A user-friendly roadmap to Pharma 4.0™ technologies and applications and webinar filled with Pharma 4.0 examples.
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For life sciences companies, Industry 4.0 offers a path to make manufacturing facilities more efficient.
Scaling cell and gene therapy operations with Industry 4.0
Cost savings. Agility. Speed to market. Regardless of what you’re producing, nearly every biopharma company shares this trifecta of goals, which is why we’re seeing so much interest in Pharma 4.0™ (an ISPE initiative to bring Industry 4.0 to the life sciences) operating models. As our Horizons: 2022 Life Sciences report found, the majority of biopharma companies are eager to embrace advances in such technologies as artificial intelligence (AI), data analytics, and cloud computing.
Everyone seems to agree—the future of pharma is here. Industry 4.0 is used to describe an industrial revolution, which means manufacturers expect order-of-magnitude improvements in productivity from their digital transformation. Industry 4.0 is useful for manufacturing operations, as well as in the design and construction of the manufacturing facilities themselves.
How to reduce costs in manufacturing with Industry 4.0?
Industry 4.0 technology combined with operational expertise can map a company’s main cost drivers to show exactly where digital technology can provide value. This data-driven approach can reveal overlooked opportunities in areas ranging from waste reduction to the use of robotics, such as automated mobile robots (AMRs). However, it can be overwhelming to figure out what technologies will actually deliver results for your business. So, how can companies leverage Industry 4.0 to reduce costs in manufacturing and R&D?
- Use information technology (IT) to solve operational technology (OT) problems
- Reduce CapEx by embracing the Cloud
- Improve asset utilization through increased performance visibility
- Develop a data strategy and get insights from your data
1. Use information technology (IT) to solve operational technology (OT) problems
Manufacturers use operational technology (OT) to deal with production equipment, process technologies, and systems integration. Legacy OT software has been installed in thousands of places and is familiar to millions of customers. It’s gone through extensive, repeated audits by life sciences companies and is 21 CFR Part 11-compliant. It’s a safe bet and, as the saying goes, “Nobody ever gets fired for buying IBM.”
However, as in all industries, there are disruptors in the marketplace. Innovative alternatives are often much less expensive, more open, and perform better than their counterparts. Industry 4.0 provides a way to use information technology (IT) to solve OT problems. Most importantly, while these solutions may not yet have as large an install base, their architectures are open and secure, allowing companies to access all their data across their enterprise, thus providing increased accessibility and utilization. These data architectures are designed to give context to data at its source and feed that contextualized data to designated individuals within the organization within a few clicks. One of the biggest pitfalls in legacy systems is having your data locked into a vendor-based ecosystem instead of being able to choose open-architecture tools for specific use cases.
Much of this software is no-code or low-code, making it easy for computer-competent users to maintain themselves. This concept of ‘citizen development’ allows solutions to come from people who solve these problems every day. Not only that, but no-code/low-code applications allow for easier validation as a GAMP Category 4 (configured) system, as opposed to a system created using custom scripting, which would require additional validation.
Cost-saving examples within your OT systems
If we keep making the same technology choices we have been making for the past 30 years, we are going to end up with the same results. Not every disruptive technology is right for every business, but it’s important to evaluate them so you can take advantage when they are.
Data historians
Also called process historians, these store all the information about your manufacturing operations. Licensing fees can be quite expensive, which is why new providers with lower licensing fees are disrupting the data historian space. For example, a different process historian might lead to savings of more than 50% on capital licensing costs, in addition to reductions in ongoing maintenance costs. These niche products are outcompeting market leaders, as well as newer offerings that are cloud native. Don’t be scared—these alternatives are all 21 CFR Part 11 compliant and meet all ALCOA+ guidance.
Supervisory control and data acquisition systems (SCADA)
A SCADA system is used to collect OT data from process equipment located anywhere and to control manufacturing processes. Unlike conventional SCADA software, newer applications provide unlimited connectivity and unlimited operator interfaces, as well as running natively in a web browser. This type of technology interacts well with other software, making it easy to add more devices to the system. Because this software runs in a web browser, no additional software needs to be installed. This reduces computer systems validation (CSV) expenses by allowing the use of standard computer builds without custom software installed. Further, browser-based SCADAs have much lower risks of operating system updates and patches causing interoperability problems, which is a big fear of IT teams and a cybersecurity risk.
Manufacturing execution systems (MES)
MES facilitate production operations and bridge the gap between enterprise resource planning (ERP) software and the factory floor. In life sciences, this is often seen as facilitating the execution of a batch record or electronic batch record (EBR). Newcomers in the marketplace are embracing cloud technologies and software-as-a-service (SaaS) models that scale with customers with minimal upfront costs and competitive ongoing fees. They can also augment existing MES systems with a more user-friendly environment to allow change management to be performed by production personnel instead of MES specialists.
2. Reduce CapEx by embracing the Cloud
For startups and small companies lacking onsite infrastructure, harnessing the Cloud for computing saves on floor space, maintenance, and server management, as well as reducing the time spent on equipment procurement and installation. Using cloud technologies can also be valuable for preclinical projects, where data storage is “rented,” until the product’s value has been proven—it allows you to scale up as your product does. This an added benefit to cloud-based MES.
Bypassing the need to purchase physical servers to handle a data historian, a SCADA system, and an MES results in significant savings. While you still need physical sensor connections to equipment, controllers or edge devices, and people to administer the applications, savings as high as 60% can accrue for typical OT workloads. As business grows and workloads increase, with the right architecture it’s easy to move those workloads on premises or to a hybrid architecture.
Some larger companies opt to have their own data center, like a private Cloud fully managed by the company, where they run their applications remotely. This bypasses the need for onsite hardware and the maintenance that goes with it. After all, you don’t want onsite servers in a facility tucked out of sight, with unmonitored error lights.
Cloud deployments are a fast and cost-effective solution that can scale with your business. Often, these can be deployed in days with zero capital cost.
3. Improve asset utilization through increased performance visibility
If you don’t know a piece of equipment is underperforming, you can’t fix it. Industry 4.0 can make this type of hidden waste more visible.
In many factories, operators need to identify and communicate manufacturing equipment problems, such as downtime. This approach is susceptible to human error, like making mistakes in correctly identifying the underlying problem, and it takes time to sift through shift reports. All of this costs money.
When these assets are digitally connected to a performance monitoring system, you can easily access comparative data between machines and take advantage of a method to optimize the equipment to help reduce manufacturing costs.
For example, if you have eight bioreactors and two of them are not as efficient as the others, this data provides a way to analyze those two. Or, to assess whether your facility has the capacity to fill a large order for a drug product, you can determine the efficiency of the current equipment. Perhaps, if you’re able to squeeze an additional 10% out of your fill-finish line, then you won’t need to expand your operation to meet the order. Instead of spending millions of dollars on additional equipment, you can spend a fraction of that to optimize existing processes.
These types of solutions are often part of a facility health management program to make sure all your assets are performing at their best. Understanding where the weak points are in your operations allows you to better utilize your people and costs to solve problems and provide the best returns.
4. Develop a data strategy and get insights from your data
It’s estimated that data scientists spend as much as 80% of their time cleaning up data so it can be used across a company. Much of this time is spent sourcing data from different places, formatting it so it’s readable by other systems, and inputting it into a software program. It’s a labor-intensive process that’s not only time-consuming but leaves room for human error.
On top of that, manufacturers produce so much data that it’s impossible for people to analyze all of it. With a proper data governance strategy, and ensuring data is contextualized at the source and stored in an organized way using a unified namespace or ISA-95 modeling, this data can be made available to the business. It also eliminates the time-consuming work of contextualization and cleansing.
AI-powered technologies can be used to optimize equipment, especially on complicated systems like a bioreactor (see sidebar). Even if you do not go all the way with AI, advanced analytics can also produce a great return on investment. The most important thing is to model your data and have policies in place to govern it.
Here are two good examples of how you can reduce manufacturing costs with data:
Predictive maintenance
As opposed to scheduling yearly repairs on equipment, consider adding software to monitor a vibration sensor on the motor to determine when maintenance is actually needed. It’s critical to understand the cost of downtime of a particular motor. It’s even more critical to start collecting data so when you are ready to make predictions, it’s easy to do.
The efficiency of a heat exchanger can be measured by comparing the input and output temperatures. An AI model can predict a failure risk if the efficiency falls below an acceptable limit, signaling the need for repair. This can also be helpful for sustainability initiatives, as well as similar use cases applied to the use of water, compressed air, other gasses, electricity, and steam.
Technology transfers
Tech transfers are faster when all data is digitized. For example, Industry 4.0 data combined with computational fluid dynamics can accurately model the scale-up of a biologic’s production. AI-powered algorithms can show that the drug substance from a 2,000-L bioreactor is equivalent to that used in clinical trials from a 2-L bioreactor in terms of safety, identity, strength, purity, and quality (SISPQ). Combining this with process analytical technology (PAT) allows for real-time release of products and reduced laboratory costs.
Jeff Bezos at Amazon had a famous application programming interface mandate that insisted all information of the business was available to all users at any time. This was critical in their transformation and allowed the company to grow orders of magnitude. Having internal data policy standards that your vendors must follow makes future technologies like plug-and-produce easier to implement. It also ensures you don’t get hit with change orders when you realize that data you need is in a vendor-locked black box.
How to address the CapEx of Industry 4.0?
Some technologies require a large investment; fortunately, Industry 4.0 adoption can proceed in manageable steps.
When properly designed, you can take small steps that drive continuous improvement. These steps allow you to realize value quicker, identify and mitigate risks, and build toward an overall solution without incurring technical debt. This agile approach is common in many industries but requires extra scrutiny in life sciences to make sure change control procedures are adaptable.
Example: digitizing paper records as part of your EBR project
A fully electronic batch record solution comes with enormous benefits—but it doesn’t come cheap. Implementation can take a few years with a long return on investment which can delay projects and, more importantly, delay time to value. Explore taking on smaller versions of this project to realize significant gains faster.
Digitizing paper records is a good place to start adopting 4.0 methods. It’s relatively straightforward to digitize paper records, which can be fragile or illegible and are more prone to human error. Digitalization also increases speed of production, which is particularly important for personalized medicines—it’s difficult or impossible to operate cell and gene therapy at scale with paper records. It also allows for process engineers to understand what steps of an operation are taking the most time and ensures contemporaneous and accurate data entry, thus reducing deviations and quickening batch release.
Every piece of paper that’s turned into “glass” delivers incremental value and helps build a digital culture within the company. These small steps also help work out the kinks in the digital transformation journey and help take the risk out of larger investments as well.
Competing with other investments
Industry 4.0 investments are no different than any other business decision in that they come with associated benefits and risks. Fortunately, we are seeing that the benefits—savings in both capital and operating expenses, more productive operators, reduced quality costs, improved efficiencies, reduced repetitive tasks, increased agility, and reduced time to market—greatly outweigh the risks. Not only that, the benefits of building a connected culture can produce exponential returns. The sooner you get started, the better.
Let’s get your Industry 4.0 roadmap started. Talk to our consultants.
