Mature Quality Systems: What Pharma Can Learn from Other Industries

Pharma can take inspiration from industries with mature quality systems and advanced quality capabilities

By Alessandro Delfino, Alessandro Faure Ragani, Vanya Telpis and Jonathan Tilley, McKinsey & Company

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Quality has a special meaning in pharmaceuticals, where production or distribution errors can jeopardize human life. But other industries face similar challenges, and some have developed sophisticated quality systems. As pharmaceutical companies look for ways to improve their quality practices and performance, they can take ideas from these quality leaders and adapt them for pharma.

Quality can be defined in various ways, from “fitness for purpose” to “meeting customer expectations” to predictability in statistical terms. Each industry understands quality differently, and priorities depend on the specifics of products and markets. In the automotive industry, for instance, companies generally define quality as the ability to meet (or exceed) customer expectations on products and services. To do so, they work toward Six Sigma levels of high predictability. In addition to avoiding defects, they also strive to maximize the appeal of their vehicles along more subjective criteria such as design, aesthetics, touch and feel, and ease of use. 

Aerospace manufacturers, by contrast, still define quality more along the formal requirements of customers and regulators, always ensuring 100 percent airworthiness. They have such complex products that they see quality as an ongoing process of continuous improvement—no airplane was likely ever built right the first time without any deviation or repair. Semiconductor makers focus on design for manufacturability to ensure consistent process quality, as well as to achieve high yield from the precious wafer materials.

Industries with mature quality systems rely on tight protocols and standard operating procedures customized for their respective concerns. They are also known for extensive, systematic, and continuous learning that allows them to address emerging problem areas. Four structural components drive these efforts as a unified “House of Quality”: building quality into the product and into functional processes; organizing for quality in all functional areas and achieving compliance and solid governance; instilling a quality mind-set and behaviors by developing capabilities in everyone, not just the quality organization; and factoring quality into strategy and performance management.[1]

From client surveys and expert interviews, McKinsey has assessed these components in pharmaceuticals as well as three of the strongest industries for quality control: automotive, aerospace, and semiconductor. 


Quality pressures on pharmaceuticals come mainly from regulatory agencies, which are increasingly driving the industry toward preventive and “intelligent” systems, rather than the control- and audit-based approach sufficient in the past. Quality differences are usually not directly visible to consumers, who are therefore represented by regulators. As a result, Pharma’s quality teams have historically focused heavily on complying with regulatory demands through documentation and testing of samples.

Factories typically show relatively poor quality, with low right-first-time metrics (2 to 3 sigma[2]). But screening by the quality assurance function helps maintain very high quality in terms of the product that reaches customers.

Functional quality processes (built-in quality):

Process quality in Pharma is often fairly low, because processes typically do not translate well from the R&D lab to the reality of the factory floor. Designers are often under pressure to launch quickly, and many do not use quality gates to ensure a scaled-up design for manufacturability. Several issues result:

  • Process capability often does not easily meet the design specification, thereby providing a perennial yield issue and an unwelcome deviation-investigation work load. In those cases, production adds secondary processing, such as to remove moisture.
  • Factories rarely apply error proofing (poka-yokes[3]) effectively. 
  • Work standardization is still low, and equipment across sites is different.
  • Insufficient validation and verification, combined with overly tight specifications for many things not critical to product quality, create problems that require secondary processing and compensatory testing ,and cause yield loss.

Many companies are streamlining quality control processes, especially in labs, and adopting lean practices, but most have yet to reach best-practice levels.

Quality organization and governance:

Quality groups are independent of production units by regulatory design, and many heads of quality report directly to the CEO or board.[4]Yet many companies still have multiple layers in these groups, leading to cumbersome decision-making. They are also often focused on compliance and documentation, rather than on problem solving, increasing process robustness, and supporting production on demand and in real time.

Quality strategy and KPIs:

Most companies track common production metrics such as first-pass yield, deviations, rejects and rework, compliance failures, recalls, as well as customer complaints. But these metrics are mainly lagging, not leading indicators, and are often followed inconsistently across sites. More important, companies generally lack reliable systems for measuring the true cost of quality and tend to miss or underestimate costs. That leads to decisions on quality investments that are made without understanding either the full cost or the financial value of improving quality.[5]

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