OPERATIONS
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ost factories these days contain some automation, usually of repetitive tasks. Autonomy, however, is a long leap away. While technologies like AI are developing and can fill part of that gap, workplace culture and operational strategy also need to change, Bob Buttermore feels.
As Senior Vice President and Chief Supply Chain Officer at Rockwell Automation, Bob oversees the company’ s Integrated Supply Chain organisation, covering manufacturing operations, supply chain strategy, sourcing, logistics and quality functions. Throughout his tenure, he has spearheaded a multi-year initiative to rigorously apply Rockwell’ s software, automation and AI technologies directly to its own operations. Bob shares his expertise with Manufacturing Digital.
Q. WHAT IS THE DIFFERENCE BETWEEN AN AUTOMATED FACTORY AND AN AUTONOMOUS ONE?
» An automated factory excels at executing predefined, repetitive tasks with speed and precision, but it relies on humans to analyse data, troubleshoot errors and make any operational changes. Conversely, autonomous operations are capable of self-optimising through humans in the loop and supercharging the ability of humans to work on continuous improvement of the operations that deliver real time business value. Instead of handling task-level execution, autonomy operates at the system level by processing inputs, evaluating scenarios, making decisions and adapting to changing conditions. While many industrial facilities are automated in some form or another, many of them lack the digital tools to analyse and contextualise data, predict inefficiencies and implement corrective measures.
For Rockwell’ s Asia Pacific Business Center( APBC) in Singapore, we designed our strategy to move from automation to autonomy through data visibility, predictive analytics and data-driven process optimisation. We started with a comprehensive review of current energy consumption patterns to identify inefficiencies. From there, we integrated a real-time monitoring platform to capture dynamic feedback on power usage, enabling precise energy tracking across operations. To power our data-driven optimisation, we leveraged machine learning models to analyse historical trends, predict demand fluctuations and recommend corrective actions.
By pairing these insights with prescriptive controls that automatically adjust production parameters, this structured roadmap moved APBC beyond simple energy monitoring into true, real-time optimisation and predictive decision-making.
Q. HOW LONG DID THE TRANSITION TAKE FOR THE APBC?
» The APBC transition took about two years. After moving beyond traditional automation toward more autonomous operations, the facility increased labour productivity by 43 %, improved workforce time-to-competency by 67 %, improved quality by more than 25 %, reduced
70 September 2026