How do maritime organizations make predictive insights truly scalable? (Hint: don't start with AI!)
The maritime sector is in a state of flux. Fuel costs are rising, connectivity on the ships is a challenge and regulations are being tightened in all kinds of areas (from cybersecurity to CO2 emissions). Predictive insights are more relevant than ever. And in doing so, many organizations like to reach for the 'newcomer' that is now radically transforming every sector: AI.
Understandable, because as a maritime organization you can also benefit greatly from this within your logistics and production processes. But let's take a step back for a moment. After all, is your organization actually ready to use AI?
What should you think of when you think of 'predictive insights'?
The possibilities are enormous. We list some examples:
- Predicting arrival times
When does something come in and when does it go out again? When you know that, you (drastically) reduce waiting times.
- Dynamic capacity planning
How many people or trucks do you need to transport a load? Accurate forecasting of incoming and outgoing quantities ensures that you have the right capacity of resources at your disposal.
- Saving on fuel
Can you plan routes in such a way that you use less fuel? Or can you use other means? Good forecasts save on fuel. And as a result, you also achieve cost savings.
- Reducing CO2 emissions and optimising the route
What are your CO2 emissions on a certain transport route? What can you do to reduce these based on what you encounter along the way? With predictive insights, you determine the optimal route with minimal CO2 emissions.
Three-step plan: cloud strategy, data, AI
Before you can make these kinds of predictive insights scalable, you need to have the basics in order. First of all, it consists of a good cloud foundation. From workstation to IoT: you want everything you have connected in the cloud. From there you can secure and monitor it properly. And if something 'breaks', you can automatically take actions to fix it.
Important to know: a cloud foundation alone is not enough. It is also crucial to have a good overview of your data. Creating it is therefore the second step you need to take. Because how else can you orchestrate all incoming information (such as manuals, system information and signals from sensors)?
Only when you have set up the cloud and have your data in order, can you safely start experimenting with AI. (Many organizations think they have to start with this, but this is actually the third step!)
Want to get started with AI quickly? Build in parallel
Before you get discouraged: we don't recommend waiting until everything is perfect!
Yes: a solid cloud strategy is the foundation you need. But such a strategy is most efficient when you incorporate AI.
Our advice: start with small AI pilots while building your cloud foundation. Pick up use cases for AI in your organizations so you can work in parallel. It is smart not to take AI as a starting point. Rather ask about the bottlenecks and costs. Once you have mapped these out, you can determine which AI experiments can concretely improve them. You run pilots on these use cases.
When you see that a pilot is successful, you are already a lot further with the cloud and you can start scaling immediately.
Need support and guidance in taking the three steps? We are happy to help you. For example, through a Data Governance Assessment, shaping and implementing a cloud strategy or AI Envisioning.