Tag Archives: Technology Management

Happy World Quality Day 2018!

Each year, the second Thursday of November day is set aside to reflect on the way quality management can contribute to our work and our lives. Led by the Chartered Quality Institute (CQI) in the United Kingdom, World Quality Day provides a forum to reflect on how we implement more effective processes and systems that positively impact KPIs and business results — and celebrate outcomes and new insights.

This year’s theme is “Quality: A Question of Trust”.

We usually think of quality as an operations function. The quality system (whether we have quality management software implemented or not) helps us keep track of the health and effectiveness of our manufacturing, production, or service processes. Often, we do this to obtain ISO 9001:2015 certification, or achieve outcomes that are essential to how the public perceives us, like reducing scrap, rework, and customer complaints.

But the quality system encompasses all the ways we organize our business — ensuring that people, processes, software, and machines are aligned to meet strategic and operational goals. For example, QMS validation (which is a critical for quality management in the pharmaceutical industry), helps ensure that production equipment is continuously qualified to meet performance standards, and trust is not broken. Intelex partner Glemser Technologies explains in more detail in The Definitive Guide to Validating Your QMS in the Cloud. This extends to managing supplier relationships — building trust to cultivate rich partnerships in the business ecosystem out of agreements to work together.

This also extends to building and cultivating trust-based relationships with our colleagues, partners, and customers…

Read more about how Integrated Management Systems and Industry 4.0/ Quality 4.0 are part of this dynamic: https://community.intelex.com/explore/posts/world-quality-day-2018-question-trust

Quality 4.0 in Basic Terms (Interview)

On October 12th I dialed in to Quality Digest Live to chat with Dirk Dusharne, Editor-in-Chief of Quality Digest, about Quality 4.0 and my webinar on the topic which was held yesterday (October 16).

Check out my 13-minute interview here, starting at 14:05! It answers two questions:

  • What is Quality 4.0 – in really basic terms that are easy to remember?
  • How can we use these emerging technologies to support engagement and collaboration?

You can also read more about the topic here on the Intelex Community, or come to ASQ’s Quality 4.0 Summit in Dallas next month where I’ll be sharing more information along with other Quality 4.0 leaders like Jim Duarte of LJDUARTE and Associates and Dan Jacob of LNS Research.

Quality 4.0: Let’s Get Digital

Want to find out what Quality 4.0 really is — and start realizing the benefits for your organization? Check out this month’s issue of ASQ’s Quality Progress, where my new article (“Let’s Get Digital“) does just that. Quality 4.0 — which we’re working to bring to the practice of quality management and quality engineering at Intelex — asks how we can leverage connected, intelligent, automated (C-I-A) technologies to increase efficiency, effectiveness, and satisfaction: “As connected, intelligent and automated systems are more widely adopted, we can once again expect a renaissance in quality tools and methods. The progression can be summarized through four themes:

  • Quality as inspection: In the early days, quality assurance relied on inspecting bad quality out of the total items produced. Walter A. Shewhart’s methods for statistical process control helped operators determine whether variation was due to random or special causes.
  • Quality as design: Inspired by W. Edwards Deming’s recommendation to cease dependence on inspection, more holistic methods emerged for designing quality into processes to prevent quality problems before they occurred.
  • Quality as empowerment: TQM and Six Sigma advocate a holistic approach to quality, making it everyone’s responsibility and empowering individuals to contribute to continuous improvement.
  • Quality as discovery: In an adaptive, intelligent environment, quality depends on how quickly we can discover and aggregate new data sources, how effectively we can discover root causes and how well we can discover new insights about ourselves, our products and our organizations.”

Read more at http://asq.org/quality-progress/2018/10/basic-quality/lets-get-digital.html  or download the PDF (http://asq.org/quality-progress/2018/10/basic-quality/lets-get-digital.pdf)

Practical Poka-Yoke

[Note: I’ve been away from the blog for several months now in the middle of very significant changes in my life. That’s about to change! In the next post, I’ll tell you about what happened and what my plans are for the future. In the meantime, I wanted to share something that happened to me today.]

A couple hours ago, I went to the ATM machine.

I don’t use cash often, so I haven’t been to an ATM machine in several months. Regardless, I’m fully accustomed to the pattern: put card in, enter secret code, tell the machine what I want, get my money, take my card. This time, I was really surprised by how long it was taking for my money to pop out.

Maybe there’s a problem with the connectivity? Maybe I should check back later? I sat in my car thinking about what the best plan of action would be… and then I decided to read the screen. (Who needs to read the screen? We all know what’s supposed to happen… so much so, that I was able to use an ATM machine entirely in the Icelandic language once.)

PLEASE TAKE YOUR CARD TO DISPENSE FUNDS, it said.

This is one of the simplest and greatest examples of poka-yoke (or “mistake-proofing”) I’ve ever seen. I had to take my card out and put it away before I could get my money! I was highly motivated to get the money (I mean, that’s the specific thing I came to the ATM to get) so of course I’m going to do whatever is required to accomplish my goal. The machine was forcing me to take my card — preventing the mistake of me accidentally leaving my card in the machine — which could be problematic for both me and the bank.

Why have I never seen this before? Why don’t other ATMs do this? I went on an intellectual fishing expedition and found out that no, the idea is not new… Lockton et al. (2010) described it like this:

A major opportunity for error with historic ATMs came from a user leaving his or her ATM card in the machine’s slot after the procedure of dispensing cash or other account activity was complete (Rogers et al., 1996, Rogers and Fisk, 1997). This was primarily because the cash was dispensed before the card was returned (i.e. a different sequence for Plan 3 in the HTA of Fig. 3), leading to a postcompletion error—“errors such as leaving the original document behind in a photocopier… [or] forgetting to replace the gas cap after filling the tank” (Byrne and Bovair, 1997). Postcompletion error is an error of omission (Matthews et al., 2000); the user’s main goal (Plan 0 in Fig. 3) of getting cash was completed so the further “hanging postcompletion action” (Chung and Byrne, 2008) of retrieving the card was easily forgotten.

The obvious design solution was, as Chung and Byrne (2008) put it, “to place the hanging postcompletion action ‘on the critical path’ to reduce or eliminate [its] omission” and this is what the majority of current ATMs feature (Freed and Remington, 2000): an interlock forcing function (Norman, 1988) or control poka-yoke (Shingo, 1986), requiring the user to remove the card before the cash is dispensed. Zimmerman and Bridger (2000) found that a ‘card-returned-then-cash-dispensed’ ATM dialogue design was at least 22% more efficient (in withdrawal time) and resulted in 100% fewer lost cards (i.e. none) compared with a ‘cash-dispensed-then-card-returned’ dialogue design.

I don’t think the most compelling message here has anything to do with design or ATMs, but with the value of hidden gems tucked into research papers.  There is a long lag time between recording genius ideas and making them broadly available to help people. One of my goals over the next few years is to help as many of these nuggets get into the mainstream as possible. If you’ve got some findings that you think would benefit the entire quality community (or quality management systems or software), get in touch… I want to hear from you!

 

Reference:

Lockton, D., Harrison, D., & Stanton, N. A. (2010). The Design with Intent Method: A design tool for influencing user behaviour. Applied ergonomics41(3), 382-392.

Value Propositions for Quality 4.0

In previous articles, we introduced Quality 4.0, the pursuit of performance excellence as an integral part of an organization’s digital transformation. It’s one aspect of Industry 4.0 transformation towards intelligent automation: smart, hyperconnected(*) agents deployed in environments where humans and machines cooperate and leverage data to achieve shared goals.

Automation is a spectrum: an operator can specify a process that a computer or intelligent agent executes, the computer can make decisions for an operator to approve or adjust, or the computer can make and execute all decisions. Similarly, machine intelligence is a spectrum: an algorithm can provide advice, take action with approvals or adjustments, or take action on its own. We have to decide what value is generated when we introduce various degrees of intelligence and automation in our organizations.

How can Quality 4.0 help your organization? How can you improve the performance of your people, projects, products, and entire organizations by implementing technologies like artificial intelligence, machine learning, robotic process automation, and blockchain?

A value proposition is a statement that explains what benefits a product or activity will deliver. Quality 4.0 initiatives have these kinds of value propositions:

  1. Augment (or improve upon) human intelligence
  2. Increase the speed and quality of decision-making
  3. Improve transparency, traceability, and auditability
  4. Anticipate changes, reveal biases, and adapt to new circumstances and knowledge
  5. Evolve relationships and organizational boundaries to reveal opportunities for continuous improvement and new business models
  6. Learn how to learn; cultivate self-awareness and other-awareness as a skill

Quality 4.0 initiatives add intelligence to monitoring and managing operations – for example, predictive maintenance can help you anticipate equipment failures and proactively reduce downtime. They can help you assess supply chain risk on an ongoing basis, or help you decide whether to take corrective action. They can also improve help you improve cybersecurity: documenting and benchmarking processes can provide a basis for detecting anomalies, and understanding expected performance can help you detect potential attacks.


(*) Hyperconnected = (nearly) always on, (nearly) always accessible.

Quality 4.0 and Digital Transformation

The fourth industrial revolution is characterized by intelligence: smart, hyperconnected agents deployed in environments where humans and machines cooperate to achieved shared goals — and using data to generate value. Quality 4.0 is the name we give to the pursuit of performance excellence in the midst of technological progress, which are sometimes referred to as digital transformation.

The characteristics of Quality 4.0 were first described in the 2015 American Society for Quality (ASQ) Future of Quality Report. This study aimed to uncover the key issues related to quality that could be expected to evolve over the next 5 to 10 years. In general, the analysts expected that the new reality would focus not so much on individual interests, but on the health and viability of the entire industrial ecosystem.

Some of the insights from the 2015 ASQ Future of Quality Report were:

  • A shifting emphasis from efficiency and effectiveness, to continuous learning and adaptability
  • Shifting seams and transitions (boundaries within and between organizations, and how information is shared between the different areas)
  • Supply chain omniscience (being able to assess the status of any element of a global supply chain in real time)
  • Managing data over the lifetime of the data rather than the organization collecting it

The World Economic Forum (WEF) has also been keenly interested in these changes for the past decade. In 2015, they launched a Digital Transformation Initiative (DTI) to coordinate research to help anticipate the impacts of these changes on business and society. They recognize that we’ve been actively experiencing digital transformation since the emergence of digital computing in the 1950’s:

 

Because the cost of enabling technologies has decreased so much over the past decade, it’s now possible for organizations to begin making them part of their digital strategy. In general, digital transformation reveals that the nature of “organization” is changing, and the nature of “customer” is changing as well. Organizations will no longer be defined solely by their employees and business partners, but also by the customers who participate – without even explicitly being aware of their integral involvement — in ongoing dialogues that shape the evolution of product lines and new services.

New business models will not necessarily rely on ownership, consumption, or centralized production of products or provision of services. The value-based approach will accentuate the importance of trust, transparency, and security, and new technologies (like blockchain) will help us implement and deploy systems to support those changes.

 

How to Assess the Quality of a Chatbot

Image Credit: Doug Buckley of http://hyperactive.to

Quality is the “totality of characteristics of an entity that bear upon its ability to meet stated and implied needs.” (ISO 9001:2015, p.3.1.5) Quality assurance is the practice of assessing whether a particular product or service has the characteristics to meet needs, and through continuous improvement efforts, we use data to tell us whether or not we are adjusting those characteristics to more effectively meet the needs of our stakeholders.

But what if the entity is a chatbot?

In June 2017, we published a paper that explored that question. We mined the academic and industry literature to determine 1) what quality attributes have been used by others to determine chatbot quality, we 2) organized them according to the efficiency, effectiveness, and satisfaction (using guidance from the ISO 9241 definition of usability), and 3) we explored the utility of Saaty’s Analytic Hierarchy Process (AHP) to help organizations select between one or more versions of chatbots based on quality considerations. (It’s sort of like A/B testing for chatbots.)

“There are many ways for practitioners to apply the material in this article:

  • The quality attributes in Table 1 can be used as a checklist for a chatbot implementation team to make sure they have addressed key issues.
  • Two or more conversational systems can be compared by selecting the most significant quality attributes.
  • Systems can be compared at two points in time to see if quality has improved, which may be particularly useful for adaptive systems that learn as they as exposed to additional participants and topics.”
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