As World Quality Month celebrations are replaced with attention to holiday celebrations and November’s focus fades into the distant past, facing a new year in the darkness of December may represent an opportunity to pay attention to issues related to developing and managing technology and contemplating the future of a company or organization.
Last month’s issue of Harvard Business Review, with a cover story related to “What really keeps CEOs awake at night,” addressed the timing of innovative technologies in an article authored by Ron Adner and Rahul Kapoor (https://hbr.org/2016/11/right-tech-wrong-time). We all know of technological innovations that have been released too late and missed the revolution (the article cites Blockbuster’s failure to address the shift from rentals to streaming, for example), as well as those that have been ready too soon, falling into a market that does not perceive their value.
To avoid the “right tech, wrong time” scenario, Adner and Kapoor suggest looking more closely at the ecosystems that support technologies. Understanding the competition between the new and the old ecosystems can help to assure more accurate predictions about the timing of transitions, and to render decisions about allocating resources more effective.
If you’re keeping track of exercise in your daily life, your electronic tracker is loaded with data—but seeing trends and patterns requires charting. See the visual information generated from this data.
Setting aside time to celebrate quality offers an opportunity not only to reflect on our own quality improvement efforts, but also to recall other years and other celebrations, and to consider the history of the designation as well as of our own quality improvement efforts.
National Quality Month (October) started in 1988 in the U.S. and Canada, while Japan has been celebrating Quality Month (November) since 1960. World Quality Month was instituted in 2010, acknowledging the global impact that quality improvement has had on organizations, and recognizing that quality in products and services is important for organizations throughout the world.
The role of W. Edwards Deming and others is not to be forgotten as we reflect on the meaning of this month and recall its history.
PC satisfaction: After a three-year slide, customer satisfaction with computers has rebounded.
Focus on supply chain quality: This company utilizes the work of the quality department to assure efficient performance in its supply chain.
Reducing wait time: Steps to reduce hospital wait time or delivery of baggage at airport may not immediately affect customer satisfaction.
Product recalls: Information about product recalls is available from the U.S. Consumer Product Safety Commission.
Statistics has gotten a bad rap. People love to quote Mark Twain (“There are lies, damn lies, and statistics,” alternatively attributed to Benjamin Disraeli), Vin Scully (“Statistics are used much like a drunk uses a lamppost: for support, not illumination”), or Stephen Leacock (“In ancient times they had no statistics so they had to fall back on lies”).
For statisticians, these jokes have become quite tedious. Avoiding small talk at cocktail parties where quips are likely to come up or lying about one’s profession (“I’m a kind of mathematician” sometimes works) are not really satisfying alternatives to the lines that people have saved to shower on the innocent professional. What’s a statistician to do?
Myron Tribus, friend of PQ Systems, died August 31 in Pensacola, FL at the age of 94. Tribus, known as an organizational theorist, was director of the Center for Advanced Engineering Study at MIT, and taught thermodynamics for much of his career. He is best known among quality professionals as a friend, supporter, and interpreter of W. Edwards Deming. For more than 20 years, he shared his expertise at quality conferences and through his prolific writing. For his work with Pensacola in applying Deming’s principles, he was awarded the keys to the city, and received innumerable awards from quality professional organizations.
Tribus attended many of PQ Systems’ annual conferences, and in 1992 addressed participants as a keynote speaker, where he shared reminiscences about interactions with Deming. I recall a colorful story he shared in his presentation about his first meeting with Deming. This account had a lasting impression on me, as well as on other participants.
Capability analysis, with its formulas and the confusion around Ppk, Cpk, Cpm, and other indices, is often perceived as far too difficult, complex, and challenging to consider utilizing. Good reasons for undertaking the analysis are sometimes not considered, in the light of this perceived complexity.
In fact, good reasons for using capability analysis not only exist, but provide compelling argument for utilizing this statistical tool. And it’s really not that hard.
Capability analysis offers a way to compare a process to a customer’s requirements, coming up with a score that facilitates communication with the customer to indicate how well a process is able to meet these requirements. Since the language and terminology surrounding capability analysis are consistent and generally agreed upon, discussions can be effective, and can initiate continued progress with meeting and surpassing the customer’s requirements.
In capability analysis, these customer requirements are expressed as specifications, not to be confused with control limits. Some examples of customer requirements (specifications):
In a rapidly changing business environment, it’s sometimes hard just to keep up with everyday demands—never mind having time to develop new and better approaches to changing requirements, needs, or markets. Staying ahead of the curve sounds as if it might demand working longer hours, hiring more people, or cloning oneself, none of which seem likely in the short term. So how does one manage to innovate in this environment?
The word “innovation” itself summons images of new products, or dramatically new approaches to customer needs, or a new version of a product or a new application of technology. Per Byland in Entrepreneur asserts that innovations often involve simply rethinking supply chains or factory operations, even in small ways that improve processes. With respect to Henry Ford’s car and Jeff Bezos’ Amazon, “the factor that made these companies great wasn’t primarily technology; it was organization.”
By developing a mindset that continually asks, “How can this process be better?” organizations will find that innovation comes naturally. Fostering such a mindset lies at the heart of improvement as well as innovation.
It may be time to recall W. Edwards Deming and his 14 Points for Management that he outlined in chapter two of Out of the Crisis (MIT Press, 2000). Generally seen as keys to product and process improvement, they also reflect the process for innovation. Perhaps we can see these tried-and-true management principles in a new light.
Baseball generates what may be the greatest array of statistics of all sports. Aficionados love comparing records of home runs, hits, runs, doubles, triples, errors, batting averages, and other performance details, not only for individual players and teams, but also against historic records, sometimes collecting ammunition for a discussion with their brothers-in-law about who’s the best player or team, and how that player or team compares to record-breaking plays and players.
As with all statistics, sports statistics can be painfully distorted or innocently quoted to “prove” a point about a player or team. But statistics being statistics, they demand that any use of data respond to an appropriate question. Stats can be specious if the wrong question is being answered. Let’s see how this works. Can you answer this simple baseball question? (Feel free to look up statistics to support your response.)
Approaching the end of the school year means focusing on graduation rates, dropout rates, and other data suggesting trends for students.
Opportunities for considering statistics abound; but one must continue to examine the way that these statistics are actually used, by asking the right questions about the data.
For example: As teachers finish state testing regimens and head into final exams, it may be useful to see data related to average pay for teachers. Is it going up?