What is a Digital Factory and Smart Manufacturing? (+10 Case studies)

Christian Berg
January 27, 2023
35 min read

Manufacturing is going through lightning-fast changes in the age of digital transformation. Companies across the globe are rushing to implement innovative technologies, transforming the manufacturing processes and setting up digital factories

Digital and smart factory illustration


Manufacturing is going through lightning-fast changes in the age of digital transformation. Companies across the globe are rushing to implement innovative technologies, transforming the manufacturing processes and setting up digital factories — a new generation of industrial machinery and software infrastructure that delivers substantial efficiency gains, allowing companies to produce highly customized products with less resources, time and human effort.

The majority of manufacturing companies nowadays realize the importance of integrating smart manufacturing solutions in order to stay competitive in the modern-day economy. Today we’d like to talk about digital factories in detail, describing the most important technologies required for smart manufacturing, as well as approaches, solutions and strategies that would allow you to achieve success in implementing a digital factory.

What is a Digital Factory?

Digital factory is a generic term that refers to a set of organizational models and technologies used to automate manufacturing processes and the exchange of information between various components of physical industrial facilities. There are several other terms used for this purpose. “Smart factory” is the most common one. Others are the “U-factory” (ubiquitous factory), the factory of things, the factory in real time frame, and the intelligent factory of the future.

There could be a number of digital factories within one organization, replicating different aspects of industrial operations.

There is no single technology or a standard for a digital factory. A typical digital factory is an ecosystem, combining various tools, technologies, processes and digital systems integrated with each other. A digital factory utilizes a number of common technologies and solutions that are often mentioned as part of the digital transformation and Industry 4.0/5.0 concepts. They include IIoT (Industrial Internet of Things) devices, Big Data, machines, robots, other types of connected equipment, as well as software solutions across all the layers of the enterprise network.

Smart factories are inextricably linked with the concept of digital transformation as both refer to implementing and maintaining an integrated system of digital solutions aligned with each other and centrally managed through a single command center.

How does it work?

The purpose of any digital factory is to streamline, automate and optimize all operational processes as much as possible. Naturally, all digital processes are driven by the flow of data collected across various network components, from sensors and machines to software applications.

The processing of data within a digital factory is focused on every aspect of the product lifecycle, starting from the product design and engineering to innovating the assembly lines and optimizing value-added processes.


Multiple technologies that are part of the digital transformation concept are applied to enable the use of data from people, robots, IoT solutions and software systems within a smart factory.

Here are some of the technologies that are playing the most crucial role in the modern-day digital factories.

Big Data

Huge volumes of information collected from various sources within a smart factory are commonly referred to as Big Data. It is mostly time series data, which can and should be utilized for business analytics, automated tracking of industrial processes and other purposes.

Artificial intelligence (AI) and Machine learning (ML)

AI and ML are another fundamental technological concept of the digital transformation and smart factories. They allow organizations to automate countless operations and mundane work processes that were manual as part of a conventional industrial facility.

Industrial Internet of Things (IIoT)

The Internet of Things (IoT) describes different kinds of computing devices embedded with sensors, software, and other automation technologies that allow them to be interconnected and exchange data with each other and external systems. IIoT refers to the extended use of IoT in industrial automation solutions: the ecosystem of sensors, machines, robotic devices and other instruments connected together via industrial communication protocols and exchanging data with both internal and external software automation systems.  

Predictive analytics

Time series data collected from IIoT systems within a smart factory is analyzed using AI and ML models to predict the needs for machinery maintenance, optimize use of resources, improve planning of workforce occupancy and other purposes centered around overall streamlining of industrial facility operations.

Cloud computing

Rapid retrieval, storage and access to time series data collected from different components of a digital factory network is enabled by cloud computing, which is one more crucial technology fueling processes within a digitally transformed enterprise.

Reasons to build a Digital factory

Clearly, transforming the old-fashioned industrial facilities and manufacturing processes into a full-scale digital factory would deliver multiple benefits and advantages to any organization. Let’s go through the most essential and crucial to understand reasons to build a digital factory today.

Increased efficiency of all processes and faster product delivery.

Certainly, the increase in efficiency across all work processes and business operations layers is one of the biggest benefits of implementing a digital factory. It allows to remove or minimize all inefficient manual processes, reducing the usage of resources (physical resources like raw materials and parts, as well as time) and increasing the speed of product delivery. Digital factories also help to remove multiple errors and inefficiencies that cause supply chain interruptions and slow down the manufacturing process.

Data-driven equipment maintenance and workforce cooperation.

Real-time data exchange between human workers, machines, software systems and other components of the operational processes is another aspect of the digital factories that delivers multiple strengths and benefits. Time series data retrieved from various IoT devices, machines, robots and other kinds of industrial solutions can be used for more cost-efficient and balanced data-driven maintenance of this equipment. The cooperation between human employees also becomes more efficient and transparent within a digital factory. Workers engaged in different operational processes and business managers are able to communicate with each other more efficiently, coming up with solutions to manufacturing bottlenecks and challenges.

Boost of innovations and human-centricity.

The establishment of a proper digital factory will inevitably lead to a boost in innovations. Thanks to the automation of multiple mundane processes, organizations are able to redirect human talent to more creative jobs that require knowledge and are more engaging and fulfilling (thus boosting the level of employee happiness). Human-centricity is one of the fundamental elements of the Industry 5.0 concept. It elevates the role of human talent and its importance to overall success of operations. This approach is also sometimes called ‘society-centric’ as it puts fundamental human needs and interests at the core of the production process and promotes adapting industrial automation technologies to the needs of industry workers. Industry 5.0 recognizes the importance of not just achieving high productivity but also creating safe and inclusive work environments where human health and well-being is a priority.

More effective business management, planning and analytics.

With the flow of real-time data from sensors, machines, human employees and software as an essential part of the digital factory concept, the implementation of smart factory components makes it possible to significantly boost the effectiveness of business management across all the layers of operational processes. The availability of a constant and timely flow of time series data makes it possible to generate automatic reports with business analytics, predictions and planning.

New level of product quality and hyper customization.

Digital factories are also inextricably connected with the concept of hyper customization and hyper personalization in customer products, which is a part of Industry 5.0 vision as well. It is focused on delivering greater customer experiences as an addition to achieving high performance by interconnecting machines and software. For consumers, this brings hyper customization and hyper personalization, which is the ability of businesses to provide customers with even better choice and product customization options while still reducing production costs thanks to robotics, automation and other innovative technologies.

Solutions and strategies for success

The advantages and benefits of building a digital factory are more or less on the surface. They all are a byproduct of the digital transformation. When different components of a digital factory are implemented properly and interconnected with each other, they can generate abundant and very measurable boosts of business performance.

Naturally, not every digital factory implementation project ends up delivering all these business strengths. When it comes to realization of the digital factory concept in practice, organizations are typically facing challenges and bottlenecks of various sorts.

Let’s talk about the challenges that occur most commonly in the process of deploying a digital factory.

Having well-planned and all-encompassing strategies.

As trivial as it may seem, starting to implement a digital factory project without thorough planning and a proper strategy that takes into account all layers of organizational IT infrastructure and business operations, is one of the most common reasons why such projects end up a failure.

A digital factory implementation strategy should include the following:

Employing qualified and experienced human talent.

Digital factory, and the technologies comprising one, is still a fairly new concept, and there is a clear shortage of qualified IT talent with sufficient knowledge and experience required to implement a digital factory strategy, as well as to operate it when the project is completed. A recent Gartner survey of manufacturing leaders revealed that 57% of them feel that their organization lacks skilled workers to support smart manufacturing digitization plans.

“Manufacturers are currently going through a difficult phase in their digitization journey toward smart manufacturing,” said Simon Jacobson, Gartner’s vice president analyst. “They accept that changing from a break-fix mentality and culture to a data-driven workforce is a must. However, intuition, efficiency and engagement cannot be sacrificed. New workers might be tech-savvy but lack access to best practices and know-how — and tenured workers might have the knowledge, but not the digital skills. A truly connected factory worker in a smart manufacturing environment needs both.”

It’s hard to argue with experts who are saying that in order to enact the digital factory concept it isn’t enough just to implement and interconnect technological components. IT employees and factory workers must evolve alongside the technology, synchronizing their efforts with machines and software solutions.

Leadership commitment and management support.

When it comes to realizing the benefits of a digital factory and supporting the idea of its implementation, the data shows that the majority of business leaders and top management are on-board. According to another survey, this time conducted by PricewaterhouseCoopers (PWC) across 200 organizations based in Germany, 91% of industrial companies are already investing in digital factories. The majority of the respondents (85%) said their factories use digital technologies widely, with some elements already connected (44%), or are using digital technologies for standalone solutions within their factories (41%). Interestingly, just 6% of all respondents described their factories as being “fully digitized” already.

Gartner analysts also note that despite a very high degree of support among the top management, the level of understanding all the needs, requirements and challenges of the digital transformation and smart factory implementation among the business leadership leaves much to be desired. “It’s interesting to see that leadership commitment is frequently cited as not being a challenge,” Simon Jacobson said. “Across all respondents, 83% agree that their leadership understands and accepts the need to invest in smart manufacturing. However, it does not reflect whether or not the majority of leaders understand the magnitude of change in front of them – regarding technology, as well as talent.”

Some of the most important business leadership challenges when it comes to implementing a digital factory project would be the ability of the management to achieve the following:

Integrating different components and layers of business technology stack.

An all-inclusive integration of various parts of the business technology stack with each other, as well as external systems and applications, is a crucial requirement for a digital factory. 29% of German industrial company top managers surveyed by PWC in 2020 said they have already implemented networking technologies that connect components, machines, production management, transportation vehicles, workers, and manufactured products. Another 60% said they intend to do so by the end of 2022.

Let’s briefly review the main technology stack elements of a connected digital factory. We will discuss them in more detail in one of the following sections of the article.

Implementation of time series data collection and analysis.

Being able to effectively collect and analyze industrial time series data is another essential component of a digital factory. In order to be able to utilize multiple kinds of data collected from industrial automation systems, such as historians, SCADA and IIoT devices, organizations need to implement a platform connected to a multitude of data sources and external components.

The data analysis platform by Clarify incorporates a number of digital factory-enabling features, allowing users to integrate, organize, collaborate and visualize industrial data. It supports a streaming data timeline technology that enables users to quickly navigate and visualize hundreds of data signals at the same time without losing overview or performance. Clarify can be easily integrated with the majority of data historians from all vendors, including the ones that only support on-premise deployments. Want to see all the capabilities of Clarify with your own eyes? Take a tour of Clarify’s right now.  

5 stages of digital factory creation

The process of implementing a digital factory is typically divided into five basic stages: planning, design, validation, building, and operation.

1. Planning.

Proper planning is crucially important for the success of a digital factory implementation project. When in the planning stage, an organization should focus on creating a full and complete digital image of all its operations that would include the logistics and all production workflows.

In order to execute this process, a digital factory project planning team should collect as much data as possible from all the sources within the organization that can generate it, including software systems, machines and various hardware equipment with integrated sensors, IoT devices, etc. The recreation of operational workflows in the digital form also requires gathering the information on the location of each piece of equipment and workstation, movements of people, materials and machinery, as well as the optimal configurations of all equipment and software solutions.

With all this data, a simulation of all organization’s operations needs to be created, which can be used as a digital twin solution to plan the proper introduction of new smart factory components into business processes without disrupting workflows or causing errors in one of the layers of the production environment.

With so much data from a multitude of sources to collect and organize, you need a powerful yet easy to use data analytics solution. Vetted by reputable industry experts, such as Walker Reynolds, Clarify is an affordable and simple data intelligence platform that can augment your data historian or enable you to properly utilize process manufacturing data collected over the years.

2. Design.

When a digital twin of the organization is made and all operational processes are recreated in a simulation, the team responsible for implementing the smart factory project can proceed to designing its execution in digital form. Having a digital simulation with all workflows and equipment locations allows engineers to add the systems required for the smart factory to it, testing the changes for potential errors and installation issues. Creating a proper digital factory design allows organizations to avoid mistakes that would interrupt actual business processes and create hard to overcome operational challenges.

3. Validation.

When a digital projection of the smart factory is created and the design part is finished, it’s time for the project shareholders to review the results. Validation is required to check the viability of a digital factory design in order to avoid costly mistakes such as the ones described in our article about digital transformation. At this stage, leadership and management of the organization implementing the project need to keep close attention, taking time to evaluate the digital design, which in most cases is created by a third-party contractor hired to integrate smart factory solutions in existing production processes.

4. Building.

When the digital factory plan is validated by the shareholders you can finally proceed to actually building it. Naturally, the process of building a digital factory has to be properly organized, split into stages and coordinated. Typically, this is done through a so-called BIM (Building Information Modeling), which is a process for creating and managing information on a construction project throughout its whole life cycle. The BIM process is normally conducted in a specialized platform such as Autodesk, allowing various shareholders of the project, including contractors, architects, engineers and other specialists, to monitor the progress and collaborate on the project.

5. Operation.

When the construction of a smart factory is completed and all the systems across the hardware and software layers are deployed and interconnected, you can proceed into the operation stage. The digital twin of the organization created initially will play a vital role in day-to-day operations as well. It can be used to centralize the management and monitoring of all production processes, optimize workflows and test new tools in a safe environment.

As part of the digital factory, the information coming from people, programs and machines across the organization should be automatically collected, stored in a secure space and analyzed, as well as being easily accessible via a cloud-based solution such as Clarify.

Digital factory system components

A digital factory incorporates a huge variety of systems, tools and technologies. A few words about the technological components that are playing the most important roles in digital factory operations.

MES (manufacturing execution system)

MES, manufacturing execution systems, are software solutions used to manage, track and document all manufacturing processes. A MES serves as a centralized layer between ERPs (enterprise resource planning systems) and process control systems, allowing people in various roles to access the data on manufacturing operations and collaborate on optimizing the productivity and efficiency of all processes.

Here are the core functions of MES solutions:

M2M (machine-to-machine) technologies

M2M (machine to machine) communication is one of the central technological solutions powering the new generation of smart factories.

M2M is an umbrella term for various technologies that enable automatic communications and data exchange between different devices within a digital factory network. There are various methods to establish M2M communications, including the integration of sensors and meters connected to the equipment and software systems, and industrial instruments connected to the infrastructure via communication protocols. Modern-day M2M communication in a digital factory environment is most commonly established by implementing a system of networks that transmits data between all the hardware and software layers as well as human users.

“Manufacturing generates twice as much data as any other industry, making it a prime candidate for new digitized processes that can lead to substantial advances in time-to-market, and significant reductions in operating and maintenance costs. IDC is seeing a major change in how companies use technology in manufacturing and its ability to remove the silos between operational technology (plant and other manufacturing equipment), information technology (software, hardware, networks), and consumer or personal technology (smartphones, tablets). Emerging M2M and mobile technologies are an important part of that change,” said Kim Knickle, IDC Manufacturing Insights Practice Director.

Here are several common applications of M2M solutions:

Digital twins

Digital twin is a virtual copy/representation of a real-time physical object, structure, process or system. Most commonly, a digital twin is powered with a flow of data from sensors that collect information from the main functional components of the simulated system. The data is processed by a software system and applied to the digital twin.

Digital twins are used to run simulations, conduct virtual integrations of tools and equipment, monitor the performance of industrial machines, study the processes for potential issues, analyze the equipment for wear out and maintenance requirements, etc.

There are various types of digital twins that are utilized in the industrial automation environment. The four most common types of digital twins are:

Digital backbone

Digital backbone is a term used to describe a variety of communication networks and solutions distributed across all the infrastructure of a digital factory, enabling real-time efficient data exchange between all the systems.

Typically, the implementation of a digital backbone is the next logical step in the smart factory construction projects, which follows the deployment of a manufacturing execution system and enterprise resource planning (ERP) system. The digital backbone forms the layer of communications between all the connected equipment, software solutions and human employees.

Here are some of the most important functions and capabilities of the digital backbone layer within a smart factory:

ERP system

ERP (enterprise resource planning) system is a software used by organizations as a centralized solution for the management of all operations and business activities, from accounting, HR, and project management to procurement, risk management, logistics and supply chain.

An ERP solution can incorporate dozens, sometimes hundreds of applications responsible for different aspects of business operations, all centrally accessible and operating in real time.

Usually various apps that are part of an ERP system are divided by general functional areas and grouped together in specific modules.

Here are the most commonly applied ERP modules:

Recipe management system

Recipe management system is a software solution designed to store and centrally manage production recipes and all the related processes. In manufacturing, a recipe is a documented set of instructions on every aspect of operating an industrial machine and executing the production process, including manufacturing steps, notes on how to combine raw materials and ingredients, and other elements that allow for the end product to be produced.

Recipe management systems are typically integrated with manufacturing execution systems and ERPs. Sometimes a recipe management system is developed as a module of a MES.

Here are the most common functions of a recipe management system:

Automated material handling systems (AMHS)

Automated material handling systems (AMHS), also sometimes being called Automated material transport systems (AMTS) or Automated transport systems (ATS), are hardware solutions designed to enable efficient transportation of materials and ingredients around different components of the manufacturing infrastructure, including elevators, conveyors, machines, autonomous vehicles, etc.

AMHS are typically integrated with a MES, using the information provided by MES to orchestrate the movement of materials. The kind and type of material is identified by a AMHS with various technologies, such as optical character recognition (OCR), sensors, radio-frequency identification (RFID), barcoding and other.

Here are the most common functions of an automated material handling system:

Augmented reality

Augmented reality (AR) is a technology that enables interaction between digital objects or information and the real world, projecting digital simulations to a real-world background.

In digital factories, AR applications can have a multitude of applications, including virtual planning of equipment positioning within a real industrial environment, AR aided assembly line, logistics, transportation and storage of materials, control of machines and robots, etc.

Here are the most common uses of AR in digital factory environments:

Digital factory core operations and processes

Aside from systems and tools that comprise a digital factory core, there are multiple important operations and processes that are enabled by the above-described technological solutions. Let’s go through some of the most crucial ones.

3D printing

3D printing, in industrial automation environments also known as additive manufacturing, is a process of producing physical objects based on a three dimensional digital model by depositing materials in layers. In order to enable a smooth 3D printing process, a digital factory requires a combination of software, hardware and material handling systems working together.

With 3D printing, a digital factory can quickly manufacture product components, prototypes, copies of objects for testing and various tools required for a smooth production process. Plastic is the most common 3D printing material utilized in industrial environments today, but other materials, such as metals, glass, cement and others, also can be used if the available 3D printing equipment supports them.

There is a large variety of 3D printing types and methods. Here are some of the most common ones:

Advanced manufacturing intelligence

Advanced manufacturing intelligence is a process of using large sets of data collected from various hardware and software components, as well as human employees, to optimize the production processes, acquire new knowledge and skills, identify production issues, what causes them, improve all key performance indicators, etc.

The advanced manufacturing intelligence is enabled by seamless collection and aggregation of data from a variety of sources within a digital factory system, including MES systems, sensors, material handling and tracking systems, equipment logs, and other components.

Here are the biggest benefits of establishing a smooth advanced manufacturing intelligence process:

WIP management

WIP (work-in-progress) management is a combination of strategies and initiatives aimed at establishing an efficient movement of materials and optimization of production processes.

WIP management requires the integration of all business operations and workflows, including inventory management, supply chain, procurement, distribution, and other processes. The system components required to implement a smooth WIP management within a digital factory include manufacturing execution systems, ERPs, and a digital backbone as an underlying layer supporting the collection of all WIP-related data, analytics and reporting.

Having a properly established WIP management process can deliver a multitude of business benefits, such as:

Data collection

Retrieval, collection and storage of data across all the layers of industrial machinery and software systems is another crucial process enabling smooth operations of a digital factory. In a modern smart factory environment, the data is being collected in a digital format from equipment sensors, manufacturing execution systems, statistical process control (SPC) systems, robotic solutions, advanced process control (APC) solutions, ERP systems, and other devices and software tools comprising the digital factory ecosystem.

Here are some examples of data that is typically collected as part of a digital factory operations:

Reporting and analytics

Automatic analytics and reporting based on the data collected from various components of the digital factory systems provides the basis for optimization of manufacturing processes, detection of production issues, development of solutions, tracking of production efficiency, and real-time visibility of all key performance indicators.

Here are some examples of analytical reports typically delivered as part of a digital factory operations:

Integrated automation

Integrated automation describes what is essentially viewed as the next step in the evolution of industrial automation systems. Integrated automation includes solutions designed to centralize and further automate the utilization of tools and management of processes in order to achieve maximum optimization and minimize the need for human involvement.

Here are several examples of integrated automation systems:

Digital factory case studies

As we learnt earlier, the delivery of a smart factory or digital transformation project can be challenging and problematic for an organization, but you can’t avoid the need to digitize business operations and manufacturing processes to match the requirements of the Industry 4.0/5.0 era.

Even though a failure of a digital factory project is not uncommon in the business world, there’s no shortage of case studies detailing a successful implementation of a smart factory solution by companies across industries and business fields. Let’s take a look at a number of the most notable digital factory case studies.


Legendary German high-performance sports cars manufacturer Porsche has a Porsche Production 4.0 project aimed at implementing all the aspects of the digital factory concept at the company’s production facility in Stuttgart.

The Porsche Production 4.0 digital factory implementation project plan is broadly divided into three fields: digitally integrated production, production work, and production technologies.

Here are a few components of Porsche’s digital factory that are already implemented and successfully operate, delivering desired results:


Siemens AG is another German company pioneering the implementation of digital factory solutions. In 2017, the corporation launched its Lean Digital Factory (LDF) program with a goal to define a conceptual holistic digital transformation plan and roadmap for all factories of Siemens Digital Industries Software, one of the main subsidiaries of Siemens.

The following are the most notable elements of the digital factory project by Siemens. What’s especially interesting for us at Clarify, a data intelligence platform designed for industrial teams and environments, is the amount of attention Siemens pays to the proper integration of data collection, storage and utilization.

Siemens Amberg factory

Siemens Electronics Works Amberg (EWA) is one of the most innovative and Industry 4.0-ready digital factories in the world. Siemens boasts of achieving the future of manufacturing automation at EWA with exceptional levels of efficiency and flexibility.

Siemens EWA has more than 1,200 products in its production portfolio, with 350 production changeovers per day and a throughput of 17 million components per year.

The Amberg factory utilizes robot farms and autonomous guided vehicles powered by AI, machine learning algorithms and pattern recognition for predictive maintenance, efficient supply chain processes, optimal work arrangements and organized logistics. Intelligent sensors are integrated into industrial machines that are part of EWA for maximum productivity and throughput.

“13 years ago, the EWA was already a highly automated factory using PLCs, HMIs, SCADA, and MES systems to automate and optimize as many processes as possible. New technologies like simulation, virtual commissioning, AI/ML, and edge were integrated into the production process in recent years. For example, to predict the failure of machines, a planned maintenance interval is always better compared to unplanned downtime in the middle of the night. For the machines which are cutting the PCB boards, this helped to save yearly around 200,000 euro. It's just one of many examples,” said Bernd Raithel, Siemens Factory Automation director of product management and marketing.

Fast Radius

Fast Radius, an American provider of manufacturing and fulfillment services, implemented an advanced proprietary technological platform with 3D printing, virtual warehousing solutions and additive manufacturing at its facility in Chicago, which was recognized as one of nine top smart factories in the world at the World Economic Forum in 2018.

Fast Radius’ proprietary platform is able to collect data from all parts of product design and testing, powering real-time analytics and orchestration of all operation processes. All the data collected and generated by the digital platform is stored at the company’s virtual warehouse. This data is used by various teams and departments within Fast Radius to find suitable applications for 3D printing, test the production processes for various components and evaluate potential risks and challenges.

Additionally, Fast Radius uses its virtual parts warehouse, which includes more than 3,000 different items, for supply chain optimization, orders management, improvement of logistics efficiency and in multiple other business applications.


Eurobank, one of the largest financial institutions in the European Union, decided to implement a digital factory as part of a larger digital transformation strategy with a goal to redesign its digital channels and customer experience. Eurobank hired Accenture to deliver end-to-end services for the digital factory.

In line with this project, a totally new technology platform was implemented to support consistent omni-channel experiences of Eurobank’s customers. Specifically, Eurobank successfully migrated to cloud-based microservices and adopted a seamless omni-channel cloud environment for its IT infrastructure.

This enabled the financial organization to cut the average time to market for new functionality to eight weeks and the release cycle to two weeks. The solutions implemented as part of Eurobank’s digital factory project include a selection of smart financial management tools, innovative techniques for securing electronic transactions, contactless payments, digital onboarding, and a bunch of other digital ecosystem components.

Rockwell Automation

Rockwell Automation, one of the oldest industrial automation solution providers in the world, is a good example of a thoughtful and consistent approach to digital transformation. The company began the process of implementing a digital factory by combining a number of different enterprise resource planning systems that were in use across teams and departments of Rockwell Automation into a single centralized ERP.

The single ERP rollout was augmented by the launch of a centralized manufacturing execution system (MES) to manage all its factories and manufacturing processes. Using this as the foundation, Rockwell Automation also implemented unified IT and OT (operational technology) systems that enabled organized collection, access and monitoring of all manufacturing and business data.

These innovations allowed the company to establish standardized workflows and processes across all facilities and operational teams. They have also achieved significant measurable improvements of key performance indicators, accelerating time to market for their products, increasing the speed of supply chain delivery by 96%, reducing day sales of inventory (DSI), etc. These achievements allowed them to improve their annual productivity by 4-5% on average.

On the next stage of digital factory project implementation, Rockwell Automation integrated FactoryTalk InnovationSuite, developed by PTC (one of Rockwell’s subsidiaries) across six manufacturing facilities around the globe. This allowed them to empower their operations with augmented reality (AR), machine learning, IoT, edge-to-enterprise analytics, and other industrial automation capabilities that are part of the digital transformation processes. The integration of the FactoryTalk InnovationSuite, which is one of the industrial automation solutions that can be easily integrated with Clarify, allowed Rockwell Automation to accelerate the pace of further digital factory transformations, providing them with data for business intelligence and optimization of all operational processes across teams and facilities.


In 2020, Haier, a Chinese multinational home appliances and consumer electronics company, in association with GSMA, China Mobile and Huawei, implemented a digital factory proof of concept project, integrating edge computing, machine vision and 5G technologies into Haier’s manufacturing environment.

The utilization of 5G and edge computing enabled Haier to implement a near-real time analysis throughout the whole production line of stainless steel refrigerators, automating the inspection of the components and completed refrigerators for defects and potential errors.

A 5G network, implemented by Huawei and China Mobile, allowed Haier to establish a multi-access edge computing (MEC) architecture inside its factories. This solution enabled the company to deploy a minimal-latency high-volume image processing solution that ensures the automated production line functions with no delays.

Another notable industrial innovation implemented by Haier and its partners is a robotic arm with a 500W industrial camera able to scan refrigerators when they come off the production line. The system, which is based on a machine vision algorithm, identifies damage to the refrigerators exterior.

The capabilities of a 5G network, with its high connection speed and low latency, support this solution by allowing the transmission of high-definition images produced by industrial cameras inside the robotic arm to the centralized AI-based system.

5G technology in digital factories and industrial automation

A few words about the 5G technology as it is supposed to play a major role in the implementation of future digital factory projects.

The rollout of 5G, the fifth generation of broadband cellular networks, provides organizations building and operating digital factories with the ability to leverage high speed, low latency and other benefits of 5G technology to empower their industrial automation systems with real-time AI algorithms, IIoT, augmented reality and other solutions.

What is 5G?

5G is the successor to 4G cellular networks that are currently used to provide connectivity for mobile communication around the globe. According to a study by Ericsson, 5G networks will account for almost half of mobile subscriptions globally by 2027, reaching a total of 4.4 billion subscriptions.

Having multiple strengths and benefits over the previous generation of cellular network technology, the implementation of 5G networks is expected to be a major driver of further penetration of Industry 4.0/5.0 solutions, IoT and IIoT, and, of course, digital transformation across all industries and economic sectors.

Benefits of 5G

Here are some of the most important advantages of 5G technology:

High connection speed.

The Internet connection speed for devices in 5G network will range from 50 Mbps to 1 Gbit/s (1,000 Mbps), able to reach up to 4 Gbit/s with MIMO-based (multiple-input and multiple-output) equipment.

Number of devices in the network.

5G networks are able to support up to a million devices per square kilometer.

Low latency.

The latency in 5G networks should be as low as 5 milliseconds or less, which is significantly lower than 4G networks with average latency between 60 and 100 milliseconds.

Extremely low block error rates.

5G technology also allowed to minimize the network block error rate (BLER), which is the ratio of the number of erroneous blocks to the total number of blocks transmitted on a digital circuit.

Human talent in digital factory environments

Even though the implementation of new technologies plays the first violin in the digital factory projects, building up the so-called digital workforce is essentially important to reap the benefits of industrial innovations and leverage them to achieve measurable business results.

The majority of business leaders implementing digital factory projects realize that the approaches to recruiting, retaining and teaching employees also need to change. According to a survey of German companies conducted by PWC, more than half of the respondents (56%) expect the size of their factory workforce to increase or stay the same as a result of digitisation.

Half of the survey respondents said that they believe digital technologies will help older workers continue working longer. At the same time, 81% companies expect that finding employees with sufficient qualifications will be the biggest human-related challenge for them to face as a result of the digitization.

The number of job offers and opportunities for job seekers with the right qualifications is expected to increase in a digital factory environment. 89% of PWC’s survey respondents expect to hire new employees who have the qualifications needed to make the most of digitisation.

Source: https://www.pwc.de/de/digitale-transformation/digital-factories-2020-shaping-the-future-of-manufacturing.pdf

According to Gartner, one of the largest technological research and consulting firms in the world, the so-called fusion teams—multidisciplinary digital business teams—are a critical success component in digital transformation and smart factories.

“The rise of fusion teams is evidence that the boundaries between IT and the rest of the business are blurring at an accelerated rate — and that business leaders have a growing appetite for planning, running and managing their own digital initiatives. Progressive CxOs are leveraging fusion teams to accelerate time to value and manage risk in digital business initiatives. Foster rather than fight the rise of the distributed digital delivery model and maximize value by focusing on the human aspects of managing digital business risk,” said Janelle Hill, distinguished VP analyst at Gartner.

Guide to distributed fusion teams
Source: gartner.com

What are fusion teams?

Fusion teams are composed of people with multidisciplinary knowledge and expertise. The purpose of a fusion team is to blend technology skills, analytical skills and business domain expertise, sharing the accountability for the products built among the team members.

One of the characteristics of fusion teams is the organization of work processes. Instead of organizing operations by responsibilities and business functions, fusion teams organized to digitize business capabilities by business or customer outcomes.

Multidisciplinary digital business teams typically utilize talent from various business areas as well as IT specialists, aiming to blend technology with other types of domain expertise. The team members share business objectives related to specific outcomes.

According to Gartner’s data, at least 84% of companies and 59% of government entities have already set up fusion teams. A survey of more than 1,000 fusion teams has shown that 43% of them already report outside corporate IT. The research done by Gartner also showed that the utilization of fusion teams allows organizations to achieve progress on their projects and business goals 2.5 times faster compared to traditional centralized efforts.

Key aspects of managing distributed teams in digital factory environments

The success of any digital factory largely depends on implementing the right industrial automation solutions and software tools. But, like we said earlier, the organization and management of human employees in a smart factory environment still plays an incredibly important role.

Here are some of the most crucial aspects of comprising and managing the multidisciplinary “fusion” business teams in modern-day digital factory environments.

Engage all employees in the process of creating policies and standards for digital business governance.

It is highly advisable to motivate all team members to participate in the creation of standards, policies and guidelines that are supposed to govern digital business decisions. According to Gartner experts, fusion team leaders are 5.4 times more likely to have high digital judgment when they are involved in creating business governance rules.

Promote cross-functional collaboration.

The promotion of cross-functional collaboration among teams and individual employees is another key characteristic of successful employee management in digital factory environments. In order to properly support the technological solutions implemented as part of a digital factory, company leadership should approach working teams and projects as a joint effort of human employees with business and IT qualifications.

Active involvement of business leadership and HR in hiring talent for fusion teams.

In order for the fusion teams to be the most effective and functional, a distributed approach to forming them and hiring IT talent for the positions within such teams is necessary. Business leaders, the HR department and other functional parts of the organization should be actively involved in the process of finding and hiring the best talent for the teams operating as part of a digital factory environment.

Challenges of digital factory implementation

As we have already pointed out in our previous article, the one on digital transformation, most organizations have the implementation of various parts of the digital factory concept on top of their operational agenda. However, the factual rates of success in the implementation of digitization initiatives are considerably lower than the industrial automation industry likes to acknowledge.

Failures in digital transformation and smart factory projects implementation are more frequent than they could be due to a number of common problems that companies across industries are facing on a regular basis. Let’s talk about the most notable issues and challenges in this field.

Lack of IT skills and expertise.

The IT talent gap is one of the biggest challenges the global workforce market is facing these days. A report by Korn Ferry, a management consulting firm, says that by 2030, more than 85 million jobs (worth more than $8.5 trillion) could go unfilled because there aren't enough skilled people to take them. Naturally, the majority of these jobs will be tech-related positions.

Digital factories require high-qualified IT staff more than any organizations outside of the tech industry. Not being able to find and hire properly qualified IT talent restricts the ability to implement digital transformation for many companies across industries and business fields. According to a survey done by Cloud Industry Forum, 40% of businesses believe they don’t have the necessary skills in-house to pursue new technologies.

Digital strategist was the position the highest number of companies (43%) indicated as the one they need to fulfill most of all. Other jobs that companies in various industries struggle to find the right talent for the most include cybersecurity experts, technical architects, advanced data analysts, and a number of other positions.

Legacy industrial infrastructure that is difficult to upgrade.

Naturally, the implementation of digital factory concepts assumes the integration of new digital systems and machines across all the layers of business operations. When it comes to manufacturing and industrial companies, it isn’t uncommon for them to have considerably outdated machinery and equipment installed at their facilities. Such legacy tools can be very difficult to replace and, in many cases, incredibly hard or impossible to upgrade, digitize and/or connect to modern industrial automation IT systems.

In order to be functional and operating in line with up-to-date standards, digital factory infrastructure needs to be fully interconnected, with high-speed real-time data exchange among components, access to cloud environments, centralized automation and support for all the other modern-day technological innovations. The industrial machinery produced in the pre-digitization era typically needs heavy and expensive upgrades in order to become a part of a digital factory. This means that in many cases, it is easier and cheaper for companies to purchase new equipment instead of trying to modernize legacy machinery.

High costs and insufficient budgets.

Very often, companies simply lack the funds to pay for modernizations of their industrial environments to turn them into digital factories. This is especially common among small and medium-sized businesses that often struggle to keep up with the digital transformation demands and match the level of industrial innovations large enterprises can afford to implement. Insufficient investments into digital factory projects is also one of the main reasons why they end up a failure. Another reason is poor understanding of the funding needs for digital factory implementation projects by the business leadership. Very frequently, the top management is focused on short-term goals and quick ROI as anticipated results of a digital transformation investment. Needless to say, more often than not such an approach leads to disappointments as implementing digital innovations is a long-term game.

Resistance to change on leadership and employee level.

It is also very typical for us to witness the resistance to implementing digital factory innovations on every level of organizational structures. The resistance can manifest itself in different forms. When it comes to shareholders and business leadership, on their level it is mostly related to the financial reasons described above, as well as to the desire to avoid risks and challenges associated with digital innovations and smart factory implementation initiatives. On the employee level, the resistance is mostly caused by the opposition to automation, lack of understanding of the employee benefits of technological innovations and concerns about robotic solutions replacing human labor.

Cyber security risks.

Growing cyber security risks is one of the biggest problems associated with digital transformation of the business in general and smart factory projects in particular. According to a report by the Cyber Resilience Centre, 47% of Britain’s manufacturers faced at least one cyber attack in 2022. A study conducted by Trend Micro Incorporated revealed that 61% of global manufacturers have experienced cybersecurity incidents in their smart factories. 75% of those companies suffered system outages as a result, 43% of which lasted more than four days.

"Manufacturing organizations around the world are doubling down on digital transformation to drive smart factory improvements. The gap in IT and OT cybersecurity awareness creates the imbalance between people, process and technology, and it gives bad guys a chance to attack,” said Akihiko Omikawa, executive vice president of IoT security for Trend Micro.

Lack of clear vision and proper planning.

As we mentioned in the section about solutions and strategies, starting to implement a digital factory project without thorough planning and a proper strategy that takes into account all layers of organizational IT infrastructure and business operations, is one of the most common reasons why such projects end up a failure. Very often organizations initiate minor digitization projects without having a well-defined long-term vision and a general plan that accounts for all the technological and communication layers of business operations. Unsurprisingly, in many cases this leads to the emergence of an extremely fragmented and poorly integrated network of software solutions and machinery tools.

Digital factory and smart manufacturing trends

According to a report by AlliedMarketResearch, the global digital manufacturing market is expected to grow from $276.5 billion in 2020 to $1.4 trillion by 2030, at a CAGR of 16.5%.

Let’s go through some of the most notable trends of the digital factory and smart manufacturing industry.

Increasing adoption of digital factory technologies in the automotive and transportation industry.

Companies in the automotive and transportation industries today are among the most active adopters of digital factory and Industry 4.0/5.0 technologies. Digital manufacturing solutions provide vehicle manufacturers and equipment suppliers with tools to achieve benefits and advancements in all layers of their business operations, from vehicle parts design and fabrication operations to testing of completed vehicles and optimization of automobile downtime and fuel consumption.  

Rise in robotic technologies adoption.

Robotic technologies are getting more and more widespread in the digital factory environments. Companies in medical device manufacturing, automotive and defense industries are implementing robotics at their production facilities more actively than others. According to the International Federation of Robotics, more than 3 million industrial robots were in operation worldwide in 2020. This is a huge increase compared to 1997, when there were 700,000 industrial robots in use. The adoption of robotics in industrial automation is growing at 14% per year on average, more than doubling within the 2014-2020 period. Robots today are utilized in a wide range of industrial automation applications and processes, including welding, painting, assembling, material handling, packaging, palletizing, product inspection, testing, etc. With the development and adoption of latest tech innovations (mainly machine vision, AI, and Edge computing), the industrial robotics field received a new boost of development, leading to the emergence of increasingly complex and powerful solutions able to take care of a growing number of tasks that were previously considered to be non-automatable and thus, had to be performed by humans.

Growing concerns about cyber security.

With continuous implementation of IoT technologies, interconnections of all hardware components and machine learning, comes a predictable disadvantage — a growing number of cyber security incidents and sensitive business data leaks. Increased risks of a cyber security breach and hacker attack is one of the main factors restricting the adoption of IoT, Big Data and other digital transformation technologies for companies across industries in the post-COVID era.

More measured benefits of digital innovations for businesses.

Even though there is already an abundance of data proving the business benefits of digital transformation and smart factory innovations, there will be even more in the coming years. According to the prediction by IDC, by 2026, enterprises that successfully generate digital innovation will derive over 25% of their revenues from digital products, services and/or experiences. IDC’s analysts also predict that by 2024, companies that have already invested in building a developer ecosystem will expand their customer base by 25%. Additionally, they expect that by 2026, 30% of software development teams will be focused on turning traditional products into outcomes as a service.

Tech and IT talent shortage.

The shortage of qualified tech and IT talent has been a rising trend for a number of years now but it has especially grown in the post-COVID era with the so-called Great Resignation, which left companies facing a dearth of qualified job candidates to fill more than one million openings. Gartner expected more than 37.4 million people to quit their jobs in 2022. A study by Korn Ferry projected a global human talent shortage of 85 million people by 2030, resulting in about $8.5 trillion in unrealized annual revenues. IDC predicts that by 2024, 55% of organizations will use cloud marketplaces and tech startup acquisitions as their most important approaches to software sourcing to help alleviate the developer skills shortage. They also expect that half of the Global 500 companies will have insourced software development by 2025, exacerbating the software engineering skills shortage and fueling interest in software development efficiencies.

Gartner quote that says heavy manufacturing experience disruptions

Increased competition driving digital innovations in the manufacturing industry.

According to Gartner’s study titled “Manufacturing Industry Scenarios in 2023: Leading Through Innovation,” increasing competition in the manufacturing industry puts pressure on organizations to reduce costs, improve customer experience and increase profitability. 36% of heavy manufacturing CIOs whose enterprise had recently experienced some type of disruption said that operating costs competitiveness had fallen behind. “Organizations armed with digital forces are disrupting business models with new value propositions. This disruption causes a challenge for manufacturing organizations but is also a chance to adopt digital themselves,” note the authors of Gartner’s report.

Equipment as a Service (EaaS) growth report

Equipment-as-a-Service model gaining popularity.

One business model that is expected to gain wide adoption in the coming years is  Equipment-as-a-Service (EaaS), when companies are renting out the industrial machinery to customers, with pricing tied to machinery use/time, wear or output. The EaaS model was first introduced in 1997 by Rolls-Royce company that allows airlines to pay for the engines produced by Rolls-Royce based on the number of flight hours. According to research by IoT Analytics, the EaaS market will reach $131 billion by 2025, growing from $22 billion in 2019.

Data-driven digital innovations in manufacturing and industrial automation.

Even though data and data-driven solutions already play a central role in powering smart factories and digital innovations, the importance of proper data intelligence and integration of data across the layers of organizational tech infrastructure will continue to gain momentum in the following years. Industry professionals expect that manufacturers and other organizations with industrial infrastructure will heavily rely on using data, analytics and proofs of concept to support innovation, production processes and business operations. “It is very difficult to adopt technology disruptions without a good research and data foundation. While not risk-averse, companies in the 2020s will exemplify the concept of incubating multiple projects on a small scale, evaluating benefits, and then scaling up quickly and sustainably as needed,” noted the authors of Gartner’s study.

Recommendations on digital factory projects for CIOs and IT leaders

Based on our extensive experience in industrial automation and smart factory projects implementation across industries, here is a list of recommendations that we at Clarify would give to CIOs, IT managers and other business leaders related to the approach to digital innovations that would allow them to maximize their chances of success in this field and avoid failures.

Final words

Data and data-driven technologies are powering digital factory systems, enabling more efficient and productive outcomes in every layer of business operations, from resource management and equipment maintenance to manufacturing and quality control. Any company implementing a digital factory project needs to master both the connectivity tools that allow it to retrieve and exchange data and software solutions helping to visualize and analyze the data for valuable information and business insights.

Clarify is an affordable and simple data intelligence platform that can augment your data historian or enable you to properly utilize process manufacturing data collected over the years.

Clarify can be easily integrated with the majority of data historians from all vendors, including the ones that only support on-premise deployments, allowing you to combine data from multiple time series databases, visualizing and accessing it in real time. Clarify also simplifies the process of connecting third-party data science tools and applications to your data for advanced analysis.

Regardless of your data management requirements, the Clarify platform is a versatile solution that can be used as a universal intermediary tool, augmenting your data management infrastructure and solving challenges with processing, integrating and visualizing time series data across industrial automation systems and software components.

Want to see the capabilities of the Clarify platform with your own eyes? Take a tour.


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