Restoring Automation: How to Minimize Downtime

18/09/2026

Why is the machine running but profits are falling?

Recently, a common trend has emerged in food and beverage processing plants in Vietnam, as well as among partners in Thailand and Mexico: machinery operates normally with no hard errors, yet actual output is 10-15% lower than design capacity. The issue is not mechanical failure, but a gradual drift in operating parameters over time. An oven temperature fluctuating by a few degrees, a slight change in pump pressure, or variations in raw material moisture. These small deviations accumulate, resulting in inconsistent product quality and forcing operations teams to stop the machine for manual adjustments.

This directly impacts the goal of reducing downtime. Each time a technician is called in to inspect, measure, and adjust manually, the plant loses 20 to 45 minutes. If this happens a few times a day, total downtime increases significantly. More importantly, relying on an operator's personal experience to restore the process is a major risk. When staff change or someone is fatigued, accuracy drops. This is when manufacturing automation is no longer a luxury, but a mandatory tool to stabilize operations.

Three Approaches: From Simple Alerts to Automated Control

When it comes to using machine data and operational logs to automatically restore optimal parameters, businesses typically consider three main directions. Each approach has a different level of process intervention, leading to different investment costs and operational risks.

The first approach is Monitoring & Alerting. The system collects sensor data, detects when values exceed allowed thresholds, and sends notifications to staff. Humans remain the decision-makers and execute the adjustments. The advantage is low cost and easy deployment. However, it does not fully solve the downtime reduction problem because manual intervention is still required. Response time depends on human speed and processing.

The second approach is the Recommendation Engine. The system not only alerts but also suggests adjustment values based on historical data. For example, if raw material moisture increases, the system suggests increasing the drying temperature by 5 degrees. The operator reviews the suggestion and decides whether to apply it. This reduces the cognitive load on operators, helping them make faster decisions. However, it remains a semi-automated loop. If the operator is busy with other tasks or ignores the alert, the process still drifts.

The third approach is Closed-Loop Control. This is the highest level. The system not only suggests but directly sends control commands to the PLC (Programmable Logic Controller) to change machine parameters, then measures the result and fine-tunes further. This loop occurs within seconds. When the process drifts, the machine automatically restores to the optimal state without human intervention. This is the true definition of manufacturing automation in the context of AI. However, it is also the most complex and risky approach if not carefully designed.

Executive Perspective: Cost vs. Risk

For executives, the question is not "how good is this technology," but "is it worth the investment." If you stop at the alerting level, the business may save on hardware and software costs, but still pays the price in wasted productivity and labor costs for monitoring. In the food and beverage industry, where profit margins are thin, every minute the machine runs with incorrect parameters is money down the drain.

Investing in automated control requires a larger budget, not just for AI software but also for tight integration with existing SCADA/DCS systems. However, if the process is highly repetitive and precision significantly impacts final quality, this investment often pays for itself within 12-18 months. I have seen many cases where businesses, fearing risk, chose the safe option of alerting, only to later complain that productivity did not improve. That is a vague trade-off. You cannot want manufacturing automation while refusing to give the system control authority over the machines.

Operations Team Perspective: Peace of Mind or New Burden?

Operations teams are often the group that reacts most strongly to AI projects. They fear job loss or excessive control. However, in practice, when the system is only at the alerting level, they become more tired because they must constantly monitor screens and react to notifications. This is a form of "noise" that causes stress.

When moving to the recommendation level, they feel supported. The system says: "You should adjust it like this." They quickly check and press the confirm button. The work becomes lighter. But when the system moves to automated control, their role changes completely. They are no longer the ones adjusting the machine, but the ones supervising the system. They need skills to read and understand data, recognize when the AI is "lost," and intervene manually. This is a higher requirement, not a lower one. Without retraining, the operations team will feel sidelined, leading to passive resistance. Reducing downtime is only truly sustainable when operators trust and clearly understand what the system is doing.

IT Team Perspective: Integration and Security Challenges

For the IT team, the biggest challenge is not the AI algorithm, but data connectivity. Machines in the plant often use different protocols: Modbus, OPC UA, MQTT... Data from old sensors and legacy PLCs is often unclean, unlabeled, or has high latency. To accurately restore the process, input data must be of high quality. If the data is noisy, the AI will make wrong decisions, and if that is automated control, the consequences could be serious machine failures.

Security is also a sensitive issue. Allowing a software system to access and control PLCs opens a large door into the OT (Operational Technology) system. The IT team must ensure that the AI system cannot be attacked from outside, and that all control commands have clear, traceable logs. Many Vietnamese businesses lack personnel specialized in OT security, making this a major bottleneck. At AIVISION, when deploying for partners in the instant noodle or lubricant industries, we always start by assessing data integration and security capabilities before considering the AI model. If the data foundation is not solid, all ideas about manufacturing automation are just illusions.

Common Customer Questions

How long does it take for the system to learn the optimal process?

Typically, it takes 3 to 6 months of stable operational data for the AI model to clearly understand the relationship between input and output variables. If the process changes frequently due to different raw materials, the retraining time may be longer. In the initial phase, the system should run in shadow mode to compare with human decisions before allowing direct control.

Do we need to replace all old machinery?

Not necessarily. Most current manufacturing automation projects focus on integrating with existing PLCs and controllers. Only when machinery is too old, lacks digital connection ports, or cannot transmit control commands stably should hardware upgrades be considered. AI can work well with data from old systems if the communication protocols are standardized.

What is the biggest risk when deploying automated control?

It is lack of trust and integration failures. When the AI issues an adjustment command that humans do not clearly understand the reason for, or when the system encounters a communication error causing the machine to stop abruptly, trust collapses. To mitigate this risk, hard safety limits must be established on the PLC, independent of the AI system, ensuring the machine never exceeds safe physical limits regardless of what the AI decides.

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