Category Archives: Socio-Technical Systems

Leadership – No Pushing Required

Brene Brown on leadership

When I was younger, I felt like I was pretty smart. Then I turned 23, was thrown into the fast-faced world of helping CxOs try to straighten out their wayward enterprise software implementations, and realized just how little I knew. My turning point came around 6pm on a hot, sticky, smelly evening on Staten Island in a conference room where a director named Mike Davis was yelling at a bunch of us youngster consultants. I thought he was mad at us, but in retrospect, it’s pretty clear that he just wanted something simple, and no matter how clearly he explained it, no one could hear him. Not even me, not even when I was being smart.

The customer was asking for some kind of functionality that didn’t make sense to me. It seemed excessive and unwieldy. I knew a better way to do it. So when Mike asked us to tell him, step by step, what user scenario we would be implementing… I told him THE RIGHT WAY. After about five attempts, he blew up. He didn’t want “the right way” — he wanted “the way that would work.” The way that would draw the most potential out of those people working on those processes. The way that would make people feel the most engaged, the most in control of their own destiny, the way that they were used to doing (with maybe a couple of small tweaks to lead them in a direction of greater efficiency). He knew them, and he knew that. He was being a leader.

Now I’m in my 40s and I have a much better view of everything I don’t know. (A lot of that used to be invisible to me.) It makes me both happier (for the perspective it brings) and unhappier (because I can see so many of the intellectual greenfields and curiosities that I’ll never get to spend time in — and know that more will crop up every year). I’m limited by the expiration date on this body I’m in, something that never used to cross my mind.

One of the things I’ve learned is that the best things emerge when groups of people with diverse skills (and maybe complementary interests) get together, drive out fear, and drive out preconceived notions about what’s “right” or “best”. When something amazing sprouts up, it’s not because it was your idea (or because it turned out “right”). It’s because the ground was tilled in such a way that a group of people felt comfortable bringing their own ideas into the light, making them better together, and being open to their own emergent truths.

I used to think leadership was about coming up with the BEST, RIGHT IDEA — and then pushing for it. This week, I got to see someone else pushing really hard for her “best, most right, more right than anyone else’s” idea. But it’s only hers. She’s intent on steamrolling over everyone around her to get what she wants. She’s going to be really lonely when the time comes to implement it… because even if someone starts out with her, they’ll leave when they realize there’s no creative expression in it for them, no room for them to explore their own interests and boundaries.  I feel sorry for her, but I’m not in a position to point it out. Especially since she’s older than me. Hasn’t she seen this kind of thing fail before? Probably, but she’s about to try again. Maybe she thinks she didn’t push hard enough last time.

Leadership is about creating spaces where other people can find purpose and meaning.  No pushing required.

Thanks to @maryconger who posted the image on Twitter earlier today. Also thanks to Mike Davis, wherever you are. If you stumble across this on the web one day, thanks for waking me up in 2000. It’s made the 18 years thereafter much more productive.

Happy 10th Birthday!

10 years ago today, this blog published its first post: “How Do I Do a Lean Six Sigma (LSS) Project?” Looking back, it seems like a pretty simple place to have started. I didn’t know whether it would even be useful to anyone, but I was committed to making my personal PDSA cycles high-impact: I was going to export things I learned, or things I found valuable. (As it turns out, many people did appreciate the early posts even though it would take a few years for that to become evident!)

Since then, hundreds more have followed to help people understand more about quality and process improvement in theory and in practice. I started writing because I was in the middle of my PhD dissertation in the Quality Systems program at Indiana State, and I was discovering so many interesting nuggets of information that I wanted to share those with the world – particularly practitioners, who might not have lots of time (or even interest) in sifting through the research. In addition, I was using data science (and some machine learning, although at the time, it was much more difficult to implement) to explore quality-related problems, and could see the earliest signs that this new paradigm for problem solving might help fuel data-driven decision making in the workplace… if only we could make the advanced techniques easy for people in busy jobs to use and apply.

We’re not there yet, but as ASQ and other organizations recognize Quality 4.0 as a focus area, we’re much closer. As a result, I’ve made it my mission to help bring insights from research to practitioners, to make these new innovations real. If you are developing or demonstrating any new innovative techniques that relate to making people, processes, or products better, easier, faster, or less expensive — or reducing risks and building individual and organizational capabilities — let me know!

I’ve also learned a lot in the past decade, most of which I’ve spent helping undergraduate students develop and refine their data-driven decision making skills, and more recently at Intelex (provider of integrated environment, health & safety, and quality management EHSQ software to enterprises and smaller organizations). Here are some of the big lessons:

  1. People are complex. They have multidimensional lives, and work should support and enrich those lives. Any organization that cares about performance — internally and in the market — should examine how it can create complete and meaningful experiences. This applies not only to customers, but to employees and partners and suppliers. It also applies to anyone an organization has the power and potential to impact, no matter how small.
  2. Everybody wants to do a good job (and be recognized for it). How can we create environments where each person is empowered to contribute in all the areas where they have talent and interest? How can these same environments be designed with empathy as a core capability?
  3. Your data are your most valuable assets. It sounds trite, but data is becoming as valuable as warehouses, inventory, and equipment. I was involved in a project a few years ago where we digitized data that had been collected for three years — and by analyzing it, we uncovered improvement opportunities that when implemented, saved thousands of dollars a week. We would not have been able to do that if the data had remained scratched in pencil on thousands of sheets of well-worn legal paper.
  4. Nothing beats domain expertise (especially where data science is concerned). I’ve analyzed terabytes of data over the past decade, and in many cases, the secrets are subtle. Any time you’re using data to make decisions, be sure to engage the people with practical, on-the-ground experience in the area you’re studying.
  5. Self-awareness must be cultivated. The older you get, and the more experience you gain, the more you know what you don’t know. Many of my junior colleagues (and yours) haven’t reached this point yet, and will need some help from senior colleagues to gain this awareness. At the same time, those of you who are senior have valuable lessons to learn from your junior colleagues, too! Quality improvement is grounded in personal and organizational learning, and processes should help people help each other uncover blind spots and work through them — without fear.

 

Most of all, I discovered that what really matters is learning. We can spend time supporting human and organizational performance, developing and refining processes that have quality baked in, and making sure that products meet all their specifications. But what’s going on under the surface is more profound: people are learning about themselves, they are learning about how to transform inputs into outputs in a way that adds value, and they are learning about each other and their environment. Our processes just encapsulate that organizational knowledge that we develop as we learn.

Why FEMA is Monitoring Waffle House this Weekend

This article originally appeared on the Intelex Community on 9/14/2018 at https://community.intelex.com/explore/posts/why-fema-monitoring-waffle-house-weekend Sometimes the most informative metrics show up in the strangest of places. Case in point: with a hurricane making landfall today in North Carolina, and the prospect for catastrophic flooding over the weekend and into next week, emergency managers are mobilizing for action – and if you’re in the path of the storm, you may be doing the same. Have you started monitoring the Waffle House Index? The US Federal Emergency Management Agency (FEMA) has. Originally devised by W. Craig Fugate, former FEMA Director, the Waffle House Index is based on the observation that the popular 24-hour breakfast chain has historically been unusually well prepared for disasters. Part of their business model is to be the spot for emergency personnel to rely on for their coffee and nourishment – a valuable role when power crews, rescue teams, and debris removal workers are working long, hard hours. To do this, they make sure all employees have disaster training and stock all their restaurants with generators, and have a reduced menu specifically to be offered in the aftermath of a disaster. Over time, this even led to a more formal partnership between the organizations. FEMA first responders are known to set up initial operations in Waffle House locations. Waffle House now reports the status of each location to FEMA after a disaster to facilitate data collection. The Waffle House Index is a red, yellow, or green marker placed on a map wherever a Waffle House location is found. Under normal conditions, the marker is green. If the restaurant has shifted into emergency operations and is offering their limited menu, the marker is yellow. If the marker is red, that means that the Waffle House is closed – either the site itself is damaged or destroyed, emergency staff can not reach the site, the emergency generators are down or out of fuel, or there is a food shortage. When FEMA sees one or more reds, they know an area is in particularly bad shape – and they’ll need to help. What can you learn about risk-based thinking from the Waffle House index? Three things: first, that you can (and should) look outside your organization for risk indicators that might help you make better (and faster) decisions, particularly when those risks are activated. Second, that you should explore crowdsourced risk data as a source of up-to-date information. And finally – if Waffle House is closed, there’s a serious problem.   Additional Reading: McKnight, B., & Linnenluecke, M. K. (2016). How firm responses to natural disasters strengthen community resilience: A stakeholder-based perspective. Organization & Environment, 29(3), 290-307. Walter, L. (2011, July 6) What do waffles have to do with risk management? EHS Today. Available from https://www.ehstoday.com/fire_emergencyresponse/disaster-planning/waffles-risk-management-0706

Risk-Based Thinking: In ISO 9001 and Beyond (Interview)

On August 31, Quality Digest interviewed me on Quality Digest Live in advance of the webinar on Risk-Based Thinking that we held (sponsored by Intelex) on September 6. You can see it here on YouTube (13:42)! I answer the questions:

  • Is risk-based thinking different than enterprise risk management (ERM) or operations risk management (ORM)?
  • Who is risk-based thinking for?
  • Are there good and bad risks? Is opportunity really the “flip side” of risk?
  • Can focusing on risk inhibit innovation?

I’ll also be capturing the information from the webinar in a series of reports later this month that will be available to everyone. Stay tuned!

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.

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.

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