Jun 15 2014
The Goals That Matter: SQDCM | Mark Graban
See on Scoop.it – lean manufacturing
Blog post at Lean Blog : “Today is the start of the 2014 World Cup, which means much of the world will be talking about goals.I’m not really a soccer, I mean football, fan but I’m all for goals. In the Lean management system, we generally have five high-level goals. These were the goals taught to us in the auto industry, where I started my career, and they apply in healthcare.”
As I learned it, it was “Quality, Cost, Delivery, Safety, and Morale” -(QCDSM) rather than SQDCM. I am not sure the order matters that much. The rationale for grouping Quality, Cost, and Delivery is that they matter to customers, while Safety and Morale are internal issues of your organization, visible to customers only to the extent that they affect the other three.
They are actually dimensions of performance rather than goals. “Safety,” by itself, is not a goal; operating the safest plants in your industry is a goal. In management as taught in school, if you set this goal, you have to be able to assess how far you are from it and to tell when you have reached it. It means translating this goal into objectives that are quantified in metrics.
In this spirit, you decide to track, say, the number of consecutive days without lost time accidents, and the game begins. First, minor cuts and bruises, or repetitive stress, don’t count because they don’t result in the victims taking time off. Then, when a sleeve snagged by a machine pulls an operator’s hand into molten aluminum, the victim is blamed for hurting the plant’s performance.
Similar stories can be told about Quality, Cost, Delivery and Morale, and the recent scandal in the US Veterans’ Administration hospitals shows how far managers will go to fix their metrics.
To avoid this, you need to reduce metrics to their proper role of providing information an possibly generating alarms. In health care, you may measure patients’ temperature to detect an outbreak of fever, but you don’t measure doctors by their ability to keep the temperature of their patients under 102°F, with sanctions if they fail.
Likewise, on a production shop floor, the occurrence of incidents is a signal that you need to act. Then you improve safety by eliminating risks like oil on the floor, frayed cables, sharp corners on machines, unmarked transportation aisles, or inappropriate motions in operator jobs. You don’t make the workplace safer not by just rating managers based on metrics.
In summary, I don’t see anything wrong with SQDCM as a list. It covers all the dimensions of performance that you need to worry about in manufacturing operations, as well as many service operations. Mark uses it in health care, but it appears equally relevant in, say, car rental or restaurants. I don’t see it as universal, in that I don’t think it is sufficient in, for example, research and development.
And, in practice, focusing on SQDCM easily degenerates into a metrics game.
See on www.leanblog.org
Aug 8 2014
The meaning(s) of “random”
In this sense, a side-loading truck provides random access to its load, while a back-loading truck provides sequential access.
While these uses of random are common, they have nothing to do with probability or statistics, and it’s no problem as long as the context is clear. In discussion of quality management or production control, on the other hand, randomness is connected with the application of models from probability and statistics, and misunderstanding it as a technical term leads to mistakes.
In factories, the only example I ever saw of Control Charts used as recommended in the literature was in a ceramics plant that was firing thin rectangular plates for use as electronic substrates in batches of 5,000 in a tunnel kiln. They took dimensional measurements on plates prior to firing, as a control on the stamping machine used to cut them, and they made adjustments to the machine settings if control limits were crossed. They did not measure every one of the 5,000 plates on a wagon. The operator explained to us that he took measurements on a “random sample.”
“And how do you take random samples?” I asked.
“Oh! I just pick here and there,” the operator said, pointing to a kiln wagon.
That was the end of the conversation. One of the first things I remember learning when studying statistics was that picking “here and there” did not generate a random sample. A random sample is one in which every unit in the population has an equal probability of being selected, and it doesn’t happen with humans acting arbitrarily.
A common human pattern, for example, is to refrain from picking two neighboring units in succession. A true random sampler does not know where the previous pick took place and selects the unit next to it with the same probability as any other. This is done by having a system select a location based on a random number generator, and direct the operator to it.
This meaning of the word “random” does not carry over to other uses even in probability theory. A mistake that is frequently encountered in discussions of quality is the idea that a random variable is one for which all values are equally likely. What makes a variable random is that probabilities can be attached to values or sets of values in some fashion; it does not have to be uniform. One value can have a 90% probability while all other values share the remaining 10%, and it is still a random variable.
When you say of a phenomenon that it is random, technically, it means that it is amenable to modeling using probability theory. Some real phenomena do not need it, because they are deterministic: you insert the key into the lock and it opens, or you turn on a kettle and you have boiling water. Based on your input, you know what the outcome will be. There is no need to consider multiple outcomes and assign them probabilities.
There are other phenomena that vary so much, or on which you know so little, that you can’t use probability theory. They are called by a variety of names; I use uncertain. Earthquakes, financial crises, or wars can be generically expected to happen but cannot be specifically predicted. You apply earthquake engineering to construction in Japan or California, but you don’t leave Fukushima or San Francisco based on a prediction that an earthquake will hit tomorrow, because no one knows how to make such a prediction.
Between the two extremes of deterministic and uncertain phenomena is the domain of randomness, where you can apply probabilistic models to estimate the most likely outcome, predict a range of outcomes, or detect when a system has shifted. It includes fluctuations in the critical dimensions of a product or in its daily demand.
The boundaries between the deterministic, random and uncertain domains are fuzzy. Which perspective you apply to a particular phenomenon is a judgement call, and depends on your needs. According to Nate Silver, over the past 20 years, daily weather has transitioned from uncertain to random, and forecasters could give you accurate probabilities that it will rain today. On the air, they overstate the probability of rain, because a wrong rain forecast elicits fewer viewer complaints than a wrong fair weather forecast. In manufacturing, the length of a rod is deterministic from the assembler’s point of view but random from the perspective of an engineer trying to improve the capability of a cutting machine.
This categorization suggests that that a phenomenon that is almost deterministic is, in some way, “less random” than one that is near uncertainty. But we need a metric of randomness to give a meaning to an expression like “less random.” Shannon’s entropy does the job. It is not defined for every probabilistic model but, where you can calculate it, it works. It is zero for a deterministic phenomenon, and rises to a maximum where all outcomes are equally likely. This brings us back to random sampling. We could more accurately call it “maximum randomness sampling” or “maximum entropy sampling,” but it would take too long.
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By Michel Baudin • Data science, Technology 2 • Tags: Quality, Quality Assurance, Randomness