We struggle to set target conditions. Is there a system to do so?
Dear Gemba Coach,
We work with Toyota Kata, which we find very helpful, but we struggle to set target conditions. Is there a system to do so?
First of all please bear with me as I go on a tangent – I’m not sure what kind of system you’re looking for? A systematic way of setting target conditions, as a formula? In many cases to understand something fully, we need first to backtrack to clarify our assumptions, or risk missing the answer in front of our very eyes. The very notion of “system” reflects how profoundly we are all influenced, even conditioned, by Taylorism.
Frederick Taylor’s intent is clearly stated in his introduction to Principles of Scientific Management in 1911:
“We can see and feel the waste of material things. Awkward, inefficient, or ill-directed movements of men, however, leave nothing visible or tangible behind them. Their appreciation calls for an act of memory, an effort of the imagination. And for this reason, even though our daily loss from this source is greater than from our waste of material things, the one has stirred us deeply, while the other has moved us but little.
As yet there has been no public agitation for “greater national efficiency,” no meetings have been called to consider how this is to be brought about. And still there are signs that the need for greater efficiency is widely felt.
The search for better, for more competent men, from the presidents of our great companies down to our household servants, was never more vigorous than it is now. And more than ever before is the demand for competent men in excess of the supply.
What we are all looking for, however, is the readymade, competent man; the man whom some one else has trained. It is only when we fully realize that our duty, as well as our opportunity, lies in systematically cooperating to train and to make this competent man, instead of in hunting for a man whom some one else has trained, that we shall be on the road to national efficiency.
In the past the prevailing idea has been well expressed in the saying that “Captains of industry are born, not made”; and the theory has been that if one could get the right man, methods could be safely left to him. In the future it will be appreciated that our leaders must be trained right as well as born right, and that no great man can (with the old system of personal management) hope to compete with a number of ordinary men who have been properly organized so as efficiently to cooperate.
In the past the man has been first; in the future the system must be first. This in no sense, however, implies that great men are not needed. On the contrary, the first object of any good system must be that of developing first-class men; and under systematic management the best man rises to the top more certainly and more rapidly than ever before.”
From Taylor to Toyota
Apologies for the long quote, but this is to show how much Taylor’s ideas have percolated within our culture – I for, one, agree with every one of his points, although maybe not with his extreme conclusion that the system must be first.
One thing Toyota has taught us is that it’s all about people! In one of the best illustrations of kaizen ever, as seen in this video, http://www.youtube.com/watch?v=EedMmMedj3M, a young lady who is part of the kaizen states her belief that “if you have a good system the work kind of takes care of itself”. This we ALL believe. But what actually happens in the video is much closer to Mike’s kata http://fr.slideshare.net/mike734/kata-walks than actually setting up a system – the team identifies a problem and then sets to solve it, and improves the situation by a multiplicity of small improvements and ideas – something the “system” would never give you.
In my experience, there is no systematic way of defining target conditions in any given situation – defining target condition is precisely part of the iterative work needed to do with the management and the teams to actually understand what the problem really is. There are, however, rule-of-thumb trick that can help in clarifying the target condition on the gemba.
I was on a gemba yesterday with the Ops VP of a service company as we toured kaizens. As we see in Toyota’s meal per hours clip, the discussions went back and forth on a number of topics:
- What is the overall business challenge? What problems does the business need to resolve? In the case of the service company, one overall issue was tough market pressure on price, which made competition on quality even more of a challenge as the only way to preserve price was to demonstrate superior service as well as lowering costs. In the video, the challenge is stated as “having one last box and seven families to feed on the line – that kills me.” Then “I like to put smiles on people’s faces.”
- What is the local problem you’re trying to resolve? In the case of the service company, these are missed interventions because the technician misses either a part or critical data. This is both poor quality and an cost overrun on operations. In the clip’s case it’s the number of boxes per truck against the demand from families. These are very practical problems that we call agree on.
- How will you measure this problem? There, it often gets tricky. Measuring the problem is not the same as tracking activity; it’s measuring the gap between what we’d like and what we have. There is no set way to come up with a good measure, and it is part of the thinking to have the teams and management discuss back and forth until they can agree on a practical measure (it has to be practical or it won’t be used) that kinda sorta represents the problem. In service, it was counting the times the technician could not do the intervention. In the clip, the number of boxes in the truck, the time it takes to prepare the boxes and then the time it takes to hand them over to hungry families.
- How do you go about solving the problem? This again, is not systematic, it’s about working out the causes, sometimes trivial, sometimes deep, or sometimes simply outside the box. This is where the “muda, muri, mura” or “waste, overburden and unlevelness” heuristic is so useful – these basic rules-of-thumb are prompters to look into what is going wrong and what could be fixed in very practical ways. In the service case, the un-levelness of the parts supply to the technician is an obvious culprit – then the overburden imposed on the system by unusual requests manually put in to the system, which screws up the operations and so on.
- Did it work or not? Many problems have a marginally results curve – basically, you’ve found one cause, which delivers – say you’ve taken some air out of the boxes so you can now put more boxes in one truck – but at some point progress is stopped. What tends to happen is that the kaizen itself will have created new conditions which require new analysis. It’s tough for teams because they now have to backtrack and rethink their causal analysis to be able to go forward again.
Targeting Target Conditions
I agree with Mike in that there is not a formulaic way to solve any of theses steps, other than observation and discussion, and, to my mind, this is the great thing about lean – this is where you engage with people, and they engage with their work, this is the fun of creation and being human – it’s NOT a system!
Target conditions: well, once you have an agreed measure in place there is no system, but a few tricks:
- Target 0 or 100% - go to extremes: what would it look like to solve the problem completely?
- 50% improvement – when in doubt, always a good stretch target
- The best day every day: in every data set, normal variation shows good days and bad days. The best day every day is a reasonable target, and as things improve and we get closer to the best day of two months ago, luck will have it that a new best day has appeared and so on.
But none of this is as important as constantly picking on the fundamental question of: are we solving the right problem? Are we trying to improve something that should simply be eliminated? Are we fixing old solutions when we should be exploring new ones? I am currently faced with this issue often concerning the impact of Big Data and open source manufacturing on many aspects of every product. At the end of the day, studying the link between the practical actions of kaizen and the formulation of the larger challenges is the key to meaningful target conditions. It’s not a system, it’s a practice.
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