Tag Archives: innovation

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.

Perception of Value & Today’s Cryptocurrency “Crash”

Artist’s rendering of Bitcoin. THERE ARE NO ACTUAL COINS THAT LOOK LIKE THIS. Don’t ever let anyone sell you one.

Today, many cryptocurrencies lost ~35-50% of their value. Reddit even posted contact information for the National Suicide Prevention Hotline in /r/cryptocurrency, knowing how emotional investors were bound to be today. Bitcoin, which was nearly $20K in mid-December and has been hovering near $14K this past week, dropped nearly $4K and almost sunk below the $10K milestone. I usually track the price of Bitcoin at http://bitcointicker.co, which can show the posted prices from several exchanges (web locations where people go to buy and sell, like Ebay). There are hundreds of cryptocurrencies and many of them dropped in value today.

Why did the prices drop so much on Tuesday? Here are some likely influences:

Market prices are usually driven by supply and demand — for example, if there aren’t that many lobsters available in a particular area at a particular time, and you go to a restaurant hoping to order one — you’ll pay a premium. But that price is also influenced by the quality of the product, the image of the product, which influences your perception of its value. Quality reflects how well something satisfies stated and implied needs or expectations.

Value, however, is quality relative to price, and influenced by image. And people are not always rational: they’ll pay a premium for image, even if the value of a product isn’t particularly high. Just think of all the Macs on display at schools, coffee shops, and airports. Price is related to value… usually, price goes up as value goes up.

Where’s the value of cryptocurrency? A Bitcoin does not, on its own, have any inherent value — just like a dollar or a Euro (a “fiat currency”). But the prospect of an asset that will increase in perceived value — where you can buy low, hold (sometimes just for a few days), and sell high because there are lots of people willing to buy it from you — will have perceived value. Hundreds of early adopters — or “Bitcoin millionaires” — are getting people excited about the prospect of making small investments and reaping huge rewards. That this has happened so recently lends a mystique to ownership of cryptocurrencies and Altcoins (or “alternatives to Bitcoin,” like Ether) in addition to the novelty.

Value is attributed to things by people, and cryptocurrencies are no exception. The quality of the currency itself, and the technical solidity of the platform upon which one is based, isn’t really tied to the cryptocurrency price right now — although this will probably change as knowledge and awareness increases.

Is this the end of Bitcoin? That’s doubtful — there are too many innovators who insist on exploring the technological landscape of cryptocurrencies and blockchain technology, and lots of investors willing to fund them. In the meantime, there are unlikely benefits: because cryptocurrencies are not yet mainstream, a “crypto crash” is not as likely to ripple through the whole economy (no pun intended) like the subprime mortgage crisis of 2008. But if you do decide to buy cryptocurrency, don’t invest any more than you can afford to lose.

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.”

What Protests and Revolutions Reveal About Innovation

The following book review will appear in an issue of the Quality Management Journal later this year:

The End of Protest: A New Playbook for Revolution.   2016.  Micah White.  Toronto, Ontario, Canada. Alfred A. Knopf Publishing.  317 pages.

You may wonder why I’m reviewing a book written by the creator of the Occupy movement for an audience of academics and practitioners who care about quality and continuous improvement in organizations, many of which are trying to not only sustain themselves but also (in many cases) to make a profit. The answer is simple: by understanding how modern social movements are catalyzed by decentralized (and often autonomous) interactive media, we will be better able to achieve some goals we are very familiar with. These include 1) capturing the rapidly changing “Voice of the Customer” and, in particular, gaining access to its silent or hidden aspects, 2) promoting deep engagement, not just in work but in the human spirit, and 3) gaining insights into how innovation can be catalyzed and sustained in a truly democratic organization.

This book is packed with meticulously researched cases, and deeply reflective analysis. As a result, is not an easy read, but experiencing its modern insights in terms of the historical context it presents is highly rewarding. Organized into three sections, it starts by describing the events leading up to the Occupy movement, the experience of being a part of it, and why the author feels Occupy fell short of its objectives. The second section covers several examples of protests, from ancient history to modern times, and extracts the most important strategic insight from each event. Next, a unified theory of revolution is presented that reconciles the unexpected, the emotional, and the systematic aspects of large-scale change.

The third section speaks directly to innovation. Some of the book’s most powerful messages, the principles of revolution, are presented in Chapter 14. “Understanding the principles behind revolution,” this chapter begins, “allows for unending tactical innovation that shifts the paradigms of activism, creates new forms of protest, and gives the people a sudden power over their rulers.” If we consider that we are often “ruled” by the status quo, then these principles provide insight into how we can break free: short sprints, breaking patterns, emphasizing spirit, presenting constraints, breaking scripts, transposing known tactics to new environmental contexts, and proposing ideas from the edge. The end result is a masterful work that describes how to hear, and mobilize, the collective will.

 

Reviewed by

Dr. Nicole M. Radziwill

 

Innovation Tips for Strategic Planning

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

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

Over the past 15 years, I’ve helped several organizations with continuous improvement initiatives at the strategic, executive level. There are a lot of themes that keep appearing and reappearing, so the purpose of this post is to call out just a few and provide some insights in how to deal with them! 

These come up when you are engaged in strategic planning and when you are planning operations (to ensure that processes and procedures ultimately satisfy strategic goals), and are especially prominent when you’re trying to develop or use Key Performance Indicators (KPIs) and other metrics or analytics.

 

1) How do you measure innovation? Before you pick metrics, recognize that the answer to this question depends on how you articulate the strategic goals for your innovation outcomes. Do you want to:

  • Keep up with changing technology?
  • Develop a new product/technology?
  • Lead your industry in developing best practices?
  • Pioneer new business models?
  • Improve quality of life for a particular group of people?

All of these will be measured in different ways! And it’s OK to not strategically innovate in one area or another… for example, you might not want to innovate your business model if technology development is your forte. Innovation is one of those things where you really don’t want to be everything to everyone… by design.

 

2) Do you distinguish between improving productivity and generating impact?

Improving quality (the ability to satisfy stated and implied needs) is good. Improving productivity (that is, what you can produce given the resources that you use) is also good. Reducing defects, reducing waste, and reducing variation (sometimes) are all very good things to do, and to report on. 

But who really cares about any improvements at all unless they have impact? It’s always necessary to tie your KPIs, which are often measures of outcomes, to metrics or analytics that can tell the story about why a particular improvement was useful — in the short term, and (hopefully also) in the long term.

You also have to balance productivity and impact. For example, maybe you run an ultra-efficient 24/7 Help Desk. Your effectiveness is exemplary… when someone submits a request, it’s always satisfied within 8 hours. But you discover that no tickets come in between Friday at 5pm and Monday at 8am. So all that time you spend staffing that Help Desk on the weekend? It’s non-value-added time, and could be eliminated to improve your productivity… but won’t influence your impact at all.

We just worked on a project where we had to consciously had to think about how all the following interact… and you should too:

  • Organizational Productivity: did your improvement help increase the capacity or capability for part of your organization? If so, then it could contribute to technical productivity or business productivity.
  • Technical Productivity: did the improvement remove a technical barrier to getting work done, or make it faster or less error-prone?
  • Business Productivity: did the improvement help you get the needs of the business satisfied faster or better?
  • Business Impact: Did the improvements that yielded organizational productivity benefits, technical productivity benefits, or business productivity benefits make a difference at the strategic level? (This answers the “so what” question. So you improved your throughput by 83%… so what? Who really cares, and why does this matter to them? Long-term, why does this awesome thing you did really matter?)
  • Educational/Workforce Development Impact: Were the lessons learned captured, fed back into the organization’s processes to close the loop on learning, or maybe even used to educate people who may become part of your workforce pipeline?

All of the categories above are interrelated. I don’t think you can have a comprehensive, innovation-focused analytics approach unless you address all of these.

 

3) Do you distinguish between participation and engagement?

Participation means you showed up. Engagement means you got involved, you stayed involved, your mission was advanced, or maybe you used this experience to help society. Too often, I see organizations that want to improve engagement, and all the metrics they select are really good at characterizing participation.

I’m writing a paper on this topic right now, but in the meantime (if you want to get a REALLY good sense of the difference between participation and engagement), read The Participatory Museum by Nina Simon. Yes, it is “about museums” — and yes, I know you’re in business or industry — and YES, this book really will provide you with amazing management insights. So read it!

Where is Quality Management Headed?

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

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

[This post is in response to ASQ’s February topic for the Influential Voices group, which asks: Where do you plan to take your career in 2016? What’s your view of careers in quality today—what challenges is this field facing? How can someone starting out in quality succeed?]

We are about to experience a paradigm shift in production, operations, and service: a shift that will have direct consequences on the principles and practice of design, development, and quality management. This “fourth industrial revolution” of cyber-physical systems will require more people in the workforce to understand quality principles associated with co-creation of value, and to develop novel business models. New technical skills will become critical for a greater segment of workers, including embedded software, artificial intelligence, data science, analytics, Big Data (and data quality), and even systems integration. 

Over the past 20 years, we moved many aspects of our work and our lives online. And in the next 20 years, the boundaries between the physical world and the online world will blur — to a point where the distinction may become unnecessary.

Here is a vignette to illustrate the kinds of changes we can anticipate. Imagine the next generation FitBit, the personalized exercise assistant that keeps track of the number of steps you walk each day. As early as 2020, this device will not only automatically track your exercise patterns, but will also automatically integrate that information with your personal health records. Because diet strategies have recently been shown to be predominantly unfounded, and now researchers like Kevin Hall, Eran Elinav, and Eran Siegal know that the only truly effective diets are the ones that are customized to your body’s nutritional preferences [1], your FitBit and your health records will be able to talk to your food manager application to design the perfect diet for you (given your targets and objectives). Furthermore, to make it easy for you, your applications will also autonomously communicate with your refrigerator and pantry (to monitor how much food you have available), your local grocery store, and your calendar app so that food deliveries will show up when and only when you need to be restocked. You’re amazed that you’re spending less on food, less of it is going to waste, and you never have to wonder what you’re going to make for dinner. Your local grocery store is also greatly rewarded, not only for your loyalty, but because it can anticipate the demand from you and everyone else in your community – and create specials, promotions, and service strategies that are targeted to your needs (rather than just what the store guesses you need).

Although parts of this example may seem futuristic, the technologies are already in place. What is missing is our ability to link the technologies together using development processes that are effective and efficient – and in particular, coordinating and engaging the people  who will help make it happen. This is a job for quality managers and others who study production and operations management

As the Internet of Things (IoT) and pervasive information become commonplace, the fundamental nature and character of how quality management principles are applied in practice will be forced to change. As Eric Schmidt, former Chairman of Google, explains:  “the new age of artificial intelligence is beginning, and it’s a big deal.” [2] Here are some ways that this shift will impact researchers and practitioners interested in quality:

  • Strategic deployment of IoT technologies will help us simultaneously improve our use of enterprise assets, reduce waste, promote sustainability, and coordinate people and machines to more effectively meet strategic goals and operational targets.
  • Smart materials, embedded in our production and service ecosystems, will change our views of objects from inert and passive to embedded and engaged. For example, MIT has developed a “smart band-aid” that communicates with a wound, provides visual indicators of the healing process, and delivers medication as needed. [3] Software developers will need to know how to make this communication seamless and reliable in a variety of operations contexts.
  • Our technologies will be able to proactively anticipate the Voice of the Customer, enabling us to meet not only their stated and implied needs, but also their emergent needs and hard-to-express desires. Similarly, will the nature of customer satisfaction change as IoT becomes more pervasive?
  • Cloud and IoT-driven Analytics will make more information available for powerful decision-making (e.g. real-time weather analytics), but comes with its own set of challenges: how to find the data, how to assess data quality, and how to select and store data with likely future value to decision makers. This will be particularly challenging since analytics has not been a historical focus among quality managers. [4]
  • Smart, demand-driven supply chains (and supply networks) will leverage Big Data, and engage in automated planning, automatic adjustment to changing conditions or supply chain disruptions like war or extreme weather events, and self-regulation.
  • Smart manufacturing systems will implement real time communication between people, machines, materials, factories and warehouses, supply chain partners, and logistics partners using cloud computing. Production systems will adapt to demand as well as environmental factors, like the availability of resources and components. Sustainability will be a required core capability of all organizations that produce goods.
  • Cognitive manufacturing will implement manufacturing and service systems capable of perception, judgment, and improving quality autonomously – without the delays associated with human decision-making or the detection of issues.
  • Cybersecurity will be recognized as a critical component of all of the above. For most (if not all) of these next generation products and production systems, quality will not be possible without addressing information security.
  • The nature of quality assurance will also change, since products will continue to learn (and not necessarily meet their own quality requirements) after purchase or acquisition, until the consumer has used them for a while. In a December 2015 article I wrote for Software Quality Professional, I ask “How long is the learning process for this technology, and have [product engineers] designed test cases to accommodate that process after the product has been released? The testing process cannot find closure until the end of the ‘burn-in’ period when systems have fully learned about their surroundings.” [5]
  • We will need new theories for software quality practice in an era where embedded artificial intelligence and technological panpsychism (autonomous objects with awareness, perception, and judgment) are the norm.

How do we design quality into a broad, adaptive, dynamically evolving ecosystem of people, materials, objects, and processes? This is the extraordinarily complex and multifaceted question that we, as a community of academics and practitioners, must together address.

Just starting out in quality? My advice is to get a technical degree (science, math, or engineering) which will provide you with a solid foundation for understanding the new modes of production that are on the horizon. Industrial engineering, operations research, industrial design, and mechanical engineering are great fits for someone who wants a career in quality, as are statistics, data science, manufacturing engineering, and telecommunications. Cybersecurity and intelligence will become increasingly more central to quality management, so these are also good directions to take. Or, consider applying for an interdisciplinary program like JMU’s Integrated Science and Technology where I teach. We’re developing a new 21-credit sector right now where you can study EVERYTHING in the list above! Also, certifications are a plus, but in addition to completing training programs be sure to get formally certified by a professional organization to make sure that your credentials are widely recognized (e.g. through ASQ and ATMAE).

 

References

[1] http://www.huffingtonpost.com/entry/no-one-size-fits-all-diet-plan_564d605de4b00b7997f94272
[2] https://www.washingtonpost.com/news/innovations/wp/2015/09/15/what-eric-schmidt-gets-right-and-wrong-about-the-future-of-artificial-intelligence/
[3] http://news.mit.edu/2015/stretchable-hydrogel-electronics-1207
[4] Evans, J. R. (2015). Modern Analytics and the Future of Quality and Performance Excellence. The Quality Management Journal22(4), 6.
[5] Radziwill, N. M., Benton, M. C., Boadu, K., & Perdomo, W., 2015: A Case-Based Look at Integrating Social Context into Software Quality. Software Quality Professional, December.
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