Digital Workflow Orchestration: Enabling Human-Machine Collaboration in Smart Manufacturing

How workflow-based systems bridge the gap between ERP and automation to improve operational efficiency and empower frontline workers

Digital transformation initiatives in manufacturing typically focus heavily on two areas: enterprise systems like ERP and CRM at the top level, and automation systems like SCADA and PLCs at the bottom level. Both areas have seen significant investment and technological advancement over the past decades. However, a critical gap exists between these layers—the middle layer where humans and machines collaborate to execute manufacturing processes.

Rafael Amaral, Chief Technology Officer at Tillit, a cloud-based digital operations platform, has observed that this middle layer remains largely analog in most factories. Operators still carry paper forms, standard operating procedures exist in thick binders, and plant managers lack visibility into what actually happens on the shop floor. This represents not just a challenge, but a missed opportunity to create significant operational value through technology.

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Understanding the Gap Between Automation and Enterprise Systems

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Manufacturing technology investment has concentrated at the extremes of the automation pyramid. At the enterprise level, companies run sophisticated ERP systems managing supply chains, accounting, and business processes. These systems are mature, well-integrated, and essential to operations. No company operates without them.

At the automation level, SCADA systems provide robust control capabilities. PLCs manage equipment with precision. These systems handle the mechanical aspects of production reliably and have evolved significantly over decades of development.

The middle layer—where manufacturing execution happens—presents a different picture. This is where work orders translate into production activities, where operators make decisions, where quality checks occur, and where the complexity of manufacturing processes becomes visible. This layer often remains dependent on paper forms, manual coordination, and tribal knowledge.

The maturity level at this middle layer is considerably lower than at either extreme. When manufacturers talk about digital transformation, they often focus on collecting more machine data or implementing better analytics. These initiatives have value, but they miss the core of manufacturing operations: the orchestration of people and processes that actually make products.

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What Workflow Orchestration Means in Manufacturing

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Workflow orchestration applies concepts that are well-established in business systems like ERP and CRM to the manufacturing environment. Think of the approval processes for travel expenses or purchase orders in corporate systems. These workflows define steps, assign responsibilities, track progress, and ensure procedures are followed. They are digital by default.

Manufacturing processes are fundamentally workflows with additional complexity. Raw materials arrive, get processed through multiple stations, undergo quality checks, move through work-in-progress storage, and eventually ship as finished products. Each product might have variations. Different customers require different processing steps. Complexity accumulates quickly.

Traditional approaches ask operators to remember all these variations or reference thick SOP documents to ensure they follow correct procedures. This places enormous cognitive burden on workers. When someone is unwell or distracted, mistakes happen. These mistakes are expensive—scrap, rework, delays, and quality issues.

Workflow orchestration in manufacturing means implementing a digital system that understands business rules and guides operators through every step. Instead of operators remembering what to do next, the system tells them. Instead of managers hoping processes are followed, the system tracks execution. Instead of quality issues discovered later, the system prevents errors at the point of action.

The system monitors signals from machines and understands process state. It knows when a cycle completes, when changeovers occur, when equipment stops. It combines this machine awareness with knowledge of what operators should do at each point. The result is synchronized operation where machines and people work as a coordinated unit rather than separate entities hoping to stay aligned.

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Moving from Paper to Digital Without Falling into the Paper-on-Glass Trap

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The obvious first step in digitizing manufacturing processes is converting paper forms to digital equivalents. This provides some benefit—forms do not get lost, handwriting is not an issue, and data becomes searchable. However, this approach falls into what can be called the paper-on-glass trap.

Digital forms that simply replicate paper forms on tablets miss the opportunity that digital systems provide. You still rely on operators to remember when to complete forms. You still lack visibility into whether processes are actually followed. You gain some efficiency from eliminating paper handling, but you do not fundamentally change how work gets done.

True workflow orchestration goes beyond digital forms to active process management. The system knows what should happen and when. It issues tasks to operators proactively rather than waiting for them to remember. It integrates with machines to trigger activities based on equipment state. It enforces sequence where needed and provides flexibility where appropriate.

This represents a shift from passive documentation to active management. Documentation records what happened after the fact. Management guides what happens in real time and prevents problems before they occur.

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Implementing Workflow Systems Without Big Bang Projects

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The complexity of manufacturing processes creates a challenge for implementation. No single person in a factory knows every step required for every product variant with every customer specification. Quality managers know their domain. Maintenance personnel know theirs. Each area has expertise, but comprehensive knowledge is distributed.

Traditional implementation approaches would attempt to document everything before starting, creating a complete blueprint of all processes before building anything. This approach faces two problems: it takes enormous time before delivering any value, and it is nearly impossible to capture complete process knowledge through upfront design.

An alternative approach implements workflow systems iteratively and incrementally. Start with critical processes. Digitize the current state even if it is not optimal—capture the as-is process into the system first. This provides immediate value: process execution becomes visible, operators receive guidance, and mistakes decrease.

Critically, getting the current process into a digital system enables improvement. When a process exists only on paper or in people's heads, analyzing and improving it is difficult. When the same process executes digitally with data captured at every step, patterns become visible. You can see bottlenecks, identify errors, and measure time accurately.

This iterative approach also addresses change management. Rather than disrupting operations with a complete process redesign, you digitize existing practices first. Operators continue doing what they already do, but now with digital guidance and tracking. Once they are comfortable with the system, you can introduce process improvements based on data rather than assumptions.

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The Role of MQTT in Connecting Workflow Systems to Factory Infrastructure

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Workflow orchestration systems need to communicate bidirectionally with factory equipment and enterprise systems. They receive events from machines, send commands when appropriate, pull work orders from ERP, and push production data back. This creates specific communication requirements.

MQTT has emerged as the preferred protocol for this type of integration, particularly when workflow orchestration systems are cloud-based. Security policies in manufacturing facilities typically prevent inbound connections from external systems. Opening ports and allowing external systems to initiate connections violates security practices that protect operational technology networks.

MQTT's publish-subscribe architecture works naturally with outbound-only connection requirements. Edge devices in the factory establish outbound connections to an MQTT broker. The workflow orchestration system also connects to the same broker. Data flows through the broker without requiring inbound connections to factory networks.

The event-driven nature of MQTT aligns well with workflow orchestration needs. When a machine completes a cycle, it publishes an event. The workflow system subscribes to these events and triggers appropriate operator tasks. When operators complete activities, the workflow system publishes events that other systems can consume.

The amount of data required for workflow orchestration is modest compared to historian or analytics applications. A few tags per machine—status, counts, basic states—provide sufficient information. This makes the integration lightweight and focused on events that matter for process orchestration rather than comprehensive data collection.

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Understanding Human Efficiency Through Behavioral Analytics

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Manufacturing organizations typically have detailed data about machines—OEE, downtime, cycle times, energy consumption. They have detailed data about materials—consumption, waste, inventory levels. They have almost no data about human work—operator workload, task completion patterns, or time allocation across activities.

Labor represents a significant portion of manufacturing costs and is absolutely essential to operations. Yet it remains a black hole in terms of data and understanding. Workflow orchestration systems change this by capturing behavioral data as a natural byproduct of process execution.

When the system issues a task to an operator, it records when the task was issued, when the operator started working on it, and when they completed it. This creates a dataset about workload distribution, response times, and execution patterns. This data is not about surveillance or micromanagement. It is about understanding how work flows through the operation.

For example, workflow data might reveal that one station receives twice the task load of another station. Without data, this imbalance is invisible. Managers assume work distributes evenly. Workflow data makes imbalances visible and actionable. Redistributing tasks between stations improves efficiency without adding resources.

Combining workflow data with machine performance data reveals additional insights. Correlations between shifts, operators, quality issues, and waste patterns become discoverable. You might find that certain error types cluster around specific conditions that were previously unnoticed.

The goal is not to track individuals for performance management. The goal is to understand the manufacturing process holistically—machines, materials, and human activities—so that improvements address real constraints rather than assumed bottlenecks.

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Preventing Errors Rather Than Only Detecting Them

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Quality management traditionally focuses on detecting problems after they occur. Quality control checkpoints find defective products. Root cause analysis determines what went wrong. Corrective actions prevent recurrence. This approach works but is reactive and expensive.

Workflow orchestration enables proactive error prevention. Consider a bakery production line where products must be labeled with shelf life dates based on production date and product type. An operator packages bread and applies labels. If they accidentally use tomorrow's date instead of today's date, the entire batch is mislabeled.

With paper-based processes, this error is not detected until quality control review. By then, multiple pallets may be affected. Unpackaging and relabeling the product represents significant waste.

With workflow orchestration, the system knows what shelf life date is correct based on production date and product. When the operator scans or enters a date, the system validates it immediately. If the date is wrong, the operator receives instant feedback and can correct it before completing even a single package.

This proactive validation extends to many error types. Using wrong materials for a product. Skipping required safety checks. Operating equipment outside specified parameters. The system acts as a co-pilot, preventing mistakes at the point of action rather than discovering them later.

The co-pilot analogy is apt. The system does not replace operator judgment but provides guidance and validation. Modern drivers rely on GPS for directions without losing the ability to navigate. Operators benefit from workflow guidance without losing their expertise.

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Capturing and Retaining Tribal Knowledge

Manufacturing expertise accumulates through years of experience. Understanding how to handle specific product variations, what to do when equipment behaves unexpectedly, or how to optimize changeovers for particular circumstances comes from time on the job. In some industries, becoming a fully qualified solo operator requires five to seven years of training and experience.

This tribal knowledge represents intellectual property that companies rarely capture systematically. When experienced operators retire or leave, knowledge leaves with them. COVID-19 accelerated this problem as many workers changed careers or retired early. Facilities that could not run shifts due to lack of qualified operators experienced the cost of knowledge loss directly.

Traditional approaches to knowledge capture create documentation—thick SOP binders and training manuals. These documents help but have limitations. They get stored somewhere and rarely accessed. They become outdated but are not maintained. Operators faced with an unfamiliar situation cannot quickly find the relevant section in a 50-page document.

Workflow orchestration provides a different approach to knowledge capture and delivery. Instead of creating documentation separate from work execution, embed knowledge directly in the workflow. When an operator encounters a specific situation, the system provides relevant guidance at that moment.

This contextualized knowledge delivery is far more effective than reference documentation. The operator does not need to remember to check a manual or search for the right section. The guidance appears automatically when needed. Photos, videos, or detailed instructions can be embedded directly in the task.

Capturing knowledge this way also reveals gaps. When errors occur, you can enhance the workflow to prevent recurrence. Add a validation check. Include additional instructions. Require a specific sequence. The knowledge continuously improves based on real operational experience.

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Metrics for Measuring Workflow Orchestration Success

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Organizations implementing workflow orchestration systems need ways to measure success and monitor ongoing performance. Two metrics provide clear insight into how well the system is working.

The first metric is automation percentage—what percentage of tasks are automatically triggered by the system based on events and business rules versus manually initiated by operators? High automation percentage indicates that business processes have been successfully translated into digital workflows. The system proactively manages work rather than operators manually starting activities.

Typically, successful implementations achieve 90 percent or higher automation. The remaining 10 percent represents exceptions and situations that cannot be fully automated. This is acceptable. The goal is not 100 percent automation but capturing the vast majority of routine process execution in the workflow system.

The second metric is adherence—what percentage of issued tasks do operators complete on time versus expire or get skipped? High adherence indicates that workflows match operational reality and that operators can execute as designed. Low adherence signals problems that need investigation.

Low adherence might indicate workload imbalances. If operators consistently cannot complete tasks on time, they may be overloaded. The workflow data makes this visible so management can redistribute work or add resources.

Low adherence might also indicate workflow design problems. If tasks routinely expire because operators need to handle other priorities, the workflow may not account for real operational constraints. Redesigning the workflow based on this feedback creates better alignment with reality.

These metrics are simple to calculate and directly actionable. They focus on the fundamental question: is the digital workflow successfully orchestrating the manufacturing process? Sophisticated analytics can be added later, but these core metrics provide clear signals about system effectiveness.

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Integration with Unified Namespace Architecture

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Organizations implementing unified namespace architectures create favorable conditions for workflow orchestration systems. The unified namespace organizes factory data in a structured, accessible way using MQTT publish-subscribe patterns. All machine data publishes to a common namespace that any system can subscribe to.

For workflow orchestration, this eliminates significant integration work. Rather than connecting to each PLC using different protocols and configurations, the system subscribes to relevant topics in the unified namespace. Tag discovery, organization, and access are handled by the namespace infrastructure.

Workflow orchestration systems also contribute data back to the unified namespace. When operators complete quality checks, when changeovers finish, when production runs start and stop—these events publish to the namespace where other systems can consume them.

This bidirectional flow creates a powerful integration pattern. The workflow system is not isolated but participates in the broader factory data ecosystem. An HMI might prevent equipment startup until operators complete pre-start checks. An analytics system might correlate operator activities with machine performance. Enterprise systems might trigger workflows based on work order changes.

However, unified namespace is not a prerequisite for workflow orchestration. The amount of machine data needed for basic workflow orchestration is modest—equipment status and production counts cover most requirements. Organizations can implement workflow orchestration using direct connections to equipment and later integrate with a unified namespace when available.

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Choosing Technology Platforms for Workflow Orchestration

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The term MES (Manufacturing Execution System) creates confusion in this space. Traditional MES platforms are prescribed, structured systems built around specific industry standards and models. They solve complicated problems and provide comprehensive functionality. They also require significant implementation projects, specialized expertise, and substantial investment.

Not every manufacturing problem is complicated. Many problems are relatively simple but still valuable to solve. A three-question form is not technically difficult to create. But ensuring that form appears at exactly the right moment, that operators complete it, and that data is captured accurately creates significant value.

Technology platforms for workflow orchestration should handle both scenarios. They need capability for complex requirements when needed but should make simple tasks easy to accomplish. This typically means low-code or no-code platforms that quality managers and continuous improvement specialists can use without extensive software development expertise.

The workflow-first approach prioritizes understanding and digitizing the manufacturing process before deciding which MES functions to implement. Track and trace is just recording consumptions and production within the workflow. OEE calculation needs shift start events and stoppage reasons, which can integrate into existing workflows for shift procedures and maintenance checks.

This approach inverts the typical MES implementation. Instead of implementing MES modules and training operators to use them, you digitize operator workflows and add MES capabilities where they naturally fit. Operators see a consistent interface focused on their work. MES functions are available but do not dominate the user experience.

Look for platforms that can be implemented incrementally. The technology should support connecting to a few machines, creating initial workflows, and delivering value quickly—weeks, not months. Then business users should be able to expand the system, adding more processes and capabilities without requiring extensive software development projects for each addition.

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