Lean(ing on) Six Sigma and Predictive Modeling in a New Industry Paradigm
Monte Carlo simulation can allow drug manufacturers to gain new insight and drive further improvements across their organizations.
By John Danese, Director, Life Sciences Product Strategy, Oracle, and Fred Ciochetto, Solutions Specialist, Oracle
The pharmaceutical industry finds itself at the crossroads of change. Pharmaceutical manufacturers, which traditionally have enjoyed enviable margins even in times of economic downturn, are facing a number of emerging challenges that threaten to transform the industry paradigm moving forward. Challenges range from upcoming expirations of blockbuster drug patents, which can shift more than $1 billion in U.S. sales within weeks, a dwindling pipeline of blockbuster drugs, increased pressure from generic drugs, and uncertainty about the future of healthcare reimbursement in the United States. In addition, merger and acquisition activity in the industry has seen a significant uptake in recent months, driving organizations involved in such activities to ponder how best to integrate and manage their new organizations (minimize time-to-value) and spurring organizations external to these activities to reassess their own strategies and operations to continue to compete effectively.
To position themselves for continued profitability, pharmaceutical companies of all sizes are taking a hard look at their organizations with an eye toward optimizing productivity, efficiency and quality across the enterprise. Manufacturing operations are far from exempt from the microscope’s lens as pharmaceutical organizations seek to drive down costs in every corner of the organization. To this end, progressive manufacturers are looking to extend the effectiveness of their Lean Six Sigma initiatives―which blend Six Sigma for quality and Lean Speed to enhance efficiency as well as reduce waste―with predictive modeling techniques, such as Monte Carlo simulation, to gain new insight and drive further improvements across their organizations.
Breaking Old Habits
For a number of years, the pharmaceutical industry lagged behind other sectors in terms of manufacturing efficiency and productivity, largely because of the cost and burden involved in fulfilling regulatory mandates that require revalidation of processes following a change. Once manufacturers confirm or validate their processes as compliant, they traditionally have been very reticent to change them.
The industry’s focus on maintaining the status quo in its manufacturing environment has produced inefficiency and waste. It is estimated that the potential world-wide cost savings from efficiency improvement could be as high as $90 billion . Quality has also suffered under the status quo. The reject percentage in the pharmaceutical industry ranges from five percent to 10 percent (<2 Sigma), compared to 0.0001 percent (6 Sigma) in the semiconductor industry. This reject-percentage costs the industry between $4.5 billion and $9 billion per year based on $90 billion spent on manufacturing annually .
Pharmaceutical manufacturers, which historically have enjoyed consistently robust profit margins, have had little economic incentive to introduce change . . . until now, as they face an environment teeming with new economic constraints, uncertainty about the scope of future reimbursements, blockbuster drug patent expiration and more. By some estimates, drugs with sales of over $73 billion from the largest 10 pharmaceutical companies will be initially exposed to generic competition over the next four years .
Fortunately, they have, in recent years, received some support for innovation from the regulatory community. The U.S. Food and Drug Administration (FDA) and other regulatory bodies are acknowledging that the industry has fallen behind other sectors in terms of efficiency and quality, and are now endorsing a Quality by Design model that contrasts with the industry’s historical “quality-by-test results” approach.
As part of this shift, FDA launched its Process Analytical Technology (PAT) initiative, a risk-based guidance model that seeks to direct pharmaceutical manufacturers toward consistent, predictable and higher quality levels. With PAT, manufacturers build in quality improvements on the factory floor through a deep understanding of how variable process attributes, as well as the relationship among raw material and manufacturing processes, affect product quality at a fundamental level.
As pharmas seek to transform manufacturing operations in the industry’s rapidly evolving business climate and embrace PAT, many are turning to or are expanding their focus on two highly regarded management approaches―Lean Manufacturing and Six Sigma―that have proven effective in other complex industries. Lean Manufacturing focuses on eliminating manufacturing waste, with the objective of making manufacturers more responsive to customer demand and market changes. Six Sigma is a business process methodology that focuses on minimizing variation in product and process to reduce product defects.
When drug manufacturers implement Lean and Six Sigma concepts, they have a powerful methodology to help them improve quality, compliance, productivity, costs and speed, ultimately enabling them to bring better products to market, faster and more cost effectively.
The Move to Monte Carlo
With the PAT approach, manufacturers must understand how variations within core ingredients or manufacturing processes affect outcomes. Many manufacturers, however, still rely on traditional analysis of quality outcomes―which does not provide probability information on quality, cost and speed, all essential components to advancing PAT.
To effectively leverage PAT and the Lean Six Sigma methodology that supports it, pharmaceutical manufacturers require modeling tools that ideally help to improve quality long before a product is ever manufactured. These models enable predictive analysis that forecast the impact of various changes to ingredients or manufacturing processes on the quality of the finished product. Monte Carlo simulation―a sampling technique that uses probability distributions as process inputs, as opposed to a single or average value―is one such modeling tool that is beginning to gain traction in the pharmaceutical industry.