The Challenges Ahead for Quality

Discussions about Industry 4.0 and Quality 4.0 often focus on technology. However, technology is just one of the challenges that Quality organizations face. Many trends are converging to create constant disruption for businesses, and the Quality unit must be ready for these changes. Rapid changes in technology, work, business models, customer expectations, and regulations present opportunities to improve quality management but also bring new risks.

The widespread use of digital technology has raised the expectations of stakeholders beyond what traditional quality management can offer. As the lines between companies, suppliers, and customers become less distinct, the scope of quality management must expand beyond the traditional value chain. New work practices, such as agile teams and remote work, are creating challenges for traditional quality management governance and implementation strategies. To remain relevant, Quality leaders must adapt to these changes..

 ChallengeMeansImpact to Quality ManagementHow to Prepare
Advanced AnalyticsThe increase in data sources and improved data processing has led to higher expectations from customers, regulators, business leaders, and employees. They expect companies to use data analytics to provide advanced insights and improve decision-making.Requires a holistic approach that allows quality professionals to access, analyze and apply insights from structured and unstructured data

Quality excellence will be determined by how quickly data can be captured, analyzed, shared and applied  
Develop a talent strategy to recruit, develop, rent or borrow individuals with data analytics capabilities, such as data science, coding and data visualization
Hyper-AutomationTo become more efficient and agile in a competitive market, companies will increasingly use technologies like RPA, AI, and ML. These technologies will automate or enhance tasks that were previously done by humans. In other words, if a task can be automated, it will be.How to ensure these systems meet intended use and all requirements

Algorithm-error generated root causes
Develop a hyperautomation vision for quality management that highlights business outcomes and reflects the use cases of relevant digital technology

Perform a risk based assessment with appropriat experts to identify critical failure points in machine and algorithm decision making
Virtualization of WorkThe shift to remote work due to COVID-19, combined with advancements in cloud computing and AR/VR technology, will make work increasingly digital.Rethink how quality is executed and governed in a digital environment.Evaluate current quality processes for flexibility and compatibility with virtual work and create an action plan.

Uncover barriers to driving a culture of quality in a virtual working environment and
incorporate virtual work-relevant objectives, metrics and activities into your strategy.
Shift to Resilient OperationsPrioritizing capabilities that improve resilience and agility.Adapt in real-time to changing and simultaneously varying levels of risk without sacrificing the core purpose of QualityEnable employees to make faster decisions without sacrificing quality by developing training to build quality-informed judgment and embedding quality guidance in employee workflows.

Identify quality processes that may prevent operational resilience and reinvent them by starting from scratch, ruthlessly challenging the necessity of every step and requirement.

Ensure employees and new hires have the right skill sets to design, build and operate a responsive network environment.
Rise of Inter-connected EcosystemsThe growth of interconnected networks of people, businesses, and devices allows companies to create value by expanding their systems to include customers, suppliers, partners, and other organizations.Greater connectivity between customers, suppliers, and partners provides more visibility into the value chain. However, it also increases risk because it can be difficult to understand and manage different views of quality within the ecosystem.Map out the entire quality management ecosystem model and its participants, as well as their interactions with customers.

Co-develop critical-to-quality behaviors with strategic partners.

Strengthen relationships with partners across the ecosystem to capture and leverage relevant information and data, while at the same time addressing data privacy concerns.
Digitally Native WorkforceShift from digital immigrants (my generation and older) to digital natives who are those people who have grown up and are comfortable with computers and the internet. Unlike other generations, digital natives are so used to using technology in all areas of their lives that it is (and always has been) an integral, necessary part of their day-to-day.Increased flexibility leads to a need to rethink the way we monitor, train, and incentivize quality.

Connecting the 4 Ps: People, Processes, Policies and Platforms
Identify and target existing quality processes to digitize to offer desired flexibility.

Adjust messages about the importance of quality to connect with values employees care about (e.g., autonomy, innovation, social issues).
Customer Expectation MultiplicityCustomer expectations evolve quickly and expand into new-in-kind areas as access to information and global connectedness increases.Develop product portfolios, internal processes and company cultures that can quickly adapt to rapidly changing customer expectations for quality.Identify where hyperautomation and predictive capabilities of quality management can enhance customer experience and prevent issues before they occur.
Increasing Regulatory ComplexityThe global regulatory landscape is becoming more complex as countries introduce new regulations at different rates. Increased push for localization.Need strong system to efficiently implement changes across different systems, locations, and regions while maintaining consistent quality management throughout the ecosystem.Coordinate a structured regulatory tracking approach to monitor changing regulatory developments — highly regulated industries require a more comprehensive approach compared to organizations in a moderate regulatory environment
Challenges to Quality Management

The traditional Value Proposition of quality management is no longer sufficient to meet the expectations of stakeholders. With the rise of a digitally native workforce, there are new expectations for how work is done and managed. Business leaders expect quality leaders to have full command of operational data, diagnosing and anticipating quality problems. Regulators also expect high data transparency and traceability.

The value proposition of quality management lies in predicting problems rather than reacting to them. The primary objective of quality management should be to find hidden value by addressing the root causes of quality issues before they manifest. Quality organizations who can anticipate and prevent operational problems will meet or exceed stakeholder expectations.

Our organizations are on a journey towards utilizing predictive capabilities to unlock value, rather than one that retroactively solves problems. Our scope needs to be based on quality being predictive, connected, flexible, and embedded. For me this is the heart of Qualty 4.0.

Quality management should be applied across a multitude of systems, devices, products, and partners to create a seamless experience. This entails transforming quality from a function into an interdisciplinary, participatory process. The expanded scope will reach new risks in an increasingly complex ecosystem. The Quality unit cannot do this on its own; it’s all about breaking down silos and building autonomy within the organization.

To achieve this transformation, we need to challenge ourselves to move beyond top-down and regimented Governance Models and Implementation Strategies. We need to balance our core quality processes and workflows to achieve repeatability and consistency while continually adjusting as situations evolve. We need to build autonomy, critical thinking, and risk-based thinking into our organizational structures.

One way to achieve this is by empowering end-users to solve their own quality challenges through participatory quality management. This encourages personal buy-in and enables quality governance to adapt in real-time to different ways of working. By involving end-users in the process of identifying and solving quality issues, we can build a culture of continuous improvement and foster a sense of ownership over the quality of our products and services.

The future of quality management lies in being predictive, connected, flexible, and embedded.

  • Predictive: The value proposition of quality management needs to be predicting problems over problem-solving.
  • Connected: The scope of quality management needs to extend beyond the value chain and connect across the ecosystem
  • Flexible: The governance model needs to be based on an open-source model, rather than top-down.
  • Embedded: The implementation strategy needs to shift from viewing quality as a role to quality as a skill.

By embracing these principles and involving all stakeholders in the process of continuous improvement, we can unlock hidden value and exceed stakeholder expectations.

Deaing with these challenges and implications requires the Quality organization to treat transformation like a Program. This program should have four main initiative areas:

  1. Build the capacity for targeted prevention through targeted data insights. This includes building alliances with IT and other teams to have the right data available in flexible ways but it also includes the building of capacity to actually use the data.
  2. Expand quality management to cover the entire value network.
  3. Localize Risk Management to Make Quality Governance Flexible and Open Source.
  4. Distribute Tasks and Knowledge to Embed Quality Management in the Business.

Across these pillars the program approach will:

  1. Assess the current state: Identify areas requiring attention and improvement by examining existing People, Processes, Policies and Platforms. This comprehensive assessment will provide a clear understanding of the organization’s current situation and help pinpoint areas where projects can have the most significant impact
  2. Establish clear objectives: Establish clear objectives to h provide a clear roadmap for success.
  3. Prioritize foundational elements: Prioritize building foundational elements. Avoid bells-and-whistles for their own sake.
  4. Develop a phased approach: This is not an overnight process. Develop a phased approach that allows for gradual implementation, with clear milestones and measurable outcomes. This ensures that the organization can adapt and adjust as needed while maintaining ongoing operations and minimizing disruptions.
  5. Collaborate with stakeholders: Engage stakeholders from across the organization,to ensure alignment and buy-in. Create a shared vision for the initiative to ensure that everyone is working towards the same goals. Regular communication and collaboration among stakeholders will foster a sense of ownership and commitment to the transformation process.
  6. Continuously monitor progress: Regularly review the progress, measuring outcomes against predefined objectives. This enables organizations to identify any potential issues or roadblocks and make adjustments as necessary to stay on track. Establishing key performance indicators (KPIs) will help track progress and determine the effectiveness of the Program.
  7. Embrace a culture of innovation: Encourage a culture that embraces innovation and continuous improvement. This helps ensure that the organization remains agile and adaptive, making it better equipped to take advantage of new technologies and approaches as they emerge. Fostering a culture of innovation will empower employees to seek out new ideas and solutions, driving long-term success.
  8. Invest in employee training and development: It is crucial to provide employees with the necessary training and development opportunities to adapt to new technologies and processes. This will ensure that employees are well-equipped to handle the changes brought about by these challenges and contribute to the organization’s overall success.
  9. Evaluate and iterate: As the Program unfolds, it is essential to evaluate the results of each phase and make adjustments as needed. This iterative approach allows organizations to learn from their experiences and continuously improve their efforts, ultimately leading to greater success.

To do this leverage the eight accelerators to change.

The Role of Mixed Reality in Quality 4.0

Last night I had the honor to speak at the ASQ Boston Section monthly meeting on some of the exciting work Thermo Fisher Scientific is doing in mixed reality and how it fits into the industrial transformation that we are all taking stabs at, as well as the broader concept of Quality 4.0.

A small group, but it was really fun to discuss some of the stuff I’ve gotten involved with in the 5 months I’ve been here, and where we see it going.

Slides are available here.

AI/ML-Based SaMD Framework

The US Food and Drug Administration’s proposed regulatory framework for artificial intelligence- (AI) and machine learning- (ML) based software as a medical device (SaMD) is fascinating in what it exposes about the uncertainty around the near-term future of a lot of industry 4.0 initiatives in pharmaceuticals and medical devices.

While focused on medical devices, this proposal is interesting read for folks interested in applying machine learning and artificial intelligence to other regulated areas, such as manufacturing.

We are seeing is the early stages of consensus building around the concept of Good Machine Learning Practices (GMLP), the idea of applying quality system practices to the unique challenges of machine learning.

WCQI Day 2 – morning

My day 2 at WCQI is Day 1 of the conference proper. I’m going to try to live blog.

Morning keynote

Today’s morning keynote is the same futurist as at the LSS in Phoenix last month, Patrick Schwerdtfeger, and not only was I dismayed that it was they exact same  I was reminded yet again how much I dislike futurists. I’m all about thinking of the future, but futurists seem to be particularly bad at it. It is all woowoo and bro-slapping and never ever a serious consideration of the impact of technology. Futurists are grifters.

These grifters profit by obscuring facts for personal gain. They are working an angle, all of them: the health gurus and the life hackers peddling easy solutions to difficult problems, the futurists who basically state current trends as revelations.  They are all trying to pull off the ultimate con – persuading people they really matter.

They are selling themselves: their books, their podcasts, their websites, their supplements, their claims to some secret knowledge about how the world works. But I fundamentally doubt that anyone who gave 40 talks in the last year has the bandwidth to do anything that really matters. It is all snake-oil. And as quality professionals, individuals who are dedicated to process and transparency and continuous improvement, we deserve better.

I’m not sure how these keynotes are selected but I think we need to holistically view just what we want to be as a society and the pillars we want for our conferences.

Anyone know a good article that evaluates futurists and life hackers with the prosperity gospel? Seem like they are coming from a similar place in the American psyche.

Ooh, artificial intelligence. Don’t get me wrong there is real potential (maybe not the potential people feel like there is) but most discussions on artificial intelligence is hype and bluster, and this presentation is no different. Autonomous vehicles block chains. Hype and bluster.

There is definitely people thinking this seriously, offering real insights and tools. We’re just not there. The speaker admits he gets 90% of his income from speaking. Pretty sure he isn’t actually doing that much. He might be an aggregator (as most of his slides with real content were attributed) but I keep struggling to see value here.

Gratuitous Steve Jobs picture.

After this there is some white space for vendor stuff.

At least I could multi-task and did some work.

Quality 4.0 Talks

I didn’t attend many of these last year because they were all standing and had a thrown together feeling. This year the ASQ seems to have upped the game. Shorter sessions can be good if the presentation is tight. At 15 minutes that is a hard bar to set.

First up we have Nicole Radziwill on “Mapping Quality Problems to Machine Learning Solutions” Nicole’s very active in the software section, which under the new membership model I’ll start paying a lot more attention to.

In this short presentation Nicole focuses on hitting the points of her 2018 Quality Progress article. Talking about quality 4.0’s path from Taylorism as “discovery & learning.”

She references Jim Duarte’s article Data Disruption.

Hit on big data hubris and the importance of statistics and analysis. The importance of defining models before we use them.

From ” Let’s Get Digital” in Quality Progress

Covers machine learning problem types at a high level.

  1. Prediction
  2. Classification
  3. Pattern Identification (Clustering)
  4. Data Reduction
  5. Anomaly Detection
  6. Pathfinding

High level recommendations were domain expertise, statistical expertise, data quality and human bias.

Next up is Beverly Daniels on “Risk and Industry 4.0”

It is telling that so many of the talkers make Millennial jokes. As a Gen-Xer I am both annoyed because no one ever made Gen-Xer jokes (the boomers never even noticed we were at the conference) AND frustrated because this is something telling about the graying of the ASQ.

Quick review of risk as more than probability. Hit on human beings as eternally optimistic and thus horrible at determining probability. As this was a quick talk it left more questions than answers. Getting rid of probability from risk is something I need to think about more, but her points are aligned to my thoughts on uncertainty.

Focusing on impact and mitigation is interesting. I liked the line “All it does is make your management feel good about not making a decision.”

Understanding Data – A Core Quality Skill

A critical skill of a quality professional (of any professional), and a fundamental part of Quality 4.0, is managing data — knowing how to acquire good data, analyze it properly, follow the clues those analyses offer, explore the implications, and present results in a fair, compelling way.

As we build systems, validate computer systems, create processes we need to ensure the quality of data. Think about the data you generate, and continually work to make it better.

I am a big fan of tools like the Friday Afternoon Measurement to determine where data has problems.

Have the tools to decide what data stands out, use control charts and regression analysis. These tools will help you understand the data. “Looks Good To Me: Visualizations As Sanity Checks” by Michael Correll is a great overview of how data visualization can help us decide if the data we are gathering makes sense.

Then root cause analysis (another core capability) allows us to determine what is truly going wrong with our data.

Throughout all your engagements with data understand statistical significance, how to quantify whether a result is likely due to chance or from the factors you were measuring.

In the past it was enough to understand a pareto chart, and histogram, and maybe a basic control chart. Those days are long gone. What quality professionals need to bring to the table today is a deeper understanding of data and how to gather, analyze and determine relevance. Data integrity is a key concept, and to have integrity, you need to understand data.