Product Is Not Concerned With
Many people on this side of the sea accept greeted the hullabaloo over Japan's miraculous achievements in manufacturing over the last decade with a yawn. "So what else is new?" they say. "Deming said this years ago." W. Edwards Deming is the godfather, if not the real begetter, of found productivity. He was mainly responsible for instituting in plants the statistical control procedures that the Japanese accept and then adroitly adopted. This commodity describes the Deming approach to productivity and quality: considering only management has the authority to change the production system to eliminate them, product defects are a managerial responsibility.
To change the arrangement, direction first needs to distinguish aberrant from normal variation. Information technology also needs to specify operationally what the system is supposed to produce. With these controls in hand, the organization can predict functioning, costs, and quality levels, and managers tin can communicate effectively with customers and people on the shop floor. And this is most important, for when management sees the organisation and not the workers equally the crusade of bug, many of the morale-sapping results of poor decisions, targets no ane believes in, and motivating slogans that implicitly blame workers disappear.
John Henry, president of Global Manufacturing Visitor, leaned dorsum in his chair, sighed, and stared at the ceiling. On the desk in front of him was a report from two statisticians on the productivity and quality problems at Global's Nightingale manufactory.
Henry and his vice presidents had known that things were bad. Customers were complaining, prices were too high, accounts receivable were escalating, repair calls were increasing, costs were upwardly, worker morale was down, and the union was threatening to strike considering of management's incessant demands for better productivity. Too, most of the machines were not upward to the chore. But they hadn't bargained, Henry thought ruefully, on what the statisticians would find. He picked upward the study, sighed another time, and looked at it once more.
"Your mill at Nightingale," the report said, "is running along day afterwards solar day sending out items, 15% of which (on the average) have i or more than major defects… This proportion of major defects in your product may well explain some of your problems with sales and profits. The amount of rework your operators have to do along the production line is as well stifling your profits.
"Your problems start this fashion. An operator on the line turns out an item. She looks it over. If she finds a major defect she may rework it herself because she knows that otherwise it may come up back to her afterward to fix. Simply, she thinks, the inspector down the line might not spot that defect. If she does, she may rework it or send information technology back to the operator. Just even if the inspector sees information technology, the supervisor may intercept the item on its way dorsum to the operator and send it on through product to avert getting defenseless curt at a later phase down the line.
"From the operator's betoken of view, why non have a chance with both minor and major defects? Send them down the line; chances are they won't come dorsum. From the inspector's signal of view, the supervisor may intervene, so fixing defects can be a waste of time. From the supervisor's point of view, she can risk the defect. She can't lose, and she might gain if she keeps her production tape high.
"In other words, Mr. Henry, your operator's job is to produce defects. She gets paid for them. This is the system, and the operator is not responsible for it. Direction is."
The point backside the statisticians' memo to poor John Henry is that defects are not free: somebody makes defects and gets paid for making them. If a substantial proportion of the piece of work force corrects defects, then the company is paying to correct defects likewise as to make them. If the Nightingale factory is producing 15% lacking products, then 15% of the total price is spent making bad units. Obviously, low quality means high cost.
All the issues of Henry and his vice presidents stemmed from the mismanagement of quality. In other words, and this could be the statisticians' second bespeak, management achieves a loftier-quality product by improving the process. If managers can improve the manufacturing process, they can transfer resources from the product of defectives to the manufacture of additional good production.
Suppose management at Nightingale is able to meliorate the procedure past making some changes at no additional price so that simply 9% of the output is defective. What has management accomplished?
1. Productivity has risen. The factory now produces 6% more than units at the same cost. (If the factory reworks defectives, then operators can use the time they would take taken to rework the vi% defectives to make more practiced product. This creates an boosted—complimentary—increase in productivity.)
ii. Aggregate quality has improved. At present only 9% of the output is defective instead of 15%.
3. Chapters has increased. The manufactory produces half-dozen% more than good units with the same organisation—labor, machines, materials, and so along.
4. The cost per unit is lower. The manufacturing plant manufactures more units at the same cost.
five. The toll tin exist cut.
One can see that process control (i.e., the proper direction of quality) tin can convalesce John Henry'due south bug. With improved quality, customers will stop complaining and returns will drop, sales people will exist able to compete effectively due to a higher quality product and a lower price, service and repair calls volition decline, accounts receivable volition go downwards (because satisfied customers are likely to pay their bills), costs will decrease, productivity will get up, the spousal relationship will finish threatening to strike, and direction will accept capital to maintain equipment properly.
Improving the process is the cardinal to increasing productivity and quality and to reducing unit costs. Managers can attain these goals through understanding the sources of variation in a procedure and using the appropriate operational definitions.
The Sources of Variation
Let'southward look at a manufacturing procedure that produces steel rods. Although the average diameter of the rods is two.00 inches, we can't await every rod'south diameter to be exactly that. We would expect some variation depending on how the measurement was rounded off.
Variation in a process is natural. In fact, nosotros should all expect it and not be surprised when it occurs. But processes are subject to ii sources of variation: normal and abnormal. Aberrant variation is due to a special or specific cause and may or may not be present in a process. In our example, let's say that we produce a rod with a one.96-inch diameter. Is the .04-inch discrepancy an abnormal variation in the process? Or is it a normal variation that we ought to expect? If it is an abnormal variation, we would want to intervene and, say, adjust a machine. If it is not, we shouldn't arbitrate. In fact, past adjusting the car without cause, nosotros'd run the take a chance of throwing the process out of whack.
Some researchers estimate that abnormal variations cause 15% of the problems in a process, while normal variations crusade the remaining 85%.1 Normal variations are common to all elements of a process—a whole grouping of workers, an unabridged department, and fifty-fifty a whole company—and create most of the loftier costs of production and service and low-output problems. Confusion between common and special causes of variation leads to frustration at all levels, more variation, and higher costs. Unable to distinguish between the ii sources of variation, management may react by blaming the workers.
A worker is powerless to act on a normal cause of variation. Workers have no authority to sharpen the definitions and tests that determine acceptable quality. They cannot do much about machines or test equipment that is out of order. They can written report such events, but management must do the follow-upwards and brand the necessary changes. Workers cannot alter the specifications and policy for procuring incoming materials either, and they are not responsible for the product's design. These are all part of the system, and only managers tin change the system.
It is difficult to overestimate how loftier morale would go in most factories if direction held workers responsible only for what they could command and not for the handicaps of the system.
What Kind of Variation Is It?
Because workers tin can't be responsible for the system, managers need to be able to distinguish between abnormal and normal variation and so they'll know when and how to alter the process. The just safe fashion to differentiate the 2 sources of variation in a process is through statistical signals that command charts generate.
Command charts
A system control chart has a center line that represents the process average, and two control limits, upper and lower. Suppose you lot want to examine the keypunching functioning in a data processing department. First, according to statistical theory, you make up one's mind a sample size, let's say 200 cards per mean solar day.2 And then you accept random samples of 200 cards from each day's output and inspect them for errors. Showroom I shows how to construct a control nautical chart for a keypunch operation.
Exhibit I Formulation of Control Chart for Keypunch Performance Note: Both of the points (twenty-four hours 8 and day 22) that lie higher up the UCL send a statistical signal to management to search for possible sources of abnormal variation on solar day viii and solar day 22.
Exhibit Ia shows the per centum of keypunch cards that are defective. Exhibit Ib is a plot of "percentage defective" (cavalcade 4 in Ia) confronting "day" (column 1 in Ia). Exhibit Ic shows the computations you lot'll need to construct the center line (in this example, the average percentage defective for the procedure) and the upper and lower control limits.
Y'all construct the control chart (Exhibit Id) by connecting the points plotted in Ib and drawing the center line and upper and lower command limits across the points. Finally, you analyze the control chart. If a sample value falls within the upper and lower control limits, and if a trend or some other systematic pattern is absent, the variation is probably normal. If, nonetheless, a sample value falls exterior the command limits, the variation is probably aberrant.
The chart shown in Exhibit I is just one of many kinds of control charts, each of which has a special purpose. (You can find examples of other charts in the sources listed at the end of the article.)
If the Variation Is Abnormal
By comparing Ib and Id the reader will run across how difficult it is to differentiate between the two causes of variation with the naked eye. Showroom Ib does not permit managers to distinguish between the ii sources of variation, while Showroom Id clearly shows that on days 8 and 22 something aberrant happened, not attributable to the organisation, to crusade defective cards to be keypunched.
When a manager determines that the cause of the variation is abnormal, she should search for and eliminate the causes that are owing to a specific worker or group of workers, a machine, a new batch of raw materials, and so on. Once direction eliminates all assignable causes of variation it is left with a stable process that is in statistical control.
Let's reexamine the keypunching functioning shown in Exhibit I in more particular. Look at the control chart for the percentage of cards with errors (Id).
It is customary to base of operations the control limits on a multiple of the standard error. Commonly this multiple is 3 and the limits are chosen 3-sigma limits. This means that there are approximately iii chances in 1,000 that the location of a point exterior the limits is due to the natural random variation of the system. If we look at the charts in Exhibits I and Two we can come across that two points are outside the upper control limit, indicating that the procedure is non in statistical control.
Exhibit Two Control Charts for Keypunchers
What should direction's next step be? To bring the process nether control, management should investigate the points that were out of control to remove assignable causes of variation from the process. Let's say that management found that on 24-hour interval 8, a new keypunch operator had been added to the work force, and that the one twenty-four hours information technology took the worker to acclimate to the new environment probably acquired the unusually high number of keypunch errors. To ensure that this assignable cause would not be repeated, the visitor instituted a one-mean solar day training program.
Investigation of mean solar day 22 showed that the night before, the department had run out of cards from the regular vendor and did non wait a new shipment until the morning of day 23. Consequently, the department purchased ane twenty-four hour period'due south supply of cards from a new vendor. Management found that these cards were of inferior quality, which caused the large number of keypunch errors. To right this assignable variation, management instituted a revised inventory policy and operationally divers acceptable quality for keypunch cards.
Later on eliminating the days for which assignable causes of variation were found, managers recomputed the control chart statistics:
Showroom IIb shows the revised control nautical chart (IIa shows the original nautical chart). The process is now stable, in statistical command.
A stable procedure that exhibits only variation due to inherent system limitations allows a manager to determine its capability, that is, what is normal. Here are some of the advantages of achieving a stable process:
i. Management knows the process'south adequacy and can predict its operation, costs, and quality levels.
2. Under the present system, productivity is at a maximum and costs are at a minimum.
3. Management can measure the effects of changes in the system with greater speed and reliability.
4. If management wants to alter specification limits it has data to back upwards its argument.
The capability of the procedure becomes a given. A stable process that produces an unacceptable number of defects volition continue to do and then every bit long every bit the organisation, as currently divers, remains the same. And only management is responsible for changing the arrangement.
Normal variation
Once a process reaches stability, which is non a natural land but an accomplishment, management is set to act on the organization to better productivity and quality. Managers can improve the arrangement by:
ane. Shifting the process boilerplate. For example, management may desire to decrease the percentage of defects or increment the average output.
2. Changing the amount of variation. Given the economic demands of the marketplace, management may want to decrease the amount of variation to obtain a more consistently uniform product or increase it to obtain a less compatible product.
Certain inputs and procedures, such as labor, training, supervision, raw materials, machines, and operational definitions, define the system. To meliorate the arrangement, management must modify these factors. Once again we stress that merely direction has the responsibleness and authority to brand these changes. Workers on their own cannot touch on the system.
How can management set about changing the keypunch process to improve productivity and quality? Past instituting training procedures that reduce the average percent of defective cards and the amount of common variation (resulting in narrower command limits), management can assist employees produce more than mistake-free cards consistently.
Exhibit IIc shows the new control nautical chart later on management instituted training and procedural changes. The average percent of keypunch cards with errors has decreased from .017 to .008 and the process variation has decreased also.
It is important to stress that the concepts we've been discussing encompass more than but control charts. Companies may use command charts without any agreement of the approach nosotros're concerned with, namely, management'southward responsibility for improving the organization, no habitual dependence on final inspection, elimination of slogans, emptying of arbitrary piece of work standards, and then on.
We take come total circle. We know that improving the process increases productivity and quality. Past distinguishing betwixt abnormal and normal variation, and by eliminating the abnormal variation, managers tin obtain statistical control. But this on its own isn't sufficient to upgrade productivity and quality.
If management fully understood sources of variation besides as saw that its responsibility is to improve the process, but did non understand operational definitions, its efforts would still be in vain.
What's Existence Produced?
If direction can't precisely define its products, how can information technology sell them, describe what information technology wants to people on the shop floor, or improve the production process? It can't. Without an operational definition, people can't do business organisation. Here's an case of the confusion that the absence of a precise thought of what's being produced can cause:
"The label on a blanket reads 'fifty% wool.' What does this mean? Half wool, on the average, over this blanket, or half wool over a month's production? What is half wool? Half by weight? If so, at what humidity? Past what method of chemical assay? How many analyses? Is the bottom half of the blanket wool and the top half something else? Is it fifty% wool? Does 50 per cent wool mean that there must be some wool in whatsoever random cantankerous-section the size of a half dollar? If and so, how many cuts should be tested? How do you lot select them? What benchmark must the average satisfy? And how much variation between cuts is permissible? Obviously, the significant of 50% wool can only be stated in statistical terms."3
What is an exact or true definition of a term? For example, what is "exactly circular"? No one definition exists that will help u.s. tell if something actually is round. The dictionary is no aid either. Webster's says that a figure is circular if it has "every part of the surface or circumference equidistant from the center." This definition is very useful for formal logic, but if we try to use information technology to determine if our disk is round, we volition have insurmountable difficulty. The dictionary provides a concept, not a definition for use in manufacture.
How and so tin nosotros define a term that is understandable at the shop level? Operational definitions are of two types: one for attributes, east.yard., success versus failure, and one for variables, e.g., sales volume. An operational definition for an attribute consists of:
ane. A criterion to be applied to an object or group.
2. A procedure to select the object nether study.
3. An operation, such every bit measuring or observing the object.
4. A record of the result.
5. A examination of the object to decide whether information technology conforms to the criterion.
6. A aye or no decision about whether the object meets the criterion.
To derive an operational definition for a variable, managers would take the same first 4 steps they took to derive a definition of an aspect. (Steps 5 and vi for attributes exercise not employ to variables.)
Now, the question is, What is the significance of operational definitions to the productivity of a company? We know how important information technology is that producers and users understand each other. Without operational definitions, a specification is meaningless. Conflict and confusion betwixt companies and between departments in a company ascend from managers' failure to country in advance, in meaningful terms, the specifications for an particular or its performance. Think of the productivity and quality issues that tin ascend when an inspector who is responsible for finding defects is inconsistent over time in her judgments, or when inspectors are inconsistent with each other. The workers don't know what is acceptable or what is defective. They need an operational definition of a defective product.
Let's suppose we industry round disks. Are the disks round? Why exercise we care? If a disk is besides far from round, information technology volition jam the customer'southward motorcar, cause equipment damage, and cause downtime. If we want to remain in business, we had amend care.
Let'south write downwardly an operational definition of round for the disk. Since we are measuring an aspect (circular versus not round), we will work on the first type of operational definition.
Step ane: First nosotros desire to derive a criterion for the object.
a. "Apply calipers that are in reasonably good order." (You perceive at in one case the need to question every word.)
"What is 'reasonably practiced club'?" (Nosotros settle the question by letting y'all apply your calipers.)
"But how should I use them?"
"We'll be satisfied if you merely utilise them in the regular way."
"At what temperature?"
"The temperature of this room."
b. "Have 6 measures of the diameter well-nigh xxx degrees apart. Record the results."
"Only what is 'about 30 degrees apart'? Don't you mean exactly 30 degrees?"
"No, there is no such thing every bit exactly 30 degrees in the physical world. So endeavour for 30 degrees; nosotros'll be satisfied."
c. If the range between the half dozen diameters does not exceed .007 centimeters, we will declare the disk to exist round. Nosotros have determined the criterion.
Step 2: Let's select a particular disk. (We could at this point specify some sampling scheme.)
Steps 3 and 4: Take the measurements and tape the results in centimeters—three.365, three.363, 3.368, three.366, 3.366, and iii.369.
Step v: The range is 3.369 to three.363, or a 0.006 difference. We test for conformance by comparing the range of 0.006 with the criterion range of less than or equal to 0.007 (from Step one).
Footstep half dozen: Because the disk passed the prescribed exam for roundness, we declare information technology to be round.
If a visitor has workers who sympathise what round means, and a customer who agrees, the problems the company may have had satisfying the customer will disappear.
Allow's look at some other example where operational definitions improve agreement within the company. In this example we measure a variable (sales), so we use the second type of operational definition.
A salesperson is told that her performance will be judged in respect to the percentage of change in this twelvemonth's sales over concluding twelvemonth's sales. What does this mean? Average pct change each calendar month? Each week? Each twenty-four hours? For each product? Percent change between Dec 31, 1980 and December 31, 1981 sales?
How are nosotros measuring sales: gross, net, gross profit, net profit, and so along? Is the percentage change in constant or inflated dollars? If information technology's in constant dollars, what is the base year? If it's in inflated dollars, is information technology at last year'south prices or this year's prices? Under what economic conditions?
A loose definition of per centum change can only lead to defoliation, frustration, and ill will betwixt management and the sales force—hardly the way to better productivity. How should management operationally define a percentage change in sales?
Pace 1: A percentage change in sales is the departure between 1981 (Jan ane, 1981 to December 31, 1981) sales and 1980 (January 1, 1980 to Dec 31, 1980) sales divided by 1980 sales:
S80 is measured in abiding dollars, with 1979 every bit the base year, using June fifteen, 1979 and June fifteen, 1980 prices to derive the constant dollar prices, and total unit of measurement sales less returns (due to any cause) as of December 31, 1980 for each product.
S81 is measured in constant dollars, with 1979 as the base year, using June fifteen, 1979 and June 15, 1981 prices to derive the constant dollar prices, and total unit sales less returns (for any reason) as of December 31, 1981 for each product. (Pi79 remains the same for all products.)
This procedure for computing the pct change in sales between 1980 and 1981 will exist in outcome regardless of the economical weather. Further, management may revise the definition of a pct change in sales after the 1985 sales evaluation, just not before unless the sales strength and sales direction hold.
Step 2: The salesperson and her sales records are the object under study.
Steps 3 and four: The sales managing director will employ all 1980 and 1981 invoices and sales render slips to compute the net number of units sold for each product in 1980 and 1981. The sales manager will record the computations and results.
The prior definition of sales might non suit some other manager and sales force; however, if the sales manager adopts it and the sales force understands it, it is an operational definition.
Operational definitions are not trivial. If direction doesn't operationally define many critical variables and attributes so that workers too every bit customers agree, serious bug will follow. The control chart becomes a useless managerial tool due to an entirely new source of variation: measurement variation. It is management's responsibility to operationally define the characteristics being charted. If inspectors don't agree with each other, or with themselves from twenty-four hour period to day, chaos will develop. Workers do non know what is expected of them. Their output is OK for Inspector 1 and non for Inspector ii; an employee'due south work may accept been passed by Inspector 1 yesterday but may not exist today.
Pinnacle Management'southward Job
Numbers of people take recently written guidelines that tell management what it should do to improve productivity:
Create an institution that has a constant purpose and long-term, meridian direction commitment.
Intermission down barriers between departments.
Create an environs in which people are not afraid to report problems.
Defuse built-in levels of defects, mistakes, poor materials, and then along.
Do non blame productivity and quality problems on the workers.
The reader no doubt is familiar with these. Additional managerial guidelines that may not be so obvious follow, even so, from the approach we've outlined here.
i. Don't expect inspection to solve the quality problem. Past the fourth dimension the inspection is made, the product is already acceptable or defective. Y'all cannot audit quality into a production.
Mass inspection does not cleanly dissever good items from bad. A better mode is to monitor small samples of product for control charts to achieve or maintain statistical command. In this way managers might eliminate the demand for inspection and put inspectors' talents to other uses. Sellers and customers could also compare their instruments and tests; sellers and customers could brainstorm to speak the same language. Inspection under pressure is often a farce: whether it is coming in or going out, anything passes. And because divided responsibility means that nobody is responsible, 200% inspection is less reliable than 100% inspection.
2. As a matter of policy, stop application business to the lowest bidder. Without a measure out of the quality beingness purchased, price has no meaning.
To judge quality, purchasing managers crave instruction and experience in evaluating statistical prove of quality. If purchasers become experts in assessing quality, near of them will drastically reduce the number of vendors they bargain with. A vendor that does not know its costs, nor whether it tin repeat today's distribution of quality tomorrow, is not a skillful business organisation partner.
three. Eliminate targets, numerical goals, slogans ("nada defects"), pictures, and posters that supervisors so often plaster in plants urging people to increase productivity. Unfortunately, such "productivity improvement" programs leave the defects right where they are. They practice not uncover or correct faults of the system, nor practice they provide the statistical signal managers need to take corrective action. They practise not answer the critical question, "How can nosotros improve productivity?"
4. Eliminate work quotas. Work quotas practice non take into account normal variations in the organisation. They do not include a way to notice the need for corrective action or a way to assign responsibility to management or to management'south delegate on the line. For example, a bank manager may determine the number of customers he thinks a teller ought to handle in an 60 minutes, the number of computations of interest and penalty someone ought to compute in an 60 minutes, and a similar effigy for every other action. However, the standards don't say anything about the quality of work or give the director any style to understand the variation in the process. Standards exercise non signal what action managers should take or how to meliorate the process.
five. Institute training programs in statistics so that managers and supervisors tin understand how to manage quality. Supervision is part of the arrangement and is, of course, the responsibleness of management. Statistical methods are vital aids to foremen and production managers to indicate causes of waste, depression productivity, and poor quality. Managers can also use them to determine when employees are fully trained and when farther grooming would help.
• • •
These guidelines indicate what top management must practise to improve productivity and quality. Though post-obit each of the guidelines will non produce tangible results, at the aforementioned fourth dimension, a company that starts today fully committed will presently realize impressive gains.
A close second for quick results would be to drive out fear, to help people feel secure, and to assistance people get over the fear of reporting problem with equipment or with incoming materials. Managers tin accomplish this goal within two or three years and reap powerful economical results.
1. See, for example, Joseph M. Juran, Quality Control Handbook, 3d ed. (New York: McGraw-Colina, 1974).
2. A discussion of how to compute a sample size can be found in numerous texts, some of which nosotros've listed at the end of the article.
3. W. Edwards Deming, Quality, Productivity, and Economic Position (Cambridge: M.I.T. Centre for Advanced Engineering, 1982).
A version of this article appeared in the September 1983 issue of Harvard Business concern Review.
Product Is Not Concerned With,
Source: https://hbr.org/1983/09/product-defects-and-productivity
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