This article outlines key challenges that Life Science & Healthcare companies face in their digital transformation: managing customer data, reducing paper documentation, improving supply chain performance, or implementing compliance updates. The focus lies on four areas of action in pharmaceutical production, which help executives address these challenges and create tangible added value through digitalization.

Practical measures are provided for each action area. For example, how to implement rapid pilot applications to access critical process data online at any time is illustrated by the example of a MEMS (Micro Electro Mechanical System) equipped with sensors that enable flexible recording and evaluation of important parameters such as pressure, temperature, and humidity in pharmaceutical manufacturing.

Could your next team member be a robot? You might find that unlikely unless you already work in a highly automated pharmaceutical production line. However, machine assistants now do far more than just dose active ingredients or label packages. Software robots (RPA, Robotic Process Automation), for instance, perform complex data analyses across all phases of drug development or clinical studies. This is particularly helpful for quickly identifying suitable drug doses based on pharmacodynamic and pharmacokinetic specifications—which can replace human-based studies through "in silico" simulations and tests. Additionally, software robots will increasingly take over routine tasks involving simple processes, such as quantity controls or workforce management. In R&D, they reduce human risks and accelerate development cycles. Overall, across the entire value chain, they enable significant time and cost savings.

A digitalization agenda with such goals is both sensible and realistic for the pharmaceutical and life science industry. Technologies like RPA, AI (Artificial Intelligence), and many others are already sufficiently developed and affordable. Government research initiatives are also driving this development. For example, the project “KI.RPA” was launched in early 2019: funded by the Federal Ministry of Education and Research (BMBF), it aims to develop a self-learning system that automatically captures and analyzes process knowledge (1).

However, while AI-driven digitalization scenarios sound promising in theory, they are rarely implemented in practice by chemical, pharmaceutical, and life science companies. Compared to other industries, they have lagged behind in digitalization efforts in recent years (2). This is surprising, as challenges such as managing customer data, reducing paper documentation, and improving supply chain efficiency have long been top priorities, just as they are for digitalization pioneers in the IT or financial sectors.

Compliance and Validation Requirements Create Barriers

The issue is not a lack of awareness but rather the significant compliance and validation requirements that the chemical and pharmaceutical industries must meet. This creates a paradox: on one hand, authorities like the FDA require as much data as possible to verify compliance—naturally in digital form. On the other hand, companies must invest immense effort in validation processes. Every minor process change must be submitted, reviewed, and approved to maintain compliance. Many pharmaceutical companies are still reluctant to take on this effort. For example, if a company uses a 20-year-old tablet press, paper printouts containing existing data such as composition and batch numbers are typically added to the batch documentation.

This approach involves significant effort and risk: a lost printout or an incorrectly transcribed number can quickly lead to time-consuming follow-up inspections. Such difficulties could be resolved with new equipment that integrates with other IT systems. However, unlike in other industries, this equipment must also be revalidated to ensure it continues producing at the exact same quality. This investment, combined with the associated effort, still deters many companies today. Instead, they accept the risk of errors in documentation.

Effort vs. Outcome Slows Digitalization in Operations

At the same time, pharmaceutical companies must always be prepared for inspections of product and process certifications. They are obligated to ensure that specifications, risks, tests, and changes are correctly validated and compliant—and during inspections, they must transparently present the flow of data and information. These controls are particularly strict in the pharmaceutical industry, which should provide sufficient pressure to accelerate digitalization.

Two key factors, however, create a significant gap between this pressure and the opportunities of digitalization on one side and its actual implementation on the other. First, as mentioned earlier, companies are often deterred by the effort involved; cost increases of up to 30% for individual projects are not uncommon. Many companies therefore cling to the status quo with the mindset, "We keep things the way they are because we know how it works."

Second, very few implemented digitalization projects deliver an impressive ROI (Return on Investment), particularly in manufacturing operations that are already highly efficient and automated. In such cases, large investments in new technologies and IT infrastructures only pay off after several years. Worst-case risk scenarios of outdated information management—such as disruptions, production downtime, and high error rates in documentation—seem, incorrectly, to be a manageable evil in comparison. Instead, companies often invest in additional systems to stabilize their outdated, error-prone systems or in flashy apps and guerilla marketing campaigns that aim to present a digitally transformed image.

Three Key Action Areas for Digital Transformation

A more effective approach is to identify significant but manageable deviations and calculate what could have been saved had the process been digitalized. For example, if a handwritten transcription error occurs, it must be verified whether it was a human error or an actual deviation in value. This can take days. In a digitalized process, every value could be directly validated. In case of deviations, a comment explaining the reasons would be required. This would significantly accelerate the process. Before taking such steps, it is worthwhile to consider three action areas that generally simplify the leap across the "digital divide" in chemical and pharmaceutical companies:

• Creating secure and efficient processes through integrated data flows
• Establishing transparency through online availability of critical process data
• Achieving process improvements through innovative technologies

Creating Secure and Efficient Processes through Integrated Data Flows

The digital maturity of some pharmaceutical companies today can still be described—in exaggerated terms—as "Industry 2.0" rather than "Industry 4.0." Yet digitalization options have been available for years. Especially vertical and horizontal integration of data flows, a core principle of Industry 4.0, would enhance security and operational excellence across the industry.

Vertical data flow involves transferring data from the MES (Manufacturing Execution System) level to the ERP (Enterprise Resource Planning) level. Decisions can then be made based on results—for example, using temperature curves to determine whether a batch can be released. Automatically transmitting production data from the MES to the LIMS (Laboratory Information Management System) in quality control opens up entirely new evaluation options and accelerates conventional daily processes. Instead of manually distributing paper protocols across departments and locations, data is instantly available to the right employees.

The data flow itself often already exists in well-structured formats. Unfortunately, this opportunity for information generation is rarely utilized because many companies still rely on paper. Especially when acquiring new machines, this approach should change: since the machine must undergo a validation process anyway, new technologies can be integrated directly into the process. Manual inputs and media disruptions should be minimized to eliminate sources of errors.

Tips:

• Reduce manual inputs and media disruptions.
• Embrace paperless production and review-by-exception methods.
• Identify all points necessary to ensure compliance (e.g., data integrity) in a checklist.

Establishing Transparency Through Online Availability of Critical Process Data

For companies with complex, legacy infrastructures that have developed over many years, transitioning to a largely paperless information flow is often only possible step by step. Significant differences in the communication maturity of machines and IT systems often make incremental advancements the only feasible option. Employee expertise must also align with integrating and operating these (new) systems and technologies. The common goal of these steps is to ensure existing systems communicate effectively—for example, when validated MES data is automatically transmitted to the ERP system and processed there. (see figure 1).

By centralizing key data, companies can track the origin of batches, document deviations retrospectively, or simplify processes such as shipping and invoicing with standardized templates. In production, this system correspondence allows production staff to monitor critical process data, leading to faster management decisions and better outcomes.

An additional use case in the pharmaceutical industry is monitoring temperature curves with predefined minimum/maximum thresholds digitally connected to production. These values are stored, analyzed, and deviations can be identified and addressed immediately. This transparency of critical processes and error sources optimizes the overall system.

An "online availability" of information in this context means that employees at a given site can access all data relevant to their area of work at any time, allowing them, for example, to review process workflows and monitor machine utilization in real time. Digitalization technologies, such as sensors for data collection, the provision of data via Wi-Fi, and the virtual localization of weak points and vibrations in material flow, are also helpful in this regard. Security concerns often prove to be unfounded here, as interfaces to external online networks are not necessary, and systems for direct machine control should, in any case, be isolated in separately secured networks.

Tips:

• Use improved process understanding to enhance order lead time metrics.
• Optimize the non-regulated sections or processes of production as well; for example, predictive maintenance tools can further increase equipment availability.
• Involve internal engineering as a key advisory body to identify obstacles and develop realistic solution ideas.

Process Improvement through Innovative Technologies

When introducing new technologies, many companies—rightly so—remain skeptical: is it worth the effort to invest employees, time, and acquisition costs in innovations that could already be outdated by their first use? This skepticism is shaped by experiences from decades of industrial IT development. However, digitalization introduces entirely new rules in this regard: hardware such as sensors, cameras, and processors is available at significantly lower purchase prices and already comes with industry-specific modifications (see practical example MEMS / figure 2). Furthermore, many work steps can be digitally mapped without requiring costly expertise from IT specialists, as corresponding software solutions and IT platforms are now much better visualized and easier for non-experts to operate.

Even in light of compliance and validation hurdles, the same basic rule applies to manufacturing pharmaceutical and life science companies as in mechanical engineering and the automotive industry: it is the small, individual pioneering projects at various “hotspots” that collectively lead to successful digitalization. In the pharmaceutical industry, for example, it is worth conducting practical tests with the following four technologies:

AR (Augmented Reality) Glasses

Digitalization pioneers already use AR (Augmented Reality) glasses to guide manual tasks, such as machine cleaning, with optimal precision. The data glasses display the SOP-based cleaning process step by step. Each completed step is confirmed by the user through a hand gesture. Technically, this application is easy to implement. However, questions about GMP-compliant documentation remain open. Initial practical tests show that employees achieve process reliability faster, thereby shortening workflows while maintaining or even improving thoroughness.

Wearables as Digital Guides

In addition to AR glasses, wearables such as data gloves or smartwatches are useful in production lines to guide employees step by step through their tasks like a “digital guide” at every location. These devices replace handhelds, which tend to slow down work processes. Moreover, information is no longer stored on individual devices where it can be “forgotten”: employees must confirm central work steps to proceed. At the same time, the data generated is automatically transmitted to the system. This ensures that all steps in production or logistics are traceable, verifiable, and additionally secured.

Robotic Process Automation (RPA)

The aforementioned software robots (RPA) are particularly useful in the pharmaceutical and chemical industries for quickly compiling documents and data from various sources, cross-checking them, and performing plausibility checks. Deviations from predefined standards are immediately visible, enabling employees to respond accordingly. The high quality of results, speed, and 24/7 availability make automating these processes highly advantageous. Implementing RPA does not interfere with the existing IT landscape but connects components through an interface- and system-independent electronic workflow. Additionally, work in the pharmaceutical industry is often very labor-intensive, with many tasks involving data verification and manual transfer between applications. RPAs can usually perform these tasks with higher quality and reproducibility. The modular structure of automated processes ensures the necessary scalability in operations. All actions performed by RPAs are precisely documented, making them traceable and verifiable at any time.

Digital Twins

“Digital twins” are a crucial step toward Industry 4.0, even in the pharmaceutical industry. This concept involves creating a virtual replica of a real machine or even an entire production process (Digital Process Twin). The advantages are significant: all data from the entire system, individual components, and maintenance information are centrally available. In the area of fault identification, digital twins offer significant benefits. Errors are automatically reported, localized, and identified. Employees—guided, for example, by a tablet—can immediately begin repairs or maintenance. The virtual model shows precisely where the error is located and which components are needed for the repair. This significantly reduces machine downtime, and planned changes in the production flow can be pre-simulated on a computer to prevent disruptions altogether.

Tips:

• Start with simple use cases to save time using Augmented Reality, such as during changeovers on packaging lines or with notifications for batch refills.
• Simplify authentications with wearables, for example, through electronic signatures or image recognition.
• Small, straightforward technology solutions make digitalization tangible as a cultural shift—encourage pilot projects in small teams, such as experimenting with wearables.

MEMS process mechanical and electrical information within a small area. As cost-effective and powerful sensors, they enable the rapid development of pilot applications. In everyday life, MEMS components are already used, for example, as vibration detectors in airbags and smartphones, as well as thermal flow sensors in air conditioning systems. Potential applications in the pharmaceutical and life science sectors include pump calibration, microfluidics, or medical dosing. Particularly, a barometric pressure sensor that continuously adjusts air-liquid mixtures to environmental conditions opens up interesting usage options.

Leveraging Value Creation Potentials Together

There is certainly no shortage of practical technological innovations. However, good tools alone do not play the decisive role in bridging the digital gap within a company. This responsibility remains—and will likely continue to remain—primarily with the employees. It is the leadership team, in particular, that must face this challenge: how can they make digitalization accessible to their employees? How can they dispel fears and prejudices and convince their teams of the benefits of a digital factory? What motivates the team to identify new value creation potentials together—whether through paper savings, shorter routes, or redesigned workplaces? This shift in mindset is successful when employees are involved as early as possible in the digital transformation process.

Openness and proactive engagement are also the right approach to initiating timely discussions with the relevant authorities during validation processes. Those who address changes early with the appropriate agencies can integrate feedback for compliant implementation directly into their digital transformation process. Together with the steps described, this significantly contributes to bridging the “digital gap” within the company as well as connecting to the broader digitalized business world.

References

(1) ) Project KI.RPA: https://www.aws-institut.de/ki-rpa

(2) Industry Atlas for Digital Transformation, available at http://www.di-i.org/impulse/praxishilfen/branchenatlas-digitale-transformation