Pharmaceutical production is complex due to many factors.
Pharmaceuticals come in various forms such as tablets, capsules, liquids, creams and injectables, each requiring different unit operations and materials. The ability to handle such a diverse range means the typical production process involves a vast array of software and hardware.
Furthermore, modern pharmaceutical manufacturing is highly automated to improve efficiency and reduce human error. This involves the integration of advanced machinery, robotics and software systems for tasks such as filling, sealing, labeling and secondary processing. Add to this the implementation of serialization and track and trace systems, involving the integration of further sophisticated software and hardware, and the reasons for production complexity become clear.
All of this produces a large amount of operational data. Indeed, in the era of Industry 4.0, manufacturing plants are becoming smarter and more interconnected, leveraging advanced technologies to optimize operations. The accurate collection and analysis of data can enhance efficiency, reduce costs, and improve overall plant performance.
Let’s look at how the digitization of the factory floor is shaping operational decision-making.
Understanding plant floor data
Plant floor data refers to the information collected from multiple sources within a pharma facility, often in real time. This data encompasses a wide range of metrics and parameters related to the operation of machinery, production processes, environmental conditions and workforce activities.
There are many different types of plant floor data, including machine data, relating to the operational status of individual machines — such as whether they are running idle or offline — production metrics, which incorporates their data relating to production rates, cycle times and output counts and the maintenance logs of individual machines.
Another important source of plant floor data comes from sensor data which incorporates environmental conditions, such as the temperature and humidity within the facility and machine conditions, referring to factors such as vibration pressure and sound levels.
Further information can be collected from quality data, incorporating product quality and defect rates, energy consumption data which includes power usage and efficiency metrics, labor data covering workforce activities and safety records and process data which includes flow rates and process parameters.
Making plant floor data-informed decisions
By using plant floor data correctly, pharma companies can make many operationally critical decisions.
- How to achieve operational efficiency: The insights generated by data analysis can be used to optimize the entire production process. This includes reducing bottlenecks on a production line, which can have a negative impact for the rest of the line, and generally ensuring that all components of a line are running in perfect synchronization.
- When to carry out predictive maintenance: Using data to predict when machines will require maintenance, thereby preventing unexpected breakdowns, is a major benefit to pharmaceutical companies.
- How to ensure rigorous quality control standards are in place: The monitoring of product quality in real time aids the early detection of defects and ensures overall consistency of products.
- How to encourage ESG: Data can be used to ensure that specific machines, or the entire production line, is performing at optimal energy levels. By tracking energy consumption, not only will an important aspect of ESG initiatives be actioned, but overall cost savings will be made, an aspect which can never be overlooked in today’s ever-competitive business landscape.
- How to ensure a rigorous inventory management process is in place: Effective data analysis will mean that materials are available as they are needed, without overstocking, which can lead to high levels of wastage if the goods are not used in time. Not only will this help the bottom-line of a company by reducing wasted goods, it will also help ESG levels.
A look at predictive maintenance
Predictive maintenance is a proactive strategy that involves monitoring the condition and performance of equipment during normal operation to predict equipment failures before they occur.
This approach relies on real-time data and advanced analytics to forecast potential equipment failures and extending the lifespan of equipment.
By predicting failures before they occur, maintenance can be performed during planned outages, significantly reducing unexpected downtime. It also saves costs by preventing unexpected failures and any resulting secondary damage. It also optimizes the use of maintenance resources and reduces the need for spare parts inventory.
The collective result of these benefits is a vastly improved level of overall operational efficiency and productivity.
How data analytics improve plant floor operations
Pharma processing and packaging equipment is often complex and costly to purchase and install. While it therefore will need to be professionally maintained, it will also need to function at its maximum potential to pay for itself as quickly as possible.
Real-time data can also aid efficient capital planning by enabling the managers of a packaging line to make better-informed decisions about when to invest in a new piece of equipment. This, in turn, will help a much broader continuous improvement program to be implemented, ensuring all equipment is replaced or upgraded at the most beneficial time for the company.
A further benefit includes the efficient management of the workforce. Data on a worker’s performance can help to identify any training needs, optimize a shift pattern, and improve the overall efficiency of a workforce. Analytics can also help with employee safety by tracking safety incidents and near misses, identifying patterns and potential hazards. This helps in implementing measures to improve workplace safety.
Real-time monitoring and control is also an important result of data analytics. Operational dashboards provide a comprehensive view of plant floor operations in real-time, displaying key performance indicators (KPIs) and allowing for quick identification of issues.
Automated systems allow the integration of analytics with plant-floor controls, enabling process adjustments to be made based on data insights, leading to more consistent and efficient operations.
Challenges and considerations
There are, of course, challenges and considerations that pharma companies must address before considering whether to implement a comprehensive data analytics program.
The first is to be aware of the current state of existing packaging line equipment. How old is it? What will be the cost of adding a level of automation? Might it be more cost-efficient in the long run to completely replace older equipment, even if it is performing well?
Another factor to bear in mind is the skillset within the workforce.
Does the right skillset exist within a company to harness the information produced by detailed data? A large, multinational manufacturer, with many thousands of staff spread across different disciplines will be much more likely to have the skills required, whereas a smaller manufacturer might struggle.
A final perspective
The process of manufacturing produces two things. The first is, of course, well-made, safe products that can be tracked across the entire supply chain, from production to the end consumer.
However, the second thing it produces — and something that is rapidly becoming just as important as the products themselves — is data. This data has the very real ability to revolutionize the entire operations of a producer, improve its efficiency, and its overall profitability.
If any pharma company ignores the potential this data has as a tool to aid their business, they are ignoring one of the most valuable assets they have.
