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道,领导也。领导必需要不断呼唤,教导下属以及以身作则。下属的过和错皆因领导懒惰。

 
 
 

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Chapter 23: Speed losses  

2012-06-24 11:46:21|  分类: Buffer Mentality |  标签: |举报 |字号 订阅

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Late in the afternoon on May 22, 2008 John Zeneski asked me this question, “In the definition of Operation Equipment Efficiency, it mentioned speed losses and quality losses. Can you share with me with real life examples how these two factors take effect on the OEE?”

I replied, “John, you are asking me a very tough question. Let’s take on to discuss the first factor, that is, speed losses.”

“Okay, let’s begin with speed losses,” said John.

I said, "John, this is going to be a long and boring session. Do you have the patience to listen to my illustrations?”

John replied, “Of course I have the patience. It is very kind of you to share with me your experiences.”  

I explained, “In 1911, the founder of scientific management, American industrial engineer, Federick W. Taylor published a theory, “Scientific management theory” in The American Society of Mechanical Engineers. In it he mentioned there are four principles. The first principle is practical scientific management theory. The second principle is selection of workers through scientific means. The third principle is scientific method in workers’ coaching and training. The fourth principle is management and workers must work together closely.

Let us drop the second to fourth principles from our discussion. What he meant by practical scientific management theory is based on the analysis of motion study. Every process step or task takes a specific amount of time to complete in one cycle. This is called, standard time. With the establishment of the standard time for all the tasks, an industrial engineer can execute the power of scientific management.

However it is a pity. Not many companies hire industrial engineers. Even if an industrial engineer was hired but he could not find time to establish the standard time for all the process steps and therefore, he cannot unleash the power of scientific management.

What is standard time?

For example, if a task takes a standard time of 2 seconds to complete the assembly of a unit of product, in an hour, 1,800 units (3600 seconds per hour divided by 2 seconds per unit) will be assembled. In an 8-hour shift, a worker can assemble 14,400 units of the product.

What if a company does not establish the standard time for its processes? What could be the most likely scenario? Speed losses ensue.

Following are three examples commonly seen in most companies and the fourth example is an illustration of a company that avoided succumbing to speed losses.”

Let me hear of your first illustration of how standard time plays a part in speed losses,” said John.

 

Illustration #1: when the company takes on a new order

 

I explained, “Everyone in the company will be overjoyed when the company lands a big order. The factory will be busy for quite a while and the employees will be expecting good company performance bonuses at the end of the year. Of course only if this increased in order leads to good profits.

Increased orders certainly lead to higher revenue. But not all employees are aware that increased revenue does not necessarily mean more profits. It depends on how well the total cost of production is contained. One of the key costs is labor cost.

More often than not, upon receiving a big order, the first thing the management does is to assess whether there is sufficient capacity to produce them or not. In terms of machinery, most companies will not be in the position to increase machine capacity because the purchase of machine generally requires a very long lead time.

In most situations, the increase in orders will be catered by running the machines through overtime or over the weekends.

If an operation is labor-intensive, the likelihood of increasing the number of workers is very high. And quite rapidly, more production workers can be hired and trained to cope with the increased in order. So the hiring order goes out. More workers will be hired.

But how does the management arrive at the number of workers to hire? This is one of the most likely scenarios.

The factory is producing, say, 10 million units per month now. The new order will increase the factory loading by another 2 million units per month over the next six months (a 20% increase). It is a straightforward, mathematical decision: Increase the number of workers by 20 percent to accommodate the 20% increase in orders. On the surface, there is no error in the computations and in most cases the instruction is to hire 20% more workers.

Six months later the company’s orders dropped down back to 10 million units per month. What do you think the management will do? Will they fire or retrench the excess 20 percent of the now bloated workforce?

The answer is usually, “No”. The company will find ways to re-deploy the excess workers so that everyone seems to be busy at work.

Having more people is not easy to be seen as a problem. The workers simply work at a slower speed of about 80 percent of the old speed. This is speed losses. A loss as a result of having more workers on hand means having more available time than is necessary.

Slowing down in working speed obviously means more time is required than necessary to complete the same amount of work. A lesser known concept to describe this situation is: Work expands with time. And the bloated workforce is now seen as capable of producing 10 million units per month.

A few months down the road, another big order in the magnitude of another 2 million units per month comes in. Do you think the company will go out to the job market and hire more workers?

If the company has not been monitoring its labor productivity, quite likely it will go out and hire again. Its management has very short memory and it has quickly forgotten that recently it had to re-deploy its excess workers.  

Let’s go back in time to when the first order of 2 million units per month was brought in. Does the company actually have to hire more workers then? The answer lies with the production speed of its workforce. If the workers are already working at their normal speed at 100 percent efficiency, just to produce 10 million units per month, fine, it is alright to go ahead to hire the extra workers.

What if the normal speed of the total workers is more than able to produce above 12 million units per month? Then there was no need to go out and hire additional workers. The question, then, was whether the company knew the exact production capacity of its current workforce at their normal operating speed or not.

In other words, does it monitor its labor productivity measured under circumstance where the workers work at a normal pace and not at the speed loss condition?

If the answer is “yes”, it should know exactly how many workers to hire or not at all.

If not, there is no way it can get to know its actual production capacity. The decision to hire more workers is certainly affirmative and the actual number of workers hired could be anyone’s guess; any number above or below 20 percent.”

John nodded his head and said, “Yes, it is indeed difficult to measure the productivity level of the workers. What is the normal pace of working? It is quite difficult to determine what is considered as the normal pace.”

I said, “Let’s not debate how an industrial engineer determines the standard time. Let me discuss with you the second example.”

 

Illustration #2: Determination of worker normal capacity

 

I explained, “Let’s go back to the example where the current pool of workers is assumed to be working at 100 percent efficiency when they were producing 10 million units a month. To produce 12 million units a month, there is a genuine need to increase the manpower by 20 percent or to add overtime hours that is equal to 20 percent of the normal working hours. In this case, assume the company chose to employ 20 percent more workers.

Six months later the demand went down back to 10 million units per month. These 20 percent excess workers, more often than not, will not be retrenched. Now with 20 percent more workers than necessary to produce 10 million units per month, the workers know very well they can slow down by 20%, thus, lowering their direct labor productivity by 20%.

How does this situation turn out to be the most likely case?

Imagine. If you were to walk the production shop floor, you would never be able to observe that the workers have slowed down in speed. At the time when you were walking down the line, the workers would either speed up if they had already slowed down or just maintain their normal operating speed. There is no chance for you to see them working at a slower speed than the normal pace.

Once you leave the shop floor, the workers will go back to the slower speed. After all, they are telling themselves, ‘What’s the point of maintaining our normal speed and finish our job one and a half hour earlier than the shift end? That could invite the production manager or our production supervisor to query us why we had stopped work early.

Isn’t it much better to work at a more leisure, comfortable, reduced speed that leads to a situation where the day’s job is finished just in time before the end of the day and nobody questions our productivity level?”

In other words, work expands with time. What do I mean by that?

A worker knows how much work she can do in a day’s time. For example, she works a normal shift consisting of 8 hours a day. If her supervisor assigned her 8 hours’ worth of job to do for the day, she knows she has to work at her normal best, at 100 percent efficiency. If her supervisor were to assign 6 hours worth of job, she can slow down to 75 percent efficiency. This is a speed loss of 25%.

In both cases, she finishes her job just before the day ends.

In another likely scenario, the worker will work at her normal speed for a short period of time before slowing down for a considerable period of time. After having noticed her output is behind schedule, she then speeds up a little to catch up with the shortfall. This scenario would be repeated several times in a day. The crux of the point for her is, at the end of the day, she finishes the job assigned to her just on time; irrespective of whether the volume of job could fill up her full eight-hour day or not.

But it is quite an astonishing fact that a vast majority of the companies do not measure their direct labor productivity. They don’t know what level of productivity their workers are currently operating at. Being unable to gauge the current level of productivity, more likely than not, these companies will not set up a productivity improvement plan with projections that tells that it can meet the increased in orders by merely raising the workers’ productivity but instead, they took the easy way out by calling for a manpower expansion hiring plan.

Indeed, I have never come across a company that says it can meet a sudden influx of increase increased orders solely by increasing its workers’ productivity.

An experienced manager understands the concept ‘work expands with time’ very well. He asks for the productivity level of its workers to be measured all the time. That will give the management a very good factual basis to decide whether the increase in orders could be met by raising the level of productivity of the workers or not. In other words, it knows the presence of ample amount of slack time that could be fully utilized to meet the increased in orders.

If the slack time is inefficient to fulfill the increased in orders, the management team could comfortably plan for over-time or prudently go out to the labor market to hire the least number of workers to top up the shortage of manpower to top up a workforce that is already operating at 100 percent productivity.”

John said, “I agree with you. Most managers do not measure the direct labor productivity of its workers. Few had attempted to set up a productivity measurement.

But the way how do you set up a productivity indicator that accurately measures the direct productivity level of the workforce?”

“John, let’s leave the answer to how to measure productivity to chapter 16. Let me continue with the third example,” I said.  

 

Illustration #3: Determination of equipment capacity

 

I continued, “Interestingly, most companies do not have a good grasp of their production capacity. This is because capacity and utilization interweave together and become something dynamic. Let’s ignore the discussion on the utilization factor as this was already explained in chapter 9.

Assume there is no addition of any piece of equipment and the production capacity fluctuates in accordance with the level of manpower assigned to this piece of equipment. That is, the output rate of a piece of equipment is directly related to the level of manpower attending to this piece of equipment. Therefore, its net effective production capacity can vary by quite a big margin. Why?

The single biggest factor that causes this variation is the speed losses. The speed losses factor can range from the low tens in percentage up to close to 100%. In determining the factory capacity, most of us assume the speed loss factor is zero. However, this is not true.

The effective capacity of a piece of equipment can be subsequently brought down by quite a large factor if its speed loss is considerably high due to insufficient number of manpower to keep it running at its highest efficiency level.

The most critical question is, ‘Does the piece of equipment need to be operated at its maximum designed capacity?’ Under most circumstances, the market demand does not justify the piece of equipment to be operated at its maximum capacity.

The next question is at what level of production capacity should it be running in order to meet its demand and yet does not over-produce in order to keep the cost of production at the lowest level?

Let’s take a piece of equipment. The computation of the effective production capacity could be quite complicated. The piece of equipment requires a group of operators to attend to it in order to achieve a pre-set operating speed. If less than the optimum number of operators is assigned to this group, its effective production capacity will be reduced because its speed loss can be considerably high.

A more dynamic management team would know the different production capacities under different manpower manning scenarios. Depending on the manning level, the speed lose can be determined. Let’s take a simple example of a piece of bottling equipment.

Five operators assigned to a piece of equipment forming a work group of five can produce 100 units per hour. A work group of four operators can produce 85 units per hour. Three operators in a work group can produce 70 units per hour. Two operators forming a work group can produce 50 units per hour and we assume it is not possible to operate the equipment with just one operator.

Thus, a decision to add the number of operators to man a piece of equipment is a straight forward calculation. In fact, given a specific ratio of man-to-machine relationship, the management should be expecting a certain level of output rate based on a specific man-to-machine ratio and thereby, accepting a certain factor of speed losses.


Figure 23-1: Man to machine manning ratio

Number of operators

Unit produced/ hour

5

100

4

85

3

70

2

50

1

0

 

However, if management did not monitor the productivity of a piece of equipment closely in accordance with its right level of man-to-machine ratio, despite the assumed speed loss, the machine could be severely under-utilized leading to much lower than expected production capacity at the said level of manning.

For example, four operators in the group should be producing 85 units per hour. If they are producing goods at 65 units per hour, the productivity level is only at 77% efficiency. Therefore, to meet the 20 percent increase in orders, without adding any resources the existing four operators should be able to cope by operating the piece of equipment at close to 92% efficiency (120% x 77%).

If the management had not been monitoring the productivity of a piece of equipment closely, the production manager might have gotten his way and asked for an increase in manpower: from four to five operators in a workgroup. After all, adding another operator is equal to a 20% increase in manpower. This is a number that fits in nicely with the 20% increase in orders.

The correct practice is the current level of productivity of the piece of equipment should be computed first before granting the request to increase manpower. If the productivity level is already met at the said level of speed losses, there is no need to add production capacity using the next higher level of man-to-machine ratio. The speed loss must be fully made used of first to fulfill the increased in orders.”

John exclaimed, “The introduction of a man-to-machine ratio makes speed loss a given factor. However, it makes the determination of the utilization factor and the productivity so much more complex. In this case, speed losses must be factored in to arrive at a much lower OEE.”

“Yes, you are right,” I replied, “However, most managers prefer the easy way out by simply factoring in the speed losses. Once a certain level of speed loss is used to determine the net reduction in OEE, most often than not, it is not being reviewed again.

Unless the service of an industrial engineer is employed, the management can whimsically covers up its buffer mentality by sticking to the same old level of speed loss. Under this complex operating condition, I assume there is a need for an experienced industrial engineer to carry out time studies in order to set the right level of productivity expected from the several different levels of man-to-machine ratio.

Let me continue with an example of an excellent CEO who understood the presence of buffer mentality among his subordinates that had adversely hindered the operations from picking up speed to the desired productivity level.”

“Okay,” said John, “At least I can see a good example how the utilization factor is being applied correctly.”   

 

Illustration #4: Investment in a steel mill

 

I explained, “In NatSteel Ltd, a Singapore steel reinforcement bar (abbreviation, rebars) rolling mill operation, the management had asked for an investment budget of $46 million Singapore dollars to upgrade one of its three rolling mills. It had set itself a target for the new rolling mill #5 to reach a rolling speed of 100 feet per second based on the production of hot-rolled rebars at diameter size 16 millimeter.

Compared to the speed of the old equipment which was operating at about 70 feet per second, the management had estimated that the mill shall reduce its cost of production by a certain number of dollars of savings per ton of rebars produced. The investment was approved.

One year later, the new mill was fully commissioned and it had been running for about six months. But it was not hitting the designed rolling speed of 100 feet per second[1]. Its productivity level was a mere 85% of the designed rolling speed. The CEO of the mill became jumpy and decided that it was about time he had to camp over-night in the rolling mill to make sure that all efforts were thrown in to achieve a productivity level consistently at 100 percent of the designed rolling speed.

Of course the workers do not want the CEO to be breathing down their backs at the shop floor. Three months later, the rolling mill was running at the designed rolling speed.

A plan to revamp the rolling mill #4 was made half a year later. Again a target was set for the second new set of rolling mill. Upon completion of this upgrading of this rolling mill, the desired productivity level was met in 3 months time. This was because the workers had learnt a lesson from the upgrading of the first mill #5.

Rolling mill #1 was then shutdown and transplanted to Vietnam.

This is a rare case of an aggressive management that used an accurate indicator of its plant’s productivity as a measure to gauge the success of in its capital investment.”

John exclaimed, “Indeed. This is an excellent management team who knows how to measure its plant productivity accurately and had pushed its workers to achieve the desired level of productivity.

Speed loss was not tolerated.”


 



[1] This is a true figure.

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