<\/p>\ngaps in deployment<\/p>\n
<\/a> are and what a national level of implementation in each of these sectors might look like. <\/p>\n
AI is often used for predictive maintenance to forecast when machines are likely to fail so repairs can be carried out before breakdowns disrupt production. <\/p>\n
Predictive maintenance systems typically analyse real-time data from sensors, tracking factors such as vibration, temperature and equipment performance to identify patterns that indicate potential faults.<\/p>\n
Professor Stefan Winkler from Singapore Institute of Technology\u2019s (SIT) engineering cluster said: \u201cThese tools are sometimes deployed for individual machines, or perhaps a production line, rather than across entire supply chains, limiting productivity gains.\u201d <\/p>\n
For example, semiconductor foundry GlobalFoundries deploys the technology in its 300mm semiconductor fabrication plant in Woodlands.<\/p>\n
Separately,\u00a0A*Star and the Ministry of Trade and Industry have been operating a Sectoral AI Centre of Excellence for Manufacturing since September 2024 to help the industry tap AI for purposes such as predictive maintenance, product design and industrial automation. Thirteen companies, including drinks producer Coca-Cola and tech manufacturer Philips, were working with the centre at launch.<\/p>\n
Mr Zavier Wong, market analyst at Israel-headquartered online trading platform eToro, said that machines that perform predictive maintenance can be linked to systems that manage production scheduling and the procurement of spare parts.<\/p>\n
So even before a manufacturing plant breaks down or receives scheduled maintenance, other plants can automatically take over the production. A linked procurement system can also automatically order the necessary spare parts for repair.<\/p>\n
Currently, many of these systems exist in isolation and human reviewers are manually ordering parts or shifting production work to other facilities. <\/p>\n