Ai Insights

"Mastering the Digital Transformation: AI and Machine Learning in Manufacturing"

Mastering the Digital Transformation: AI and Machine Learning in Manufacturing

In a revealing dialogue with Asia Business Outlook, BN Shukla, the Operations Director at Jabil, India, with over 28 years of expertise in operational excellence and business management, sheds light on the transformative influence of artificial intelligence (AI) and machine learning (ML) in manufacturing. He delves into the inherent challenges of deciphering the decision-making mechanisms of AI and ML, strategies for their efficient deployment, safeguarding critical production data, ensuring compliance with regulatory standards, and more.
Manufacturers face a plethora of challenges in the digital transition, including substantial capital investments for integrating IoT and machine learning technologies, managing change, bridging technical skill gaps, and handling the growth and security of data. To navigate these complexities, Shukla emphasizes a structured approach, beginning with "datafication" — transforming business processes into quantifiable data for analysis. This process is supported by an enterprise-wide Data & AI council, guiding the definition, lineage, and sources of critical data.
AI and ML are pivotal in extracting value from vast data collected daily, enabling Jabil to deliver business insights, automate tasks, and refine system capabilities. Shukla highlights their strategy of employing AI algorithms for enhancing factory inspection processes and leveraging advanced data analytics for business intelligence.
The migration to SAP S4 Hana in January 2022 marked a significant leap for Jabil, shedding decades of technical debt for improved data processing speeds and streamlined data structures. This transition is not just about technical upgrades but about unlocking new value for users, enhancing productivity, and embracing new functionalities for process transformation.
Moreover, robotic process automation (RPA) has played a crucial role in automating repetitive tasks, with Jabil personifying these digital tools as part of their digital workforce. This approach not only achieves maximum ROI but also fosters a digital-first culture within the organization.
To overcome the challenges in AI/ML implementation, Jabil adopted a customer-centric strategy, focusing on solving business problems and enhancing safety, quality, delivery, and cost metrics. This entails a deep understanding and communication with stakeholders to align efforts with business priorities.
Regarding scalability and adaptability in manufacturing operations, Shukla underscores the importance of centering on people as part of a digital-first approach. He advocates for upskilling and reskilling workers to harmonize human capabilities with intelligent machines, fostering an environment that values human insight alongside technological advancement.
Addressing cybersecurity, Shukla describes Jabil's comprehensive Defense-in-Depth approach, encompassing threat identification and blocking, rapid incident response, and minimizing downtime post-incident. Guided by the National Institute of Standards and Technology (NIST) Cybersecurity Framework, Jabil ensures robust data protection, continuous threat monitoring, and enterprise resilience against cyber threats.
Through this interview, Shukla provides a comprehensive overview of the strategic, technical, and security considerations involved in leveraging AI and ML technologies in manufacturing. His insights highlight the potential of digital transformation to enhance operational efficiency, foster innovation, and secure the manufacturing sector against emerging threats.