biotech lab with colorful technology overlay

Industry 4.0: Examples and benefits for manufacturing

Industry 4.0 may be a buzzword but it’s also a force driving the evolution of the manufacturing industry. It takes advantage of data, connectivity, and potent computational power to digitally transform manufacturing operations. It’s not about any specific technology, like AI or robots, or even a set of technologies like the Internet of Things (IoT). Rather, Industry 4.0 and facility digitalization is the synergy of these technologies working together to produce order-of-magnitude increases in efficiency, reliability, quality, and agility that we expect from an industrial revolution. In a connected facility, data can flow from sensors inside your equipment directly to your ERP systems, triggering real-time action and unleashing new levels of operational productivity and business success.

Manufacturers who have leaned into the benefits of a connected facility have reduced unplanned equipment downtime, successfully navigated worker shortages, reduced expensive errors, and unlocked flexibility in their operations to pivot quickly when consumer preferences change. Manufacturers who drag their feet are exposing their business to costly and growing risks.

Here, we’ll use tangible, detailed Industry 4.0 examples with proven technologies to demonstrate the value it can bring to your manufacturing operations. Skip ahead to examples here.

What is Industry 4.0?

How does Industry 4.0 differ from digital transformation?

Digital transformation applies to all businesses, not just manufacturing. In fact, the retail and banking sectors are far ahead of manufacturing in terms of digitalization. This is great for manufacturing companies because the technology to drive this change is already available and proven, greatly reducing the risk of adoption. Becoming an Industry 4.0 factory, though, is about more than just technology—we must consider people and processes as well.

diagram of technologies that fit within industry 4.0 and digital transformation diagram of how Pharma 4.0 fits within Industry 4.0 and digital transformation

What are some proven Industry 4.0 technologies?

New, expensive technology can be intimidating, and nobody wants their neck on the line for a questionable project that hasn’t been properly tested. This is why many of our pharma and food manufacturing clients ask if there are proven technologies to help optimize their operational technology (OT) on the factory floor. The short answer is yes, and we recommend using proven IT tools to solve OT problems all the time. Take the numerous companies already leveraging Cloud computing platforms. These established technologies power advanced warehouses and banking systems, and are used to seamlessly communicate with both internal and external applications. Not only that, adopting these platforms where a use case demands it gives companies access to tens of millions of skilled people who are certified for developing on these platforms compared to only tens or hundreds of thousands available to develop some niche pieces of software.

Another example of a validated technology ripe for integration is MQTT, which is a machine-to-machine messaging protocol relied on by many IoT sensors, wearables, and devices with resource constraints or limited bandwidth. MQTT was designed to monitor oil pipelines more than 20 years ago and today is being used in ‘smart cities’ to track traffic flow and water use, by Facebook Messenger for its lightweight publishing, and by some manufacturers to connect machines and increase uptime. It’s actively used by billions of people globally. Adopting something like MQTT to develop a Unified Namespace for manufacturing operations can significantly reduce system integration costs as well as provide a company with a single place to know the current state of its business.

The technology itself is not the issue—there are plenty of proven technologies that are primed for manufacturing operations and efficiency.

Manufacturing tends to be slower

Industry 4.0 solutions that integrate IT and OT involve people, processes, and technology. We have to make sure our processes and people are aligned to reap the rewards of technology. Manufacturing needs to develop operating procedures and training programs designed for Industry 4.0 technologies. This is of greater importance for life sciences companies as their procedures are necessarily risk averse. It’s important to think critically and take a risk-based approach.

diagram of pillars of Industry 4.0: People, Process, and Technology diagram of pillars of Industry 4.0: People, Process, and Technology

What does a connected manufacturing facility look like?

A connected plant has a high level of automation, as well as integrated and standardized systems. All systems are able to exchange information with all other systems. It ensures all the data in your plant is FAIR—findable, accessible, interoperable, and reusable. To reach this level of maturity requires investment in infrastructure that does not pay dividends by itself. Fortunately, at this point the hard work has been completed and a factory can start taking advantage of this infrastructure to unlock the value found in Industry 4.0.


Industry 4.0 manufacturing examples and their benefits

Generally speaking, we all know what Industry 4.0 is, but how do you implement it and what does it look like in action? Using real-life case studies we will show how to apply Industry 4.0 to your manufacturing operations.

processing data

Industry 4.0 example #1: Performance management using Big Data analytics, AI, and machine learning

There are a lot of great metrics to track packaging equipment, especially overall equipment effectiveness (OEE). Measuring OEE is especially important for high-volume manufacturing, like food & beverage or oral solid dose medications for which knowing how your equipment is operating is critical for packaging. A surprising number of companies don’t track this metric.

On the contrary, we don’t really have a lot of good metrics to track manufacturing processes, such as processes within a mixing tank, heating or cooling a recipe, or pumping it through another system for processing. Those metrics aren’t tracked nearly as carefully, and often they are measured as batch cycle time. Tools like computational fluid dynamics can help develop optimized processing times to measure against.

Getting to the Golden Batch

Performance management allows us to use data to measure effectiveness and support how batch tanks operate. Metrics might include the cycle time and temperature profile that lead to the ideal product that is within specifications, the so-called Golden Batch.

But it’s a difficult problem to solve, especially when you don’t have contextualized process data. A tank, for instance, is composed of equipment such as an agitator, pump, temperature transmitter, and heat exchanger. Each of these have subcomponents, such as a setpoint, an actual value, and alarm limits. Without digitally integrated systems, you need to organize data to identify which information is coming from that tank.

A user-defined data type within a unified namespace contextualizes all of the information from the tank and its subcomponents. Now, whenever you retrieve information from your data historian, you know the precise context of the tank that ran a Golden Batch. You have all the field information to know what a future Golden Batch should look like—the specific tank, the recipe it was running, the process value and setpoint for each analog device, and any alarm conditions. Then you can compare it, in real time, to the status of your current batch. What’s different? Is the tank not heating up? Is the agitator too low? If the tank isn’t heating up is that due to a problem with the heat exchanger or the steam supply?

Scaling asset management becomes much easier

Contextualizing your data to enable performance management works especially well for high-volume manufacturing since it improves asset utilization at scale. Not only does it empower the tank operator, it allows a supervisor to follow what’s going on in 10 different tanks simultaneously. Once you’ve taken the time to structure your data like this, you can consider using AI or machine learning to do advanced analytics, supplementing the experience of your operators.

But to do this effectively at scale, you can’t be custom designing each piece of equipment. It’s important to ensure you’ve structured data for multiple pieces of equipment. Then you can plug it in across 10 different tanks or 10 different unit operations.

green cloud computing icon

Industry 4.0 example #2: Cloud computing

Accessing Cloud computing and storage offers a short path to value and can be effective for startups with minimal infrastructure on site, such as servers, computers, spare parts, and people to maintain it. Even a large manufacturing company with these resources in its business-wide enterprise data center may choose to operate a manufacturing plant without its own local data center.

Cloud services can be turned on or off as needed

Consider a startup company building a new facility that orders $100,000 worth of computing hardware. While the facility is being built the hardware is delivering no value. Instead, they could consider outsourcing computing resources to a Cloud service provider, eliminating the need for on-site equipment and the staff to maintain it. And this could be turned on only once the facility is up and running. This can also work for a brownfield facility that needs to add a new computer or service. They can turn on a new Cloud instance and have it running almost immediately.

  • Reduce costs: Pay only for what you need and avoid maintenance costs.
  • Scale quickly as needed: Quickly pivot as project demands change.
  • Protect against outages: Engage multiple Cloud providers to prevent downtime.
  • Smooth coordination with suppliers: Most suppliers have Cloud-based systems.

A quick example of the benefits of Cloud: 6 months vs. 2 weeks

A CRB client wanted to add an OEE system to tie in equipment performance evaluation. Their IT team estimated it would take six months of work to get servers on site. We used a Cloud solution, turning it on within two weeks instead.

Eliminates on-site storage challenges

Storing data requires servers and the infrastructure to maintain them, including IT maintenance staff and a dedicated air-conditioned server room. We’ve seen many factories where the server sits in a backroom and has four flashing warning lights that nobody ever looks at.

But what happens if your Internet connection goes down? Most companies that rely on the Cloud choose to have duplicate Internet service providers for just this concern. You could also add a 5G cellular network as long as you make sure your data is structured in such a way that, when the 5G network is activated, only the important data gets out, turning off things like video conferencing to save bandwidth.

Security concerns

The International Electrotechnical Commission (IEC) has created IEC 62443, which are international standards dealing with cybersecurity for automation and control systems. The infrastructure of Cloud service providers tends to be more secure than what’s traditionally provided at any factory. Then it’s a matter of securing connections between the factory and the Cloud provider. This just means installing at least one firewall going into your site.

checklist icon

Industry 4.0 example #3: Electronic batch records

In a paper-driven biopharma facility (Digital plant maturity model, or DPMM, Level 1), a batch record will have thousands of pieces of paper operators have checked, dated, and signed ensuring everything has been executed per the operating procedure. A supervisor reviews all of these records, then again for the batch release. It’s a really time-consuming process.

Read more on Pharma 4.0™ (Industry 4.0 for biotech and pharmaceuticals) here.

How electronic batch records can provide you with an easy Industry 4.0 win.

An electronic batch record (EBR) is a digital version of this. Converting paper record-keeping to inputs on mobile tablets can decrease human errors, reduce administrative work, add visibility to your operations, and quickly provide alerts when a process is running slower than it should.

3 levels to digitizing paper records

While doing a full EBR implementation is time-consuming and expensive, there are incremental steps that deliver a lot of this value quickly.

1. Paper-on-glass transformation

Digitizing paper records is a low-risk, high-reward activity that often yields a quick ROI. A PDF is made to record information that had been recorded on paper. It has the benefit of including timestamps, which allow you to know how much time it takes for an operator to perform each process step. This level doesn’t always include automatic error checking—records still need to be reviewed. It can eliminate entry errors and the time consuming review of legibility issues.

2. Automating workflow reduces costs quickly

Automating the workflow is the next level. It includes PDF files with dropdown menus, real-time error checking, and ensuring steps are executed in the correct order. Workflow management can integrate with your company’s active directory system so you know who’s executing this, and uses electronic signatures.

  • Reduction of errors: It eliminates transcription errors and allows automatic error checking. EBRs make manufacturing errors obvious a lot sooner.
  • Review by exception: Unlike with paper records, operators and quality staff are notified immediately when a process step is out of specification, such as having an incorrect temperature. Value-added steps could then be stopped, avoiding the cost of having to scrap or rework material. At this level equipment is not directly integrated.
  • Cost savings: In our experience, digitizing a single page of a record costs on the order of $100–500, including licensing and the engineering work that ensures error checking. This is easily recouped from the savings you get from quality and workflow improvements.

This automated workflow level provides the quickest and best return on investment (ROI). You’re probably spending 60 percent of the budget on a complete EBR and getting 70–80 percent of the value. Every life sciences manufacturer should be aiming for at least this level.

3. Fully-implemented EBRs are expensive, but needed to scale

To have automatic data collection and record-keeping, without having a human entering any of this information at all, requires connecting directly to the sources of digital information. For example, this could mean having an automation controller that reads the temperature of a step automatically, and enters it into the batch record. Eliminating the human point of contact makes this data collection and recording faster and more accurate.

While fully integrated EBRs require significant investment compared to the first two levels, they allow scaling due to reduced overhead activities requiring operators to write down data for every batch. This won’t matter as much when you’re running a handful of batches each week. But it makes a big difference when the number of batches increases, as it does for personalized medicines that require hundreds of batches weekly. At this rate, the odds of making mistakes also increase. However, this level is not always possible due to equipment limitations. Working with equipment manufacturers to establish data requirements is a critical step in this process.

The ROI for this level can take two to four years, but is a good long-term investment. A personalized medicine facility with an aspiration to supply 15,000 patients per year must have this technology to scale effectively.

How to begin your Industry 4.0 journey

Conduct a digital plant maturity assessment

A digital maturity assessment gives a snapshot of your facility’s state of digital readiness, compares that to where your peers are, and highlights the major infrastructure investments you need to make. These need to be aligned with your business goals, including revenue, flexibility, sustainability, time to market, or other key performance indicators.

The assessment can be used to choose some small projects to start off, designing standards for equipment or technology you’re procuring to align with your long-term goals. This is an easy place to start and delivers results quickly. You can complete the assessment in as little as four weeks at a cost of roughly $15,000.

Be sure to avoid technical debt.

What is technical debt? Technical debt results from beginning a digitalization project without a big-picture strategy. An example would be a company that has separate projects to put in a new manufacturing line, make changes to the warehouse, and upgrade a quality lab. Yet it’s doing each project in isolation without a long-term strategic plan. The company brought in three different technology sets, some of which align with its goals, some do not. Some have people within its facility who know how to maintain them, others don’t. Technical debt is the cost, time, and related setbacks that ensue due to these short-sighted projects.

Technical debt problems include:

  • Rework to match standards because a team proceeded without clarifying standards, or proceeded without knowing its company had standards.
  • Needing to hire external consultants because the company doesn’t have anybody who was trained on the technology.
  • Investing in technology that will be obsolete in a few years in an ever-changing landscape.

Understanding where you want to go is critical to avoid technical debt on your way there. While you may not know exactly which technologies you will be deploying, it’s still important to lean on your digital roadmap for guidance at the earliest stages of your project.

Identify infrastructure requirements

First, identify what you’ll need to digitally transform the factory floor. A good example is a plant network, which will incur significant capital cost. Once you have this list, choose potential pilot projects to focus on, referring to your digital plant maturity assessment for ideas. You may choose to leverage your strengths or try to improve your weaknesses. An example is a proof of concept on connecting a piece of equipment to a data historian.

Referencing your data strategy and its connectivity standards early will influence your equipment selection process, which is especially helpful for long lead-time items and avoiding costly change orders. Using this information, you can include the appropriate computing and connectivity specs in your proposal requests.

You need to invest in connectivity and data infrastructure early. By investing in Industry 4.0 infrastructure, facilities can adapt and expand with less cost, as well as adopting new technology as it becomes available. For both new builds and retrofits, your most important decisions are those with the longest lifespan that will be difficult, disruptive, and expensive to modify in the future.

This step takes one–three months.

Create a value-led roadmap

This will outline how to get where you want to be—whether tactically in a year or with a strategic five-year plan. You add potential opportunities to a calendar or a Gantt chart in a logical order. This ensures development work and testing is done in the correct order to avoid doing things twice or having one project block another.

Success begets success.

This step provides clarity on the total timeline, estimates investment, and benefits you expect to achieve. Getting some wins builds confidence that your team is getting value from the investments and adds a level of accountability. This can take about one to three months to plan and cost depending on the level of detail and duration of the initiative.

Implement and iterate

Each step can’t be done in a vacuum. The maturity assessment should be reviewed once a year and the roadmap every six to 12 months, depending on the pace of implementation, to make sure you’re achieving what you want to achieve. What progress did you make? What benefits did you gain?

It’s important to know before you start that you won’t get everything right the first time.  During proof-of-concept projects it’s particularly important to identify high-risk functions so you can prove or disprove hypotheses, and fail fast.

Navigating the Industry 4.0 revolution

Every vertical, company, and facility is different, and Industry 4.0 will take on a different look depending on where you sit. But one thing rings true despite where you are: there’s a tectonic shift actively changing the manufacturing landscape. The value of Industry 4.0 continues to evolve, with new use cases emerging. AI may entirely reshape the drug discovery process, reducing timelines and the cost of goods and, despite questions about the actual ROI, industry leaders continue to move their vision forward.

It’s important to know where you are currently so you can get where you want to be, unlocking the benefits and developing new use cases that weren’t available before. This is not a technology project. It’s a continuous transformation of a business to ensure your people are empowered and your business processes are aligned to support digital transformations.

Ready for help with your Industry 4.0 initiatives? Our team can help. 

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