From regulatory compliance and globalization to production and pricing pressures, the pharmaceutical industry faces overwhelming
demands to remain profitable and competitive. In light of indications that drug manufacturing currently wastes $50 billion
per year, many companies are waking up to the value of creating more efficient operations (1).
Some have turned to traditional Lean Manufacturing practices to transform their operations from the high margin, low-productivity
model of the past. Unfortunately, success has been elusive: traditional Lean practices are not well suited to pharma's complex,
highly variable, shared-asset production environments.
Others have looked to technology to provide leaner, demand-based production. Our company recently surveyed manufacturing performance
at 1500 pharmaceutical manufacturers. We found that—even after spending millions on enterprise resource planning (ERP) systems,
advanced planning and scheduling (APS) systems, and other traditional technologies—many are still chasing performance improvements
at the plant level. Only a third of those surveyed felt that their IT systems have delivered their expected ROI. Respondents
cited lack of information visibility, no awareness of variability and lack of support for Lean principles.
Global visibility, global optimization
To achieve the benefits of Lean, manufacturers need global visibility of production performance and optimization and simulation
solutions that help model scenarios for more-agile performance. They must battle manufacturing's performance killer: variability.
Few industries deal with as much variability in products and processes as drug makers, whose product mix averages to 20% in
new SKUs per year and to 60% in overall SKU volatility.
Most successful Lean implementations have been in high-volume, low-mix manufacturing, which isn't surprising given Lean's
links in the automotive industry: the Toyota Production System. Implementing traditional Lean is a struggle in the pharmaceutical
arena, where hundreds of products, dozens of work centers, and significantly more process and demand variability are the norm.
The good news is that this is changing. Next-generation optimization software and methodologies can adapt traditional Lean
Manufacturing techniques to account for variability and mitigate its impact. By changing traditional performance metrics,
using flow-path management to derive more flexible approaches to define value streams and organizational structure, and using
alternate means of calculating inventory, capacity planning and lot sizing, companies such as Bristol-Myers Squibb have cut
cycle times and inventory in half or more, while achieving on-time delivery rates as high as 99%.
To begin reducing the $50 billion per year waste, manufacturers must leverage this Modified Lean approach and combine it with
flow-based manufacturing methodologies, simulation and analytics software, and variations on demand-based pull scheduling.
This next generation solution will enable companies to:
- Break down organizational silos and integrate manufacturing operations. Decisions can be made on the basis of product flow
through the factory, not by individual departmental metrics, improving control over key performance indicators.
- Improve decisions that impact the bottom line. Improved real-time data are fed back into the ERP and other business systems
for better visibility, planning, and decision-making. By feeding more accurate data from the plant floor into other business
systems, manufacturers can slash inventory and reduce cycle times up to 80%.
- Determine best-practices and prioritize metrics based on current business goals and challenges. Simulations allow manufacturers
to model scenarios to answer questions such as Which scenario will enable me to reduce cycle times more effectively? and Where
will reductions in variability benefit the bottom line most?
is president and CEO of Invistics, 5445 Triangle Parkway, Suite 300, Norcross, GA 30092, tel. 770.559.6386, fax 770.582.9298,
1. J. Macher and J. Nickerson, "Pharmaceutical Manufacturing Research Project Final Benchmarking Report," Washington University
in St. Louis, September 2006.