When the term “Digital Factory” is mentioned, many businesses often envision a brand-new facility equipped with robots, automated production lines, and state-of-the-art machinery. However, transitioning to a digital factory does not necessarily require replacing all existing equipment. The core of a digital factory lies in the ability to connect, digitize, and leverage production data.

Instead of data remaining fragmented across PLCs, machinery, paper logs, or Excel spreadsheets, a digital factory creates an environment where production data is collected, interconnected, and visualized. This enables managers to gain an accurate understanding of line operations—such as which machines are idle, which stages are lagging, current output levels, the line’s OEE, and where losses are occurring. When these questions can be answered through data—rather than relying primarily on manual observation and end-of-shift reports—a business has laid the vital foundations for its digital factory journey.

► Read more: What is data-driven manufacturing management?

I. What is a Digital Factory?

A digital factory can be defined as a manufacturing model in which data from machinery, production lines, personnel, and manufacturing activities is digitized, interconnected, and utilized to support monitoring, analysis, and decision-making. The goal goes beyond merely transferring data to a computer; the true value lies in transforming operational data into actionable information to detect issues, enhance performance, and support production management.

For instance, instead of operators manually recording product counts and machine downtime in an Excel spreadsheet at the end of a shift, data can be automatically collected from equipment and displayed on a real-time dashboard. Production Managers can monitor line status without waiting for end-of-day reports. Maintenance Managers gain data to identify equipment frequently causing downtime. Plant Managers can track production KPIs via a centralized system. To achieve this, a digital factory is typically built upon multiple layers of technology.

1. Machinery and IIoT Connectivity

This foundational layer enables the collection of data directly from the production environment. Depending on the factory’s existing infrastructure, data can be sourced from PLCs, sensors, machine controllers, gateways, or current management systems. This data—which may include running/stopped status, output, cycle times, machine speeds, downtime, and other critical operating parameters—serves as the basis for monitoring and analyzing production performance.

2. Real-Time Production Data

Data collection is merely the first step. To derive true value, information from machinery and production lines must be centralized within a single system and updated continuously, providing an up-to-the-minute reflection of production status. Instead of simply learning that “yesterday’s shift missed its targets” via an end-of-shift report, managers can pinpoint specific issues—such as: “The packaging machine’s performance began to drop at 10:15, followed by recurring instances of micro-downtime.”

The ability to precisely identify where, when, and for how long losses occur allows businesses to rapidly diagnose root causes and implement improvements, rather than merely reviewing aggregated results after the problems have already taken place.

3. Intuitive Dashboard

The volume of data generated by production lines can be immense. Dashboards serve to transform this data into visual information that is easy for users to monitor. Depending on implementation goals, dashboards can display metrics such as OEE, Availability, Performance, Quality, Downtime, Cycle Time, and Output, or track performance by individual machine, production line, and shift.

4. Data Analysis

Once comprehensive and detailed production data has been collected, businesses can move beyond merely monitoring current operational status. Historical data enables trend analysis, performance comparisons across shifts, the identification of recurring errors, downtime root cause analysis, and the pinpointing of process stages that constrain line capacity.

Based on these analyses, businesses can determine which issues require priority attention and identify areas for improvement. This marks a crucial shift from simply “viewing data” to “leveraging data to enhance production performance.”

II. Benefits of the digital factory for manufacturing enterprises

A digital factory should not be viewed merely as a technology project. The value of digitalization must be demonstrated through the ability to solve real-world production challenges.

1. Transparency of production data

A common limitation of traditional production management models is that data is fragmented across various systems and departments. A Production Manager might manage data using Excel, a Maintenance Manager might use separate reports, and a Quality Manager might track metrics on a different system, while machine operational data resides within PLCs or HMIs.

When these data sources are connected, digitized, and centralized within a single system, departments can access and work from the same information base, eliminating the need to spend time consolidating and reconciling disparate reports. Consequently, the enterprise can gradually establish a “single source of truth”—a unified and reliable data foundation for production management.

2. Faster decision-making

If data is only aggregated at the end of a shift, an issue arising at 9:00 AM might not be fully detected and assessed until the end of the day. This causes the business to miss the optimal window for addressing the problem and mitigating its impact on production.

With real-time or near-real-time data updates, the time elapsed between problem occurrence, detection, and resolution can be significantly reduced. When performance drops, downtime spikes unexpectedly, or output falls behind schedule, the operations team can quickly identify the issue, investigate the root cause, and implement timely corrective measures.

3. Clearer identification of losses

A production line may not experience major breakdowns yet still suffer from a silent loss of efficiency due to the accumulation of numerous minor losses during operation. These losses can stem from recurring micro-downtime, machines running below standard speeds, prolonged changeover times, waiting for materials, process imbalances, or bottlenecks that shift depending on the production stage.

Relying solely on total output figures or end-of-day reports makes it difficult for businesses to pinpoint where and when losses occur or to understand their impact on efficiency. Digital Factory solutions digitize these losses, converting them into measurable, trackable, and retrievable data; this enables businesses to identify root causes and focus their improvement efforts on the right issues.

► Read more: 6 Big Losses Affecting OEE in Manufacturing

III. OEE — A common starting metric for factory digital transformation

One of the key questions when embarking on the creation of a “Digital Factory” is: Where should a business begin its digitalization journey? In manufacturing operations, OEE (Overall Equipment Effectiveness) is an ideal starting metric, as it allows businesses to evaluate the operational efficiency of machinery and production lines based on three core factors: OEE = Availability × Performance × Quality.

Specifically, *Availability* reflects the equipment’s operational readiness relative to scheduled production time; *Performance* indicates the equipment’s operating level compared to its ideal speed; and *Quality* reflects the ratio of conforming products to total output.

The value of OEE lies in more than just a single aggregate figure. Through its three components—Availability, Performance, and Quality—businesses can pinpoint exactly where efficiency is being lost: whether due to excessive machine downtime, equipment running below standard speeds, or rising rates of non-conforming products.

For instance, two machines might both achieve an OEE of 70%, yet the underlying reasons for this performance level could differ vastly. One machine might suffer from low Availability due to frequent downtime, while the other experiences few stops but low Performance because it consistently operates below standard speed.

Therefore, implementing automated OEE measurement does more than just calculate a metric; it helps businesses build a data foundation to gain a clear understanding of how machinery and production lines operate, where losses occur, and which issues require priority improvement. This serves as a practical starting point for businesses to gradually expand their factory digitalization journey.

VI. Roadmap for a Phased Digital Factory Implementation

Not every enterprise needs to implement a full-scale digital factory system from the outset. A more practical approach is to build the system layer by layer, starting with production issues that can be clearly measured.

Step 1: Measurement

Before making improvements, an enterprise must understand its current state. The initial phase should focus on identifying the KPIs that truly require monitoring—such as OEE, downtime, cycle time, output, machine speed, or other critical parameters. Simultaneously, the enterprise needs to determine where the data currently resides and how it is recorded.

Step 2: Data Connectivity and Digitization

Once the necessary data has been identified, the next step is to connect the data sources. Depending on the factory infrastructure, data can be collected from PLCs, sensors, controllers, or existing systems. The goal is to reduce reliance on manual recording and establish a consistent data flow from the shop floor to management systems.

Step 3: Visualization and Analysis

With data connectivity established, dashboards transform discrete signals into actionable information for Production, Maintenance, and Management teams. This is the stage where businesses begin to address deeper questions: Which machine experiences the most downtime? What are the most frequent causes of issues? When does performance drop? Which process step is the bottleneck? Which SKUs or shifts show low efficiency? At this point, data begins to directly support continuous improvement efforts.

Step 4: Automation and Optimization

Once data is stable and processes are well understood, the next step may involve expanding into automated alerts, digitized workflows, system integrations, or advanced analytics. A crucial principle is to avoid automating processes that have not yet been measured and fully understood. Thus, a sustainable Digital Factory roadmap can be visualized simply as: Measure → Connect → Visualize → Analyze → Improve → Automate.

V. Why start the digital factory transformation journey with an automated OEE measurement solution?

Implementing a digital factory across the entire facility from the outset can be a complex undertaking involving numerous systems, departments, and data sources. Therefore, rather than attempting a wholesale digital overhaul, businesses can start on a smaller scale—such as by implementing automated OEE measurement for a specific machine or production line.

This approach allows businesses to focus on clearly measurable metrics, such as: How much actual operating time does the machine have? What is the downtime? Does the actual speed meet the standard? How does the output compare to the plan? And where do the primary losses lie—in Availability, Performance, or Quality?

By automatically collecting and recording this data, businesses can gradually build a reliable production data foundation for subsequent stages of their digital transformation.

More importantly, businesses can conduct pilot implementations on a single machine, area, or line to evaluate effectiveness, refine data collection methods, and standardize processes before scaling up to other lines or facilities. This step-by-step approach ensures the digital factory journey remains focused and aligned with operational realities, avoiding the pitfalls of trying to “digitize everything” right from the start.

VI. i-OEE: A Starting Point for the Production Data Digitalization Journey

i-OEE is a module within the i-Soft Platform, developed by the i-Soft engineering team to help enterprises digitize the collection, monitoring, and analysis of production performance data. Moving beyond a reliance on Excel and end-of-shift reports, i-OEE enables the transfer of data from the production floor to a centralized dashboard, allowing for the tracking of key metrics such as OEE, Availability, Performance, Quality, Downtime, Cycle Time, and Output.

This provides Production, Maintenance, and Plant Managers with the data needed to identify underperforming machinery, pinpoint where losses occur, and determine which issues require priority improvement. Thanks to a phased implementation approach, enterprises can start with a single machine or production line—standardizing their data collection model—before scaling up as part of their journey toward building a Digital Factory.

VII. Building a Digital Factory Based on Real-World Plant Data

Transitioning to a Digital Factory does not necessarily require replacing all machinery or launching a massive automation project. The starting point can be much simpler: using data to gain precise insight into what is happening on the production line.

By automatically collecting, visualizing, and analyzing equipment data in real-time, businesses establish a foundation to incrementally improve OEE, reduce losses, and expand into other digitalization initiatives. i-Soft develops solutions designed to connect data and digitize production operations in phases, tailored to the specific current state and objectives of each factory. Sign up for a consultation with i-Soft to assess your facility’s current status and develop a digital transformation roadmap suited to your needs.

i-Soft Joint Stock Company – specializing in providing factory digitalization software solutions

► Address: 115 N2 Road, Long Truong Ward, Ho Chi Minh City, Vietnam
► Hotline: 0989 739 488
► Email: info@i-soft.com.vn

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