Certain industry analysts describe artificial intelligence’s (AI’s) value in driving rapid economic recovery and growth in the industrial space as ‘undeniable’.
Fuelled by a fundamentally different approach via data-driven decision making, AI has, for example, the dramatic potential to lift profitability rates in the manufacturing sector by an average of 39% by 2035.
A 2020 global survey among AI experts by MIT Technology Review Insights noted that almost 9% of Asian respondents reported AI deployments by 2019, above the 85% average of respondents from the other regions. The report also shows that Asia-Pacific is leading the pack in adopting AI to personalise products and services, among others.
Countering this with a broad picture view from a new IDC benchmark reveals that 42% of Asia Pacific enterprises only deploy AI in isolated projects. However, the analyst firm’s IDC MaturityScape Benchmark for AI in Asia Pacific 2021 acknowledges that AI is fast becoming the focal point for many organisations on the hunt to unlock greater value from their data.
Additionally, 52% of organisations in the region that ‘have invested in AI are still in the earlier maturity stages, in which AI is used in silos by select individuals/groups or for isolated projects.’
As AI, machine learning, data analytics and other digital revolution drivers often paint rosy pictures, Disruptive.Asia recently interviewed Astri Ramayanti Dharmawan, Schneider Electric’s country general manager for Malaysia and Brunei, to gain a reality check on frontier technologies’ role in the road to the current recovery.
In tandem with growing Schneider Electric in Malaysia and Brunei, Astri is focused on integrating the company’s energy distribution and automation processes with the broad aim of making energy safe, reliable, sustainable and efficient for customers.
“Last year, we announced a multi-million-dollar investment in machine learning tools and data science,” she said in our introductory remarks. “This investment brings AI-assisted advising to our energy and sustainability services offer, giving our clients access to next-generation digital tools.”
“This improves the insights and analysis of the company’s energy and sustainability portfolio, driving more efficient value and bottom-line impact in pursuit of climate change resilience and other resource-reduction related targets,” Astri explained.
Sustainability is the keynote behind this investment, which hopes to optimise corporate resource consumption and reduction data during the ongoing worldwide energy and climate transition.
Recovery challenges
“Most companies today still face data challenges that limit their sustainability approach,” Astri continued. “Companies struggle with inconsistent, incomplete, overabundant, and poor-quality resource consumption and cost data. The introduction of AI allows companies to get more value out of the data they produce and provides more accurate and efficient analysis as the foundation of an energy and sustainability strategy.”
She said that adding machine learning and data science to Schneider Electric’s legacy of traditional energy and sustainability consulting also enhances its clients’ approach to sourcing and procuring energy and resource management.
“Our investment in technology will allow clients to benefit from improved predictive capabilities and enhanced insights. These enhancements will more rapidly convert data into meaningful insights, better-tailored recommendation, and better support of clients in reaching long-term environmental goals.”
“Users will also be able to draw more confident conclusions that help reduce costs, manage risks, harvest opportunities, and build resilience into their sustainability strategy,” she added.
Astri pointed out that the current crisis has shown that large manufacturing companies have not yet experienced genuine productivity improvements other than on the shop floor.
“Many of our C-level customers are convinced that a step function in productivity can be generated through digitisation of work processes, and they are now becoming more important than ever.”
Adaptation curve
She also outlined examples of how digitisation is enabling accelerated acquisition of data and big data analysis. “[Thus] enabling our customers to envision non-traditional approaches for driving productivity of both their people and physical assets.”
“Now, the push is on for digitisation to permeate their organisations from shop floor to the top floor. But in order to implement such changes, customers require solutions that furnish the desired data, create the transparency required paired with in-depth process knowledge to drive up production output volumes.”
One example is a plastics manufacturer in Singapore, which digitised its operations with connected products and apps and analytics. Digitisation has helped the company free up manpower and opened up capacity for more value-added, business-oriented work. The solutions also allow the company to cope with natural attrition arising from an aging workforce and enable greater productivity from current employees.
Remote technology is another key area in digitisation, which not only advances processes but empowers people.
“On the shop floor, technology including simulations and augmented reality, resolve issues faster,” Astri continued. “It maximises support in operations and builds collaboration, allowing sites to connect with talent around the world to get the deepest experience and training.”
The events of 2020 have indeed catapulted IT and network capacity upgrades as part of a rapid adaptation initiative
“Reflecting back on 2020, we saw companies accelerating their digitalisation strategy as that once mapped digital strategy in one- to three-year phases must now scale their initiatives in a matter of days or weeks.”
The digital age has redefined the way companies operate and do business. “Nowadays, you can see businesses pivot and leverage eCommerce platforms and virtual events to remain relevant to their audience and retain their competitive edge.”
With the ongoing pandemic, we know our customers are increasingly seeking solutions that enable them to perform remote monitoring, real-time collaboration, and predictive technology to increase efficiency and ultimately ensure business continuity,” she said.
Astri expects the focus in 2021 will be on innovating computing architectures and networks to drive higher performance and efficiencies through automation.
“This digital transformation and automation are exciting, and new innovations could provide additional advancements, setting the stage for even more possibilities in 2022.”
More than a digital panacea
MIT Technology Review’s 2020 survey includes the advice to set the proper foundation when embarking on AI use cases. A primary aspect lies in clarifying the goals.
Astri holds that: ‘”As more aspects of the end-to-end industrial infrastructure become connected, and AI gets deployed throughout the systems, decision-making becomes faster, better, and more profitable.”
“The closer industrial companies can get to delivering on the core value that customers want when they want it, the closer AI’s innovative promise becomes a reality.”
“Industry players alike recognise that we have a responsibility to address important challenges that new technologies like AI creates.” She said. “Central to many of these challenges are questions of trust. Questions such as how do we ensure that AI is designed and used responsibly? How do we ensure that everyone has access to it? And how will AI impact employment and jobs?”
“We believe that building trust must begin with a human-centred understanding of AI that sees these technologies as powerful tools to augment human talents rather than as a substitute for human labour, creativity, judgment, and initiative. By enabling manufacturers to automate tedious and repetitive tasks, it will free workers to focus on areas where they can apply their knowledge and skills to solve problems and drive innovation.”
Living up to this understanding of the role technology should play in people’s lives will require an ethical approach to developing and deploying AI solutions.
“At Schneider, our ‘Principles of Responsibility’ serve as an ethics and compliance framework for everything we do,” Astri answered.
“In addition to ensuring AI is used ethically, we must ensure that new technology is secure and respects privacy. AI systems in manufacturing should be designed so that personal data is used in accordance with Europe’s GDPR [General Data Protection Regulation] and other applicable privacy practices and laws. “
Indeed, to facilitate compliance with privacy laws, industrial AI solutions should track how personal data is accessed and used and include capabilities to ensure transparency and accountability.
Key factors to consider
Turning to her thoughts for leadership considerations, Astri highlights a few key factors to advance digital adoption of AI and related technologies as part of the holistic transformation journey.
“Building AI models without knowing how to interpret, manage, and act on the insights leaves any of us with just a shiny object that has no real, applicable value,” she said. “The gap here is brought on by a lack of maturity or ability of business processes to consume these insights because they were never designed for this purpose, i.e. they were never designed to be AI-first.”
Astri presented some recommendations:
- Adopt an AI-first mindset and revisit business processes, workflows with an AI lens by looking at every step along the value chain with an AI perspective: “For example: Can my maintenance and operations teams respond to and act on predictive models that flag potential asset failure? Do the workflows they follow account for AI to create insights from data?”
- Speed up change management: “This step involves first ensuring buy-in at the executive level as part of a broader strategy. From there, communicating across the enterprise is critical, focusing on topics such as customer-centricity, data privacy, and security.”
- Define the “what” and “why” with scale in mind: “As AI is about much more than just integrating data and showing it in a dashboard. For example, in the O&G [oil and gas] segment, maintaining asset performance and efficiency of onshore oil pumps is expensive. We co-innovated with Microsoft a way to harness machine learning at the edge. Our Realift rod-pump with edge analytics improves operator efficiency by proactively identifying pump problems/abnormalities through machine learning algorithms. This application can rule out false positives with predictive analytics and, better, know the onshore pump jacks’ condition two miles below the surface. This proof of concept has broader-reaching appeal, as we have seen a 15% increase in productivity when the onshore pump is fully optimal thanks to edge analytics.”
- Prioritise: “Determine which AI projects will have the most business impact by focusing on the bigger problems instead of scattering AI projects to address too many smaller problems.”
“AI is not all hype if done right and at scale,” she said. “Business and customer value can be created: By overcoming the glaring obstacles, we can ensure a favourable and smooth tipping point when we know and implement the mechanisms to consume and act on insights from AI models.”
Given the fragile global environment, Astri’s closing thoughts stress the need to transform as a matter of urgency.
“If done right, AI can deliver businesses great benefits. It can also do the following:
Moving forwards, Astri concludes: “As we work closely with our partners to remove roadblocks while embedding cybersecurity safeguards at every step, the future of AI is beyond exciting. It already holds real value as it drives tangible business outcomes today.”
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