Posts in category: Industrial IoT

Why Is Tracking Industry 4.0 Trends Especially Important Today?

Companies that follow modern trends and the real possibilities of their application do not operate more efficiently because they want to appear “innovative.” They operate more efficiently because they can identify opportunities earlier where time can be saved, costs reduced, or performance increased—without immediately having to invest in new machines or expand production capacity.

An example is the use of artificial intelligence in procurement processes. Today, systems can easily contact 15 suppliers, summarize price offers, and prepare a comparison. What once took a person days can now be completed by a system within hours.

Without monitoring trends, you would arrive at such efficiency improvements five years later, most likely at a time when it has already become the market standard and you are simply catching up. And this is not some futuristic scenario. It is a practical acceleration of processes that reduces administrative burden and frees up people’s capacity for more valuable tasks.

A very similar situation can be seen in manufacturing digitalization. Companies that build a solid data foundation will be able to respond more quickly to market fluctuations, optimize capacities, and make decisions with lower risk. On the other hand, those that follow trends only passively will be implementing in a few years what their competitors are already using as a standard today.

What Challenges Will Companies Face in 2026?

1️⃣ Geopolitical Uncertainty and Difficult Predictability

The year 2026 is characterized by a high level of unpredictability. Threats of trade restrictions, sudden tariff changes, and tensions between global players can have an immediate impact on supply chains, input costs, and material availability. In a highly globalized environment, a single geopolitical decision can affect the entire market.

For many companies, success may simply mean maintaining the status quo. Not in the sense of stagnation, but in terms of stability. Maintaining margins, performance, and delivery reliability despite external shocks. And it is precisely the companies that have a clear overview of their capacities, efficiency, energy consumption, and bottlenecks that can respond to market fluctuations without panic.

2️⃣ Pressure for Flexibility and Rapid Adaptation

In the past, it was possible to plan production months in advance. Today, the situation is different. Orders fluctuate, customers change priorities, delivery times are shortening, and input prices can change practically overnight. What was true last quarter may no longer apply today. Companies therefore need to be prepared to quickly adjust production capacity, redirect production, or optimize costs.

Such flexibility, however, does not emerge from improvisation. It emerges when you have a clear overview of the real utilization of machines, where downtime occurs, and where hidden reserves exist. A company without data reacts reactively, solving problems only after they arise. A data-driven company, on the other hand, can act preventively, before the problem affects results.

3️⃣ ESG, Energy Efficiency, and Regulation

ESG is no longer just a topic for large multinational corporations. Increasingly, it also affects medium-sized manufacturing companies, either directly through legislation or indirectly through the requirements of customers and partners. If a company wants to comply with standards such as ISO 50001, it must be able to systematically monitor energy consumption at the level of individual devices, evaluate energy efficiency, implement specific measures, and demonstrate their benefits.

In 2026, however, ESG is not just a “reputational” topic. Energy represents a significant cost component. Yet many companies still cannot say exactly which machine consumes the most energy, where unnecessary peaks occur, or what the relationship is between production performance and energy consumption. Without this data, energy management is only an estimate. A company that does not have energy under control also does not have a significant part of its margin under control.

What Risks Do Companies Face If They Neglect Innovation?

A company that changes nothing today may feel stable. After all, machines are running, people are working, and orders are being fulfilled. At first glance, nothing dramatic seems to be happening. The problem is that the loss of competitiveness does not happen suddenly, but gradually. First, costs increase by a few percent. Then delivery times become longer. Later, margins decrease. Eventually, it becomes clear that competitors can produce cheaper, faster, or more flexibly.

Companies that fail to innovate systematically therefore risk:

❌ Greater risk, because in times of crisis, reserves are often what determine survival.
❌ Low ability to respond to market fluctuations, where improvisation replaces real adaptation.
❌ Higher invisible losses, as operating costs increase without companies even realizing it.

One thing is important, however: It is never too late to start. Not all innovations require major investments. Often, it is about systematic work with data, identifying hidden reserves, and gradually improving processes. And perhaps in times of an unpredictable market, focusing on efficiency improvements is wiser than waiting for “a better time.” Because a data-driven company handles uncertainty much more calmly.

Key Industry 4.0 Trends in 2026

👉 1. Automated Data Collection

Manually recording data on paper or in Excel should no longer be the norm today. Digitalization is not new, nor is it rocket science. It is the foundation of efficient management. If a company has not started yet, in 2026 it is high time to map processes, define priorities, and most importantly appoint an internal digitalization ambassador.

👉 2. OEE (Overall Equipment Effectiveness)

If digitalization is the foundation, OEE is the next logical step. The OEE indicator can reveal hidden reserves of 20–30%. And honestly, no AI will deliver such an immediate impact. However, beware of a common misconception: the fact that your machine shows OEE on its display does not mean you are digitalized. If these data remain isolated and are not connected to reporting, you are still operating “on paper.”

👉 3. Energy Efficiency Through EMS and BMS Systems

Energy management is no longer just a “nice to have.” Systems such as EMS and BMS allow companies to monitor consumption at the level of individual machines, optimize operations based on tariffs, identify inefficient equipment, and also prepare operations for ISO 50001.

👉 4. Transition from Reactive to Predictive Maintenance

Reactive maintenance (“we fix it when it breaks”) is today a costly luxury. Transitioning to predictive maintenance means collecting operational data, analyzing trends, and most importantly planning interventions before a failure occurs. Combined with a CMMS system, this creates a managed maintenance ecosystem that reduces downtime, emergency interventions, and the secondary damage associated with them.

👉 5. Unified Platforms (Ignition)

There is no need to discard existing systems. However, if a company is starting from scratch, it is wise to choose a platform that can scale. Ignition is an example of a solution that connects all critical systems, enables ETL processes, and simplifies data integration. A unified platform reduces chaos and increases the clarity of data flows.

👉 6. Digital Workforce and High Performance HMI

This topic is discussed far less than it deserves, yet its impact in practice is enormous. The ISA-101 standard defines High Performance HMI principles such as fewer colors, more context, highlighting only critical states—all designed to reduce the cognitive load on operators. A modern interface should not be about 3D graphics and blinking flames, but about the operator making fast and correct decisions.

👉 7. Cybersecurity as an Inherent Part of Projects

The question today is no longer: “Will a company become a target of an attack?” but rather: “When will it become a target?” Cybersecurity therefore must be an inherent part of every project, just as natural as occupational safety, without compromise. Not as a separate add-on, but as a fundamental architectural layer of the solution.

👉 8. Big Data and Advanced Analytics

Big Data only make sense when a company is fully digitalized, the data are reliable, and the processes work properly. At that point, connecting data with AI can bring an additional 2–3% optimization. However, as we described in the article “How Big Data Helps Reduce Costs and Boost Performance in Manufacturing Enterprises“, advanced analytics is an extension, not a replacement for fundamental digitalization.

👉 9. AI as a Tool, Not a Goal

Artificial intelligence is currently experiencing enormous hype, perhaps even greater than Big Data once did. It is clear that AI is here to stay and will have its place in industry. However, at the moment it is often overestimated and applied in situations where it does not deliver real value.

Companies should not start with the question “How do we implement AI?”, but rather “What problem do we want to solve?”. And the solution does not automatically have to be artificial intelligence. Often, automated data collection and basic process digitalization are enough. The real value lies in the correct and justified use of technology, not in the technology itself.

How to Prepare for These Trends?

If digitalization or innovation is to be successful, it cannot be random or driven only by current trends. It requires a clear structure, realistic expectations, and a process that minimizes risk while maximizing benefits. A properly designed approach also ensures that the investment will not become a one-time project, but rather a long-term tool for optimization.

A proven approach therefore looks as follows:

• 1️⃣ Audit and process mapping
• 2️⃣ Identification of priorities and benefits
• 3️⃣ Solution design
• 4️⃣ PoC (Proof of Concept)
• 5️⃣ Implementation
• 6️⃣ Long-term monitoring and optimization

When deciding on innovations, the greatest challenge is often to objectively evaluate one’s own processes. Internal teams are naturally immersed in daily operations, and many inefficiencies gradually become the “norm” that no one questions anymore. That is why it is beneficial to involve an external partner with practical experience, who can bring an independent perspective, reduce the risk of incorrect decisions, and accelerate the path to measurable results.

The Most Common Dead Ends in Industrial Internet of Things (IIoT) Implementation

Before moving on to the right approach, it’s important to understand the mistakes where most IIoT projects fail. Recognizing them early can save you months of work, thousands of euros, and help you start in a way that has a real chance to grow into a scalable solution.

❌ 1. Starting with technology instead of the problem

Companies often invest in technology without clearly defining what they actually want to solve. Industrial Internet of Things (IIoT) becomes a goal in itself rather than a tool to address specific business problems. The result? Data is collected, but never truly used.

❌ 2. A pilot project that cannot scale

A Proof of Concept (PoC) may work on a single machine, but the architecture is not prepared to scale to dozens more. The problem is often the network infrastructure — remote areas or halls with heavy metal structures lack stable connectivity, making expansion difficult or impossible.

❌ 3. Poor collaboration between IT and OT

Many IIoT projects stall due to conflicting priorities between IT and OT. IT focuses on cybersecurity, encryption, and keeping data inside the corporate network, while OT prioritizes availability and uninterrupted production. Without a shared language and agreed rules, a gap forms that slows down every next step.

❌ 4. Creation of data silos

Another common mistake is building IIoT as a standalone solution, disconnected from MES, SCADA, OEE, CMMS, or ERP systems. Data may be collected, but without context. The company ends up with more data — yet no real value, no unified view of production, and no actionable improvements.

❌ 5. Weak adoption and missing KPIs

After deployment, success depends on daily use. Proper training, ongoing maintenance, and clearly defined KPIs are essential. Without measurable outcomes, management support fades and the project quickly turns into another “IT experiment” with no real impact.

A 5-Step Roadmap to Start IIoT the Right Way

To ensure IIoT becomes a working technology with measurable results — not just another failed pilot — a structured approach is essential. The following five steps represent a proven roadmap we use in real-world projects.

✅ 1. Define the problem, not the technology

Industrial Internet of Things (IIoT) is not about sensors. It’s about solving problems. Decisions about data and sensors should only follow after clear goals are defined.

Start by answering three key questions:

• Why do we actually need IIoT?
• What problem are we solving?
• What specific outcome do we expect?

Examples of meaningful goals:

• Reduce unplanned downtime
• Lower energy consumption outside active production
• Reduce scrap rate on a critical line
• Gain visibility into real machine efficiency
• Eliminate manual data collection

✅ 2. Choose a pilot project with fast ROI (Quick Win)

A pilot should not be a “toy.” The right pilot delivers measurable results and visible improvements — understandable even to management. This builds trust, accelerates decision-making, and turns IIoT into a strategic investment.

A good pilot should have:

• measurable outcomes
• clearly defined roles and responsibilities
• fast implementation (2–8 weeks)
• easy scalability
• no risk to production stability

Common Quick Win pilots:

• Machine status monitoring linked to OEE → fastest way to uncover downtime, bottlenecks and hidden productivity potential
• Energy monitoring → detection of hidden consumption, often 15–20% savings after first deployment
• Digitalization of manual data collection → immediate error reduction and dozens of hours saved monthly

✅ 3. Design a scalable architecture from the start

Your first pilot must not become a dead end. The architecture should grow seamlessly — from one machine to entire production lines and plants. A strong initial design makes future scaling faster, cheaper, and more stable.

A scalable IIoT architecture typically includes four layers:

Edge layer

• Identification of devices to be connected to IIoT
• Selection of appropriate hardware such as sensors, IoT modules, and PLC devices that collect data from machines, production lines, or buildings.

Network layer

• Verification of network availability or design of alternative solutions if needed
• Secure data transmission using OPC UA, MQTT, or Modbus
• Data encryption, network segmentation, and access control

Platform layer

• Definition of the types of data to be collected, how they will be used, and how they will be presented within the IIoT system
• Implementation of the platform (Ignition) for data collection, storage, analysis, and visualization

Application layer

• Transformation of data into information in the form of dashboards, trend analyses, automated alerts, predictions, and reports
• At the moment when information starts to flow, the most important phase begins — turning insights into decision-making

✅ 4. Launch the pilot project

The pilot validates the solution in real conditions. It reveals technical limits, verifies data quality, and tests how the system fits existing processes — while providing hard evidence for management.

At this stage:

• IIoT is deployed on selected machines or processes
• Communication stability is tested
• Data accuracy and consistency are validated
• Initial results and production benefits are evaluated

✅ 5. Scale from one machine to the entire plant

The biggest mistake is letting the pilot remain just a pilot. A properly designed IIoT solution should expand to additional machines, lines, buildings, and sites. At this stage, the value of IIoT grows exponentially — not linearly.

How IoT Industries Can Help

Successful digitalization doesn’t start with technology — it starts with the right process. At IoT Industries, we don’t just install sensors and platforms. We build functional, sustainable, and scalable systems with measurable results.

We will:

• Clarify goals and expectations together, so you know exactly what you want to solve and what value IIoT should bring
• Perform an IIoT readiness audit (technology, network, processes, IT/OT) to ensure the project is built on solid foundations
• Design a scalable architecture that can easily expand to dozens of additional machines, production lines, or buildings
• Deliver the first pilot (PoC) with fast return on investment, so you can see real results within weeks
• Provide clear dashboards, alerts, and visualizations that enable data-driven work across all management levels
• Train users, establish data workflows, and ensure the system is used correctly on a daily basis
• Operate continuous monitoring and optimization, ensuring that IIoT remains reliable, secure, and delivers growing value over time

Why Is Security So Important in Digital Transformation?

The shift from paper-based processes to digital ones means that a company begins to generate and store exponentially more data. At the same time, this data starts flowing between different systems — and every such connection becomes a potential point of attack. Since digital transformation connects the world of IT (information technology) with the world of OT (operational technology), it creates a complex environment where the failure of a single component can impact the entire production process.

What Threatens Companies That Neglect Security?

• ⚠️ Leakage or loss of sensitive data (customer information, production know-how)
• ⚠️ Virus or ransomware attacks leading to operational paralysis
• ⚠️ Production shutdowns and financial losses
• ⚠️ Damage to reputation and loss of trust from business partners
• ⚠️ In extreme cases, even emergency situations impacting human safety

What Are the Most Common Security Risks in Digital Transformation?

❌ Connecting outdated systems to the network

Digitalization often begins by connecting old devices to the network to collect data. However, legacy PLCs, computers running Windows XP, or unsupported applications pose a major risk. They lack security updates, don’t support modern encryption, and often operate on outdated communication protocols.

In practice, this means that even a single such element can serve as an open gateway to the entire network. Therefore, every connection of an older system should undergo a security assessment by the IT department, or the entire system should be migrated or modernized to meet current standards.

❌ Direct connection of machines to the internet

It is common practice for machine manufacturers to enable remote diagnostics so that their technicians can quickly resolve malfunctions or update the system’s software. The problem arises when such connections are established without the knowledge of the IT department. This creates so-called “backdoors” through which anyone—whether accidentally or intentionally—can access the system.

If remote access is necessary, it should always be time-limited, encrypted, monitored, and performed only with IT’s approval.

❌ Unsecured Data Transfer to the Cloud

As part of digitalization, cloud services are increasingly used for data collection and visualization. However, the customer (in this case, the manufacturing company) does not always know where their data is being sent or how it is protected. If communication is not encrypted, or if a shared account with a simple password is used, the data may become publicly accessible.

It is equally risky when a supplier operates the cloud outside the EU without informing the customer. Every cloud solution should therefore include encrypted communication (HTTPS, VPN), individual user access, and clearly defined data ownership and server location. Without these measures, the company risks losing control over information that may be strategically sensitive.

❌ Outdated Firmware and Software

Many companies postpone updates with the argument that “the system works fine, so there’s no need to touch it.” However, outdated software and firmware are among the most common entry points for cyberattacks. Older versions often contain known vulnerabilities that are publicly available online. Attackers actively search for and exploit these weaknesses without needing physical access to the system.

The solution? Implement a regular update management process, ideally within a test environment to verify compatibility before deployment. Modern platforms such as Ignition allow fast, seamless updates without downtime — often completed within just a few minutes.

❌ Weak Access Management

Shared accounts, simple passwords, and the lack of login records are still common in many organizations. When an incident occurs, it’s often impossible to determine who made a specific change and when. In addition to the risk of unauthorized access, this also makes post-incident analysis and remediation much more difficult.

Modern systems should therefore use centralized identity management (e.g., Active Directory, SAML, OAuth, OIDC), two-factor authentication, and detailed logging of all user actions. The fundamental principle should be “least privilege” — every user has access only to what they truly need to perform their role.

❌ Insufficient Collaboration Between IT and OT

IT and OT are two very different worlds. The IT department protects the company’s network, servers, and data — their top priority is data confidentiality. OT (operational technology), on the other hand, ensures the smooth running of production, where the main priority is system availability. Without proper communication between the two, a gap emerges — one that attackers are quick to exploit.

IT teams often lack understanding of industrial protocols and production logic, while OT teams are not always familiar with cybersecurity principles. The key is to establish a shared framework of security policies, ensuring that IT and OT collaborate already at the design stage of digital solutions, not only when incidents occur.

❌ Human Factor

No firewall or antivirus can prevent human negligence. Clicking on a phishing email, sharing login credentials, or being inattentive while working with a system — these are among the most common causes of cybersecurity incidents. Attackers today use sophisticated social engineering techniques and often target maintenance staff or system administrators directly.

That’s why regular training and awareness programs are just as important as technical safeguards. Every employee should know how to recognize suspicious communication, handle passwords securely, and report unusual or potentially harmful activity to the right person.

How to Prevent Security Risks?

✅ Security as Part of Every Project

Cybersecurity should never be treated as a separate chapter that comes only after a project is completed. On the contrary, every digital project should include a security analysis from the very beginning. Customers should require their supplier to provide a system interconnection diagram detailing interfaces and communication protocols. This allows the IT department to evaluate the security of the solution before deployment, not after an incident occurs.

✅ Regular Updates and Continuous Monitoring

Every system should be continuously monitored and regularly updated. It’s important to track not only server status, but also the availability of connected devices, firmware versions, and communication changes. For example, Ignition allows a quick update to the latest version without downtime — the system can be updated and secured within minutes.

✅ Access Rights Management

No generic usernames, passwords, or shared accounts. Each user should have their own account with clearly defined permissions, following the “least privilege” principle — access only to what is absolutely necessary. Ideally, identity management should be centralized (e.g., Active Directory, LDAP) to ensure full traceability of who accessed the system and when.

✅ Training and Awareness

Security is not only about technology — it’s mainly about people. Employees should understand the basics of cyber hygiene and know what to do in case of an incident. Equally important is to train IT teams in the field of OT, so they understand the specifics of industrial technologies and can respond appropriately to differing priorities (AIC vs. CIA model). All types of software, interfaces, and communication protocols should be evaluated and approved before being deployed.

Case Study: The Cyberattack That Halted Production at Jaguar Land Rover

At the end of August 2025, automotive manufacturer Jaguar Land Rover faced a massive cyberattack that disrupted its production facilities worldwide — including the plant in Nitra, Slovakia. For safety reasons, the company had to immediately shut down several internal IT systems, including those directly controlling production.

The result was a complete production stoppage and subsequent delays in vehicle deliveries across the entire supply chain. In addition to the production downtime, a data breach was also reported, turning the incident into a complex crisis — technical, logistical, and reputational.

Although the exact causes of the attack were not publicly disclosed, the case clearly demonstrated how fragile interconnected digital infrastructures can be. A single unprotected interface, missing update, or weak access point can have global consequences.

This event serves as a warning for every industrial enterprise undergoing digital transformation. Cybersecurity is not an add-on to digital transformation — it is an essential part of it. For digitalization to truly deliver value, it must be secure. Companies that address security from the very beginning not only minimize the risk of incidents but also strengthen trust among their customers and partners.

What Is the Industrial Internet of Things (IIoT)?

The Industrial Internet of Things (IIoT) builds upon the concept of the Internet of Things (IoT), familiar from everyday life — such as smart homes or wearable devices. However, in an industrial setting, it operates on a much larger scale, with significantly higher demands. The key differences lie in the volume of data, as well as its accuracy, processing speed, and security.

IIoT represents an ecosystem of sensors, devices, applications, and network components that continuously communicate with one another. Data from these elements is collected, transmitted, analyzed, and evaluated in real time, providing immediate, actionable insights for managing production, maintenance, logistics, and energy. This gives companies complete control over every aspect of their operations.

The goal of such a system is not merely to “see more data.” Its true value lies in enabling businesses to monitor critical parts of production, prevent unplanned breakdowns and downtime, and optimize the use of materials, workforce, and equipment. All of this leads to higher productivity and lower costs.

The IIoT Cycle in Practice — 4 Steps to Success

The essence of IIoT can be summarized in a cyclical four-step process. The first three steps form the foundation, but the true value emerges in the fourth, when data is transformed into concrete actions. This process continuously repeats itself — and it’s precisely this ongoing cycle that makes the Industrial Internet of Things not just a technological solution, but also a powerful tool for continuous optimization and innovation.

1. Selection and Deployment of IIoT Sensors

The Industrial Internet of Things (IIoT) begins at the production floor level. Modern sensors today can monitor a wide range of parameters, such as:

• Vibration and temperature (bearing, motor, and gearbox conditions)
• Energy consumption (electricity, gas, water, compressed air)
• Environmental factors (humidity, dust, CO₂ levels)
• Quality output (optical sensors, camera systems)
• Process parameters (pressure, flow rate, speed, torque)
• Movement and position (pallets, products, AGVs, or robots)
• Safety and maintenance (oil levels, cycle counts, gas leaks, fire risks)

Choosing the right sensors is the cornerstone of a successful IIoT implementation. It determines the quality of collected data—and therefore the accuracy of analysis and effectiveness of all subsequent actions.

2. Connectivity and Data Collection

Sensors must be able to communicate securely with one another. The Industrial Internet of Things (IIoT) relies on protocols such as OPC UA, MQTT, and Modbus to ensure that data flows reliably to higher-level systems. At this stage, security plays a crucial role — encrypted communication, network segmentation, and access control help protect sensitive production data from leaks or misuse.

3. Monitoring, Analysis, and Data Evaluation

Collecting data alone is not enough — it must be processed and interpreted to turn raw numbers into actionable insights. In this phase, several key functions come into play:

• Visualization (clear dashboards in SCADA, MES, or BI systems)
• Trend analysis (identifying deviations and long-term patterns)
• Automated alerts (notifications for anomalies or unexpected changes)
• Prediction and modeling (forecasting future developments)
• Reporting (tailored outputs for operators, management, and auditors)

Through these steps, data is transformed from complex figures into practical tools that help manage production faster, more accurately, and more efficiently.

4. Action and Implementation

The fourth and most important step is transforming data into concrete actions that have a direct impact on production operations and the company’s overall performance. The outcomes can include:

• ✅ Higher productivity (automated production control, more efficient use of resources, and minimized downtime)
• ✅ Lower costs (optimized use of materials, labor, equipment efficiency, maintenance, and energy consumption)
• ✅ Better decision-making (real-time, accurate data that reduces the risk of poor decisions)
• ✅ Continuous IIoT development (adding new sensors, expanding reports, analyses, and notifications based on company needs)

However, the IIoT cycle doesn’t end here — quite the opposite. It loops back to the first step, enabling continuous improvement and innovation in production processes.

Integrating IIoT with Other Systems

As you can see, the Industrial Internet of Things (IIoT) is not just about passive data monitoring — it enables active, real-time improvement of production and creates the foundation for long-term optimization. While IIoT can function as an independent ecosystem, it becomes even more powerful when integrated with other — even existing — systems such as:

• MES for digitalizing production processes
• SCADA for monitoring and controlling production equipment
• OEE for measuring equipment efficiency
• CMMS for predictive maintenance management
• EMS/BMS for tracking energy consumption and managing building operations
• BI for advanced data processing and reporting

In this way, an integrated Industry 4.0 ecosystem can emerge — one that connects technologies, people, and processes into a single, intelligent environment.

The Industrial Internet of Things is not merely a technological trend — it is the essential foundation of modern manufacturing. Companies that implement IIoT gain not only higher efficiency but also the ability to respond faster to market demands, minimize losses, and maintain long-term competitiveness.

Why Do You Need to Calculate Labor Productivity?

Labor productivity is one of the key indicators that reveals the ratio between inputs and outputs—that is, between what a company puts into production and what it gets out. Calculating labor productivity enables you to compare the actual performance of individual workers, machines, production lines, work shifts, or entire departments. Without this overview, it’s impossible to identify weak points, set realistic goals, or evaluate the effectiveness of implemented changes.

What Does Labor Productivity Calculation Look Like?

Labor productivity can be measured in various ways, depending on the measurement goal, production type, and level of detail desired. The formula for calculating labor productivity must always be based on what you consider a relevant output (e.g., number of units produced / value added / machine performance) and which inputs you want to track (time / labor / technologies). Only then will the result provide relevant and comparable information with real value.

1️⃣ Labor Productivity per Worker or per Hour Worked

The simplest way to calculate labor productivity is to compare output with input. In practice, this might mean dividing the number of units produced by the number of workers or hours worked.