Blog clippings

Aug 13 2013

The Measure of Efficacy of Spend is Value Adding | Bill Waddell | Manufacturing Leadership Center

Lindsay Levkoff Lynn
Lindsay Levkoff Lynn

See on Scoop.it – lean manufacturing
“A writer by the name of Lindsay Levkoff Lynn asserts that a charity should not be measured on the basis of the percentage of its money that goes to the cause for which it exists.  “We cannot measure efficacy of spend by looking purely at the ratio of overheads to programme costs,” she says.  I was curious as to how someone could not just be wrong, but absolutely, totally, dead wrong about such a subject …. and then I learned that she is a former Bain consultant with a Harvard MBA and it made more sense.  Fundamental lean principles are simply not part of her intellectual make-up.

In fact, the percentage of their money that goes to creating value for customers is the overarching measure of not just charities, but every organization.”

Michel Baudin‘s insight:

While I agree with Bill on measuring a charity by the percentage of its money that goes to the cause for which it exists, I don’t follow him in when he chides Levkoff Lynn for saying that P&G is not purely a manufacturing business but also a marketing giant.

While I am not familiar with P&G, I have consulted in the past for a competitor of theirs in detergents and personal products, and was told that, in this business, if you stop promoting a brand, it dies in six months. I don’t know whether this hypothesis has ever been tested, but the managers held it to be self-evident.

Even is you own a well-known brand in a mature market, you must keep advertising it, offering special discounts, and including toys in boxes. It is a massive direct expense, and it affects the manufacturing process, because the promotional materials are actually more difficult to procure and  have longer lead times than the raw materials used to make the product.

See on www.idatix.com

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Aug 10 2013

Guidelines for Fast Lean Transformation | M. Zinser & D. Ryeson | HBR Blog

See on Scoop.it – lean manufacturing
One of the most common mistakes that companies make when embarking on a lean program is trying to do too much at once. These “boil-the-ocean” initiatives are long, costly and often end up stalling under the weight of their own…

 

Michel Baudin‘s insight:

Scoop It just brough my attention to this 2 1/2-year old article by BCG consultants Michael Zinser and David Ryeson. Their key point is that a successful Lean implementation must start with a small number of well-chosen, pilot projects, and I agree.

I do, however, part company with them on two other issues. First, they only speak the language of money, relentlessly bringing up costs, savings,  payoffs, metrics and incentives. I understand that this language is familiar and attractive to top management.

The article only cites examples of improvements that have a direct economic impact, but there are many aspects of Lean for which the relationship is indirect. Scoring a goal in tonight’s game has a direct impact on performance; building a championship team doesn’t.

Which brings me to my second disagreement with the authors:  there is no consideration in their article of the need to develop the organization’s technical and managerial skills. They are just assumed to be there.

Lean is about developing a team that is able to compete at the highest level in your industry. If you already have such a team, you are probably not looking to implement Lean. If you don’t have it, you can’t start projects as if you did. Instead, you have to focus on projects that your team can do today and that will start it on its way. The biggest payoff and the practically possible do not always match.

This perspective is missing in their guidelines.

See on blogs.hbr.org

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Aug 1 2013

How to avoid human mistakes in production? The Lean approach | Renaud Anjoran

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Human operators make mistakes. But there are solutions: adopting the right philosophy, mistake-proofing each process, and self-inspection. (How to avoid human mistakes in production? The lean approach.

 

Michel Baudin‘s insight:

Renaud Anjoran shares his experience of mistake-proofing in Chinese factories, and quotes “Lean Assembly.” Thanks.

See on www.qualityinspection.org

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Jul 26 2013

Australia’s Lean Trojan | Troy Taylor

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Not all RTO’s are doing a great job, read this post and find out why your selection will make all the difference.

 

Michel Baudin‘s insight:

An Australian version of Certification-Shmertification!

See on www.businessleaners.com

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Jul 9 2013

Betting on Lean, or …. Analytics versus Empowerment | Bill Waddell

See on Scoop.it – lean manufacturing

“Management is all about playing the odds. […]  In operations, calculate lot sizes, generate forecasts and set quality standards with enough data and increasingly sophisticated algorithms and statistical methods and you will increase the chances of coming close enough.  At least that is the theory, and the hope.

This is the basic premise of big data and ERP.  With point of sale scanning, RFID, smart phones and all of the other data collecting technologies increasingly in use, the data to feed the engines is more and more available.  The potential and the lure of the data driven, analytical approach to finding the center line and getting more decisions closer to correctness is growing.

The other approach is empowered people.  Recognizing that management cannot be involved in every one of the individual customer interactions and operational, situational, tiny decisions, those calls are left to the people on the spot.  They are expected to rely on their knowledge, understanding of company values and goals, and the information available to them in very real time to decide what to do.[…] The basic question is whether empowered people will get it right more often than big computer.”

Michel Baudin‘s insight:

In this article, Bill Waddell presents the data-driven approach to management decision making as contradictory to people empowerment. I do not see these as mutually exclusive.

In 1993, there was a group within Toyota’s logistics organization in the US that, based on weather data, thought that the Mississippi might flood the railroad routes used to ship parts from the Midwest to the NUMMI plant in California. Four days before the flood, they reserved all the trucking available in the Chicago area, for the daily cost of 6 minutes of production at NUMMI. When the flood hit, they were able to ship the parts by truck around the flood zone, and NUMMI didn’t miss a beat.

This is what a good data scientist  does.

In Numbersense, Kaiser Fung points out that data analysis isn’t just about the data, but also about the assumptions people make about it. As an example, he points out the Republican polling fiasco of the 2012 election, as being due to the combination of flawed data collection and equally flawed modeling.

In other words, it’s not a computer that comes up with answers from data, but a human being, and the quality of these answers depends as much on the human analyst’s understanding of the underlying reality as it does on the ability to collect clicks from the web or transactions from point-of-sale systems.

Good data analysis does not require petabytes of data. In statistics, a small sample is 10 points; a large sample, 100 points. The difference matters because, with small samples, there are many convenient approximations that you cannot make. But 100 points is plenty for these approximations to work.

With millions of points, the tiniest wiggle in your data will show overwhelming significance in any statistical test, which means that these test are not much use in that context. To figure out what this tiny wiggle is telling you about reality, however, you still need to understand the world the data is coming from.

I don’t see an opposition between relying on people and relying on data, because, whether you realize it or not, you are never relying on data, only on people’s ability to make sense of it.

See on www.idatix.com

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Jul 3 2013

The Porsche Lean Story | Competitive Advantage via Quantitative Methods

See on Scoop.it – lean manufacturing

Michel Baudin‘s insight:

Along with information about the history of Porsche’s turnaround since the early 1990s, this article contains strange statements about, for example, Lean Manufacturing being “Non-Quantitative,” which must make Ohno and Shingo turn over in their graves.

It also contains the doubtful statement that “the costs of warehousing excess inventory are a hundred of times more expensive than a delay caused by a missing part.” The point of Lean Logistics is not to trade-off full warehouses for shortages!

The real paradox of stock is that hoarding parts is ineffective at preventing shortages. The Lean Logistics approach is to keep inventories low but monitored with vigilance, and to respond quickly when floods, tsunamis, or earthquakes disrupt the supply chain.

The article further asserts that “Statistical Process Control” was central to Porsche’s effort, but gives not indication that it is even used. I don’t recall it being mentioned or seeing any trace of it in the Porsche plant in Leipzig two years ago.

See on cavqm.blogspot.co.uk

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