One of the ongoing discussions I have on the genba, is the discussion on what productivity is, and how to measure it to spark and track improvement in productivity. Productivity is not the same as efficiency. Unfortunately, people commonly throw both terms into the conversation without properly defining what they mean with these words. During my tenure at a global tier one automotive supplier in the early 2000s, management decided to introduce a new measure, called Kosu (or Kousou). I was skeptical at the beginning but have become a huge fan since. I’ll try to explain how Kosu can help you to improve productivity.
Productivity is generally defined as the rate of output per unit of input or resource. Or inversely, the rate at which inputs are converted into, or resources are used to create outputs. We’ll come back to the relevance of this inversion later in this post. Productivity, therefore, links resources and inputs to (valuable) outputs.
I will try to illustrate the concept using an example from agriculture. For instance, the cucumber yield of a hectare (100 ares or 10,000 square meters) in the Netherlands rose from 106 tons to 810 tons a year from 1950 to 2015 [CBS, 2017]. Inversely, in 2015, we used 12.3 m2/to of cucumbers whereas, in 1950, we still required 94.3 m2/to. This represents a massive 86.9% reduction in the required square meters (the resource) for the same amount of output! Productivity, as you can see, is measured in units.
Efficiency, however, is something different. Unfortunately, many use it in interchangeably with the term productivity. Efficiency, however, is the comparison of what is actually produced or performed with what can be achieved with the same consumption of resources. Using the same agricultural example, the average yield of cucumbers in the EU is 53.2 tons/hectare (or 188.0 m2/to). This is only 6.5% of what the yield is in the Netherlands. Efficiency, as shown in the example, is measured as a percent. And it depends upon the reference that is taken.
I prefer discussing productivity. Productivity is about the cost of a (valuable) output. Particularly when we use a measure that enables us to speak about the resources and inputs used in producing one unit of output. This is also one of the reasons why I prefer the inverted way of looking at typical productivity measures. But more will follow.
In the early 2000s, while working as a Supply Chain and Logistics director for a major tier one automotive supplier, I was introduced to Kosu, or even phonetically better written as “Kou-Sou”. It was a Japanese term consisting of two characters, 工, signifying a (factory) worker, and 数, meaning number or amount. Joint, the term is used to indicate the number of labor hours per unit of output. As you can see, it is the inverted way of looking at productivity, be it specifically aimed at direct labor. But the concept can, of course, be used for any input or resource used in production.
Simply stated, Kosu takes the number of workers in an area and multiplies this number with the number of net hours to get to the total number of (net) labor hours in a certain period. You then divide this number by the total number of good units produced by the same area in the same period. For instance, when 4 people for 60 minutes produce 40 good units, Kosu equals 4*60 / 40 = 6 min/unit. Please note that Kosu is not the same as cycle time, which in this case is 60 / 40 = 1.5 min.
Kosu Changes the Shop Floor Dialogue
At the time Kosu was introduced in our plants, I was rather skeptical to be honest. Mathematically, it was just the inverse of what we were already using in our 140 or so plants worldwide (as so many plants still do today), viz. the number of parts per person per hour (PPPH). I was questioning the value of asking 140 plants with a multitude of areas in each plant to change their current indicator from PPPH to 1/PPPH… Just imagine the effort and explanations required…
But I started to see significant differences in behavior, plus several other advantages. First, when you are on the genba, and speak about PPPH, you create the image of working faster; of producing more. But more is not always better. It is not about more volume and creating overproduction; Lean is about producing the right numbers.
PPPH also created the wrong dialogue between frontline supervisors and operators. The subject of the discussion when you use PPPH is volume and speed. Operators feel being put under pressure to work faster and to produce more volume, whatever the quality and with whatever effort. It creates tension on the shop floor, it leads to poorer quality and it focuses the discussion on the always required productivity improvements on the operator.
Kosu changes this dialogue. You don’t ask why volume was missed. Instead, you ask where labor time was lost when Kosu deteriorates. It almost naturally eliminates volume from the discussion as Kosu is per unit of good output. Kosu helps you focus on wasted effort in the process in which the operator works; it helps people to investigate the system of work. It changes the mentality from doing something faster to doing something in less time; a subtle but important difference!
To be honest, I underestimated the impact of such a change that arguably did not make a lot of sense, mathematically. But in fact, it was very well worth the effort across the globe!
Another clear advantage of Kosu, I found, was the fact that productivity now was clearly related to the direct labor cost of our products. It very nicely aligned part of our cost reduction efforts to the way we spoke about productivity and the eradication of waste from our system of work on the shop floor. The importance of this alignment between top management concerns and everyday losses on the shop floor cannot be underestimated!
What I also liked about Kosu, is that it is an indicator that has to ultimately go to zero to indicate perfection. In that sense, it is like measures in quality such as ppm and in delivery as dpm (see my post on measuring delivery reliability: http://dumontis.com/2016/09/measuring-delivery-reliability/). I like it when all measures on the team board have to go in the same direction to show improvement and that – as a manager – I don’t have to first investigate what it exactly is that I’m looking at, and whether up or down is better.
So, Why Not Kosu?
I hope this post makes you think about the productivity measures that you use in your operations. Are you actually measuring something that makes sense? Is your indicator simple and understandable by all? And does it align your efforts with your goals? Are you clear on what, in fact, it is that you are actually measuring? Give Kosu a thought. I have been amazed by its effects and would recommend anyone to apply the concept to its productivity efforts!
the concept seems similar to the Harbour Hours per vehicle analysis performed annually within the automotive industry. Is this correct?
HPV, although a similar concept, is not exactly the same as Kosu. HPV generally includes all paid hours of the entire workforce of a production plant. So not only the value-adding activities of the direct employees at the production lines, but also their non-productive times such as breaks or personal allowances (insofar these are paid). Even indirect employees such as logisticians, maintenance technicians or quality engineers contribute with their working hours to an increase of the HPV. The same applies to managers, planning engineers and administrative staff. Furthermore, HPV is typically used in benchmarking plants.
Kosu, on the other hand, only takes net, direct hours and is used on an hour-by-hour basis to manage abnormalities during production.
Hope this helps to clarify.
very insightful article, indeed. However couple of observations.
1- Kosu behavior could be the same like traditional productivity in term of spurring more products leading to overproduction. Using your example above (6 min/ unit), the management may order to decrease kosu through increasing the denominator (from 40 units to 50 units, for example) with same number of operators. Or other bad behavior is labor reduction. Management may ask to decrease kosu through decreasing number of labor (from 4 to 3, for example). So How can you mitigate the risk of abusing kosu? I feel it has same disadvantages like productivity.
2- I can understand using Kosu in labor intensive environment or discrete industry. But in process industry, it is meaningless as process industry is bounded by machines rates. What do you think?
Thanks in advance
Kosu is, indeed, a labor-related productivity measure. So it is primarily applied in labor-intensive environments, like assembly. However, I am currently also using it to review the number of line operators in a highly automated environment, next to downtime, speed losses, and defects. It is not meaningless, as even in these environments, you can use it to plan labor hour requirements.
And to your first remark: of course, if you want to, you can always ‘game’ any system and any measurement. My experience, however, is as described: it does change the perspective, although mathematically there is no real difference of course.
Thanks Rob for your reply.
Another interesting point, who sets the kosu target? I came across Japanese study for Toyota in Indonesia, in which the author claims that cost control department is responsible for setting the target. It is interesting point. For review the study, you can download from here:
P.S.: The Kosu is mentioned is several places. It is a big study conducted and published in 1999 by Prof. Keisuke Nakamura
In my experience, kosu targets were not set by cost control departments. They were set based upon a gemba study of the actual kosu to set the so-called operational target, which can be roughly translated as the mean of the actually measured kosu, with statistically out-of-control points eliminated. Cost control targets could very well lead to continuously detecting abnormalities; the goal is to stabilize kosu by detecting and eliminating out-of-control situations (abnormalities), and to then lower the operational target step-by-step. This post may be helpful in this regard: https://dumontis.com/2018/02/setting-targets/