Pathway To Smart Factory – From Manual To AI-Powered Automation

Smart factory is no longer considered a rising phenomenon, but an absolute trend that reshapes the global manufacturing industries in the future era. While large enterprises have taken a steadfast approach to modernizing their infrastructure for large-scale manufacturing & distribution, small & medium businesses are catching up by transforming selective aspects within their production lines, from AI-driven data analytics to predictive maintenance. 

But the real barrier is not simply about the amount of spending, but the pathway to adopt and how to maximize the technologies for long-term & foreseeable impacts. 

• What if manufacturers keep relying on the manual, traditional methodology of operation, and completely avoid the smart factory pathway? 
• And in what ways, or how manufacturers should foster a pathway to transition into a smart factory that ensures a feasible modernization and sustainable impacts? 

Our Smart Factory Expert, Mr. Nguyen Huu An, addressed these concerns through a speech topic “From AI and IoT To Production Practices – The Catalyst for Vietnam’s Smart Factory Era” at the recent Vietnam Digital Infrastructure Development Forum 2025 (DigiInfra 2025) on 30th September. In this article, we would like to give you a full breakdown of what our expert covers in his presentation! 

How Does the Global Supply Chain Get Reshaped By Smart Trends? 

The global supply chain has long been redefined through different eras, from the industrial revolution to the era dominated by the ongoing waves of digital transformation. Demands for diverse goods, market expansions, and globalization set a new standard for global supply chains, where an accelerated pace, mass production, and efficient scale are becoming the new normal. And emerging technologies, including AI, Industrial IoT (I-IoT), MES & Smart Automation, are reshaping the ways supply chains function in the current dynamic landscape. PwC’s recent survey indicates a surge in the number of businesses considering the adoption of AI to maximize efficiency, with 53% of survey respondents saying AI is being used in either a few areas or widely to anticipate and mitigate supply chain disruptions.

At the presentation, Mr. An Nguyen highlighted that the role of technology in navigating global supply chains has been clearer than ever, from harnessing better automation to driving long-term strategy. Notable signatures include: 

• Smarter Analysis: Global manufacturers are harnessing the maximum power of new digital platforms to stay up-to-date with the current trends, social listening & data analytics on a real-time basis.  Through these valuable insights, manufacturers will be able to easily predict the potential demands, estimate the frequency & targeted output. 

• Automated-driven Process: The latest generation of manufacturing automation is not just about doing repetitive tasks, but also playing a pivotal role in accelerating the whole supply chain process. For instance,  orders can be generated automatically based on a schedule, then distributed to assigned suppliers to conduct mass production. This example demonstrates how automation helps maintain stable operations, ensuring the demands for goods are satisfied without disrupting the supply chain. 

• Unified Platform: Instead of using different software or platforms to monitor the production process, modern platforms are now unified into a single touchpoint to streamline the manufacturing operations. Through harnessing platforms like ERP & MES, manufacturing organizations can monitor the whole process, from multi-dimensional connectivity, interaction, to collaboration & tracking process, within a single digital platform. 

From Manual To Modernization – Pathway To Smart Factory

Key Hinders

While global experts, leading enterprises are realizing the enormous potential of emerging technologies in accelerating manufacturing performance, some manufacturers are still relying on traditional methodology to monitor & operate the production chains. In the presentation, Mr. An Nguyen indicates that while technology has dominated the ways manufacturing work, there are some cases that businesses pursue manual methodology to update the situation of the whole processes, with most common practices in specific aspects such as: 

• Updating production output manually — operators record quantities on paper or spreadsheets instead of capturing them in real-time via automated systems.
• Logging machine downtime incidents by hand — critical stoppages or faults are noted after the fact rather than being auto-detected and reported by sensors.
• Recording issues during production — defects, deviations, or quality issues are documented manually or based on personal experience, which might pose a delay, errors, and data inconsistency.
• Tracking resolution times for incidents — the time from fault detection to resolution is logged manually, making it hard to analyze response performance or improve maintenance processes.

In an era driven by accelerated pace and large-scale demands, these manual processes might interfere with manufacturers’ capabilities to capture real-time production, gather accurate data, and fuel decisions with future-proof impacts, including: 

• Delay in insight: manual logs lag behind real events, making it impossible to act in real time
• Data noise & error: human entry introduces inconsistencies, missing data, or mistakes.
• Poor root-cause analysis: without accurate timestamped and sensor data, analyzing why faults occur is much harder.
• Inefficient response: manual tracking of resolution time reduces transparency and slows continuous improvement

The Recommended Roadmap To Smart Factory

Manufacturers, regardless of their unique characteristics in scale & key product scopes, should consider the adoption of a smart factory aligned with practical impacts and foreseeable values. Determining what stages businesses are currently falling into is essential to identify the adoption journey. Mr. An Nguyen indicated that most manufacturers will be progressing maturity of infrastructure development within 4 levels of adoption including: 

• Level 1: Production planning, execution & report is conducted through manual tools, including excel sheet, forms & boards. 
• Level 2: Manufacturers begin adopting technologies and a part of automation across selective tasks within production planning & execution, including visualized planning, automated alerts, direct update & real-time monitoring
• Level 3: Aside advancing planning, manufacturers in this level modernize their execution methodology through integration of I-IoT, real-time visibility, predictive maintenance, accurate & instant data gathering. In this stage, manufacturers’ production is processed by emerging technologies & automation, data-driven operation. 
• Level 4: In this level, manufacturers integrated AI-driven tools & solutions into their production lines to drive optimized planning, efficient execution. 

The success of smart factory adoption is not just about integrating emerging technologies, but underlying within a long-term strategy & goals. Mr. An Nguyen highlighted a recommended roadmap to smart factory development centered around fostering a strong data foundation, then turning those into practical & actionable insights, covering the following steps:

Connectivity & Data Collection:

• Connect machines, equipment, and assets
• Collect real-time information and data

Simulation & Monitoring:

• Simulate operations, real-time monitoring
• Instant incident alerts, collaborative issue resolution

Analytics & Continuous Improvement:

• Multi-dimensional, in-depth data visualization and analysis
• Improve anomalies, support maintenance, and production planning

Control:

• Enable automated, bi-directional control scenarios
• Respond and predict based on context

Artificial Intelligence:

• Optimize complex scenarios
• Analyze, forecast, and plan

Alongside these stages, NTQ experts advise manufacturers to build a synchronized communication infrastructure to ensure seamless connectivity across systems:

• Apply common industrial communication protocols to standardize data flow.
• Establish a solid network infrastructure (4G/5G, Wi-Fi) for reliable connectivity.
• Strengthen the service infrastructure, including cloud platforms, servers, security layers, and backup systems to safeguard operations and data.

Finally, depending on industry characteristics, company size, and production domain, businesses are encouraged to customize their strategies. They may opt for semi-automation in specific areas or adopt full I-IoT integration across the factory, combined with tailored AI transformation solutions to maximize outcomes.

Conclusion

The journey from manual processes to AI-powered automation is not a one-size-fits-all transformation, but a carefully designed pathway. As Mr. An Nguyen emphasized, success lies in building a solid data foundation, establishing synchronized infrastructure, and tailoring strategies that fit each manufacturer’s scale and industry. By moving step by step — from connectivity to analytics to AI integration — manufacturers can unlock higher efficiency, resilience, and competitiveness.

In the era where smart factories are no longer optional but inevitable, the key differentiator will be how quickly and strategically companies embrace this pathway. Those who invest early in AI, I-IoT, and digital infrastructure will not only modernize their operations but also raise their competitive edge in the global supply chain.