best choice. For example, if the organisation is very small, or if the logistics data is very simple, it might not be worth the investment. In these cases, it might be better to stick with existing systems, or to look for a simpler solution. It's also important to consider the potential return on investment, and to make sure that the system will be able to pay for itself over time.

In general, it's wrong to assume that a new system will automatically be adopted, just because it's available. This is a mistake that many organisations make, and it can lead to a lot of wasted time and money. Instead, it's necessary to take a careful and considered approach, and to make sure that the system is properly integrated into existing routines and workflows. By taking the time to do this, it's possible to get the most out of a system like AsscherAi, and to make a real impact on logistics operations.