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论老子

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。

 
 
 

日志

 
 

Chapter 27: Setting productivity indicator  

2012-06-24 11:27:30|  分类: Buffer Mentality |  标签: |举报 |字号 订阅

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On May 22, 2008, I shared with John Zeneski about inaccurate cycle time[1]. He was very impressed with the depth of my explanation about how to go about determining the accuracy of the cycle time that is used in the determination of the production capacity for a group-technology production cell.

John asked, “Eric, assume you have determined the cycle time of a production cell how do you go about measuring its productivity? As far as I know, productivity is a very well understood concept but somehow, its definition is vague and may mean different thing to different people. In other words, it depends on how you define it.”

I explained, “John, you are dead right. A company’s productivity is usually measured as how you want it to be defined as. Do you find it surprising, don’t you?

However, the most critical determining factor of whether a company is going to make good profit or not is almost single-handedly dependent on how the productivity indicator was set. Well, you may not agree with me now. But certainly you would agree with me it is one of the main goals set by the chief executive officer who then uses it to drive the entire organization or workforce forward.

If the productivity goal set is easily achievable, it does not take the workers much effort to achieve it. In other words, they are not challenged.

If the productivity goal set is more aspiring, the workers will have to work harder. In a free market competition, the company that managed to motivate its people to work harder will win in the marketplace, secure more customer orders, gain higher market share, enjoys a bigger profit margin and eventually higher profits.”

“I fully agree with you. Setting the productivity indicator right is one of the most important management responsibilities. Please share with me how would you do it?” John asked.

I continue to explained, “A good example of setting a productivity indicator is to set it off with reference to the 100% mark; just as all primary school students do during their examinations. The 100% percent mark is the best that a student can achieve. Likewise, the 100% productivity level is what the management can achieve at its very best.

Of course, not everybody can achieve the 100% mark all the time. But then hitting close to the 100% mark should be viewed as a consistently excelling performance in the business world. This company had done its best.

What’s more, the actual performance when measured against the 100% level can be used to reveal how big the productivity gap is. The bigger the productivity gap means a mediocre management and hence, there is much more potential opportunity for this company to get its workers together to produce more goods with the current level of resources or with less resources and hence, raises its profit.”

John interrupted me, “That is a neat way to define productivity against a 100% goal line. It makes it very clear to everybody they have to hit the goal of 100% and nothing less. Hmm! It is an intelligent move.” 

I continued with my explanation, “Let’s go back a full step and make ourselves clear about a new concept of how you see a half-empty or half-full glass.

First, setting the productivity with reference to yesteryear’s productivity level is like a glass half-filled with water. A mediocre manager will see it as half full. All he needs to do is to gradually fill it up to the brim. Gradually, I mean. How fast he tops up the remaining half glass of water depends on how hard he wants to drive his workers?

Most often than not, he will add about five percent every year. A reasonable pace of growth, he thinks. But really, this is buffer mentality. Growing the productivity at a comfortable rate instead of aggressively reduces the productivity gap immediately.”

“By the way, which manager doesn’t do so? All of us do precisely that,” John interrupted.

I smiled and continue, “Seldom will there be a manager who sees the glass as half-empty. If he does, he would want to top-up the glass to the brim quickly. As a result he has to drive all his workers to work very hard and march towards the 100% productivity level and accomplishing that goal as soon a possible.

In his mind it seems to him it is rather irresponsible to accept the situation of having a big gap in the current productivity level against the 100% mark and not doing something about it. This is the right attitude.”

Impatient, John asked, “This leads me to exactly what I want to know. How do you set productivity indicators?”

I said, “Let me shared with you several real life examples to illustrate how productivity indicators had been set with the interests of the chief executive officer in mind. Most of these chief executive officers have a strong preference for buffer. Who doesn’t, anyway?

These examples are recalled from my 23 years of working experiences, beginning with an encounter with a general manager who was very proud of his double-digit increased in productivity every year for more than a decade.

These examples will enable you to grasp a better idea on how many productivity indicators had gone wrong as a tool in providing a good measure of the factory or workers’ productivity. They are rather de-motivating though, to your chagrin.”

“Wow! I appreciate your wealth of knowledge in the arena of lean production system or as a matter of fact, productivity,” said John.

 

Illustration #1: Tons per month

 

I explained, “In one of my assignments to improve the productivity of a wire mesh making factory, the general manager told me off. He said, ‘You are too aloof. You came in with a pre-conceived mind about what productivity is. Your unique argument about how to improve productivity does not apply here.

You don’t understand my business. You don’t understand what it is about productivity in my business. For more than a decade, I have been consistently pushing up my productivity by double digit growth every year.’

He was so proud of his accomplishments and he felt belittled when I said I could help him to increase his productivity by 30 percent.

Being a productivity expert who has worked with many different companies, I often hear similar unkind remarks. Yes, he was partially right. He knew a lot about how to improve productivity in his business. He had been doing that for many years and in each year he showed double digit growth in productivity. Great! It was a string of consistent accomplishments of double digit growth that seemed hard to beat.

But his wise boss saw it through. He was falling further and further behind in the competition. The biggest wire mesh maker in Singapore was now more than twice as big as his company in term of sales and more than six times more profitable. He seriously needed help.

Back in the wire mesh factory, the general manager set the factory productivity as the number of tons of wire meshes produced in a month. The factory uses electric welding machines to weld steel wires into a rectangular wire mesh. In the early days the wire mesh machines welded wires of 1.5mm diameter.

Over the years it purchased bigger and better welding machines that weld wire meshes using wires of 2.5mm, 4mm, 6mm, 7mm, 8mm, 9mm, 10mm, 12mm, 13mm, 16mm and 20mm diameters. The cross sectional areas of these wires and its percentage increase in area over the next lowest diameter size are shown in the Figure 27-1 below.  

 

Figure 27-1: Relation between diameter and cross-sectional area

Chapter 27: Setting productivity indicator - 浪里行舟 - 论老子
 For the same length of wire, wires of different diameters will produce different weights. For example, a meter of wire of 2.5mm diameter weighs 278% more than a meter of wire of 1.5mm diameter. A meter length of a piece of wire of 20mm diameter weighs almost 177 times the weight of a meter of wire of 1.5mm diameter. That is 17,700% heavier.

As the market gravitated to consume wire meshes of bigger and bigger diameters, the tonnage of wire meshes produced increase many folds. As long as the general manager goes on to produce wire meshes of bigger diameters the growth rate in the productivity number, that is measured in ton of wire mesh produced per month, should have been more than 100 percent per year and not at a mere low double digit growth rate.

The general manager knew the effect of the diameter size on his productivity formula very well. For this reason, he had chosen tons of wire meshes produced per month as the productivity indicator for his company’s performance. He knew he didn’t need to do anything but one trick. That is, focus on getting the board of directors to agree to purchase bigger and bigger machines to weld wire meshes of bigger wire diameters. He got his way.

For more than a decade, he had fooled his board of directors that he had been doing a great job. Not until one fine day, when his board of directors made a comparison of his performance with the market leader’s performance. They were totally disillusioned by his ‘double-digit growth’ performance. Shortly after, I was called in to assist him to raise his company’s productivity.”

“Most company uses tons of output per month as a key productivity indicator. Never did I suspect it could be used as a buffer to cushion for one’s mediocre performance,” said John.

 

Illustration #2: Dollar value per employee

 

I continued, “In Pepperl & Fuchs Singapore, a German company that manufactures proximity switches in Singapore, the managing director measures its factory productivity by the dollar value produced per direct worker. It directly measures value-added per employee. It seems fine to you, doesn’t it?”

John replied, “Yes, it is. In fact this is one of the commonly used accounting methods to determine the productivity of the workers. It can’t be wrong.”

I smiled and continued with my explanation, “Every year, he sets a target that is much higher than the previous year’s by a substantial margin. Again he sees the glass of water as half full and he needs to fill it up gradually. Without fail, he met his productivity target every year. Of course, the parent company in Germany was very pleased with his performance year after year.

Having gained such high level of confidence in him, the parent company moved more and more products to be produced at the Singapore plant. And more and more of these products were getting more sophisticated and fetch much higher dollar value.

Naturally, more sophisticated products cost more to build because these products require much higher labor content. But these products contributed to a much higher rate of increase in the dollar value than the incremental increased in labor hours required to produce one unit of these products.

Since these products fetch much higher prices, the net profit margin for each unit of product produced by the Singapore plant increases at a much faster rate. These products boosted up the dollar value of the total amount of products shipped back to Germany.

The managing director has chosen an easy way to show improvement in its factory’s productivity indicator. By the way, he must be showing he is doing a great job in managing his factory. Otherwise, he will not be able to bring in more new products over to the Singapore factory and grow the company production volume at a healthy rate.

Back to the business fundamentals, he needs to justify to his parent company that he is more productive than his counterpart in Germany. This is an easy feat. The labor wage in Singapore is many times lower than back in Germany.

To top it all, he showed results proving that he was able to grow his factory productivity at a rate much higher than the parent company could. After all, that was the whole purpose of setting up the Singapore factory.

To his board of directors, he was doing an excellent job in bringing down the cost of production per unit of product produced. He received big fat, fabulous bonuses year after year.

By the way, this managing director could have done much better. He knew he could do a lot more for his company.”

“Choosing a productivity indicator that measures the dollar value per direct worker has always been a good productivity indicator. When consistently applied, it will bring tremendous cost savings to the parent company. This is what out-sourcing is all about. Never did I realize that it can be turned into a soft cushion that provides a good buffer to the incumbent managing director,” said John.

“It is not easy to identify buffer mentality. You need to understand the business. The same set of productivity may mean different thing to different organization. It is easier said than done how to test whether a set of productivity is designed with buffer mentality or not,” I replied. 

“I fully agree with you. You have a pair of very sharp eyes, indeed,” admiring, said John.

 

Illustration #3: Number of tests per hour

 

I continued, “This is a classic example from the inkjet manufacturing division of Hewlett-Packard in Singapore. In one work center in the Quality department, pens (or inkjet pen cartridges) are subjected to many tests in the pen-test laboratory. Some of these tests are: ink drop size, ink drop weight, ink drop velocity, print quality, color quality and resistor life. Different tests require different amount of time to complete. Some tests take a few seconds while other tests may take several hours.

Often some of the test operators complain to their supervisors that they feel it is very unfair to them to have been assigned to their current jobs. They said they have to work almost non-stop while some of their colleagues manning other test equipment nearby were often seen idling with nothing to do.

The supervisors made close observations for a few days and indeed, found that the complaints made by his operators were true and fair statement. He brought up the issue with his manager.

After several rounds of discussion with his manager, his manager concluded the best way to resolve this situation of hugely unbalanced productivity was to start monitoring the productivity of the operators. The result of this productivity measure will be able to reveal which test operations were fairly busy and which ones were not. A subsequent exercise to re-assign the tasks would be able to resolve the complaints. Good strategy.

Two weeks later the test supervisor came out with a productivity indicator. It measured the number of tests conducted by each operator. How was this productivity indicator set? You may want to know the detail.

Every day, each production line would send a sample of pens to the pen-test lab. These pens were segregated into a few groups and distributed among the test operators to subject the pens to the above mentioned tests. Every week, the number of pens tested was collected and compiled into weekly statistics. The test supervisor and test manager monitored this weekly productivity indicator closely.

Two months’ worth of data was collected. After a careful analysis, it indicated that some test operations’ level of efficiency were low while the rest showed they were indeed very busy operations. Armed with this piece of information, the test supervisor made a decision on several test operations to be combined to boost up its low productivity while the busy test operations shall be distributed among more operators.

He called for a meeting among his operators to show his findings and asked for the opinions of his test operators. In his mind he hoped his operators would buy the idea of reassigning some of their tasks to balance out their work load. Everybody should be happy with a more balanced work load, then.

Upon hearing his proposals, however, the operators voiced their rejections strongly. Caught by a surprise, the test supervisor asked them why they couldn’t agree to the task re-assignment. Some said they were already hardly able to cope with the current test operations and now, they were asked to carry out an additional test. That would break their backs. Others said they were perfectly fine at the moment. They could handle their current operations without any further assistance.

In other words, the re-assignment plan proposed by the test supervisor was not going to level out the work load among the operators but will make it worse for the operators who already had a burdening task. On the other hand, it will lighten the load for some whose tasks were already fairly light and should not have any problem at all.

The test supervisor was really puzzled to hear these contradicting feedbacks. I was invited to study what went wrong with this re-assignment exercise.

The test supervisor collected data on the number of tests the operators carry out everyday. For example, an operator may be assigned 100 pens to be tested on two of the test equipment that were placed under her responsibility. If she finishes testing all the 100 pens for both the test operations, she has completed 200 pen-tests. In an eight hour shift, her productivity is computed as 200 pen-tests divided by 8 hour-day which equals to 25 pen-tests per hour.

But the amount of time taken to complete one cycle of test varies widely for different kind of tests. In addition, for some of the tests, the operators just load in the pens and walk away. The test equipment can be left alone to complete the cycle of test. This may takes quite a long while.

For some others tests, the operators have to stand by the side of the test equipment in order to carry out a series of operational steps in order to ensure that the test is concluded successfully. I noticed using number of tests as a common numerator to measure the productivity posed another problem. To make things simple for us to understand, I have tabulated the various tests into a Figure.

 

Figure 27-2: Different tests and measure of output

Chapter 27: Setting productivity indicator - 浪里行舟 - 论老子
Using number of tests as a productivity indicator is certainly not appropriate. For example, only one unit each was used for the drop size, drop weight and drop velocity test. The operator inserts one pen at a time into these test equipment. Over an eight hour period, an operator can test twenty four pens. That translates to three pens tested per hour.

On the other hand, an operator manning the print quality and color quality printers can operates two banks of up to eight printers simultaneously. In terms of number of tests completed, it was the total number of pens tested in all the eight printers. His productivity would easily be eight times higher than that of the productivity of an operator manning the drop size or drop weight and drop velocity test equipment.

A different scenario happens for resistor life testing. An operator is required to insert the pen into the test slot and leaves the resistor life tester to run for days without any interference. His productivity is only a fraction of a pen tested per hour. No wonder the productivity of the test operators was so uneven.

To those who were assigned to an easy test operation, they happily waste their time away while waiting for the test equipment to complete its test cycle. And to those operators who had to work almost non-stop, they gnashed her teeth and thought, ‘Why am I having such a lousy luck. Why am I working so hard while the others are idling away?’

Being unhappy with their plight, they slowed down their pace. Inconspicuously, that led to a low level of productivity.

Indeed, my study showed that these operators’ productivity level was only at the 60% mark. It is far off the 100% efficiency mark even though some of them have been kept busy the whole day long. Therefore, using a poor productivity indicator had caused more harm to the operators’ morale. It had caused the productivity of the whole team to spiral downwards.”

“This is a rather complex situation. How do you resolve that?” asked John.

“Well, let’s leave the answer to the end of this discussion. May I continue with the next example?” I asked.

Though a little disappointed, John nodded his head.

 

Illustration #4: Number of production cycles per day

 

I continued, “In NatSteel Ltd, a Singapore steel mill, scrap metal, pig iron and alloying minerals are charged into a furnace which heats them up into molten steel. The molten steel is then potted from the furnace into a ladle that transports the molten steel to the billet casting machine that transforms the molten steel into square billets of diameter 100 millimeter by 100 millimeter.

The output of one complete cycle of melting steel in the furnace is called one heat. In this particular mill, the average turnaround time for one heat ranged from 55 minutes to 65 minutes. The general manager measured the productivity of the furnace by counting the number of heats it produced in a day. That was about 22 heats per day. For the past few years, the factory had been producing 22 heats per day, plus or minus one heat. It has been a fairly consistent productivity, as this number had shown.

But the problem with measuring the number of heats per day turns out to be quite inconsistent when it is converted to another indicator: the tonnage of steel produced per hour. Some days, the tonnage of steel produced was considerably higher while at other days, the tonnage of steel produced was much lower than projected. The average tonnage of steel per hour varies by quite a large margin.

Sometimes the variation could be as high as 105 to 15% off the average daily output rate. Since the performance of the factory was measured by the total tonnage of steel bars sold, producing the highest tonnage of steel consistently is fundamental to the profitability of the factory and not the number of heats.

Looking back, it was not difficult to maintain a consistent productivity number of 22 heats per day.[2] In the beginning of the day, the workers were given a free hand to decide how much scrap metal to charge into the furnace. The more scrap metal they charged, the higher the tonnage of steel produced. If more heats were to be produced, less scrap metal would be charged into the furnace to reduce the cycle time taken to produce one heat of molten steel.

By watching the cumulative number of heats produced earlier in the day, the furnace supervisor can issue an order of how much scrap metal should be charged into the furnace towards the latter half of the day. Of course, the lesser the amount of scrap metal charged, the lower the final tonnage of molten steel will be produced for that heat. But it takes a shorter time to complete one heat.

A pattern of longer cycle time per heat in the beginning of the day and shorter cycle time per heat towards the end of the day was a daily repeating occurrence. That led to a higher tonnage of steel per heat produced in the beginning of the day and it tapered off to a much lower tonnage of steel per heat towards the end of the day.

After studying the behavior of the steel furnace production process, a recommendation was made to monitor the tonnage of steel produced per hour. The entire team of furnace workers were forced to adopt a totally different strategy to work towards ensuring that every heat consistently produced the said target of tonnage of steel per heat and at the same time, tried to hit a turnaround cycle time per heat of 55 minutes. With this total change of unit of measurement the productivity of the factory increased by more than 10%.”

“It is quite innovative for steel mills to come out with a productivity using the number of heats per day instead of number of tons per hour because it provides the foreman a much wider buffer or ease of meeting the chosen productivity number and not something that directly affects the total tonnage of steel produced even though this is the key measurement that matters to the company’s profitability,” said John.

 

Illustration #5: Number of units per month

 

I continued, “Let’s go back to the classic example of viewing the glass of water as half full in Hewlett-Packard Singapore. The ‘Tiger’ assembly line was commissioned with an initial output volume of just 100 (number has been converted to a baseline figure) pens per day. For the next five years, the targeted daily output volume was pushed to 120, 140, 170, 190 and finally to its best record of 200 pens per day. Without much effort the targets for all five years were comfortably met.

Using this productivity indicator that measures the number of pens produced per day, the team managed to raise its productivity by double digit growth every year. Is this sequel of double digit growth in productivity over five years a consistent sterling performance?

When viewed as a glass of water that is half empty, the answer is negative. Looking back, it had taken the company more than five years to raise its productivity to the true capability of the equipment. This is not spectacular at all.

How does this gradual increase in its level of productivity affect Hewlett-Packard?

In a situation where the sales volume is many times the production output volume churned out from a single set of equipment, the capital investment decision would be how many pieces of such equipment to be purchased. In Hewlett-Packard, within a short period of five years, thirteen ‘Tiger’ production lines were installed world-wide. The question is, ‘Could a lesser number of ‘Tiger’ lines be installed?’

If the management had used a productivity indicator that first gauged the full potential output of the ‘Tiger’ line and set a goal to match this full potential as the target to be met in one year, the market demand could be met by a much lesser number of ‘Tiger’ production lines. Why?

By pushing the workers to achieve the 100% mark in productivity in one year instead of a gradual increment over a period of 5 years, definitely, the total output volume could match the sales demand with a much lesser number of equipment.

That will certainly lead to the purchase of less equipment and thus, a smaller amount of capital investment sunk into installing fewer number of production lines, smaller factory space required, smaller utility bills, less fixed overheads, less workers and etc.”

“Quite unbelievable,” said John, “This management team prefers a slow, gradual improvement in the productivity. It wants to spread the full potential for improvement in productivity over several years. That is buffer mentality to its hilt.”

I nodded my head. In my mind, ‘John is a fast learner.’

 

Illustration #6: Determining the productivity indicator for new machine

 

I continued, “Most companies make decision to purchase the best equipment available from several vendors who continuingly push the most advance set of equipment to the marketplace. Of course, the most technologically advance equipment is usually often determined by the volume of products it can churn out in one hour.

For the same price or slightly higher price, a piece of equipment with the highest output rate is more desirable. The newer equipment, more often than not, is capable of producing a quantum leap in output, many times what the older piece of equipment can produce in an hour.

After the new equipment is installed, it does not take much of its capacity to meet the current sales volume. Often the case, the new equipment is required to operate at about half its capacity or less to satisfy the current sales demand.

Setting the production target is relatively easy. The factory manager conveniently uses the sales demand figure to set the production target for the new piece of equipment. The productivity indicator commonly used is the number of products produced per hour.

As the sales demand goes up, the factory manager raises the productivity target for the piece of equipment proportionately to tally with the projected sales volume. Of course, if the sales demand were to grow by 10% a year the productivity target will be raised by 10% a year.

I agree. If the sales volume is growing gradually to fill up the extra capacity of the new piece of equipment, it is fine to set the productivity target at that proportionate rate. But really, there is more than meet the eyes. From the aspect of total productivity, it is not as simple as just looking at the efficiency of the equipment. It requires a set of operators to man the equipment.

Let’s go back to the example where the newly installed piece of equipment is capable to produce at an output rate that is many times the capacity of the old equipment. The new piece of equipment could be operating at only one-third its full potential capacity and yet meeting the current sales demand amply.

Should the new piece of equipment’s full potential in output capacity be used to drive the factory productivity? If not, would a different productivity indicator be more appropriate?

If the equipment capacity is being used as the productivity indicator, management will first and foremost deploy all the manpower required to man the piece of equipment in such a way that it can run at its full capacity. Which means the equipment must operate at full speed. And to support this maximum speed, perhaps, more investments in the installation of auxiliary equipments are needed to sustain it at its full steam operating speed.

Compare that to a case where the management decides that it is quite pointless to deploy all the manpower and auxiliary equipment required to run the new piece of equipment at its manufacturer-determined optimum speed. It knew very well this piece of equipment could be operated at a much lower speed to produce a much lower output volume at a level that could constantly satisfy the market demand.

But the way, it already knew the sale demand is not high enough to operate the new piece of equipment at its full capacity. So instead of choosing a productivity indicator based on the equipment’s optimum production rate, the management chose the productivity indicator based on the number of man-hours deployed to operate this piece of equipment. With this productivity indicator in mind, he aims to reduce the manpower cost to the minimum and at the same time could have saved on thousands or millions of dollars in the investment of auxiliary equipment that is quite unnecessary in the first place. Overall, his total cost of production per unit of product produced with the new piece of equipment would be much lower.

But in my many years of experience working with many companies, most of them choose a productivity indicator that is based on the equipment capacity instead of labor-hours because they feel they have spent so much money on a piece of super machine and therefore, they must extract the maximum output out of it despite the fact that they know pretty well, the market volume does not justify such high rate of production.”

“Once a piece of equipment is purchased and installed, the total investment is a sunk cost. The management should aim for the lowest unit cost of production. Hence, a blind pursuit of productivity based on an equipment full capacity is not wise. I have learnt a new lesson,” said John.

 

Illustration #7: Piece-rate remuneration system

 

“This is the seventh and last example,” I said, “Things get simpler when a factory is in the labor-intensive operation mode. Where workers in the thousands are employed to manually assemble products day in and day out, the piece rate remuneration scheme is often employed to motivate the workers to work at the highest level of productivity.

I talked to many factory managers about what is the best way to motivate their workers. All the answers pointed to a steadfast resolution: apply a piece-rate remuneration system. It seems to me this is a one-stroke-catch-all solution. Never did I ever hear of there is a need for a complementary resolution to that. It is a panacea, it seems.

In a labor-intensive factory, a productivity indicator is almost never been used. The piece-rate incentive scheme is all that a factory manager needs to ensure that its factory churns out the goods at the lowest cost of production.

With this piece-rate incentive scheme in place, the factory manager believes that since he is not going to over pay his workers, he will not be in a losing position if his workers were to go slow in their work pace. There is no other better scheme where the wage expense dances in exact tempo with the workers’ productivity.

On the other hand, the workers feel they are receiving a fair reward for their hard day’s work. The more products they produce the more wage they get. A piece-rate incentive scheme is fair and doubtlessly, no question was asked about this scheme.

But things can get awry if the workers are not motivated. The workers may slow down in their work pace and the factory’s total production drops. The labor cost per unit may be the same irrespective of the level of productivity. This is because the wage is directly tied to the piece-rate incentive scheme where a fixed sum of money is paid out for every unit of product produced.

However, the factory overhead cost, which is a fixed sunk cost irrespective of the factory output rate, will be prorated as a much higher per unit total cost of production if the volume of production had gone down. A cost accountant can testify to this statement.

Imagine a situation where the labor cost per unit is about 30% of the total factory cost. This is already a relatively high portion attribuFigure to labor cost. If the production volume is halved, the total cost per unit of product produced would go up by 70 percentage points. The factory would no longer be competitive. The increase in unit total cost of production would easily rob the factory of its profit margin and thereby, causes it to lose money.

Therefore, a factory cannot afford not to monitor its labor productivity. The piece-rate remuneration by itself is insufficient to assure that the factory will make money. But most factory managers often gave an excuse having a good piece-rate incentive scheme is good enough. The factory manager needs not look forward to doing more for the company. This is buffer mentality.

I would like to summarize my discussion with this closing statement, “No matter what kind of production mode of operation the company is in, it must find an appropriate productivity indicator to gauge and motivate its workers to put their heart and soul into producing goods at their optimum speed.”

John nodded his head and said, “Thank you very much, Eric. These few examples had given me a clear idea that it is not easy to find the most appropriate productivity indicator. But the way, how do you set up a good productivity indictor? That is back to the question which I asked 30 minutes ago.”

I replied, “The universal method to measure productivity is to use the earned hour methodology. This method should be used to measure the factory productivity irrespective of its mode of operation. How to set the productivity indicator? You must use the earned hour methodology[3].”

 


 



[1] Please go back to read chapter 22 to refresh your mind, if necessary.

[2] In 1990, NatSteel Ltd used 22 heats per day as the productivity indicator for its furnace operations.

[3] Please go back to read chapter 3, ‘Planning with historical output rates’ if necessary.

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