Quality Management Systems

Organizations implementing a QMS usually do so according to the requirements outlined by an accepted standard, such as the ISO family of guidelines. To gain the maximum benefit from a QMS, a manufacturer will need to have their system audited and certified by an external certification body.

The details of QMS vary by industry. Industry pressure and consumer requirements shape these differences. In the automotive industry, for example, a mix of federal regulations, concern for consumer safety, and extremely high consumer standards for performant products drive quality efforts. For the pharmaceutical industry, QMS are designed to ensure compliance and documentation, and the cost of regulatory intervention fosters a risk-centric approach to quality.

This section will review some of the standard QMS systems, as well as more general methods for achieving quality.

Importantly, these are process-based standards. As described above, the particulars of quality are something that must be established and negotiated between a company, its consumers, and regulatory bodies for each product.

ISO 9000 Series — The ISO 9000 refers to a family of internationally accepted standards for quality management and quality assurance. They are not specific to any industry or product. Notable standards within the ISO 9000 series include ISO 9000:2005 (definitions of quality), ISO 9001:2008 (requirements for quality), ISO 9004:2009 ( guidelines for continuous improvement) and ISO 9001: 2015 (the “bible” of risk management).

ISO 9001:2015 — The most widely implemented quality standard. Applicable to companies of all sizes in every industry. The 2015 revision expands its coverage of the relationships between organizations and their QMS. It promotes a process-driven approach, and gives companies greater latitude for applying general quality concepts to the particulars of their operations.

ISO 13485 — The standard particular to the medical device industry. Designed to help medical device manufacturers comply with regulations and maximize quality at every stage of a product’s lifecycle. The 2016 revision emphasizes risk reduction, and expands coverage of quality concepts to the full supply chain, including sale and delivery.

IATF 16949 — Replacing the ISO 16949, this standard governs QMS in the Automotive Industry.  Industry specific focuses include the reduction of waste and scrap in the supply chain. It provides “guidance and tools for companies and organizations who want to ensure that their products consistently meet customer requirements and that quality and customer satisfaction are consistently improved.” The most recent revision emphasizes continuous improvement, and outlines best practices for applying quality concepts throughout a manufacturer’s supply chain.

AS9100 – The quality standard for aerospace industries. Considered among the most exacting of standards. Certification is required of all vendors working with aviation, space, and defense organization.

GxP — An abbreviation for “Good ____ Practices,” GxP refers to industry specific standards and best practices. These regulations all encourage manufacturers to improve traceability, accountability, and data integrity. Compliance is enforced by regulating bodies such as the FDA. Adhering to relevant GxP is an important goal of any QMS. Some of the most widely instituted GxP include GMP (Good Manufacturing Practice), GCP (Good Clinical Practice), GDP (Good Documentation Practice), and GLP (Good Laboratory Practice).

GAMP®5 — GAMP stands for “Good Automated Manufacturing Practices.” These practices represent a more general set of standards for bringing computerized systems in alignment with regulations. GAMP is a structured, project-based approach to the validation of automated systems. It foregrounds continuous process validation and the correct production and storage of documentation throughout the software lifecycle. Often, GAMP is used as shorthand for GAMP®5: A Risk-Based Approach to Compliant GxP Computerized Systems. The main objectives of GAMP include: Clarity about delivered products and processes; a “life-cycle of systems” approach to quality; enhanced scalability of processes; a scientific approach to risk management; as little generation of new material as possible.

In addition to industry specific guidelines, there are also several widely share methods that inform the implementation and execution of a QMS.

Here are some of the most common.

ALCOA – ALCOA is a method for ensuring data integrity. It is mostly commonly used in the pharmaceutical industry for guiding documentation practices. An acronym, each of the letters in ALCOA stand for a feature of high-integrity data.

• Attributable – Evidence must be attributable to the person collecting it
• Legible – All records must be filed accurately, permanently, in a form intelligible to anyone needing to access it.
• Contemporaneous – Documentation must be created at the time the process was executed
• Original – All documentation should be kept in its original form
• Accurate – Documentation should be free from error.

V Model – The V-Model is a critical part of GAMP. It is commonly used for computerized system validation. In the V-Model, product specifications are paired with verification tests. This ensures that quality checks are built into each stage of the product development lifecycle, and that verification and validation are performed routinely, in a linear fashion, not just at project completion stage.

CAPA – Also known as Corrective And Preventative Action, CAPA is a series of steps taken to identify and eliminate the source of an adverse event. Usually triggered in the event of a recurring or significant non-conformance, CAPA begins with a root cause analysis, and continues with prescribes steps to eliminate any problems. In some industries, such as Medical Device and Pharmaceuticals, CAPA must be defined and outlined in order to receive certification of a quality management system.

 

The Digital Quality Management Landscape

Broadly, Industry 4.0 refers to advances in manufacturing accompanying the widespread adoption of technologies like IoT, cloud storage/computing, and AI, among many more.

These technologies have had a significant impact on quality assurance (giving rise to the name Quality 4.0), and they promise to make quality management systems more robust, easier to implement, and more cost effective to enforce.

Industry 4.0 QMS solutions fall into two partially overlapping camps.

1. QMS software suites
2. Digital solutions that simplify the constituent parts of a QMS (e.g. documentation, training, reporting)

QMS Software Incumbents

For many years, specialized software suites have helped manufacturers automate some of the more labor intensive aspects of QMS. These programs are usually designed to ensure compliance with FDA, FAA, or other regulations, as well as particular ISO guidelines.

These software systems excelled at streamlining repetitive processes, centralizing documentation into a single source of truth, and ensuring adherence to necessary regulations and guidelines.

They weren’t perfect, however.

Many software solutions required complicated, IT intensive integrations. Getting a piece of QMS software to communicate with a factory’s machines was itself an expensive, time-consuming task. Integrating a QMS with an ERP or CRM added new complications for both operations and data storage. Once configured, these systems could be extremely rigid, requiring IT intervention to make small changes in procedures or data accessibility. The cost and complexity of these systems limited them to larger manufacturers who could afford and sustain significant investments in quality-focused software.

In the last ten years, developments in technology and demand among SMEs have birthed more flexible QMS software. These solutions are often cloud-based, alleviating the need for expensive, on-premise integrations. The SaaS business model has created modular solutions, for which companies can pay per use-case and feature, as opposed to purchasing an entire system. And more attention has been placed into designing systems that scale.

For the modern manufacturer, there are an increasing number of viable software solutions.

New Digital Solutions 

Beyond software, there are a host of new digital technologies with quality management systems. Rather than operate as a single software system, they function as part of a technological ecosystem.

The technologies in this category are easy to deploy, flexible, and customizable to a manufacturer’s operations. Rather than cover all aspects of a QMS, they attack some of the more onerous processes and components.

Interactive Digital SOPs – One of the most difficult aspects of compliance is ensuring that operators follow standard operating procedures to the letter on every run. Often, SOPs for a single process can number into the dozens of pages. Even if operators follow them exactly, errors are still possible, and recording their progress can reduce efficiency.

Increasingly, manufacturers are opting for media-rich, digital SOPs. These SOPs are dynamic and interactive, allowing engineers to embed video and images for complicated steps. With IoT systems, break-beams, light systems, and other interactive tools can integrate with these SOPs. These engage operators, and guide them through complex processes, making it impossible to make a mistake. Further, in-line quality checks like scales, calipers, and cameras can be used to catch non-conformances at the source.

All of this results in processes that are completed correctly a higher percentage of the time, with simplified documentation to prove it.  

Interactive Training Platforms – One of the most important aspects of a QMS are training programs. Especially for industries with high seasonality, turnover, or especially complex assemblies, it’s important that workers are trained effectively and quickly.

Fortunately, new technologies are increasingly being used to train employees. Just like digital SOPs, there are now digital applications that guide workers through pre-designed training programs, preventing the labor lost when operators are taken off the line to mentor new hires, and reducing the overall time spent training. On the vanguard, technologies like computer vision are being used to train workers on new processes as they perform them. These hands-free training systems build embodied knowledge of a task as the worker completes each step.

Ultimately, better trained employees are less likely to make mistakes, and more likely to produce quality products the first time.

Electronic Logs – Another compliance driven use of Industry 4.0 tech is Electronic Logbooks. These digital logs make it easy for an engineer to record and integrate data from equipment, machines, processes and operators. Using IoT connectivity and cloud storage to record and store data from multiple sources, they provide visibility into the state and usage of equipment in a factory. They place events, reason codes, notes, and photos into a single log, and eSignatures allow operators to verify that the information is correct at the source. These logbooks reduce the amount of time spent recording and archiving processes by hands, and places data in a single, easily accessible location.

Improved product traceability – Demonstrating quality requires tracking materials from the supply chain, into inventory, through the manufacturing process, and through distribution to the consumer. IoT technology has made it easier to track items from end to end, bringing the ideal of complete traceability. Manufacturers now have access to a variety of tools that help them track materials and products through the full value stream. On the horizon, technologies like blockchain have the potential to create public, immutable records of a product from supplier to consumer.

But we need not look to the future. Existing product tracking technologies, when paired with other tools, like Electronic Logbooks, manufacturers are one step closer to achieving demonstrable, reportable product genealogies.

Advanced Statistical Process Control – Quality engineers have long used statistical process control to understand and analyze the variability inherent in many manufacturing processes. One of the defining features of Industry 4.0 is more data describing a greater number of features in a factory. With advances in machine learning, artificial intelligence, and big data analytics, decision-makers can leverage this data to reduce defects, and lower cost-of-quality.

Big data and Advanced Analytics – Big data has uses beyond SPC. When paired with artificial intelligence and machine learning, this data has the potential to reveal new sources for quality improvement, and to support previously unthinkable leaps in quality management. Manufacturers are already using Big Data to identify and correct bottlenecks, improve traceability, hone KPIs, improve workflows, and nuance error categories. With truly massive quantities of data now available to manufacturers, the ideal of predictive maintenance is closer than ever to becoming a reality.

These are just a few of the most significant use cases for Industry 4.0 technology within quality management systems. Manufacturers looking to reduce the burden of compliance, as well as those who are looking to realize cost savings and improved efficiency, should consider what features of their current operations could be improved. Likely, there are flexible new technologies that can amplify their quality initiatives.  

This may be obvious for the industry that turned continuous improvement into a science, but it bears repeating: a digital transformation is not something that happens once. Disruption is now the status quo. Staying successful means building agility into the foundation of a manufacturing operation.

As one quality expert recently noted, “Quality will always be important. Compliance will never go out of fashion.”

In other words, investments in quality management will always be beneficial to manufacturers.

Whether for attaining a necessary certification, systematizing continuous improvement, or ensuring compliance with regulations, quality management systems can help manufacturers achieve their quality and financial goals.