Let’s look at some ways that manufacturers are already creating value with Pharma 4.0 solutions.
Electronic Log Book
Electronic logbooks automatically document relevant production information, streamlining a manual process while dramatically improving data integrity. These logbooks can compile and integrate information from machines and operators, expanding process visibility. Further, electronic logs can integrate photos, notes, reason codes, device history records, and locations providing a more holistic record of production than paper-based forms.
Electronic logbooks ensure that information is attributable, legible, contemporaneous, original, and accurate (ALCOA).
Because these logs are digital, they can be easily accessed to prove compliance.
Electronic logbooks make documentation and compliance reporting a seamless part of the manufacturing process.
Many line clearance processes are complex, time-intensive changeovers. With paper-based processes, workers may spend a significant amount of time looking for the next step or validating the execution of the previous one and less time progressing through the procedure.
Interactive, digital line clearance applications can make line clearance easier to navigate. Digital, IoT enabled work instructions guide users through SOPs, increasing efficiency ensuring that work is performed correctly and validated automatically. The applications record how long each step of the process takes, improving process visibility, and enabling engineers to locate areas for process improvement. Because these apps are collecting and communicating data in real time, engineers can view process status as work unfolds, leading to reduced downtime and more effective scheduling.
With Pharma 4.0, even complex procedures like line clearance can be made clear and simple. Digital work instructions replace paper SOPs and ensure compliance.
Electronic Batch Record
Batch record reviews require aggregating and reviewing a substantial quantity of manufacturing data and process documentation.
Much of the labor spent in the review process comes from identifying incorrect or illegible entries, and correcting records so that all production information is available for a given batch.
With Pharma 4.0 tools, manufacturers can make data collection and validation a continuous, seamless part of the manufacturing process. Information about manufacturing processes is automatically collected as operators and machines work, and all data is thereby attributable, legible, contemporaneous, original and accurate.
When it’s time for a batch record review, the necessary information is accessible and easy to read. Manufacturers can spend more time ensuring the quality of a product and less time correcting transcription errors. With more data available, it’s easier to flag items to review by exception.
In pharmaceutical manufacturing, the greatest barrier to process improvement isn’t always regulatory constraints. In many cases, it can be lack of process visibility.
With IoT devices and human-centric manufacturing applications, manufacturers can break complex processes into their constituent steps, creating a granular, picture of how workers perform on the line. The applications let engineers track individual operators performance at each step. This let’s them create identify situations in which more training may be necessary. It also helps engineers differentiate between poor operator performance and poor process design.
In our experience, many of these changes are incremental, and can yield significant gains in quality and efficiency without triggering an audit or requiring revalidation.
Clean Room Monitoring
IIoT makes it possible to respond to changes in environmental conditions as they develop. Connected sensors can detect when conditions may exceed established thresholds, and alert operators to take the proper action before interrupting production.
IoT technology makes Pharma 4.0 more transparent.
Training for Regulated Environments
Many manufacturing processes require a significant investment in training. Training can be slow, as manufacturers have a difficult time replicating “real-life” production scenarios in their training programs. This leads additional costs sunk in training, and quality can suffer if operators aren’t trained effectively.
Using manufacturing apps, manufacturers can design Pharma-specific training applications to get employees on the line fast. Engineers can break multi-stage processes into their constituent parts with targeted modules, and embedded media like videos and images help convey information to different styles of learner. If it’s not possible to take workers off the line during reskilling periods, training applications can be configured to facilitate on the job training (OJT).
Further, manufacturers can simulate processes in production settings by running apps in an “offline” mode. Here, training modules walk employees through a process and collect data on their performance, while governed execution mode makes sure that learners don’t impact production.
Big Data Analytics
Manufacturers generate a truly massive quantity of data in the course of operations. Yet most of this data isn’t used as the basis for production insights. This is because it can be too unwieldy or unstructured to be valuable (also referred to as Data Rich, Information Poor, or “DRIP”).
One of the promises of Pharma 4.0 is the enhanced interpretation of the data collected throughout a product’s lifecycle. With advances in AI and machine learning, systems are better able to parse and find connections within large data sets.