Evolutionary Op ration in IF
An Attitude Or
Technique?
A PROCESS operates smoothly and produces satisfactorily, why not let well enough alone? Why tamper with it and run the risk of dropping a monkey wrench in the works? Why ask for trouble? Such reliance on the status quo may be comfortable, but chances are it leaves desirable changes unmade. A conscientious superintendent may wish to explore new techniques, but because experimentation at plant level may be hazardous, he is unwilling to risk the company’s process. This is particularly true when expensive equipment or batches of materials are involved. Evolutionary operation (EVOP) techniques are tailor-made for such a superintendent. It is fairly safe to say that resorting to an EVOP
It is a statistical technique for improving a plant process during the course of operation, and forcing the process t o produce data about itself without upsetting production. Like other statistically designed experiments, i t is based on a law of probability: standard deviations of averages are l / d g times the standard deviations of single observations. I n fact, this comparison of averages is the basic concept of EVO P. The technique, named after Darwin’s wellknown theory, has two essential features: Changing levels of variables Selecting the best combination of levels Generally, each change introduced is so small t h a t its immediate effect on production is obscured by normal plant variation. By making
40 A
INDUSTRIAL AND ENGINEERING CHEMISTRY
program entails no danger of producing off-specification material. Other things may happen, or other considerations such as legal aspects may be involved, but judicious changes, introduced according to EVOP practice, will not of themselves cause risk. I n fact, the technique has a built-in safety device against making costly or catastrophic mistakes. When should EVOP be used? Dr. George Box, when he introduced the technique in 1957, used as an illustration a new process going on stream. Since then, however, the technique has attracted more attention in its application to operating commercial processes. I n this context, it is likely to pay off where volume is large, and where even a
changes according to a planned pattern, called an experimental design, information is obtained with which averages can b e compared efficiently. Usually the designs used are simple two-level (usually two or three factors) factorial designs with an added center point. A minor variation is repeatedly introduced according t o a pattern, until it can b e determined whether or not the variation results in a statistically significant difference. Process conditions are then shifted slightly i n the indicated direction, and the sequence is repeated. The concept of the procedure is simple b u t successful application may not be simple a t all. EVOP definitely means change, and this in itself can be complicated. Even one of the first steps may not be easy; that is, formulating the precise question t o be answered.
Plant-Sca
eriments
SPECIAL FEATURE trivial saving on a per unit basis can add u p to substantial amounts. A good program ensures that no opportunity for improvement is overlooked, no matter how great or small the gain. EVOP is not recommended where a process is supported by adequate theoretical knowledge. It is an empirical tool and represents the Edisonian approach. I n fact, this is the power of EVOP. Where sufficient fundamental knowledge is lacking, the process engineer need not wait until the undefined point has been developed. He can proceed immediately to adjust operating variables for at least improving, if not optimizing, factors such as yields or costs. EVOP thrives on a sense of urgency. I t is an ideal tool for the impatient engineer who is not satisfied with his process and hopes to make improvements while waiting for more fundamental information to be developed. Sometimes EVOP may appear to fail-most techniques do-and there is always the risk of improper use. It will not ferret out the important but unknown and untested variables. Proceeding first with a fractional factorial experiment for screening important variables will not help either, unless the variable is tried. O n the other hand, there are many examples of success where EVOP was not entirely applicable, and even where the process was not under statistical control. These 3ases should be looked at as less a test If EVOP than as taking a calculated -isk of failure. EVOP techniques minimize that risk. Actually, EVOP techniques are iot too different from those used in .he old days. Then a good super&or or foreman often tried minor nodifications on a trial and error ]asis, using the good trials and jiscarding the failures. EVOP nakes these trials systematically and xovides a basis for good judgment ibout the effects.
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iuman Factors
In practice, EVOP has been applied to problems involving factors such as increased yields, lower raw material requirements, or recycle time. However these factors are zphemeral-the process may not be
INDUSTRIAL AND ENGINEERING CHEMISTRY
in operation next year or even tomorrow. Therefore the real advantage of the technique is that it promotes a new way of thinking, and utilizes to its fullest extent the human factor. For example, the remark of a youthful assistant superintendent of a large plant is revealing. He said, “The old man is a little on the conservative side . . doesn’t want to fool around with the process. With EVOP now, I can propose certain studies and there’s not much he can do but to go along with the idea. This is great for us young guys.” I n his first study, this young engineer upset two long-held fallacies about the process. Experience has shown that EVOP tends to have a catalytic influence on the mental activity of plant personnel. There is an instance of a superintendent who instituted an EVOP program and then in a mood of experimentation decided to try, without benefit of EVOP, a process change he had been wondering about. His move was a gamble, but it paid off handsomely in lowered production time, increased capacity, and reduced process costs in other areas. This stimulation for original thinking is not by any means limited to the superintendent or his assistant. Operators seem to be affected too, and with greater interest in the process, they are likely to exercise closer control. EVOP is a break in the routine with something to look forward to. And, possibly another factor enters the picture as well-in eliciting operators’ assistance, management provides them with a feeling of greater importance. Man does not live by bread alone. Other personnel in the plant hierarchy are influenced as well, such as process engineers, quality control personnel, and those taking part in EVOP committee deliberations. The program provides an opportunity for individuals to present their views for consideration. Dow Chemical like many other organizations has an operations improvement ( 0 1 ) program. As a result, the employees have become so 0 1 conscious that a janitor may submit a proposal. His suggestion may not be earth-shaking, but it adds to the program and bolsters the morale of the janitor.
Why not cultivate the evolutionary technique in the same way? For example, it may seem strange that a fellow from the power department signs up for an EVOP course. But later it seems less strange when it is learned that he has succeeded in reducing stack temperature by 18" by using the technique. Blessed are those with imagination and enthusiasm! Most of the time, that is, and EVOP monitor those instances where enthusiasm can go awry. For example, extrusion is a tricky business. Dow has a plant with a number of film extruders having some eight heat zones with friction of the screw adding its share of heat. Heat zones and screw r.p.m.'s can be set a t a fantastic number of combinations, and without a definite technique for determining optimum settings, each operator becomes his own expert. Thus, it may not be unusual for the temperature chart and production record of an extruder to show that everything is running smoothly, and then suddenly the log may change drastically-heat on zone 3 may decrease lo', and that on zone 2 increase 12'. This is puzzling until it is found that the change occurred at the precise time a new operator came on duty. I t would be wrong to say that if a plastics molder or an extruder adopts EVOP, his troubles will vanish, but in the Dow plant, it did help. The foreman with his long-term experience believed that specific heat zones were controlling, but could express this only as an opinion. Subsequently EVOP techniques were applied to one machine, important zones were identified, and optimum settings were determined. I n a short time this machine increased its output by 2075 above other machines. As the study continued, yield continued to improve until undesirable characteristics appeared. At the cutoff point, output had increased by 37%. As is customary in EVOP programs, results of this study were posted in the plant, but contrary to expectations, the exemplary extruder did not become the show horse, production-wise. Rather, all machines showed improvement as the study progressed. Probably
A CONDENSED FACT FILE ON EPOXIES BY FMC
(
1
For additional information on these products write for the bulletins mentioned above-
EPOXY DEPARTMENT, FMC CORPORATION (Continued on page 44 A )
I",
I
0
161 East 42nd Street, New York 17, N . Y. Circle No. 55 on Readers' Service Card VOL. 53,
NO. 12
DECEMBER 1961
43A
S P E C I A L FEATURE each operator, who considered himself an expert, said to himself, “Might as well see how they’re running that machine. Not that they know so much, but maybe I should up the temperature of zone 3 myself.”
Dow Chemicals’ Case Histories
The familiar one step at a time, or classical procedure, has achieved many good results, but what happens when one takes a certain step and then gets clobbered, as it were? T h a t actually happened in one instance-use of recycle material was stepped up, and quality of the product went to pot. By using EVOP, several variables were studied, and the amount of recycle material used was eased upward, but nothing adverse happrned. Ultimately, the ratio of recycle material could be doubled, and by studying another variable, product quality actually improved. In addition to having a better product, the estimated saving in cost amounted to some $4000 per month. Measuring improvements in terms of dollars is an alluring method of recording results and stimulates use of further programs. In one plant, as a result of better combination of two variables, yield was increased to the point where more than a quarter million dollars was addad to the annual revenue of the plant. Understandably, other projects were undertaken, and because it is a large plant, most of them realized large estimated dollar savings. When EVOP projects are continued in this way, there is perhaps a certain risk in speaking about savings which might not otherwise be realized. Old savings tend to dry up and new ones are needed to keep the stream running. For example through EVOP, a plant learned how two variables could be combined to increase output about 10%. Normally, two things could be assumed : better operating conditions would have been found without EVOP but not as efficiently or to the same extent; also competitive advantage of this gain may be short lived, because competitors are concinually looking for better ways themselves. Thus the gain may put the plant ahead this year, but 44 A
________~
next year the plant may just break even. T o say that a $50,000 a year saving, on a process having a plant life of 10 years, amounts to $500,000, is misleading. Further, can the added output be sold? Perhaps the EVOP committee should include a cost accountant. Or, at least it may be desirable to draw from other disciplines from time to time. This point can be illustrated by using economics : A problem in a styrene plant was to find the optimum temperature, ethylbenzene feed rate, and steam rate. Several products result, including styrene, as well as by-product benzene and toluene which have a market value and can be recovered. All can be obtained in a range of combinations by controlling variables which govern conversion rate and throughput. But obviously styrene is the main product. A three-factor EVOP study was set up and the amount of styrene produced was established for each of several combinations of the three variables. With this information the superintendent could maximize production as far as these three variables are concerned. I n any production study, costs should be recorded because when the low point of the cost curve, which is dish-shaped, is passed, there is little point in increasing production, except in limited circumstances. However, in this instance, each combination of the three variables varied not only the amount of styrene produced, but also amounts of the by-products, benzene and toluene. Thus value of the byproducts-i.e., pounds per hour resulting from a given combination of variables--multiplied by respective market prices was subtracted from the over-all cost. Then in the next step, net cost per pound of styrene can be figured. Although market prices of the several products may change, the resulting cost figure should be considered as an index number of cost at the several combinations of variables. Thus, highest yield regardless of cost and highest yield commensurate with cost were ascertainable. I n an EVOP study of another process, one full phase of an operation was eliminated, and cycle time of another step was reduced; this reduced polymerization time
INDUSTRIAL AND ENGINEERING CHEMISTRY
52%. I n a case such as this, where lack of capacity is a factor, such a gain can be equated in terms of added capacity. T o use an overly simplified example: if $1 million is invested in equipment which can produce 10,000 pounds of a certain material per month, increasing the output to 20,000 pounds per month by using EVOP would be equivalent to doubling capital investment without EVOP. Educational Facilities
For EVOP programs to be accepted, many people must be trained. T o anticipate trouble spots, design programs for unusual situations, and analyze bad data, the plant statistician must know plant practices and statistics. Managers must appreciate potential benefits as well as requirements in facilities, man power, and time. Production supervisors need a basic knowledge plus confidence that the game is worth while. Also, the research chemist should realize the safety of the method so he will give permission to alter process conditions. Even the foreman is more cooperative when he is a believer. EVOP techniques can be learned through two broad types of training courses :
b b
In-plant Public
In-Plant. It should be pointed out that EVOP is not the only statistical method used in the chemical industry (7), nor is it the only occasion for training. I n a plant of 2000 employees, Monsanto has trained 90 people in EVOP. The company does not complain of the cost of this training, but it does consider the failure of many people to form the habit of statistical thinking a great loss to both the industry and the economy at large. One consequence of this failure is a long delay before new techniques are employed for maximum gain. For its in-plant courses in EVOP, Monsanto devotes an over-all teaching time of six hours. This is sufficient to gain support of the foremen and managers, and also to induce engineers, who actually run (Continzted on page 46 A ) Circle No. 27 on Readers’ Service Card --f
DMSO powerful solvent for LARGE molecules
Dimethyl Sulfoxidefor laying d o w n films, forming fibres, or carrying out reactions.
SPECIAL FEATURE
EVOPs later, to consult freely at the beginning of their programs. T h e courses may be taught by a consultant, a statistician borrowed from another company location, or the local statistical engineer. Terminology must be as elementary as possible and actual plant data is preferable. Names of actual variables should be used for realism. After an introduction, fundamental statistics is taken up, starting with a tally of data and proceeding through the chance of a rare event and confidence intervals. Confidence interval of a mean is emphasized in connection with a simple 2 x 2 factorial, together with the idea that several comparisons can be made with the same data. An important topic is the response surface concept of a chemical process, and the power of this approach for improvement by proceeding up the hill (70). Production and cost bottlenecks are illustrared on a response surface. The standard EVOP design in two dimensions is recommended, and a sample problem is worked on a calculation form (2)* T h e real meat of the course is gaming with a process analog ( 1 7 ) . A synthetic problem is formulated which includes cost of a run and the dollar benefit from each per cent of yield increase. The problem then involves deciding when to stop experimenting. The class is divided into teams of three or four people which compete for the honor of having the most net gain. Each of the four teams obtains data from an analog operated by an assistant. The instructor circulates among the teams to clarify points of doubt and to discuss their results. H e guides their thinking, but he does not point the way up the hill. At the end they have a feeling of accomplishment because they have made their own decisions. When the proper competitive atmosphere is established, learning is rapid and permanent. Two hours should be allowed for nearly maximizing the response on two variables, and for discussing progress of the teams and their results. Public. The principal public course, sponsored by the Chemical Division of the American Society for Quality Control (5), is a two-day
46 A
course with excellent instructors, which has been held in various cities from coast to coast. Designed for technical people, preferably with an elementary knowledge of statistics, the course is highly effective, but it cannot serve the broad audience which must be covered in a plant. I n addition to the courses offered by the American Society for Quality Control, many other fine short courses, usually associated with colleges, are offered throughout the country. However, these are not primarily EVOP. Colleges. In the matter of forming habits of statistical thinking, our colleges could help a great deal by requiring that statistical inferences be drawn from laboratory data. Those in the industry realize the difficulty of adding required courses; however eight or ten classroom hours during the freshman year, plus a good reference text (7, 8) would enable students to apply Student’s t and the F tests and confidence intervals to data in later laboratory work. I n each laboratory course, during the sophomore and junior years, a new topic should be introduced, where appropriate for its own data. For example, one laboratory should require a few non-parametric tests, another, calculation of the error, and another several linear least-square fits of data. During the senior year, students should be expected to select a n appropriate way to present their data statistically. At present, this program may sound too ambitious. Nevertheless it can be realized soon through two trends, one in colleges and the other in high schools. The more progressive colleges are recognizing the importance and universality of mathematics. They are strengthening this part of the curriculum by forcing students to proceed farther and faster. In some cases statistics is presented briefly as an exercise in mathematical operations, and in others, statistics is a separate required course. High schools also, to prepare their graduates for college work, are offering more mathematics sooner. I n some cases statistics is included. Such progress is gratifying. However, the needs of industry should
INDUSTRIAL AND ENGINEERING CHEMISTRY
be made known more clearly through the Committee on Professional Training. Its booklet, “Minimum Standards. . .” (I) should state on page 8: “When possible, laboratory data will be reported in statistical terms. For example, see the ASTM Manual on Quality Control of Materials. SDecial Technical publication ’ 1j-C, January 1951.” Suggested Reading (1) Amer. Chem. SOC.. Committee on Professional Training, “Minimum Standards Used as Criteria in Evaluating Undergraduate Professional Training in Chemistry,” April 18, 1960. (2) Barnett, E. H., IND.ENG. CHEM.5 2 , 500-3 11960). (3) Box, G. E.’ P., A!@. Statistics 6 , No. 2, 3-23 (1957). (4) Box, G. E. P., others (0. L. Davies, ed.) “Statistical Methods in Research and Production,” Oliver and Boyd, London, 1957. (5) Chemical W e e k , 75-7 (October 24, 1959). (6) Dixon, W. J., Massey, F. J., “Introduction to Statistical Analysis,” McGraw-Hill, New York, 1957. (7) Grohskopf, H., IND.ENG. CHEM.52, 497-499 (1960). (8) Zndust. Qual. Control 17, No. 3, 46 (1960). (9) Koehler, T. L., Chem. Eng. Progr. 5 5 , NO. 10, 76-80 (1959). (10 Koehler, T. L., Chem. Eng. 142-52 (I3ec. 12. 1960). ( l i ) Mod&, J. i.,Jr., Zndust. Qual. Control 13, NO. 4, 16-21 (1956). (12) Thomas, M. D., Webster, H. L., “Evolutionary Operation Applied to Resistance Welding of Automotive Sheet Metal,” General Gotors Institute, Flint, Mich., 1960; unpublished.
A staff-written feature based on papers
presented
before
the
Division o f Industrial and Engineering Chemistry, Symposium o n Evolutionary
Operation,
139th
Meeting, St. Louis, Mo., March 22,
1961: Brant Bonner, The Dow Chemical Co., “Some Success Stories
EVOP”;
in
W. W. Paris and R. C.
Manring, Monsanto Chemical Co., “Introduction t o EVOP”;
and E.
Harvey Barnett, Monsanto Chemical Co., ”Education for Evolutionary Operation.”
Information
contained in a private communication from H. 0. Hehner, Monsanto Chemical Co., t o the editor i s also included.
by K. A. CHATTO and R. W. KENNARD,l E. I . du Pont de Nemours & Co., Inc. Evolutionary Operation in Plant-Scale Experiments
The Simplified Concepts EVOP is a different approach to process improvement. It applies sound, well tested principles to an area where experimenters encounter more than usual restraints
SOME
O F THE considerations needed in utilizing the concepts of EVOP are highlighted in this report. T h e discussion, covering the middle ground between techniques and successful case histories, begins with the simplest statistical abstraction of a process.
We shall call the process a box
Y
I n general we cannot predict the exact value of y, but can predict only the fraction of time that it will be in some interval, Ay. However, in almost all practical situations we assume that the probability distribution of the observations is at least partly unknown. We are ignorant of some aspects of the situation; that is, we do not know the characteristics of our process. In the simplest case this amounts to assuming that the mean, 8, is unknown. T h e observations provide information about the distribution from which they come. Therefore, they guide us in determining the value of e. Statistical inference is concerned with methods of using observations on the output to obtain information about the probability distribution and the mean e. The case before us now is too simple to consider further, but it does provide the base to introduce a further degree of complexity.
The purpose of any experimentation is to learn something about the function, f. Therefore,
What we want to learn about the process, that is how it responds to controls XI and X2, can take various forms
How does output y compare with a postulated value, say 0 = f (XI = 10, xz = 20) = ZO?
The process is not a tightly sealed box. It has external leads, XI andX2, by which its characteristics can be changed Operation of the process produces an output, y, which for our purposes is a set of data or observations, as we call them-i.e., values of Y , (yl,y2 ...). We assume that the observations are values of a random variable; in other words, the values cannot be forecast exactly, but only within the confines of a probability distribution.
A simple model is that of a normal distribution with mean e and variance 0 2 I
AY
0
Y
XI
Y
These external leads are factors such as temperatures, concentrations, and rates which are under our control. We postulate that through these external controls we can change the characteristics of the process. That is we can change the values of the output. Mathematically, we say that we can change the value of e by changing the values of XI and Xz. T h e relationship between X1,X2,and e is
e
What is the range of operation for controls XI and X?? What is the extent of the nonshaded area? I n the shaded area, the process may be inoperable-e.g., no reaction, or fouling, or PIugging
XZ
XI
When experimental and analytical work is complete, what are the predicted values of 0 in some region for every conceivable combination of XI and Xz? What does a map of the process look like?
= f(X1,X2) VOL. 53, NO. 12
DECEMBER 1961
47A
Which values of XI and Xz result in a maximum 8?
Thus far, the process has not been identified. It hasn’t been defined as a laboratory, pilot plant, or a production process. There has been n o need to. The basic ideas concerning experimentation are universally applicable. They do not depend upon the location in which the experimentation is taking place. The concept behind evolutionary operation is simple. Experimentation can be carried out on a production process; the type that we discuss here is the last type-an experiment aimed primarily at determining where the function, f(X,,X,), takes its maximum value. Answering questions about a process is generally difficult, because three things operate against us: Inevitable variability. There is always noise and this tends to cloud the picture and make it difficult to determine the quantity of interest, say 0 0 General complexity of physical processes. Relationship between the controlled variables and the output is not a simple one 0 Economics. There is a limit to the money that we can invest in a n experimental program 0
These three elements impose on the experimental program, restraints which are markedly different, depending upon the context of the experimentation. There are many differences between the laboratory and the plant. When we choose to do our experiments on plant production equipment, we undertake additional responsibility-i.e., for protecting the company’s investment in the equipment and raw materials we are going to use. Engineering knowledge of the process and its equip48 A
ment is a must to prevent damage to the equipment and injury to the people running it. Also we must remember that investment in equipment was not made for running experiments, but for manufacturing a product for sale. Unlike a pilot plant or laboratory, our primary interest here is in the product we can sell. If we can improve the product while making it, so much the better. But, on the other hand, if we have selected an experimental pattern that results in a lot of poor quality product, we have failed in our responsibility, and our actions may affect the economics of the entire company. Before tampering with a production process, we had better know what we are doing. And before starting an EVOP program, plant supervision usually wants to be convinced of this. T o do this we can use a pilot plant or prototype equipment, when available, to demonstrate capabilities of an EVOP program. However, if given an opportunity to use such equipment and we cannot demonstrate the value of our program and our own competence, then we have no right to ask plant supervision to back an experiment on a plant scale where the stakes are big. Once plant supervision has been convinced that its investment will be protected and that the program has potential gain, we should dig deep into everything we know about the process. Early in the procedure, of course, we must determine which process variables are to be studied. And, because a basic variability in our observations is inevitable, we must select those variables which in a reasonable time will cause sufficient change so that the noise of natural variability does not disguise the effect of our changes. Previous experience in a pilot plant may indicate which variables are important. But care must be exercised here because other variables, not looked at in the pilot plant, may exist, which are equally important. Other variables may have changed in importance since scale-up to the commercial plant. Sometimes, major variables can be screened out of production log records. However, this procedure is likely to be more successful if deliberate changes have been made so that what we see is more than noise. If production data are not
INDUSTRIAL AND ENGINEERING CHEMISTRY
felt to be reliable, perhaps an engineer should record settings of variables over a period of time. Also, a close look at the process chemistry and mechanism should indicate candidates for test variables. Two other items must be considered: How much and how often should the variables be changed? The how-much aspect is the one that can really hurt if we are wrong. When information about what to expect is unavailable, it’s best to be a little bit conservative. Perhaps a reasonable change is one equal to the degree of control for that variable. For example, if a temperature can be controlled to f 2 O , then we would make changes of 4’. Perhaps larger changes could be made. I n any event, as much thought should go into this part of setting up a program as any other part. The how-often aspect depends on the process itself and the data collection system. There are two main principles to remember : The process should return to equilibrium before data are collected for any set of operating conditions. For mechanical processes, this is usually no problem because the system usually will return to equilibrium. within a couple of hours. For some chemical processes, however, it may take days or weeks for the full effect of a change early in a process eh :! s? of conditions should be run long enough to obtain sufficient information about 0 for that set of conditions If these two principles are met, then the changes can be made at any convenient interval-once per shift, once per day, or once per week. T o this point we have considered only one output variable, but obviously in any practical case, there are many outputs-e.g., yield, productivity, cost, profit, and a host of others. Some care must be given to the choice of the output variable that is going to be maximized. T h e point at which productivity is a maximum is probably not the point at which yield is maximum or profit highest. I n such cases, we are dealing with a constrained optimization problem-an output function is to be maximized, but subject to the restrictions imposed by the other
(Continued on page 52 A )
For the fourth time in five years, increased enabled Celanese
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4 A
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TRIMETHYLOLPROPANE
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Circle NO. 53 on Readers’ Service Card VOL. 53, NO. 12
DECEMBER 1961
49A
50 A
INDUSTRIAL AND ENGINEERING CHEMISTRY
Circle
No. 34 on Readers’ Service Card VOL. 53,
NO. 12
DECEMBER 1961
51 A
SPECIAL FEATURE outputs. Converted into practice, the simultaneous consideration of all outputs means that we must:
b Combine all output variables into b b
a single variable which is a n index of performance and optimize with respect to this index Make observations on all output variables and follow each closely to avoid taking a direction which leads to the wrong consequences for any of them Abandon the maximum-hunting type of experimentation and use another approach. For some chemical processes, the system may be so complex that a computer is needed to assist in seeking and maintaining optimization
I n connection with measuring output we should perhaps go more into detail. Because we must do some algebraic manipulation from which certain things are inferred, observations must be sufficient and of the correct form. Perhaps more measurements on one output variable are needed because of high analytical variability. Perhaps our data should be transformed to stabilize the variance, such as taking the square root of Poisson-type data. We may even have to develop or install a new measure which is more sensitive to changes in the product. Since our actions are based on values of the observations and what we do with them, we must ensure their reliability and validity. This is a n important phase of the overall program. One other thing. We must be certain to provide for following the product throughout the entire process, especially in multistep processes. Observations can then be correctly related to a specific set of operating conditions. Also, we must not lose sight of another concept for improving the economics of manufacturing processes. This is that of statistical control conceived by Shewhart. Because of high variability, it is necessary to operate a t each experimental point over a relatively long time, even though operation may not be consecutive. Therefore it is important to have the process in a state of statistical control and we must have, a t least to a first approximation, the same error distribution a t each point and the same distribution each time we run the same point. If not, then unidentified
52A
sources of variation exist, and if their effect cannot be predicted, then we may be led to false conclusions about the variables we are changing. Let us return to the process again and look at other factors :
Here, the process looks the same as it did before, except that input variables Z1and Zz, are added which are not under control
as described may not be adequate and a more sophisticated analysis may be needed. For most industrial processes, a n E V O P program will be successful provided that the rules of the game are followed. This, of course, assumes that once started, the E V O P program is allowed to continue. But if plant supervision, assured of our competence initially, has given permission for the program why should it want to stop operation? There are several possible reasons, none of which should be justifiable. The laboratory complains of too much additional analytical effort 0 Process supervision claims it upsets their daily routine. Bad product is being made 0
9=f (X13X2,ZI, Z2) Y * f ( X I , X ~ , ~ ) - S T A T I S T I C A L ANALYSIS
and Z2 may be variables which we haven’t considered and haven’t bothered to insist on their control. They may even be variables over which we have no control. These Z’s could be factors such as humidity or raw material quality. We do our statistical analysis assuming that 6 = f ( X l , X 2 ) but really Z1
e
=
f(~~,x~,z~,z~).
An EVOP program usually covers a considerable length of time, and often it is impossible to isolate the process completely from other experimental work. I t is impractical to insist that all other technical effort on the process be stopped. Therefore, there is
Another potential source for error-variables W1 and Wz resulting from other experimental work not under EVOP control
XI
x2 e’(xI#xpwI,w2,zI, z2 1
Y-f ( X l , X 2 , e ) -STANDARD EVOP ANALYSIS Y.f ( X I , X ~ , W I , W ~ . ~ ) - S H O U L D USE THIS
Thus, variables, W 1 and Wz, are under control but not by us. They represent changes in the process from other technical effort which may not be related to what we are doing, but can nevertheless affect our results. If such is the case, the simple statistical analysis
INDUSTRIAL AND ENGINEERING CHEMISTRY
T h a t a bad product is being made should not be a reason for termination, unless we have goofed. All personnel in supervision related to the process under study must be acquainted with the facts of life concerning experimental programs. They must realize that for every set of conditions which improves 8, there is another set for which it is worse, unless we are at or very close to a point of maximum e. Supervision must be assured that we will recommend a change when the results of our statistical analysis indicate that the differences in e we have been observing between sets of conditions are real differences. Agreement must be obtained prior to a n E V O P start-up that we have control over the conduct of the experimentation. This, of course, means that we must keep everyone informed of the progress. This can be done by the information board, weekly meetings, or a discussion of results after each complete cycle. T h e supervisor directly responsible for the process should have a n up-todate chart or summary of the status of the program. There must be a very close cooperation between him and us. It’s his process not ours. I n summary, we wish to emphasize that E V O P is not a new technique nor is it a new gimmick to peddle. Rather, it is a straightforward application of sound, well-tested principles of experimentation to a n area where experimenters encounter more than usual restraints. It is a new and different approach to process improvement. 1 Present address, University of Delaware, Newark, Del.
