In conversation with Marcelo Tarkieltaub, Regional Vice President, Southeast Asia, Rockwell Automation
There is a persistent assumption in food and beverage manufacturing that automation is a destination. Commission the right equipment, integrate robotics, close a few production loops, and the operation runs itself. The lights-out factory sits just over the horizon, waiting.
The reality, as Marcelo Tarkieltaub of Rockwell Automation sees it, is more complex. Tarkieltaub oversees the company’s Southeast Asian operations and works regularly with food and beverage manufacturers as they navigate the distance between having automation and achieving autonomy. These are not the same. The gap between them is where most manufacturers are stuck and understanding what causes that gap is the first step to closing it.
The real bottleneck is not the machine
When a food manufacturer’s push toward more autonomous operations stalls, the instinct is to blame technology: legacy equipment, fragmented data architectures, incompatible systems. Tarkieltaub’s diagnosis is often more uncomfortable.
“More often, what ‘breaks’ first is the alignment between technology, processes and people,” he says. “While legacy equipment integration and data architecture are common technical challenges, the more fundamental issue tends to be organisational readiness and process discipline.”
Rockwell Automation’s 10th Annual State of Smart Manufacturing Report found that 56% of manufacturers are currently piloting smart manufacturing, while only 20% have scaled it – a gap that reflects exactly this pattern: enthusiasm for transformation does not automatically translate into operational integration.
The underlying cause, Tarkieltaub explains, is a disconnected data landscape. ERP systems, manufacturing execution systems (MES), and manual inputs like spreadsheets operate in parallel, generating information that cannot be meaningfully reconciled.
“Without a single, standardised source of truth, it becomes difficult to generate reliable insights or scale automation effectively,” he says. Layering AI on top of these fragmented systems does not accelerate transformation; it exposes the deeper dysfunction beneath.
Asia Pacific factories often operate with a mix of automation systems from different vendors across production lines, and integrating these platforms into a unified digital factory architecture remains complex and time-consuming. The challenge is making existing systems communicate in a consistent, trustworthy way.
Automated islands vs autonomous plants
Most food manufacturers have what Tarkieltaub calls “automated islands”: individual machines or lines that perform well in isolation but are not connected to the broader production environment. A filling line may run with impressive precision. A filling line may run with impressive precision. A packaging station may log zero defects across multiple shifts. But if those lines do not share data, if a quality deviation on one line cannot trigger a response in another, the plant is not autonomous. It is a collection of well-behaved machines.
What separates an automated line from an autonomous-ready plant is connectivity and context. In a genuinely autonomous-ready environment, data flows continuously from the shop floor through MES, quality systems, and enterprise platforms, creating a unified, real-time operational view. Data is not only collected but contextualised to support decision making.
The plant does not just monitor what is happening. It understands what the data means and can act on it. “Automation does not equal autonomy,” Tarkieltaub states plainly. “Automating individual tasks improves efficiency, but without coordination across systems, plants remain heavily reliant on human intervention when conditions change.”
This is the architecture that closed-loop systems depend on. Autonomous plants trigger adjustments automatically: optimising production parameters, flagging quality deviations early, and initiating maintenance before failures occur. Each of these capabilities requires data integration at a level most plants have not yet reached.
Manufacturing execution systems held the largest share of the Asia Pacific smart factory market in 2025, driven by their critical role in coordinating shop-floor operations, enabling real-time production monitoring, quality management, and traceability across largescale manufacturing facilities. The technology infrastructure exists. The challenge is deploying it in ways that are genuinely connected rather than merely sophisticated.
Why MES is more than a compliance tool
When food and beverage manufacturers invest in MES, the initial driver is almost always traceability and compliance. Regulatory pressure in the region is no longer abstract. Indonesia’s BPOM enacted comprehensive new recall and traceability requirements in January 2025, mandating that Class I recall action be initiated within 24 hours of discovering a product that poses a high health risk, a requirement that demands digital readiness rather than paper-based systems.
China updated its Good Manufacturing Practice standards for health foods (GB 17405-2025) in September 2025, adding safety management and recall and traceability provisions. The ASEAN Guidelines on Nutrition Labelling were formally endorsed in August 2025, creating further harmonisation pressure across member states.
Tarkieltaub acknowledges this regulatory context but pushes back on the idea that compliance is MES’s primary value. “MES should not be treated purely as a compliance tool but as a platform that connects production, quality, and maintenance workflows,” he says.
The transformation in value comes once real-time production visibility is established. At that point, manufacturers can move beyond reactive compliance toward proactive optimisation. This involves identifying downtime patterns, addressing yield loss, and improving scheduling and labour productivity systematically rather than reactively. Traceability becomes a foundation, not a ceiling. The urgency of this shift is not theoretical.
According to the US PIRG Education Fund’s Food for Thought 2025 report, hospitalisations and deaths from foodborne illnesses doubled in 2024 compared to 2023, while recalls driven by Listeria, Salmonella, and E. coli increased by 41%. Manufacturers still relying on paper-based batch records or siloed quality systems face mounting exposure when investigations require rapid, cross-system data retrieval.
Traceability as operational intelligence
The conventional framing of traceability is defensive: maintain records, satisfy auditors, manage recalls when they arise. Tarkieltaub sees a different and more operationally significant opportunity in the same data.
“The real opportunity is moving from reactive to proactive operations,” he says. “When traceability data is integrated with real-time production and quality systems, it can be used not just to investigate issues, but to predict and prevent them.”
What this means operationally is that end-to-end traceability, properly integrated, allows manufacturers to quickly identify where and why a quality deviation occurred, whether it originates in a raw material, a process inconsistency, or a specific production stage. Investigation time compresses sharply. When that same data is linked to process conditions and equipment status in real time, patterns emerge before they become incidents.
A particular supplier’s input consistently correlates with batch variability. A production shift shows higher deviation rates. A filling parameter drifts gradually before a fill-weight complaint arrives. The precision that traceability enables in recall management is a related and growing priority.
Tight digital traceability allows manufacturers to isolate affected batches precisely rather than defaulting to broad, precautionary recalls. Pursuing lot-level traceability can significantly reduce recall costs by narrowing recall scope and coordination expenditures. It also provides near-real-time monitoring of shelf life, temperature, and product routing, delivering value through simultaneous waste reduction and brand protection.
Predictive maintenance: the quality argument
Predictive maintenance is typically positioned as a strategy to reduce downtime. In food and beverage manufacturing, however, Tarkieltaub argues its most significant impact may be on product quality rather than machine availability. “Many quality issues are not caused by sudden equipment failure, but by gradual process drift as machines wear over time,” he explains.
Fill levels that shift imperceptibly over the course of weeks. Temperature inconsistencies that accumulate across thousands of batches. Changes in mixing performance that alter texture before any alarm activates. These are the failures most difficult to detect early and most damaging when they reach consumers or trigger investigations.
“By monitoring equipment conditions alongside process parameters, manufacturers can identify correlations between machine health and product quality,” he says. The shift is from reacting to failures or visible defects to intervening earlier by adjusting processes, scheduling maintenance, and recalibrating equipment before quality is affected.
In an industry where batch consistency is brand identity, the operational stakes of this distinction are significant. The connection between equipment condition and food safety risk is increasingly well-documented.
Under the Hazard Analysis and Critical Control Points (HACCP) system and the FDA’s Food Safety Modernization Act, manufacturers are required to have preventive controls in place for processes and equipment, and AI predictive maintenance transforms compliance posture, replacing time-based maintenance schedules with auditable, time-stamped data demonstrating that critical assets were operating within designated safety and quality parameters.
In beverage bottling plants, where conveyor belts and fillers are critical assets, predictive analytics enables maintenance scheduling during planned downtime, avoiding costly production disruptions and ensuring equipment operates within precise parameters for consistent, safe production.
AI in the plant: value and hype, side by side
Few topics in food manufacturing generate more heat than artificial intelligence. The claims are sweeping; the reality is more specific and more useful to understand clearly. Tarkieltaub is precise about where AI is currently delivering value. “AI-enabled inspection and analytics are already delivering strong value in areas where quality issues are visual, repetitive and data-rich.”
Packaging integrity checks, label verification, fill level accuracy, and detection of visible product defects are applications where machine vision, combined with AI, consistently identifies anomalies at high speed and with greater consistency than manual inspection.
Quality control remains the top AI use case for the second consecutive year, with 50% of manufacturers planning to apply AI and machine learning to support product quality in 2025. AI is also effective at identifying patterns that are not visible to operators in real time, detecting early signals of process drift in historical and live production data before they escalate into quality events.
The overselling, Tarkieltaub argues, happens when AI is positioned as a fully autonomous replacement for human expertise across all quality control domains. AI models require high-quality, well-labelled data, stable processes, and ongoing calibration. In production environments with frequent product changeovers or inconsistent inputs, performance is less predictable.
“The most effective approach is to treat AI as an augmentation tool rather than a replacement. It works best when integrated into a broader quality strategy that combines automation, domain expertise and robust process control.”
The practical implication for food manufacturers is that a more deliberate approach to AI deployment is required. Rather than scattered pilots that demonstrate capability without delivering operational value, Tarkieltaub argues for anchoring AI initiatives to specific, measurable operational challenges.
Use cases that generate the fastest returns are built on existing, high-quality data and well-understood processes, predictive maintenance, AI-enabled visual inspection, and process optimisation, applied where the data infrastructure already exists and pain points are well-defined.
The data hiding in plain sight
One of the most structurally important points in Tarkieltaub’s analysis is the scale of operational intelligence that food manufacturers are already generating and largely not using.
Machine condition data, including temperature, pressure, vibration, and cycle times, is often available at the equipment level but rarely linked to product quality or batch performance. Operator logs and manual quality checks contain detailed intelligence about recurring process variability and workarounds, but when captured on paper or in isolated systems, that intelligence cannot be aggregated or analysed at scale. Alarm and event data are generated continuously, but without proper structure and analysis, they become noise rather than signals.
“Plants generate large volumes of alarms, but without proper prioritisation and analysis, they can become noise rather than actionable signals,” he says. “When structured and analysed effectively, this data can highlight systemic issues, recurring bottlenecks, and opportunities to improve reliability.”
The strategic implication is significant: the path to smarter manufacturing does not always require investment in new data collection infrastructure. It often requires investment in connecting and contextualising data that already exists. For manufacturers managing capital budgets carefully, common in Asian markets where investment must be balanced against rapid expansion, this changes the return-on-investment conversation considerably.
The Asian context: speed, diversity, and dual pressures
The smart manufacturing journey in Southeast Asia has a character distinct from transformation narratives in North America or Europe. The differences are structural. Regulatory diversity is perhaps the most underappreciated complexity. A food manufacturer operating across multiple Asian markets must navigate different food safety standards, different labelling requirements, different traceability expectations, and different enforcement approaches — all of which are actively and rapidly evolving.
The 2025 BPOM recall regulation in Indonesia, the updated Chinese food safety manufacturing standards, and the new ASEAN labelling guidelines represent the kind of regulatory flux that is now the norm rather than the exception.
Systems that cannot adapt to diverse and changing requirements create compliance risk that scales with market presence. Labour dynamics add another layer. While lower labour costs historically moderated the urgency of automation investment in some Asian markets, the calculus is shifting, workforce shortages are increasing, automation requirements are rising, and the skills needed to operate data-driven manufacturing environments differ substantially from those of the previous operational generation.
“This creates a dual challenge of investing in automation while also upskilling the workforce to manage more advanced, data-driven operations,” Tarkieltaub says.
What is distinctive about the Asian context, however, is the pace at which manufacturers are expanding. The Asia Pacific smart factory market, valued at USD 40.24 billion in 2025, is projected to reach USD 88.47 billion by 2032, at a CAGR of 11.9%, driven by investment in automation, robotics, and digital factory technologies to support large-scale production.
Manufacturers scaling at that pace have the opportunity, and competitive pressure, to build connected operations natively rather than retrofitting legacy infrastructure. That window will not remain open indefinitely.
The compliance proof gap
Compliance in food manufacturing is increasingly about being able to prove, quickly and verifiably, that those standards have been met consistently. Tarkieltaub identifies a specific and practical gap between what regulators increasingly expect and what most plants can currently deliver.
End-to-end traceability with full contextual linkage remains elusive. Many manufacturers can trace raw materials to finished products, but cannot link that trace seamlessly to process conditions, operator actions, and quality checks in a single, time-aligned view. Investigations and audits become more labour intensive and time-consuming than necessary.
Proof of process adherence is a related challenge. Regulators increasingly expect evidence that critical control points and standard operating procedures were followed consistently, not simply documented after the fact. Manual records and fragmented systems raise legitimate questions about accuracy and completeness that automatically captured, timestamped digital records do not.
“The goal is to move towards real-time, audit-ready operations,” Tarkieltaub says. “This means capturing data automatically at the source, linking it across systems, and structuring it in a way that can be easily accessed and verified.” Digital batch records, automated reporting, and integrated quality systems are the enabling infrastructure for this, not a future investment, but an increasingly urgent present one.
The next phase: from visibility to orchestration
Looking ahead, Tarkieltaub describes the next phase of smart manufacturing in food and beverage not as an incremental extension of current capabilities, but as a qualitative shift in what plants can do.
“The next phase of smart manufacturing in food and beverage will not be defined by a single dimension, but by the convergence of intelligence and autonomous optimisation across the plant,” he says. More automation alone will not deliver the next meaningful step change in performance.
The real shift is towards intelligence: systems that do not merely monitor and report but adjust dynamically in response to changing conditions — optimising production parameters in real time, balancing throughput with quality requirements, responding automatically to supply and demand signals.
The difference he draws is between visibility and orchestration. Visibility is the ability to see what is happening across the plant. Orchestration is the ability to coordinate and respond to it as an integrated system. Most manufacturers today are working towards the former. The latter is where meaningful competitive differentiation will be built.
The building blocks are available now: connected MES, integrated quality systems, predictive analytics, and AI-enabled inspection. What separates manufacturers moving towards orchestration from those still managing automated islands is not access to technology. It is the discipline to build integration deliberately, the investment in change management alongside technical deployment, and the strategic clarity to treat data infrastructure as foundational rather than supplementary.
For food manufacturers in Southeast Asia, the opportunity to build that foundation during expansion — rather than retrofitting it later under operational and regulatory pressure — is one that narrows with every year of growth taken on without it.
Originally published on Asia Food Journal