Systems to measure and understand power usage at machine level
Toyota run a lot of high value machinery which uses a significant amount of energy in their operation. They would like to be able to monitor the equipment at a very local level to get an understanding of how much energy is being used and when. The manufacturing plant would then need a version of AI/machine learning to look at the data and give an analysis of the energy use and best practices for the energy consumed. Toyota can than better manage their usage as well as optimise the machinery. This energy data may also be used by AI/machine learning to identify if something has changed in the process and address problems earlier before becoming a major barrier to production.
Opportunity
Challenge opens
03/01/2024
Challenge closes
23/02/2024
Benefit
Toyota manufacturing are looking to monitor and manage their machinery and its energy to get the best value from its use. They are looking for monitoring of machines for energy consumption with the potential to be supported by AI to get the best value and optimisation of power consumption.
Background & Challenge
Manufacturing companies are now looking more closely at their energy costs and in particular, their energy management as they move to a low carbon way of working. Toyota have a lot of machinery that they are looking to monitor on either a local level or line level depending on what gives the best value and response. By understanding how the machines are using energy, noting spikes and other anomalies, the team will be able to look for best use and smooth out the consumption.
To support the data coming in from the various machines or lines, a machine learning or AI support system would be needed to identify certain situations where the flow of energy is getting high or is not as they would expect it, and also to be able to analyse to look for energy saving opportunities.
The system that could monitor all these machines which includes hand held tools in assembly would be scoped to where there may be an opportunity for machine-based monitoring against where line based may be sufficient, smart AI/machine learning that could learn machines from a line based set of results would make the monitoring more desirable.
The installation of such a system needs to be carefully planned as the business is in a very large factory that works 24/5 producing engines for other parts of the Toyota group, therefore any disruption due to installation would need to be considered. Smart systems that have the ability monitor at a distance and and provide data at a machine level would be desirable due to less disruption to the manufacturing process.
The Toyota manufacturing plant has a full range of machinery from furnaces for casting components, machinery for manufacturing finished parts from the castings, which will include metal cutting machinery, through to the assembly shop where all the parts come together on lines, electric hand tools for assembly and handling equipment for health and safety of the team members.
The innovator would need to consider the different types, makes and systems of the machines, as well as consider potential future upgrades to facilities in their method of gathering data.
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