Covering Disruptive Technology Powering Business in The Digital Age

The booming manufacturing industry in Asia-Pacific is about to change. Modern infrastructure and applications were originally adopted slowly, but today they are embraced quickly, thanks to the rise of digital twins, robotics, cloud and edge computing, artificial intelligence (AI), and especially generative AI.  By 2027, IDC predicts that 60% of Asia-Pacific organisations, will use automation to supplement operational jobs and increase employee engagement.

In recent news, the Malaysian Industrial Development Finance Bhd (MIDF) has approved soft loans totalling RM103.54 million to automate and modernise 29 companies as an incentive to embrace smart manufacturing using Industry 4.0 (IR4.0). Modernisation is not without difficulties, though. Manufacturers in Malaysia must take into account the potential and challenges that will shape the industry as it enters the world of artificial intelligence and cloud computing.

Capitalising on emerging technology

Forecasts indicate that the manufacturing sector will generate value added of US$118.5 billion (RM560.51 billion). With the increasing influx of high-tech and innovation-driven investments in Malaysia, especially in the green manufacturing and renewable sectors, the New Industrial Master Plan (NIMP) 2030 plan seeks to support Environmental, Social, and Governance (ESG) goals while increasing workforce productivity.

Manufacturers must adopt an integrated strategy using the cloud if they hope to successfully utilise these advancements and propel end-to-end transformation. To achieve this, they must expand on the skill sets they have already acquired during their transformation journeys, particularly in big data and analytics.

More precisely, since data is essential to intelligent production, manufacturers must figure out how to do it. Businesses now have access to enormous volumes of data that have the potential to completely change their operations because of the usage of increased Internet of Things (IoT) devices. Nevertheless, converting large amounts of data from many systems into useful insights is a difficult task.

To meet this problem, organisations can take advantage of AI’s wide range of capabilities to enable thorough data integration. With this degree of connection, customised centralised dashboards may offer real-time notifications at every stage of the operation. The enterprise-wide visibility offered, when combined with digital twins, will enable manufacturers to make more intelligent and efficient operational and business decisions.

By effectively utilising these resources, manufacturers will be able to analyse data from many departments inside the company, personalise both internal and external tools, and improve visibility to facilitate improved decision-making. The foundations of effective data and knowledge sharing are AI and the cloud. Organisations are able to lay out a plan that facilitates flexibility and adaptability when they have a strong feedback loop.

One firm that has effectively leveraged artificial intelligence is Cerapedics, a pioneer in biological bone grafting. The business created an IoT-enabled smart factory to scale. Through the use of a cloud environment, they were able to integrate both digital and analogue sensors, providing real-time insights that allowed for predictive maintenance and future growth prospects. Strict data collecting was a part of the process to guarantee regulatory compliance.

Manufacturing will be driven into the future by embracing these changes as a strategic shift, which will also enable new operational paradigms and help realise the full potential of the industrial IoT.

Prospective challenges

Even though modernisation—especially AI-driven modernisation—is pervasive in businesses, serious security issues persist. According to Rackspace’s Technology FAIR 2024 AI Research report, hardly 50% of participants indicated that their companies adhere to data management and retention regulations. Furthermore, 55% of respondents are cautious about the risks AI poses to cybersecurity.

Over half of the respondents indicated that their AI governance initiatives are centred on the responsible and ethical use of AI, indicating that ethics is another important concern. When asked to define “Responsible AI,” 52% of respondents said accountability, and 60% thought it had to do with data privacy. Fifty percent of the respondents mentioned transparency. This emphasises how crucial it is to first establish the governance framework to guarantee that AI risks are appropriately reduced and controlled.

Importantly, relative to AI adoption rates, there are fewer people concerned about AI governance, suggesting that IT leaders may be placing too much faith and security in AI systems. This is something that needs to be taken care of right now.

In terms of talent, Rackspace Technology discovered that businesses are working hard to close the skills gaps in software development and data analytics (40%), machine learning, and data engineering (43%), and data governance (38%). The majority of organisations don’t provide formalised training programs, which is the issue. Just about 40% of businesses offer AI training, but about 58% indicate they intend to do so in the future.

Thankfully, there are remedies available for the problems mentioned above. It is the responsibility of manufacturers to make wise decisions and plans. Manufacturers need to move to the cloud and work with professionals who can tailor tools and solutions to meet their specific demands as an organisation and business. Manufacturers may lay a strong basis for industrial innovation by working with the right partners.

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