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If you want to explore how you can balance energy between logistics, building, renewables and grid then chat to us. We are working with a small number of subject matter experts to introduce a new digital platform.
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Systems Thinking: How do Buildings and Logistics systems collaborate in the future?
Recently we've won a grant with Connected Places Catapult to work with Welch's, a logistics company just outside Cambridge. This project is fascinating because it's a perfect example of the "system of systems" complexity that AI is particularly good at handling.
The Challenge
Picture this: you're running a large distribution centre, and you want to transition your fleet to electric vehicles. Brilliant for sustainability, but here's the catch - your grid connection can only support two charging points. Meanwhile, you've got:
A massive solar PV array on your roof
Battery storage systems
Dozens of lorries that need charging on tight schedules.
Peak demand charges that could sink your margins if you get the timing wrong.
How do you optimise all of this? How do you know when to charge from solar, when to store in batteries, when to draw from the grid? And crucially, how do you schedule your entire fleet's charging regime to maximise the number of EVs you can actually operate?
For logistics companies, scheduling is everything. They plan their diesel purchasing and route scheduling to the nth degree, that's how they make money. Get the energy management wrong in an EV transition, and the entire business model falls apart.
The feedback we've had from freight companies since announcing this project has been incredible. They're all grappling with the same grid capacity constraints, and they can see how optimization could be the difference between a successful EV transition and an extremely expensive failed experiment.
Our Approach
This is where our combination of machine learning, large language models, and semantic data integration really comes into its own. We're not just looking at charging schedules in isolation, we're modelling the entire system: energy generation patterns, storage capacity, grid availability, vehicle requirements, route planning, and even weather forecasts.
It's early days on this project, but I'm genuinely excited about where it could lead. The principles we're developing here - system-level optimisation, balancing multiple energy sources, predictive scheduling, they apply far beyond logistics. Any organisation managing complex energy systems on site could benefit from this thinking.
Chat to us
Copyright ©
2025
optimise-ai.com
Back to Blog
Systems Thinking: How do Buildings and Logistics systems collaborate in the future?
Recently we've won a grant with Connected Places Catapult to work with Welch's, a logistics company just outside Cambridge. This project is fascinating because it's a perfect example of the "system of systems" complexity that AI is particularly good at handling.
The Challenge
Picture this: you're running a large distribution centre, and you want to transition your fleet to electric vehicles. Brilliant for sustainability, but here's the catch - your grid connection can only support two charging points. Meanwhile, you've got:
A massive solar PV array on your roof
Battery storage systems
Dozens of lorries that need charging on tight schedules.
Peak demand charges that could sink your margins if you get the timing wrong.
How do you optimise all of this? How do you know when to charge from solar, when to store in batteries, when to draw from the grid? And crucially, how do you schedule your entire fleet's charging regime to maximise the number of EVs you can actually operate?
For logistics companies, scheduling is everything. They plan their diesel purchasing and route scheduling to the nth degree, that's how they make money. Get the energy management wrong in an EV transition, and the entire business model falls apart.
The feedback we've had from freight companies since announcing this project has been incredible. They're all grappling with the same grid capacity constraints, and they can see how optimization could be the difference between a successful EV transition and an extremely expensive failed experiment.
Our Approach
This is where our combination of machine learning, large language models, and semantic data integration really comes into its own. We're not just looking at charging schedules in isolation, we're modelling the entire system: energy generation patterns, storage capacity, grid availability, vehicle requirements, route planning, and even weather forecasts.
It's early days on this project, but I'm genuinely excited about where it could lead. The principles we're developing here - system-level optimisation, balancing multiple energy sources, predictive scheduling, they apply far beyond logistics. Any organisation managing complex energy systems on site could benefit from this thinking.
Back to Blog
Chat to us
If you want to explore how you can balance energy between logistics, building, renewables and grid then chat to us. We are working with a small number of subject matter experts to introduce a new digital platform.
Book a time to chat
Copyright ©
2025
optimise-ai.com
Copyright ©
2025
optimise-ai.com
Back to Blog
Systems Thinking: How do Buildings and Logistics systems collaborate in the future?
Recently we've won a grant with Connected Places Catapult to work with Welch's, a logistics company just outside Cambridge. This project is fascinating because it's a perfect example of the "system of systems" complexity that AI is particularly good at handling.
The Challenge
Picture this: you're running a large distribution centre, and you want to transition your fleet to electric vehicles. Brilliant for sustainability, but here's the catch - your grid connection can only support two charging points. Meanwhile, you've got:
A massive solar PV array on your roof
Battery storage systems
Dozens of lorries that need charging on tight schedules.
Peak demand charges that could sink your margins if you get the timing wrong.
How do you optimise all of this? How do you know when to charge from solar, when to store in batteries, when to draw from the grid? And crucially, how do you schedule your entire fleet's charging regime to maximise the number of EVs you can actually operate?
For logistics companies, scheduling is everything. They plan their diesel purchasing and route scheduling to the nth degree, that's how they make money. Get the energy management wrong in an EV transition, and the entire business model falls apart.
The feedback we've had from freight companies since announcing this project has been incredible. They're all grappling with the same grid capacity constraints, and they can see how optimization could be the difference between a successful EV transition and an extremely expensive failed experiment.
Our Approach
This is where our combination of machine learning, large language models, and semantic data integration really comes into its own. We're not just looking at charging schedules in isolation, we're modelling the entire system: energy generation patterns, storage capacity, grid availability, vehicle requirements, route planning, and even weather forecasts.
It's early days on this project, but I'm genuinely excited about where it could lead. The principles we're developing here - system-level optimisation, balancing multiple energy sources, predictive scheduling, they apply far beyond logistics. Any organisation managing complex energy systems on site could benefit from this thinking.
Back to Blog
Chat to us
If you want to explore how you can balance energy between logistics, building, renewables and grid then chat to us. We are working with a small number of subject matter experts to introduce a new digital platform.
Book a time to chat
Copyright ©
2025
optimise-ai.com
Copyright ©
2025
optimise-ai.com