“[…]Organizations dealing repeatedly with projects will soon develop templates of Work Breakdown Structures (WBS) holding the most current tasks and milestones. These canvasses speed up somewhat the project initiation and ensure some degree of standardization.
Over time though, the copy-pasting from one project to the next, the addition of “improvements” and requirements as well as countermeasures to problems kind of inflate the templates and the projects. This, in turn, extends the project’s duration as every additional task not only adds its allocated time to completion, but also the safety margin(s) the doer and/or project manager will add on top.[…]”
Sourced through Chris Hohmann
Michel Baudin‘s comments:
The project management literature astonishingly fails to provide guidance on the art of breaking a project down into tasks. The “Body of Knowledge” tells you what a Work Breakdown Structure (WBS) should look like but not how you actually break a project down into meaningful pieces, whether it is a dinner party, the construction of a bridge, or a moon shot. For a manager who has to make a plan, this makes templates irresistible: instead of thinking, you just fill in the blanks.
Chris’s questions are certainly relevant but I would like to go further and propose a few rules for generating a WBS.
The purpose of graphics for data visualization is communication, not decoration, which is often forgotten in publications as well as on company performance dashboards. A case in point is the chart on yesterday’s cover of the New York Times. It shows that solar energy currently accounts for more than twice as many jobs as coal. It also shows the numbers of jobs in different sectors and uses a color code to mark some as based on fossil fuels versus renewable and low-emission technologies.
Until recently, most publications would have used a pie chart. Now, graphic artists have found a way to square the pie chart into yet another style that will most likely trickle down to slideware and office walls, in spite of a low data-to-ink ratio and the use of two-dimensional shapes to display one-dimensional data.
Christoph Roser’s pulse line animation
“There are three different options on how to time the production lines.[…] The “easiest” one is an unstructured approach. The processes are still arranged in sequence; however, there is no fixed signal when to start processing a part. The pulse line is also a flow line, but now all parts move at the same time. […] When all processes are done, all parts move to the next process simultaneously. […] Another common way to structure the timing of flow lines is the continuously moving line.”
Sourced through All About Lean
Michel Baudin‘s comments: Christoph’s two posts are great for their rifle-shot focus on the single issue of flow line pacing and for their effective use of animation to illustrate principles. It makes the differences clear in a way you couldn’t on paper.
“Imagine going to work at 7:30 every night and spending the next 12 hours, including meals and breaks, inside a factory where your only job is to insert a single screw into the back of a smartphone, repeating the task over and over and over again. During the day, you sleep in a shared dorm room, and in the evening, you wake up and start all over again.That’s the routine that Dejian Zeng experienced when he spent six weeks working at an iPhone factory near Shanghai, China, last summer. […]. Unlike many of those workers, Zeng did not need to do the job to earn a living. He’s a grad student at New York University, and he worked at the factory for his summer project.”
Sourced through Business Insider
Michel Baudin‘s comments: Thanks to my colleague Kevin Hop for sending me this rare peek into the life of the people who assemble iPhones by hand in Chinese factories each employing tens of thousands of workers. We need to keep in mind that this is the perspective of Dejian Zeng, an American student who was there for 6 weeks, not someone who works there for a living, but it is still informative.
While his account wouldn’t make anyone want to embrace iPhone assembly as a career choice, it’s not a horror story. The work is dull and repetitive, and there is too much of it, but it’s not described as dirty or dangerous. I have seen worse in poorly ventilated paint shops and machine shops with slippery floors, and not only in China.
“This year is the 20th anniversary of the founding of the Lean Enterprise Institute (LEI). There will surely be a big celebration. But in my view, there is less to celebrate than meets the eye. Here’s why:
LEI has controlled the progressive management agenda for the last 20 years. That means they own the failures as well as the successes. By LEIs own reckoning (as well as its sister organization, the Lean Enterprise Academy in the U.K.), success has been much less than they had hoped for.”
Sourced through Bob Emiliani’s blog
Michel Baudin‘s comments: Overall, I agree with Bob’s assessment, but I think American manufacturers deserve more of the blame than the LEI, for faddishly latching on to one tool after another and mistaking it for a panacea. For example, in his introduction to “Learning to See,” Mike Rother explicitly warns the reader that, at Toyota, Materials and Information Flow Analysis (MIFA) is not a major tool. Yes, he repackaged it with the attractive but nonsensical name of “Value Stream Mapping” (VSM), but his audience didn’t have to elevate it to the status that it did.
Employees cheering 1st 787/10 in 2/2017
Today, the Boeing 787 is a successful product, with production rates at 12 units/month, and a total of 521 flying just over 5 years from launch. By comparison, in 49 years of production, Boeing built 1,528 units of the 747. And, having just flown in a 787 from San Francisco to Paris and back, I can attest that it was for me less tiring than in any other plane, which I attribute to the higher air pressure. It is close to that of Lake Tahoe (6225′) while other planes are closer to Squaw Valley High Camp (8200′).
Back in 2008-2011, however, the news coverage of the 787 was not so positive, as the plane’s product launch accumulated a delay of more than three years, with analysts pondering what had gone wrong. To keep this event in perspective, we should remember that multiyear delays in product launches have recently been the rule rather than the exception in commercial aircraft, worldwide. In Europe, the Airbus A380 was 2 years late and, in Russia, so was the regional Superjet 100. But the question remained of how Boeing, an organization with 100 years of experience in designing and building airplanes, could not have done better.
I would like to present here a few explanations that have been proposed, without passing judgment as to whether any or all of them are accurate.
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.
Patients waiting in Emergency Room
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.
With Christoph Roser
Christoph Roser, who blogs at AllAboutLean.com, is another on-line correspondent whom I had a chance to meet on this trip. We discussed our backgrounds and shared interests over a brasserie lunch in Paris, across from the Luxembourg gardens, where we walked afterward, among Parisians enjoying the early spring, playing tennis, and watching puppet shows. At one edge of the gardens is the seat of the French Senate; at the opposite, my alma mater, Mines-Paristech.
Brasserie Le Luxembourg
As authors, Christoph and I have been working with the same publisher, Taylor & Francis, and even with the same editor. His “Faster, Better, Cheaper” in the History of Manufacturing came out last year, covering the period “from the stone age to Lean Manufacturing and beyond,” which is very ambitious. I confessed to not having read it cover to cover, but the parts I did read seemed carefully researched. Unfortunately, the way things are made hasn’t been as thoroughly documented as wars and revolutions, and it’s a challenge to trace back the origins of ideas in this area that we apply every day.
Torbjørn Netland, professor of Operations Management at ETH Zürich, blogger at Better Operations, and an on-line correspondent of many years, had invited me to deliver a guest lecture in his course on Global Operations Strategy.
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.