Navigating The Challenges Of Modern Manufacturing: A Data-Driven Approach – warungku – Warungku Terkini

Joe Ong is the VP and GM, ASEAN at Hitachi Vantara. Based in Singapore, he provides overall leadership and guidance on business direction, including go-to-market strategies for the ASEAN region.

Joe Ong possesses close to three decades of experience in the IT industry, a substantial number of those years in the storage solutions business. In the first 3½ years at Hitachi Data Systems, Joe grew the local business in Singapore by 67%, bringing it from Top 6th player in the storage market to 3rd position. He continued to scale the ASEAN business, becoming the top revenue producer in the Asia-Pacific region by 2016.

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Having built up a strong selling engine, Joe led the ASEAN team to win the Top Region Award for growing the largest number of new customer accounts in 2019. Prior to joining Hitachi Data Systems in 2012, he was the Country Manager for Singapore, Brunei at EMC, where he managed the Singapore team (Direct Sales and Channel team) and enabled EMC Singapore to maintain number one market share in Singapore for 4 years , according to IDC.

Joe graduated with a Bachelor of Science in Information Systems and Computer Science from the National University of Singapore. Outside of work, he enjoys a good round of golf. He is also an avid reader, diving into business, economics and strategic management.

Navigating The Challenges Of Modern Manufacturing: A Data-Driven Approach

In an era where artificial intelligence (AI) and machine learning (ML) are revolutionizing manufacturing, the industry faces unprecedented challenges in managing vast amounts of data generated by modern infrastructure. Joe Ong explores how embracing highly-powered platforms and strategic partnerships can bridge the gap, enhancing efficiency and productivity. As AI spending soars and data deluge threatens to overwhelm, companies must navigate data sprawl, performance issues, and duplications to stay competitive. This article delves into the critical role of robust data infrastructure and governance in transforming manufacturing processes, highlighting real-world success stories and emphasizing the synergy between technology and human expertise.

Modern Manufacturing remains a topic of interest in the technology industry as more manufacturers are embracing artificial intelligence (AI) and machine learning (ML) technologies to improve efficiency, safety, and effectiveness in the industry. However, there are unique challenges arising from the implementation of modern infrastructure such as overwhelming and vast amounts of data, which may in turn impact data processing and hinder manufacturing processes altogether. Bridging this gap isn’t a simple plug-and-play issue, companies need to really consider the challenges, count on highly-powered platforms for managing large-scale AI/ML and data analytics projects, as well as take into account the ideal partner to assist in realising the vision.

AI spending has risen from just under USD 20 billion to nearly USD 50 billion, with Year-on-Year growth between 23-25% between 2022-2026. Although the initial focus was on enhancing professional services for white-collar workers, as technology advances and becomes more efficient, it is starting to affect manufacturing workshop floors. Those still deliberating the integration of AI/ML into their operations may find themselves lagging, given the swift evolution of the industry.

Statistically, the growing acceptance of AI as a productivity multiplier is evident. Forecasts predict a ten to twenty-fold increase in enterprises’ investment in AI software by 2026 compared to 2021. With a USD 14 trillion investment in AI software, the hope is to yield a four-fold return in the form of a USD 56 trillion labor output.

Unraveling Challenges in AI/ML Adoption for Manufacturing

Companies exploring the AI/ML new frontier face several challenges when implementing the technology in their manufacturing processes:

Data Sprawl: The exponential growth of data generated from various operations due to the proliferation of IoT devices means that companies are still learning how to handle and manage data. Hitachi Vantara’s 2023 report projects a doubling of data storage needs by 2025. In Asia, 60% of companies feel overwhelmed by the data deluge, with 73% concerned about infrastructure scalability. The imperative is for a flexible, cloud-like infrastructure to accommodate rapid data growth. Plus, as many manufacturing companies were established in the pre-digital era, they are finding they also need to address unstructured data or digitise paper data.

Data Performance: Businesses must invest in high-performance computing hardware and networks to match data performance to their systems’ capabilities to process and analyse data in real-time. Furthermore, it can be difficult to meaningfully access the vast amounts of data that contemporary systems are producing at the edge.

Data Duplicates: Companies are also encountering the problem of data being distributed in multiple copies among various systems, resulting in dataset discrepancies and inconsistencies that can impact decision-making and model performance. The time and effort required for data preparation is increased by this additional layer of complexity, which raises the costs associated with data management, storage, and security requirements.

Efficiency Boost Through Modern Data Infrastructure and Governance

Companies are increasingly integrating AI/ML solutions into their daily operations, requiring effective data management. This involves leveraging data infrastructure experts to establish a foundation for AI-driven businesses, optimising workflows, and creating resilient supply chains. A centralised data platform is crucial for businesses to gather, combine, and handle data from various sources, as well as connect to high-bandwidth networks for real-time processing.

A subsidiary of an electronics company faced challenges in handling massive amounts of data generated by IoT sensors and lengthy quality control and defect analysis times. By building a data platform that collected real-time data from sensors, processed it using queries, aggregated it, and stored it in a database, the company achieved 160 GB per second processing capacity, enabling data-driven decisions within hours and processing 200 million objects in real-time.

In 2024, industry leaders are expected to prioritise enhanced data infrastructure capabilities, recognising them as pivotal elements in navigating the evolving landscape of data-driven transformation. Companies can benefit from implementing a single data platform to effectively manage, aggregate, and analyse their data. However, a key aspect that must not be forgotten is the blending of technology with the human element, combining expertise with essential tools to produce actionable outcomes. To produce precise AI and ML-driven insights, both the consolidated platform and the people need to work hand-in-hand, making data access and retrieval easier, while enhancing data quality and consistency.

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