Solving Today’s Complex Pharma Manufacturing Challenges with Digital
The pharma industry is unique in that it is a conservative and highly regulated industry with famously long product development cycles. Pharmaceutical companies are facing more pressure than ever as extended value chains and networks become more global and intricate in nature. Manufacturing process complexity is growing rapidly as therapies evolve from small to large molecules to cell and gene therapy and connected drug delivery devices. This has required the pharma industry to have new levels of analytical capability to solve their manufacturing challenges.
Some of the biggest challenges we hear are:
- Pressure to get to market first and transfer products quickly
- Need for greater flexibility in manufacturing
- Imperative to increase automation and improve worker effectiveness
- Avoiding batch quarantines and reducing time for quality reviews and product release
Across the manufacturing value chain, a new digital performance management (DPM) framework is emerging; one that connects digitally enabled factory processes and their data to closed-loop, lean Continuous Improvement Programs to help manufacturers transform data into actionable performance insights.
Deriving and Applying Critical Insights from Digital Factory Data
These new uses of digital factory data are fundamentally changing how factories implement continuous improvement. Although program goals and outputs have not changed dramatically, the inputs and how they are used have. This has enabled organizations to better contextualize factory data to gain useful insights and make better decisions on improving their operations. The issue with data is no longer one of scarcity, timeliness, or accuracy. Factory executives have access to plenty of current, reliable data.
However, many companies still struggle to bridge the gap between managing, sorting, organizing and curating data with delivering valuable and actionable insights.
DPM collects operational data, analyzes it and then presents it in ways that make it easier for staff across the manufacturing lifecycle to identify and efficiently solve the problems they are facing.
Tailoring Digital Performance Management to Pharma Operations Context
With a closed-loop framework, manufacturers use tag event and reason data from factory automation, coupled with the right quantities of operator-entered data, through closed-loop problem solving frameworks.
To understand the role of closed-loop problem solving in DPM, check out our previous Viewpoint, Value of Digital Performance Management.
Pharma manufacturers have specific manufacturing processes honed to optimize performance through their automation layer or sometimes specific manual operations that augment automated events. Formal investigations and root cause analysis can be complex to handle because of intrinsic product complexities, stretched value chain and data accessibility. Manufacturing line operation issues are a major problem for many pharma manufacturers. They are looking to apply new ways to better control the manufacturing process and address the huge variability between various products, shifts and schedule variations.
An effective digital solution needs to be able to tap into the specifics of the context so the hidden insights offered by the underlying operational data metrics can be uncovered.
Key Activities to Consider When Implementing a DPM Solution:
- Map key process events and reasons for all time loss categories like downtime, changeover activities and speed losses with appropriate reasons. Align the Digital Performance Management Framework to the pharma manufacturing process by focusing on key processes and automated events that span the various work units of the specific manufacturing line.
- Identify all time loss categories and focus on the automation layer tags and combination of tags that can help derive key insights on resolving root causes of shutdowns, events and other causal factors that can turn into stoppages, costing operational times.
- Analyze stoppage events with the lens of digital factory data to help focus on priorities for Continuous Improvement and other initiatives. Automate to avoid guesswork to identify stoppages and have appropriate event mappings to capture granular reasons.
- Focus on individual steps and outcomes of the cleaning and sterilization processes, not just production. Many pharma processes have stringent quality requirements and have intricate stages and repeat tasks in case of quality failures. Ensure that the reasons are captured in detail with digital tools, so the actual underlying root causes can be addressed.
- Address any manual entry processes in problem resolution and standardize hybrid operations where manual and automated events need to coexist given the complexity of the process.
- Capture standard operating target durations for all operations including setup processes and changeover processes and track them with automated events and/or manual entries and optimize across the board for closed loop problem solving.
- Standardize processes for changeover and setup processes with external tools for digitizing training, so that simple and complex issues have resolutions rooted in tribal knowledge of the skilled workforce.
- Capture Audit Trails with enterprise security credentials to trace manufacturing operations across operators, shifts and changing priorities of the day.
Value Capture and Benefits
Pharma manufacturers that have successfully implemented a Digital Performance Management solution realized significant benefits in multiple plants and lines for manufacturing organizations. These included the ability to:
- Understand Hidden Production Issues: Rapidly identify the highest impact performance issues that demand improvement. Increase throughput, improve OEE (Overall Equipment Effectiveness) and other mission critical performance metrics. The holistic design of the solution with pharma context has yielded double-digit OEE improvements in the manufacturing line
- Identify Root Causes of Production Inefficiencies: Find, analyze and visualize production time losses. Identify root causes, then prioritize them for analysis and action by the plant’s Continuous Improvement (CI) teams
- Communicate Action Plans for Improvement: Document analyses and potential remedies using advanced analytics. Connect manual and automated data, create insights, define actions, track results of CI initiatives
- Create Investment Accountability: Measure results using RT production data to ensure actions deliver desired results – financially measurable operations improvements that improve production performance
- Reduce Cycle Time: Organizations can plan upstream capacity expansion as cycle time reduction after the deployment return sustained yield improvements
Improving into the Future
To be successful, organizations must ensure that new technology interface with the existing backbone and be of value to achieve defined objectives.
DPM is empowering pharma manufacturing organizations to use factory data like never before. By providing real-time insights into manufacturing process performance, they can make better decisions, take actions more quickly and establish a pattern of continuous learning and improvement across the organization with significant business outcomes. Our goal is to help our clients better serve patients, address unmet life science needs, and navigate a complex regulatory environment.
Authors
