Journalists and other authors who should know better routinely conflate productivity increase with automation and automation with the introduction of robots. “Productivity” covers a set of performance metrics that are increased by a variety of methods, many of which do not involve automation. Automation sometimes increases productivity, but not always. Finally, most of the time, automation does not involve robots. At last Tuesday’s Palo Alto Lean Coffee, I asked Tesla’s Omar Guerrero and Genentech’s Curtis Anderson for examples of changes that had increased productivity in their organizations.
The seven articles I posted four years ago on the art of using videos to improve operations included no pointers on what to do with the videos once you have them. This concern may seem premature in a manufacturing world where video recordings of operations are still rare, process instructions are in dusty binders and obsolete, customization specs come in the form of all-uppercase text from a 30-year old dot matrix printer with a worn-out ribbon, engineering project records reside in individual employees’ laptops, and management expects IT issues to be resolved by implementing a new, all-in-one ERP system.
In everyday life, on the other hand, videos are already in common use to explain how to pry loose a stuck garbage disposal, remove a door lock, change a special bulb in car headlight, or neatly cut a mango into cubes. You just describe your problem in a Youtube search, and up come videos usually shot and narrated by handy amateurs, and sometimes pros. It is particularly useful for tasks involving motion with key points that are difficult to explain with words or still images. The manufacturing world will eventually catch up.
Today, some automotive parts manufacturers are able to deliver one million consecutive units without a single defective, and pondering quality management practices appropriate for this level of performance is not idle speculation. Of course, it is only achieved by outstanding suppliers using mature processes in mature industries. You cannot expect it during new product introduction or in high-technology industries where, if your processes are mature, your products are obsolete.
While still taught as part of the quality curriculum, acceptance sampling has been criticized by authors like W. E. Deming and is not part of the Lean approach to quality. For qualified items from suppliers you trust, you accept shipments with no inspection; for new items or suppliers you do not trust, you inspect 100% of incoming units until the situation improves. Let us examine both what the math tells us about this and possible management actions, with the help of 21st century IT.
In Capacity Planning For 1st Responders, we considered the problem of dimensioning a group so that there is at least one member available when needed. Not all service groups, however, are expected to respond immediately to all customers. Most, from supermarket check stands and airport check-in counters to clinics for non-emergency health care, allow some amount of queueing, giving rise to the question of how long the queues become when the servers get busy.
At one point in his latest book, Andy and Me And The Hospital, Pascal Dennis writes that the average number of patients in an emergency room is inversely proportional to the availability of the doctors. The busier the doctors are, the more dramatic the effect. For example, if they go from being busy 98% of the time to 99%, their availability drop by half from 2% to 1%, and the mean number of patients doubles. Conversely, any improvement in emergency room procedures that, to provide the same service, reduces the doctors’ utilization from 99% to 98%, which cuts the mean number of patients — and their mean waiting time — in half.
This elaborates on the topics of randomness versus uncertainty that I briefly touched on in a prior post. Always skittish about using dreaded words like “probability” or “randomness,” writers on manufacturing or service operations, even Deming, prefer to use “variability” or “variation” for the way both demand and performance change over time, but it doesn’t mean the same thing. For example, a hotel room that goes for $100/night in November through March and $200/night from April to October has a price that is variable but not random. The rates are published, and you know them ahead of time.
By contrast, to a passenger, the airfare from San Francisco to Chicago is not only variable but random. The airlines change tens of thousands of fares every day in ways you discover when you book a flight. Based on having flown this route four times in the past 12 months, however, you expect the fare to be in the range of $400 to $800, with $600 as the most likely. The information you have is not complete enough for you to know what the price will be but it does enable you to have a confidence interval for it.
In a previous post, I pointed out that manufacturing professionals’ eyes glaze over when they hear the word “probability.” Even outside manufacturing, most professionals’ idea of probability is that, if you throw a die, you have one chance in six of getting an ace. 2000 years ago, Claudius wrote a book on how to win at dice but the field of inquiry has broadened since, producing results that affect business, technology, science, politics, and everyday life.
In the age of big data, all professionals would benefit from digging deeper and becoming, at least, savvy recipients of probabilistic arguments prepared by others. The analysts themselves need a deeper understanding than their audience. With the software available today in the broad categories of data science or machine learning, however, they don’t need to master 1,000 pages of math in order to apply probability theory, any more than you need to understand the mechanics of gearboxes to drive a car.
It wasn’t the case in earlier decades, when you needed to learn the math and implement it in your own code. Not only is it now unnecessary, but many new tools have been added to the kit. You still need to learn what the math doesn’t tell you: which tools to apply, when and how, in order to solve your actual problems. It’s no longer about computing, but about figuring out what to compute and acting on the results.
Following are a few examples that illustrate these ideas, and pointers on concepts I have personally found most enlightening on this subject. There is more to come, if there is popular demand.
Few terms cause manufacturing professionals’ eyes to glaze over like “probability.” They perceive it as a complicated theory without much relevance to their work. It is nowhere to be found in the Japanese literature on production systems and supply chains, or in the American literature on Lean. Among influential American thinkers on manufacturing, Deming was the only one to focus on it, albeit implicitly, when he made “Knowledge of Variation” one of the four components of his System of Profound Knowledge (SoPK).
“[…] Choose a single line that leads to several cashiers
Not all lines are structured this way, but research has largely shown that this approach, known as a serpentine line, is the fastest. The person at the head of the line goes to the first available window in a system often seen at airports or banks. […]”
Sourced through the New York Times
“Riddle me this…
If the Japanese way of management and their engagement with employees is supposedly the best, yielding the best result, why is there such a lack of trust among employment across the spectrum; employers, bosses, teams/colleagues. From Bloomberg and EY.
Lifetime employment sounds like a great thing, but not if you hate where you work. That seems to be the plight of Japanese “salarymen” and “office ladies.” Only 22 percent of Japanese workers have “a great deal of trust” in their employers, which is way below the average of eight countries surveyed, according to a new report by EY, the global accounting and consulting firm formerly known as Ernst & Young. And it’s not just the companies: Those employees are no more trusting of their bosses or colleagues, the study found.
Most of the work we do today involves interactions with machines. It is true not only in manufacturing but in many other business processes. The machinist works with machining centers, the pilot with an airplane, the surgeon with a laparoscopy robot, the engineer with a variety of computer systems,…, not to mention the automatic appliances that relieve us of household chores. In fact, I think that being good at working with machines is so essential that I wrote a book about it. For the short version, see the following A3/tabloid infographic. To enlarge it, click on the picture, and then on “View full size” in the bottom right-hand corner.