Real-World Industry 4.0

Industry 4.0, an exciting future of digitalization, integration, and auto(no)mation. A future where organizations gain significant competitive advantage or fall behind. But what is Industry 4.0 exactly and what does it look like in the real world. Which challenges are there to overcome and how to do this?

This article provides an understanding in real-world Industry 4.0, lists achievable examples, and discusses the challenges that one most likely will encounter during the transformation. And finally, the article will introduce a comprehensive approach to the real-world Industry 4.0 transformation.

Table of Contents

The concept of Industry 4.0 was first introduced on the Hannover Messe in Germany 2011 as the 4th industrial revolution. Industry 4.0 is a movement that applies smart- and emerging-technologies to industries and realizes horizontal & vertical digitalized integration within an organization and across the supply chain.

The purpose of Industry 4.0 is to further improve quality, efficiency, and performance while reducing cost, waste, error, and downtime. A clear definition of Industry 4.0 is unfortunately shining by absence, resulting in a somewhat floating concept.

In essence, Industry 4.0 is mostly data-driven and mainly relies on technology to: generate, collect, transport, share, store, and transform data into value. In other words, all technology making value creation from or with data possible in the industrial environment. Examples of this technology include (I)IoT, Blockchain, Machine Learning, Artificial Intelligence, Cloud Computing, Big Data, Digitalization, and (Real-Time) Dashboarding & Visualization. Even though Industry 4.0 was introduced in relation to manufacturing, the concepts and principles apply to nearly all industries.

To make things still a bit more complex, Industry 4.0 includes other technologies which might be less directly related to data. Some of these other technologies might technically not be part of Industry 4.0, as they arguably rely on Industry 3.0 concepts. This said, they still have a major impact on recent industrial innovation. Examples of these technologies would be the latest advancements in Robotics & Cobotics, 3D Printing & Additive Manufacturing, and Robotic Process Automation. These technologies are further empowered by the use of data-related innovations, boosting operational flexibility.

An Industry 4.0 enabled business relies on:

I like to call this, “Uniform Industry 4.0”.

The true potential of the fourth industrial revolution still has to come as the technology and innovation on which i4.0 relies is still developing at a rapid pace. Monthly breakthroughs are still achieved in the field of Machine Learning, 3D printing is still evolving rapidly, and most other technologies driving Industry 4.0 follow similar progress. The latest developments must still find their way to the industries, meaning that Industry 4.0 is not even close to its true potential!

When speaking about Industry 4.0, many verbally paint futuristic images of completely autonomous and fully dynamic robotized factories, integrated supply chains, drones, and Artificial Intelligence. Production lines being able to produce several different and fully customizable products while continuously self-optimizing performance and quality. Machines predicting their own failure, ordering their own spare parts, and scheduling their own maintenance when most convenient!

A very pretty image, but most organizations are not even close or didn’t start their innovative journey yet. These futuristic images do raise the question if it is applicable to older factories, how many millions of euros it’s going to cost, and perhaps raise doubts if Industry 4.0 is just not for their specific branch.

It is time to have a look at what Industry 4.0 looks like in the real world and what Industry 4.0’s challenges are.

Many companies have a stable installed base (Brown Field), machines doing exactly what they should do, sometimes connected, sometimes standalone, skilled operators running them or monitoring the automated process. Many of these companies, especially those who are around for a bit longer, operate a vast spread of equipment generations ranging from legacy systems to state-of-the-art technology. Most equipment is costly and has a planned lifetime of between 10 to 40 years. This equipment cannot be simply replaced because of some innovative Industry 4.0 projects, unless there is a beast of a business case behind it. Real-World businesses do not have mind-bending budgets laying around, but instead, have an operational process that cannot be stopped for a long time, and have an already hard-working organization that has limited spare time for improvement and change.

But earlier, we established that Industry 4.0 further improves quality, efficiency, and performance while reducing cost, waste, error, and downtime mainly by utilizing data, integration, standardization, and late generation technologies. This is exactly what Industry 4.0 should be about and this is something any business can start with, in small steps.

The path to Real-World Industry 4.0 must be a tailored, incremental, and self-sustaining journey, taking bite-size steps while generating continues value on the way.

The transformation to Industry 4.0 in the real world has to deal with real-world limitations. The Journey has to generate continuous value from the start in order to be applicable for real-world companies. Small value-generating steps to a larger goal, that is the only way to go.

Different studies show significant gains as a result of the implementation of Industry 4.0 principles. Gain in specific areas could in many cases reach up to, and over 50%. We won’t name any specific percentages in this article, as they are highly dependent on the situation. But one thing is clear, there is a significant amount of new business value to be gained, and those who fail to act will fall behind.

A list of potential Industry 4.0 value drivers with matching examples is provided below. The given examples might sound like significant projects, but in fact could easily and affordably be implemented in a short time with a great and continuous return, as long as the right approach is followed.

Example: Overall Equipment Effectiveness (OEE) is an excellent overall Key Performance Indicator (KPI) with a direct link to financial business results. 

Industry 4.0 principles could significantly boost OEE. Not only through digitalization and automation, but also through insight.

Industry 4.0 principles make automatic, real-time, and detailed OEE calculation possible. Real-time OEE will enable prompt and targeted action on OEE deviations, while the OEE trend over time provides crucial insight into the predictability and stability of the gauged processes. The smarter the OEE calculation is implemented, the more actionable the insights will be.

Things become even more interesting when applying smart analytical tools to OEE deviations, correlated with collected process data. Root causes of OEE deviation could efficiently be exposed and addresses, stabilizing the operational processes. Once the production processes are stable and predictable, the same smart analytical tools and process data could identify the potential for lasting performance improvement where the now stable OEE is lifted to higher averages.

Example: 

“Utilizing machine learning to early detect
breakdown or production-loss
in historic and real-time process data.”

Modern machine learning algorithms can provide insight into events, predict future events (predictive maintenance), and suggest a solution for repair before the event even occurred (prescriptive maintenance). These insights allow for planned action to prevent an upcoming event from happening, instead of hasty repair. This approach could even reduce unnecessary or over-active maintenance.

Examples under the “Improved productivity of knowledge workers” value driver below, also are highly applicable to improve maintenance quality and productivity at a reduced cost.

Industry 4.0 principles enable dramatic optimization of maintenance, reduction of the maintenance cost, optimized spare part planning, and reduction of (unplanned) downtime. Just make sure to apply the correct implementation approach.

Example: Smart sensors, process data, process parameters, quality data, and perhaps track & trace data allow machine learning algorithms and modern data analytics to predict quality. Quality inspection could be automated, and even prediction of bad quality before actual production is possible through these technologies. Machine learning algorithms could even be used to (continuously) self-optimize processes and machine parameters, boosting quality and performance in a true Industry 4.0 manor.

Industry 4.0 principles could not only automate quality inspection and, improve quality in general, but even do this at a lower cost and at higher throughput.

Example: Digital twins could speed up the design and test process of a new product, at significantly reduced development costs. Faster and cheaper prototyping can be realized through 3D printing (additive manufacturing). And digital integration across departments in the development chain could even further improve matters.

Another advantage is that these principles allow for higher levels of experimentation, which could improve the quality of the product design itself.

Adjustment time of the production lines for a new product is another point addressed by Industry 4.0 principles. Typical Industry 4.0 production lines are more flexible, adjustable, and highly digitally integrated, reducing the cost and time to reconfigure for new products.

These capabilities of Industry 4.0 production lines should also be considered in the product design process, as batch sizes become smaller and customer-specific customizations become a reality.

Example: Industry 4.0 principles could significantly optimize inventory cost, for example:

  • Collected (market) data could enable prediction of production demand through machine learning algorithms and data analytics.
  • Digital integration across systems (like ERP, MES, WMS, SCADA & PLC) and along the supply chain could optimize delivery and planning.
  • A combination of the first two could even lead to (inventory) optimization across the supply chain.
  • Optimization of internal processes, improvement of process predictability, reduced batch sizes, and predictive maintenance allow for even further reduction of inventory.

Example: Smart systems and tools could prepare data and prioritize information, make correlations between historic and recent events, align different data sources, improve the representation & visualization of information, and provide powerful search and easy-to-use analytical tools.

Knowledge workers lose a lot of their time collecting information, arranging it, correlating it, and analyzing it. Industry 4.0 enabled systems can perform a significant amount of this preparatory work, allowing scarcely available knowledge workers to perform their tasks much faster, and most likely even arrive at better results as their input data is better prepared and smart tooling enables them to find and verify better answers.

Example: Specialists who traditionally traveled between sites could be empowered by the use of Augmented Reality (AR) glasses. Local engineers could wear these glasses, while the specialist remotely provides real-time information via the AR glasses and instructs the local engineer while observing all results via the AR glasses-powered video call.

The same AR glasses could be useful for any engineer who needs both his hands to perform his work while needing real-time access to information. The AR glasses could project any information and process information, fighter pilot style, while the engineer performs his task. The glasses could record all work for later analysis or perhaps proof of work.

Example: Analysis of collected energy use data and process data could provide valuable insight into energy use and potential for optimization. These insights allow for prioritized actions to reduce energy consumption, like:

  • Reusing leftover energy like heat or motion.
  • Reducing speed, acceleration, and performance when demand is lower.
  • Reducing peak loads through optimized planning and technical solutions like buffering.
  • Reducing energy use of idling and waiting equipment through improved control and planning.

(Near) real-time energy data could provide continuous insight into energy use per production unit through live dashboarding, allowing for prompt action to improve energy use. This information must be actionable for optimal results.

Example: Industry 4.0’s digitalization principles could automate parts of the change-over processes. Automatic parameterization of equipment, automated self-checks, and the use of machine learning to ramp up performance after change-overs are some examples that could bring great improvement in this production time destroyer.

Again, also data analytics could gain actionable insight into change-over performance killers and their root causes. These new insights could help to optimize the change-over planning, improve the change-over process itself, and perhaps improve preparatory works.

Example:

“Although lots size reduction might be easiest for greenfield sites,
there is still plenty improvement potential for brownfield sites.”

To name a few, prediction of demand, reduced changeover times, and modular product design could help to optimize the lot size with relatively small improvements.

Digital integration of existing robotics and machines, introduction of centralized production management like with a Manufacturing Execution System (MES), and strategically chosen additional machinery like a 3D printer (additive manufacturing) and advanced robotics could further improve lot sizes against a higher investment.

Example: Servicification is one of the most common sources of new revenue streams as a result of applied Industry 4.0 principles. Let’s have a look at a bit less common example. A fictive company sells warehouse solutions. They traditionally produce and install entirely operational warehouses for their customers as their product.

Servicification of their warehousing products could be to sell warehousing as a service. Customers will pay per item stored in the warehouse, and the warehouse service provider (our fictional company) ensures in a Service Level Agreement (SLA) that the customer could retrieve each stored item on-demand, in the same condition as submitted, and within a specified time. The agreement will likely state a minimal duration of the service, and a minimal amount of items stored during the term of the agreement.

This allows our fictive warehouse as a service operator, to build a warehouse against its perfect specifications to match the Service Level Agreement (SLA) against minimal cost. This approach allows the operator to continuously self-improve, as it has access to all its operational data, and could make adjustments as required without confirming with the customer. The only obligation to the customer is after all the SLA.

Other service-based new revenue stream opportunities include:

  • Predictive maintenance and continuous product analytics as a service.
  • Remote analytics, remote operator services, or remote maintenance.
  • Cloud-enabled products and solutions.
  • Continues software updates and introduction of new features. Perhaps for free, in exchange for access to valuable operational data.

These new revenue opportunities are enabled through Industry 4.0 and IoT principles.

The future of Industrial 4.0 is fully integrated across the organization and supply chain. Humans, Machines, Central Planning, Warehousing, Purchasing, Logistics, Suppliers, and Customers will all become digitally connected while drastically increasing levels of automation. This might already provide some insight into the complexity of the future and the journey leading to it.

The transition to successful Industry 4.0 knows many challenges and each of them needs to be addressed in the early stages of the transition. Just postponing the transition is also no option as competitors will adapt, realizing such significant gains that competing is no longer viable. Even a badly executed transformation will leave better future chances than doing nothing.

The coming 10 years will show a vast shift in business. Those who do not change will drown, others who do adapt but fail to address all challenges likely await a similar destiny. Only those who transform while addressing each and all challenges will survive and thrive. Check the solution at the bottom of this article to find out how!

The organization is an important factor in the transition to Industry 4.0 and one easily forgotten as focus goes naturally to tech & value. The transformation to Industry 4.0 will change processes, introduce new tools, and require close collaboration with digital systems, robotics, and cobotics. People have to learn how to reply on data to make better-informed decisions. Future machinery will predict its own failure, requiring humans to fix a problem that is not there yet. Smart systems will make suggestions for improvements that have to be implemented by people, trusting these insights. Trusting “smart” machines, AI and future prediction can be very unnatural in the beginning.

Newly introduced technology and principles require new knowledge and different skills. The need for highly skilled professionals will increase with the introduction of modern innovations, as well as the need for low-level maintenance technicians who need to maintain the vast increase of sensors and IoT devices.

Industry 4.0 demands integration and involvement of every part of the organization in order to be successful.

Small projects relying on their own tools, solutions & resources, perhaps even with an individual vision, will book individual success but in the long-term result in unmanageable chaos as part of the whole, too expensive to run and squarely unmaintainable over time. Departments must rely on each other, ensuring that every department is doing what it does best while all heading to a single target on the horizon. Different departments introducing their own solutions for similar problems is a killer for long-term Industry 4.0.

Organizational & cultural change is one of the most critical aspects as part of the transformation to Industry 4.0. Ensure that organizational change is part of the transformation!

As mentioned before, Industry 4.0 is about integration. A clear business-level vision and company-wide guidance is required to achieve this level of integration and collaboration across all initiatives and innovations.

Industry 4.0 is different for each business, requiring a certain level of tailoring, development, and experimentation. Most experiments and developments will lead to magnificent results, where others might struggle. It is essential to give enough initiatives the space to grow, but also to move on to the next initiative in time, both for successful as unsuccessful initiatives.

The industry 4.0 journey requires a mindset, different than the traditional one. Industry 4.0 is dynamic, optimized for continuous change, and ensures fast responses to the environment. The mindset and approach for the journey require similar agility for optimal results. A traditional waterfall planning and project approach does not match the needs of this journey.

Industry 4.0 reaches across the entire organization and supply chain. Many different departments and disciplines are involved, and many systems become interconnected. Different initiatives will be launched in contribution to the journey. This journey brings significant value, but could increase levels of complexity. This complexity needs to be managed from the start. Every aspect of the journey should contribute to a future-proof business-level vision and fit a scalable Industry 4.0 architecture.

When the journey is not managed well, the once so ambitious and unrestricted innovation towards Industry 4.0 could result in a completely unmanageable infrastructure on which the company becomes to depend. This infrastructure itself becomes to rely on indispensable individuals. Data and functionalities are not reusable as they should, multiple sources of the truth emerge, and new innovative initiative becomes increasingly expensive to undertake. The created monstrosity restricts growth, is likely unreliable, and expensive to operate.

Industry 4.0 is largely Big Data-driven. Large amounts of data are used for different use-cases. New use-cases might use similar data as others or gather new data from new sources. When all this data is managed correctly, an impressive data library grows over time. The potential for new insights and new use-cases expands exponentially at dramatically dropping costs.

A scalable data governance strategy is key to excel in the journey to Industry 4.0. This data governance strategy must ensure scalability and prevent data duplicity, keeping data organized at increasing amounts, in different types, and from plenty sources, while ensuring a single source of truth across the organization.

Data governance must also ensure scalable data access rights, as some users or systems need access to confidential data like recipes or financial data, where others only have access to process data. Privacy is another crucial aspect to be addressed in the data governance strategy, as regulations need to be met without restricting potential.

Data should be accessible through a range of standardized interfaces, ensuring that data is always available in the right format to users, tools, and systems. Efficient interfacing will lower the bar to new insight and additional value creation while preventing people from building separate solutions and gathering their own data.

Re-arranging data governance in an operational and growing system is expensive and inefficient. That is why data governance should be addressed from the start of the journey.

Connectivity and standardization are important aspects of Industry 4.0, essential to realize integration. This poses a challenge as standardization around Industry 4.0 is only in its early stages. Available solutions do not yet meet these standards or meet different i4.0 standards. The field of Industry 4.0 is in full development and the science and tech on which Industry 4.0 relies is still experiencing regular breakthroughs.

A smart Industry 4.0 architecture needs to deal with this variation in standards, and in fact not only with these. A real-world business is likely to operate a range of equipment at different generations. This means that the Industry 4.0 architecture needs to deal with a range of standards and connectivity solutions from different suppliers and of different generations, down to legacy systems.

A smart Industry 4.0 architecture, well-designed from the beginning, will easily address this challenge!

The transition to Industry 4.0 connects many systems and machines in order to digitalize processes and gather valuable data. Cloud solutions are likely to be integrated into the Industry 4.0 infrastructure, and some data might even be (openly) accessible via the internet.

All of this new connectivity introduces a challenge. Some systems were never designed for this level of interconnectivity, especially OT legacy systems. It is crucial to embed both IT and (OT) Cyber Security as a pillar of the journey. The transition to Industry 4.0 must be Cyber Secure by design!

This is not only the most cost-effective approach, but in fact the only way to ensure a Cyber Secure journey and future.

Industry 4.0 involves digitalization and automation across the supply chain. Data will be shared between organizations and perhaps for each individual product across the supply chain. This brings a lot of questions, like:

Well, I could go on but I am sure you get the point.

These questions need to be clear in an early stage of the journey, ensuring a scalable, robust, and lasting supply chain integration. The smart Industry 4.0 architecture provides the required technical flexibility.

The transformation to real-world industry 4.0 requires different skills and knowledge. There is already a shortage of Data Scientists and other high-level skills who are essential for the journey to Industry 4.0. On the other hand, smart tools are becoming more and more powerful, allowing people with less developed Industry 4.0 relevant skills to still achieve significant results.

The journey to Industry 4.0 requires addressing this skill gap. A potential solution is to utilize these late generation smarter tools, combined with accessible training, to empower the current organization to achieve extraordinary results. Still, a certain amount of specialists, like data scientists, are required but in a smaller amount. These specialists could be allocated in an expert center, supporting & training the organization and fine-tuning results where necessary.

This approach will not only require less of the scarce specialists but deploys them more efficiently, while involving the organization by design.

Industry 4.0 in the real world involves lots of variety, different brands, and types of systems including hyper-modern, legacy, and obsolete systems. Some systems are black boxes, locked down, and only accessible for the supplier with specific tools & access codes.

In the real world, also budgets, politics, and financial decisions have their impact on the journey while little to no downtime is available to integrate Industry 4.0’s innovations.

The journey to industry 4.0 has to be dynamic, deal with the real world situation and limitations, be able to quickly address opportunities, and work around shut doors. There are plenty of ways to do this, but the right approach is crucial in order for the transformation to succeed.

The future of Industry 4.0 is exciting but knows many challenges which must be addressed in order to become and remain successful. Summarized, these challenges include:

A “bite-size steps approach” to Industry 4.0 is the only feasible approach for most businesses although, there is a major catch. Just taking uncoordinated small steps will very easily lead to a jungle of different solutions, never scaling to a truly integrated Industry 4.0 driven business. In fact, just small steps will likely lead to the very opposite, being short-term value at the cost of an unmanageable & non-integrated catastrophe in the near future, defeating all value-generating principles of Industry 4.0.

AIVHY Ltd developed an openly published guide, leading real-world businesses to a lasting future of Industry 4.0. This guide is called, the “Master Plan of Approach” and definitely worth having a look at:

Feel free to contact AIVHY Ltd for any questions or perhaps to set up an exploratory meeting to discuss what the “Master Plan of Approach” could look like in your organization:

A comprehensive guide to Real-World Industry 4.0

Industry 4.0 holds an exciting future. A future that poses entirely new sources of business value and revenue. A disruptive transformation where organizations enter the 4th industrial revolution and leave their competitors behind through digitalization, integration, auto(no)mation, and insight.

Very exciting and crucial indeed, but how do real-world businesses with real-world budgets, real-world limitations, and perhaps operational legacy systems take part in this disruptive transformation?

This guide introduces a comprehensive real-world approach to lasting Industry 4.0. It enables real-world businesses to transform their organization with small value-generating steps, to a future of lasting uniform Industry 4.0.

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What is Operational Technology (OT)?

Operational Technology (OT) is hardware with a digital or electronical core or software, that interacts with the physical world. This interaction includes sensing, actuating, direct monitoring and direct control of physical systems and processes.

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OT Cyber Security is different

Cybercrime could be a highly lucrative activity. Information could be stolen and sold, operations could be disrupted as paid criminal service or an entire company could be taken hostage for ransom. As soon as a company has value, it becomes a potential target for Cybercrime.

Recent development in digitalization and Industry 4.0 likely render OT environments not only defenseless against Cybercrime, but could even create a hard-to-close backdoor into your digital organization! IT Cyber Security applied within the OT environment will proof to be a poor fit. This is why OT Cyber Security must be approached correctly and prioritized at C-level.

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