Implementing AI in Manufacturing: Opportunites for Southeast Asia

The world is constantly evolving due to technological advancements. Today’s technology plays an important role in helping businesses, governments, and social institutions improve productivity, create new products and services, and enhance overall well-being. By leveraging Artificial Intelligence (AI) technologies, the manufacturing sectors in Southeast Asia can analyze data and gain valuable insights to automate processes, improve existing capabilities, and enhance decision-making.

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According to the McKinsey Global Survey (2021) on the state of AI, a larger proportion of respondents reported that their organizations had implemented AI, rising 12% from 2020. The survey also indicated that integrating AI can lead to financial gains, with 27% of respondents citing that AI contributed at least 5% of their companies’ EBIT (Earning before Interest and Taxes).

AI applications are rapidly expanding into various industries and business functions. The technology sector is at the forefront of AI implementation. Moreover, product development and service operations have reaped significant benefits from AI deployment.

While organizations are steadily increasing the creation and implementation of AI solutions, a few challenges, including ethical, technical, regulatory, and organizational issues, must be addressed before companies can fully realize the technology’s potential.

A Brief Look at AI’s Impact on Manufacturing Costs

Across business functions, AI has already made a notable financial impact. As per McKinsey’s survey, two-thirds of respondents saw increased revenue from AI integration, while nearly 80% saw reduced operating costs. Here is the table depicting revenue increase from AI adoption by function.

Function	Increase by ≤5%	Increase by 6–10%	Increase by >10%
Product and/or service development	34	16	15
Strategy and corporate finance	38	15	10
Human resources	30	18	15
Manufacturing	38	25	11
Service operations	26	25	13
Marketing and sales	27	15	12
Risk	30	25	15
Supply chain management	33	32	2
Average across all activities	33	21	13

Here is the table depicting Cost decrease from AI adoption, by function.

Industry	Decrease by <10%	Decrease by 10–19%	Decrease by ≥20%
Product and/or service development	12	24	51
Strategy and corporate finance	23	27	37
Human resources	20	26	40
Manufacturing	21	35	27
Service operations	17	20	41
Marketing and sales	15	27	36
Risk	22	24	23
Supply chain management	10	28	30
Average across all activities	18	28	33

What industries are most affected by the trend?

The use of AI impacts a wide range of operations across all industries, leading to more profitable value chains, improved financial results, and more efficient operations.

Information Technology and Electronics

AI is widely used throughout the technology industry and its subsectors, such as software, hardware, and electronic devices. For instance, using generative AI models to create 3-D visuals for software simulations.

Telecommunications

AI models are programmed to identify recurring customer issues and offer solutions before complaints are raised.

Pharmaceuticals and Medical Products

Researchers are using AI to explore the relationships between different medical treatments and their combined effects to discover new drugs.

Healthcare Systems and Services

AI is playing a pivotal role in enhancing healthcare services by employing functions such as diagnosis decision support and automated pathology recognition.

Financial Services

AI is supporting risk management in financial services, such as detecting credit card fraud to minimize losses.

Retail and Consumer Packaged Goods

AI is increasing sales by analyzing large sets of purchasing data, identifying patterns, and providing customized recommendations to shoppers.

Aviation, Travel, and Logistics

AI is being used to combine inputs from various sensors to help operate autonomous vehicles, leveraging multimodal fusion.

How AI is Becoming a Staple in Manufacturing

Artificial intelligence as a modern manufacturing technology can tackle some of the industry’s difficulties. Though businesses have experimented with these technologies for the past decade, many have struggled to fully realize their potential. Companies have often employed measures for increased transparency in areas such as production processes and future predictions on the basis of historical data, but few have invested in AI-powered self-controlled systems. 

AI is applied in industrial operations to help systems and machines function effectively. The World Economic Forum has listed four main ways AI can contribute:

Enhancing productivity

Companies can use AI to boost output and reduce costs. Some of its applications could include predictive maintenance to improve equipment efficiency, self-optimization of machinery and process settings, and autonomous mobile robots. These applications have the potential for substantial revenue growth and double-digit cost savings.

Improving sustainability

Artificial intelligence can also make business operations more environmentally friendly by reducing emissions. For example, AI can predict future energy usage trends and determine which equipment is causing excess consumption and emissions. It can also minimize emissions by ascertaining the optimal production parameters and sequences.

Increasing flexibility

With continuous disruptions in the supply chain, artificial intelligence can improve the flexibility of business operations and lessen the effect of external shock. This could include demand forecasting, better network optimization, and advanced planning and scheduling for production.

Supporting the workforce

Finally, artificial intelligence can help address the skill shortages manufacturers face. It can automate tedious and repetitive tasks, freeing up the workforce for more valuable activities. Moreover, AI can assist and support employees in their work, such as in decision-making or through collaboration.

How AI Is Improving Manufacturing In 2023 

• General Motors (GM) has partnered with Autodesk to incorporate generative design algorithms into their product design process. The algorithms, based on machine learning techniques, take into account constraints and provide optimized designs.

• Nokia has developed a video application that employs machine learning to monitor the production process and alert assembly operators of any inconsistencies.

• Real-time image analysis is also being utilized in industries such as automotive and consumer products to perform product quality inspections and ensure compliance with regulatory standards.

• The accuracy of demand forecasting has improved across various industries, with consumer packaged goods manufacturers showing great results.

• Thales, a leading provider of electronic systems, is using machine learning to predict maintenance needs for high-speed rail lines in Europe.

• The BMW Group is using AI to inspect images of components from their production line and detect any deviations from quality standards in real-time.

AI Adoption in Southeast Asia

Nikolai Dobberstein, Asia Pacific Head of Communication, Media and Technology and Partner at Kearney, emphasized the significant impact AI can have on businesses in the SEA region and noted that, although there is a growing investment in AI, only Singapore is leading the way. AI investments are focused on four key areas: Machine Learning, Robotic Process Automation, language-based AI, and intelligent operations.

Moreover, compared to leading AI global centers such as the US and China, ASEAN countries are lagging behind with USD 2 million per capita in AI investments (2015-2019). However, Singapore stands out with USD 68 million.

In Southeast Asia, the following are some factors that are hindering the growth of AI adoption.

Overstated talent gap

While local AI skills are scarce, especially outside of Singapore, the technical skills required for developing and implementing AI solutions have become less specialized with the availability of ready-made AI models.

Fragmented and immature AI ecosystem

The underdeveloped AI ecosystem in Southeast Asia (not including Singapore) makes it difficult for companies to find and work with suitable AI providers to develop new applications.

Evolving data governance and infrastructure

The lack of quality within a company’s data infrastructure and governance is seen as a challenge in harmonizing and extracting data.

New regulations

Regulatory constraints, particularly in Indonesia, Vietnam, and the Philippines, are a significant obstacle to AI investments. More harmonized regulations across SEA will accelerate AI investments and adoption.

User resistance to AI adoption

AI will have an impact on jobs, and the pace of change can be concerning for some. A third of survey respondents expressed concern over a potential backlash from job displacement.

Principles of Successful AI Implementation

As compiled by PwC, your business needs six main readiness requirements. to successfully integrate AI into manufacturing

Business Applications:

• Determine the most essential uses for analytics in your business
• Consider the different perspectives from multiple departments
• Make use of all available data

Technology:

• Establish a centralized data platform
• Utilize cloud services, big data architecture, and open-source tools
• Incorporate IoT connectivity and machine learning

Data:

• Make sure you have access to the necessary data
• Establish a single source of truth
• Properly handle data quality
• Utilize external data sources and services
• Process unstructured data and handle real-time data processing
• Create data dictionaries

Talent and Organization:

• Ensure you have the right skills and expertise
• Hire data scientists and IT specialists
• Have business analytics translators and UX designers on staff
• Establish a center of competence or shared services
• Appoint a Chief Data Officer and seek advice from start-ups

Process:

• Promote data democratization
• Ensure proper data governance, security, and privacy
• Encourage knowledge exchange and collaboration
• Use agile development practices

Culture:

• Foster a culture of experimentation and data-driven decision-making
• Encourage the adoption of analytics tools
• Build trust in data and algorithms.

How to Start the AI Implementation Process

Implementing AI is a new and unexplored territory for many businesses. Professor Neale O’Connor from the Monash University gave advice for businesses looking to embrace this trend. 

Hire a factory auditor or utilize a third-party visibility provider to audit your factory. Factory audits are important for manufacturers to understand how to improve their operations. By conducting a factory audit, a manufacturer can identify areas for improvement, such as documentation, assembly lines, and processes.

Once the audit is complete, start the documentation process and begin measuring elements, such as water usage, electricity, quality control, machine downtime, and bottlenecks. Moreover, build relationships with original equipment manufacturers (OEMs).

Takeaways for Asian Region

• Transparency is the most crucial aspect for small and medium-sized enterprises (SMEs) in Asia, as international clients place great importance on compliance, quality control, and brand management. To enhance transparency, factories can adopt digital systems, which help them collect data digitally without costly equipment changes.

• Asian manufacturers should focus on continuous improvement and digitalization, especially for SMEs. Other challenges such as talent and security should also be considered. To move forward, companies in Asia should measure more, learn from third-party providers, factory audits, and OEMs in developed nations.

Conclusion

In brief, Industry 4.0 and AI offers significant benefits that are applicable to specific industries and well-suited for large-scale businesses. Currently, industry players who have made significant investments in digitizing their processes are taking advantage of opportunities. For instance, predictive maintenance, defect detection and optimizing production output, quality, and costs.

For Asian manufacturers, the primary focus is on continuous improvement and digitalization, particularly for SMEs, despite facing challenges in talent acquisition and security. Moving forward, the initial steps involve increasing measurement, conducting factory audits, and gaining insights from OEMs in developed nations.