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Tim Lozier

Risk Management

Risk-Based Thinking and the Digital Transformation of Quality

Visibility and control bring better decision making

Published: Wednesday, March 21, 2018 - 12:01

The concepts and frameworks behind quality management are evolving. As more companies adopt new technologies, and standards like ISO 9001:2015 begin to shift in focus, there is a concept that is arising out of Industry 4.0, the factory of the future, and the industrial internet of things (IIoT). This concept, known as Quality 4.0 and the digital transformation of quality, combines the elements of technology, process innovation, and risk-based thinking to provide greater visibility and control into quality processes.

There are various ways in which Quality 4.0 has built technology into the overall story of quality, specifically through companies incorporating more data into their processes.

This article will consider where risk-based thinking fits in, and how current quality management system (QMS) processes and concepts feed into the Industry 4.0 story to create a Quality 4.0 dynamic.

Risk-based thinking defined

Risk has always had an implicit role in ISO standards, but newer versions are giving risk a more prominent place in quality management system standards. In the context of ISO 9001:2015, risk-based thinking replaces what was called preventive action in the previous version. Where ISO 9001 once assigned preventive action a separate clause, the 2015 version of the standard now incorporates risk throughout. Risk-based thinking requires companies to evaluate risk when establishing processes, controls, and improvements in a QMS.

However, risk isn’t limited to just negative possibilities. Companies can also use risk-based thinking to pinpoint opportunities, which represent the positive side of risk.

Using technology to mitigate risk

One of the most important parts of applying risk-based thinking to quality management is to make it part of the process rather than a siloed activity. This means having risk tools built into the QMS rather than using a separate point solution or time-consuming manual processes.

The key capabilities of a risk-enabled quality management solution include:
An integrated risk register. An organization needs a centralized place to record and monitor individual hazards and risk items. Although this is not formally part of ISO standards, consistently using a risk register will help satisfy several requirements.
Flexible risk tools. Organizations should be able to activate risk assessment tools such as a risk matrix or decision tree within any QMS application, from audits to deviations to regulatory compliance tracking.
Risk-based effectiveness checks. Adding a final risk-based verification step for processes like corrective action helps satisfy performance evaluation and improvement requirements.

One of the most important ways companies use technology to reduce risk is through automation. Creating automated risk management processes ensures nothing falls through the cracks and provides a documented history to look back on.

Risk meets Industry 4.0

Experts are calling Industry 4.0 the fourth Industrial Revolution. The concept represents a push towards manufacturing digitization that is predicted to deliver massive improvements in efficiency, costs, and profits in as little as just a few years.

Industry 4.0 has huge implications for quality, with LNS Research calling this side of this revolution Quality 4.0. Potential benefits include:
• Increased reliability of production output with less variation
• Improved overall equipment effectiveness (OEE) and lower maintenance costs
• More tightly integrated supply chain management

Making digital transformation a reality comes with numerous challenges. Risk management tools will be more important than ever under Industry 4.0, with potential concerns around: 
Security. Interconnected cyber-physical systems raise questions about data security and protecting proprietary information.
Systems reliability. Keeping automated systems up and running is a top priority and could severely affect quality if not properly managed.
Data talent. Managing the flow and volume of data that Industry 4.0 is designed to deliver requires skilled data scientists. Unfortunately, the current shortage of data experts is only getting worse, so companies will need to deliberately recruit and cultivate digital talent.

Industrial internet of things (IIoT)

Quality 4.0 is the application of IIoT to quality, according to LNS.

Disruptive technologies like ride-sharing, 3D printing, and self-driving vehicles are driving rapid transformation across many industries. IIoT is no exception, with manufacturing ripe for disruption due to the prevalence of manual processes.

How can companies expect to improve quality through the interconnectedness of devices?

Big data
IIoT has the capability to deliver vast amounts of data that companies can mine for important trends, triggers, and leading quality indicators. The size of datasets possible with IIoT far outpaces what companies can collect manually, with machine sensors providing a level of detail that can only be analyzed with advanced computing capabilities.

It’s a stark comparison to existing manual and paper-based data-collection methods still in use at many companies. Manual data can take weeks or even months to analyze, by which point it may not even be actionable anymore. IIoT and big data have the potential to change that, providing real-time insights into production processes to improve quality performance.

Companies need a way to effectively manage all the data that IIoT can provide, and advanced reporting and analytics can help.

Companies not only need large amounts of quality data, they also need that data integrated with the rest of their production process, as well as the company’s performance as a whole. That means having a QMS that links information from areas that include equipment calibration and maintenance, nonconforming materials, corrective action, quality records, and customer complaints.

Predictive maintenance
IIoT enables organizations to automate data collection on equipment performance. Rather than relying on a person to detect calibration and maintenance issues, machine learning capabilities can provide advance notification of when equipment is about to fail. By delivering this information directly to the QMS, companies can repair or replace equipment before it malfunctions.

This knowledge can result in less unplanned downtime, errors, and waste. When combined with an automated QMS, IIoT has the potential to revolutionize manufacturing as we know it.

Risk management
The IIoT revolution is moving forward and companies must be prepared to capitalize on the opportunities while minimizing risks.

One way to reduce risk is to break it down into steps, rather than creating a fully connected smart factory all at once. Another approach is to start with groups of connected devices. Once those are in place and delivering positive returns, it will make more sense to connect them end-to-end.

Companies should also make sure any changes to equipment, people, or processes involve formal change management procedures. Without a robust change management process, it’s difficult to identify risks and ensure a smooth implementation.

Finally, it’s necessary to incorporate risk management principles into the overall IIoT strategy. Risk management tools within the QMS can help, allowing an organization to track items, link any compliance issues, and see how IIoT fits into the larger risk picture within the company.

Machine learning
Companies can also expect to see major improvements in quality through machine learning. Machine learning can transform quality management, from reducing defects to improving efficiency.

Machine learning uses algorithms or calculation sequences that allow equipment to learn from data and improve performance. Prediction-generating models are continually updated based on data outputs, allowing the system to refine the model itself.

The machine learner compares the model’s predictions against the actual outcome, using that data to adjust the parameters feeding the model’s predictions.

Machine learning is already revolutionizing the manufacturing world, with applications that include:
Quality control. Training machines with a library of visual data can help equipment learn to spot both in-specification products and defects. Analyzing component data may also help companies predict which parts are likely to fail quality control, and trace defects to their origin in the production process. An organization also needs its quality-related data integrated with the rest of its production process—this means having a QMS that links information from areas such as nonconformances. This enables the organization to build a background on the nature of the nonconformity as well as use the data to pinpoint trends that will eventually lead to a nonconformance.
Predictive maintenance. Instead of creating maintenance schedules based on time intervals, companies can use sensors to detect unusual conditions long before equipment fails. A QMS that can take real-time equipment data and automatically initiate maintenance workflows and calibration activities results in tying in the processes associated with maintenance in a more real-time fashion.
Production optimization. Smart manufacturing systems can help companies boost product yields. More reliable equipment, optimized production planning, and equipment sensors that monitor and adjust outputs in real time all make this possible. This can help feed into the optimization of the product life cycle by initiating management of change in processes that need to be optimized as well as new product introductions. The QMS’s change management process takes components from all aspects to incorporate quality data into the changes we make.
Supply chain integration. Machine learning will allow manufacturers and suppliers to more tightly integrate planning and ordering, helping speed delivery while reducing material shortages. Couple this with real-time supplier quality, quantitative score carding, and risk-based supplier ratings, and companies can maintain efficiency in the supply chain as well as ensuring the highest levels of quality.

Overall, these applications have the potential to vastly improve the efficiency of operations while reducing defects, helping to cut quality costs, and improve customer satisfaction.

Closing thoughts

Risk is becoming embedded in all aspects of the business and beyond, playing a role in Quality 4.0 initiatives. There is more visibility and control, which leads to better decisions. Businesses can translate what they do on an operational level to a risk-based paradigm.

Risk-based thinking combined with Quality 4.0 can greatly improve the visibility and control a company has in its operations.

For more information on this topic, join me and Quality Digest editor in chief Dirk Dusharme for the webinar, “Risk-Based Thinking and the Digital Transformation of Quality,” on Tues., March 27, 2018, at 2 p.m. Eastern, 11 a.m. Pacific. Click here to register.

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About The Author

Tim Lozier’s picture

Tim Lozier

Tim Lozier is the director of product strategy for EtQ, in Farmingdale, New York. He has extensive experience in the software industry, and has been involved in the creation of leading-edge technologies in user-interface design and development. He began his career in digital marketing before taking a turn into software design and marketing at Quark Inc. Since then, he’s never looked back—helping to foster the development (and blog about) leading quality management software solutions.