B E N J A M I N P.
COE
C A P A CI T Y
S T R E T C H
Equipment size is not the
onb element controlling capacip.
Blmding operations research and chemical engineering methods may reveal ways to postpone plant investment
ix years ago, General Electric’s Silicone Products
S Department embarked on a systematic program
aimed at defining, and where possible, stretching system capacity. As more precise techniques were used to measure capacity, it was found that the measurements were bringing into focus many ways that capacity could be stretched and investment postponed. Failure to measure capacity accurately can be disastrous to a business. If a process runs out before new capacity can be put into place, sales are lost. If new facilities are installed too soon, profits suffer. The timing factor is critical, especially if the business is growing rapidly and requires expansion frequently. The six-step program below used for maximizing capacity is basically a specific application of the engineering approach to a problem. Implementing such a capacity-stretch study calls for combining the traditional disciplines of the chemist and chemical engineer with operations research and industrial engineering methods. STEPS FOR CAPACITY STRETCH
c
-Measure the elements of capacity to show present status and define stretch potential -Gather ideas on how to improve each element -Test these ideas by experimentation -Evaluate the ideas to point out profitable plan of action -Revise system or operating methods -Remeasure elements of capacity to determine progress
As for result, we can cite six major processes where production rates have been significantly increased ; these processes range from simple unit operations to complex reaction systems. Over $5 million investment was deferred for an average of about three years. In addition, and much to our surprise, we found that some of the following side benefits were almost as important as the postponement of cash outlay: -Deferral
of expansion allowed time for technical development
so that process improvement could be incorporated in future facility design -Less investment per pound capacity was required f o r new facilities when they werefinally needed -Almost invariably the capacity-stretch measurements yielded direct cost savings. Labor savings resulted because more is made in the same size unit; and utilities and maintenance costs were reduced in systems where costs are primarily a function of hours run rather than throughput THE ELEMENTS OF CAPACITY
Defining “system capacity” would, on the surface, seem unnecessary. There is a problem here, however, of multiple meanings. A capacity number used successfully for scheduling production would result in serious errors if used for predicting when new facilities should be in place. For example, an engineer is asked to measure the capacity of a system. From past material balance data he sees that 2 million pounds of product went out the back door in one month when the plant was running at “full steam.’’ He mult$lies this by 12 and reports a 24 million pound capacity. If this number is used for production scheduling it should prove fairly accurate. If, on the other hand, f i e is asked to plan a schedule for constructing new facilities, his calculated capacity may be much too high. How could this be? Take a situation where sales juctuated and only 6 million pounds of product are made in the Jirst half of the year. Even if a fullsteam 12 million pounds are made in the second half, the most that can be made f o r the year is 78 million pounds. If the business normally juctuated this much, 78 million pounds is the true capacity and the process will probably run out seueral years earlier than predicted.
AUTHOR Benjamin P. Coe is a Project Superuisor of General Electric Co.’s Silicone Products Department at Waterford, N . Y. VOL. 5 4
NO. 3
MARCH 1962
47
F&re 7
Possible production rate is therefore only one element of capacity. Other elements can be defined by two equations.
For short term production scheduling:
C,ad. = R(8736)F
Equation (7) hours 8736 hours = 52 weeks X 168 week Where: . , ,C , = schcduling capacity and is thc maximum rate at which a process can produce on a short term basis, say 7 to 4 month = product rate, Ib.lhr. = servue factor; fraction of time process is on stream, limited by normal activities such as downtime, shutdown, maintenance, start-up
L&),
For predicting capacity run out: C.xd.
=
R(8736 - M ) (F) ( Z )
Equation (2)
Where: CaM.= standard capacity, Ib./yr. Most probable product that can actually be produced in a year M = avcrage h r d y e a r system is down due to major
Z
infrequent maintenance or process s h u t d m = sales factor; limitation in cafmiy expressed as a fraction of total output, accounting fm lower production rates because sales juctuationr and invmtmy capaciy will not allow full production the year around
Capacity, expressed in t h i s manner, is useful for d d b i g continuous processes. For batch processes, where the product comes out only during one part of a sequence of operating steps, the term B / ( T T’)is equivalent to (RYF), where B is average pounds product made per batch; T, average on-stream batch time in hours; and T’, average n o d turn-around time between batches in hours. Capacity has to be predicted; it can be m e m e d only when it is reached; and in a growing businegs this is too late. This means that when measuring the elements of capacity we are not dealing with absolute answers. Each element has a didbution of possible average values about the most probable average value. The prediction of capacity from the above equations will be reliable if for each element this disbibution is narrow and not badly skewed (Figure 1A). This is generally the ease for well defined processes. Every now and then, however, the distribution looks l i e Figure 1B and use of an average value will not give an accurate capacity. This sometimes occurs in semicontinuous p m s e s
+
48
I N D U S T R I A L AND ENGINEERING C H E M I S T R Y
where not many runs per year are made-i.e., run time is long, and the elements B, T,and T’ are highly variable. In this case the most probable capacity and the distribution of possible capacities can be determined using a Monte Carlo analysis (7). To use this approach three sets of cards representing the distribution of probable values for B, T,and T’are made. By drawing these cards in the proper order a year’s operation can be run through on paper. By repeating this a number of times, a distribution of possible capacity values is obtained. On beginniig such a study, we often found that some of the needed data were already in hand, a result of rewrds kept during operation. Usually, however, additional more carefully planned plant tests were desirable.
STEP 1.
MEASURE STATUS OF ELEMENTS OF CAPACITY
Measuring Hourly Production Rate Although the element R is straightforward to measure, there are certain precautions that must be taken if the numbers are to be wrrect. We must choose units of capacity that are most meaningful. If a system produces a crude product, capacity might be best measured in terms of the usable portion of the crude. This defines the present situation more precisely, and also points out an 3m for capacity increase-raise the percentage of product in the crude. Make certain that the equipment is being operated at the true maximum rate. This may require the piping up of additional storage capacity, or installing metm having a higher range. The limitations of minor auxiliaries are best overcome at this point. Make certain that the production figures obtained during testing are unbiased and representative. When attempting to measure capacity, there is a natural tendency to use production figures that have been achieved during a good period, with the feeling that “if we run the right way we can do this all the time.” This might well be true, if the methods were provided to run the right way. Periods of high and low prodnction rate can tell much about what makes the process tick, but they do not deline the present status. The possibilities of seasonal effects and raw material variations must not be overlwked. Sequential averaging will aid in determining how much data is necessary. -Average the data as they are obtained, always increasing the number of data pieces included -Plot the sequential average against time -When the plot becomes a horizontal line with no fluctuations, it represents a true average rate (see graph). Batch processes present pitfallr of their own. The equivalent to rate, R, in a batch system is product per hatch, B, divided by processing time, T. The final result of a batch process wmes out during the last of a series of processing steps. Much can therefore be learned from a log sheet that separates the processing
Ir
time into these steps. As in handling R data, sequential averaging of time data on each step is helpful. Quite frequently, when maximum production is not required, batch processes are scheduled on a labor limiting basis, and batches sit idle in the equipment awaiting labor availability. Care must be taken to eliminate such time periods from the rate measurement. Measuring Major Infrequent Shutdown Hours
If a yearly plant shutdown for preventive maintenance is standard practice, this factor, M, is immediately predictable. Other major infrequent maintenance shutdowns, however, are often unpredictable, and it may require several years to get enough data for a reasonable 7
*
estimate of frequency and duration. In this case the estimate will have to be made from experience with other similar processes. When a process is one of a series of processes, its capacity can be affected by shutdowns in the processes before and after. Since there is generally inventory capability between processes to eliminate the effect of normal shutdowns, this effect is infrequent and can be included as part of M. Whenever any type of irregular shutdown begins to become regular, its hours must be included in the F factor and, thus, be reflected in scheduling capacity. Measuring Service Factar
This factor, F, can be measured by taking the ratio of actual production for a reasonable time interval to average production rate, R, times total time in the interval. Alternatively, average downtime and ons t r e a m hours per batch can be measured, and :
T F= T + T’ Whenever the service factor has a significant effect on capacity, we have found it worth while to take a good look at its make-up. Downtime is generally a period of great and varied activity. It is usually very difficult to see at a glance how the time is being spent, and virtually impossible to log the time spent on each turn-around activity. One good way of getting around t h i s dilemma
% of TOTAL
I I I I I I I I
08s.
I,dd
Ratio delay form and example of rwulfr. An obsmotion would consist of mcrely a check for each system under the proppcr activity. Comments w d d be expected on each “Idle” and “Other” observation. Note that informafirmisgaincd not only about service factor ar a wkole but about cnch of the threc system. Fm instancc, the longn start-up and shutdown times for System 3 might indicate poorer heat transfer than the others. The instrument and pump maintenaruc in Systems 2 and 3, respcctiwly, also indicate spsciel problmu
is to use random observations to pull together the story. This is done by aratio delay study (9,illustrated above: -List job categories felt to contribute significantly to downtime -Schedule a fixed number of observations per shift using a table of random numbers (foremen can make good observers) -As you gather the data daily, ask for details on items where cause is not clear from the observation alone -Obtain an accurate per cent of total time in each category by sequential averaging Measuring Sales Factor
Because it must be based on history of fluctuating sales, the element Z is the most difficult to predict. The most important pieces of information necessary for predicting the sales factor are: -History of sales fluctuations --Inventory capacity For instance, if a business with no inuentwy capacity ran last year at 50% c a f ~ a dfor y thfirst h.lfyear and 75% for th last half, it can be cxjected that at maximum capacity, it will run at 6v0 thfirst h d f and 700% the last half. This giues a Z factor of 0.83. If,on t h 0 t h hand, it hns +ient iwmtory c a w i t y , to run at t h last three parters of the y e w , Z = 0.25 X 0.66 0.75 X 7.00 = 0.915.
+
Time lags between sales, production scheduling, and production can also affect this factor. No panacea for obtaining the sales factor can be given here. In general, the operations research approach of deriving a sufficientlyaccurate mathematical model of the business situation is necessary.
tn
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!
I
I
1
6
! k
I
I
I
I
9
10
II
12
I IJWEgS
The scqumtiol averagc of these data points lewled out after only about 5 obsnwtians, giving an averagc of 75 + 0.8. For this study, 6 to 8 data poinfr WNC sufient
STEP 2.
GATHERING CAPACITY STRETCH IDEAS
Once capacity is defined, and it is decided that a program for stretching capacity would benefit the business, a good brain-storming session is in order. It has VOL 5 4
NO. 3 M A R C H 1 9 6 2 49
been our experience that the process of measuring the variables in itself will shed light on many good stretch approaches. The disciplines and techniques for successful ideation have been covered heavily by literature in the last 10 years (6). Perhaps most important is the need for dividing the over-all problem into many small subproblems so that thought can focus on a subject without wandering or becoming too general. Even when studying one element of capacity, pick out the important controlling concepts and study them separately. For inrtance, in an exothermic reaction gstm w b e hat transfer, UAAT, is a controlling concept, attack each variable separately: REACTION TIME
Problem: to increase A -Putjnr on h a t transfer side with lowest h -Insmt a heat h m f m coil -Increase jacketed hight of vessel
A few hints based on our experience may be helpful.
When looking at the R measurements for a complex system, don’t overlook the possible effect of the a u x i k i e s . If an auxiliary is the bottleneck and not the main portion of the process system, capacity can often be increased at relatively small cost. Several cases are known where distillation columns, originally sized conservatively because of lack of information, were stretched to over 133% of previous capacity by purcbasing a larger reboiler, or condenser, or flow instruments. Batch systems have their own peculiar concepts to bok for. For instance, it is possible in one system to increase capacity by increasing the per cent yield per batch and in another by decreasing per cent yield per batch. This occurs because equilibrium reactions having a logarithmic yield curve reach a point where v u y little extra yield is obtained for an increase in reaction time as shown opposite. This maximum capacity point may be different from the minimum direct cost point. If so, an over-all cost evaluation will be necessary. Try to squeeze value out of every cubic inch of vessel when running a batch process. A recent example of capacity increase occurred in a system where product is boiled out of a kettle and catalyst left behind for the next batch. Because the amount of catalyst remaining varied, a constant conservative quantity of charge was fed in each run to prevent overcharge of the kettle. Sometimes the kettle was only two thirds full; almost never was the level up to the maximum possible. When a radiation detection unit was installed to detect high level and allow complete filling for each batch, a 20% capacity increase resulted. Some ideas that have been used to reduce downtime, T‘, or increase the service factor, F, are: -Reduce start-up and shutdown time by converting batch processes to semicontinuous or continuous -Reduce idle time due to lack of parts by improving spare parts ordering procedures 50
INDUSTRIAL AND E N G I N E E R I N G CHEMISTRY
MAXIMUM CAPACITY
.J
3J
I
I
I
I
I
I
REACTION TIME
-Reduce maintenance downtime through a better preventive maintenance program -Study a particularly important maintenance job, such as cleanout of a kettle using a flow process chart (5, 7), a time and motion tool of the industrial engineer. Don’t dismiss the sales factor, Z, as without potential for capacity stretch. Far a manufacturing operation with many sequential steps, the further a process is removed from sales, the greater the amplification of sales fluctuations. Generally the fluctuations in rate of production for such processes should be carefully scrutinized; the fluctuations may be greater than necessary. Finding more sensitive methods for setting inventory levels and scheduling production or perhaps installing more inventory and storage capacity might allow Z to be increased. STEP 3.
TEST IDEAS BY EXPERIMENTATION
This step consists of measuring the effect of certain independent or “cause” variables on the dependent elements of capacity. The idea list will generally point out the need for two types of measurement programs. The least expensive of the two is measuring the dfect
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-11-13-15-17-19-21-23
WEW
POINTS IF DATA AVEPAGED ONLY M E R EVERY SIX POINTS COLLECTED
75 73
ROLLING AVEPAGE
Use of a rolling avnage of six obrnontions xhows trends as each tlcw doh point is obtained. If obscrvotiom wnc nat amaged on a rolling basis, it would bc six week before thc third amogc point (18th week) would show thc upward trend
UMlTS
t
I1
of variables within the present capability of the system. The ratio delay study to determine the effect of various maintenance jobs on service factors is an example. Another is the optimization of average production rate, R, by EVOP techniques (4), or, when it is difficult to hold all variables under control, multiple regression (2). The other measurement program category calls for a deeper probing into basic design variables such as heat transfer surface and agitator speed. Naturally it would be very expensive to change these variables significantly. But often by making small inexpensive modifications to achieve a little change, enough can be learned about the trend to show that the risk of a major alteration is worth takiig. At this time it may be wiser to go back to the pilot plant where the basic design variables can be changed drastically at less cost. Pilot plant work is definitely necessary if “playing” with the production unit might affect quality of a finished product and subsequently jeopardize the customer, which, of course cannot be tolerated. At this point, two warnings are in order. Make sure that when an optimum setting is found for a variable affecting one element, it does not affect another element adversely. For instance, a higher temperature may increase production rate, R, but it may also decrease the service factor by fouling surfaces. Also,when measuring the effect of certain variables on capacity. be prepared to determined their effect on direct wst. The ultimate value of the capacity stretch will have to be measured in terms of dollars. STEP 4.
EVALUATE IDEAS
Evaluation in terms of one parameter, dollars, is now necessary to pull .the measurements and ideas together to arrive at the best capacity stretch program. Deferred investment benefits require a profitability measure which recognizes the time value of money. Such methods as annualized cost, capitalized cost, present worth, and discounted interest rate of return are acceptable.
STEPS 5 AND 6. REVISE, REMEASURE, AND CONTROL After a system has been changed, it is important to evaluate the success of the program and reassess when new facilities will be necessary. Also a production control program to ensure that capacity elements do not drift back to the old operation may be worthwhile. Two measurement techniques, useful for measuring the improvement trends for the various elements and to make improvements stay in effect, are: -Use rolling average of N pieces of data (when each new piece is added the oldest piece is discarded). A plot of this average, as illustrated above, shows trends more clearly than plotting each piece of data where random variation can be mistaken for trends. The best N can be arrived at by trial and erroyi.e., plotting the data with increasing N and noting the value of N that shows trends soonest, without confusing trends with random variation. --If control is desired, the rolling average plot can be converted to a control chart. It is better than the standard control chart which works with an average value for every N pieces of data, because an average control point is obtained for each data piece (figure above).
Susgesled Remdhn
( I ) Churchon, C. W., Ackoff, R. L., Arkoff, E . L., “Zntroduction to Opnotiom Research,” Wiley, New York, 1957. ( 2 ) Davies, 0. L., ‘‘Dei@ and Analysis of Indwtriol Expnimcnfs,” Hafnn Publishing Co., New York, 1954. ( 3 ) Douies, 0.L., “Sturirricql Methodr in Research and Production,” Ibid. ( 4 ) Huntn, J . S.,Chem. Eng. (Scpt. 19, 1960). (~, 5 ) Industrial Ewineeriw Handbook f H .B. Movnnrd, cd.), Chop. 3, ~. McGraw-Hill,%ew Y k , 1956. ’ (6) Osborn, A . F., “Applied Imagination," Sm’bnn‘s, New York, 1067 .I”,. ( 7 ) Robbins, M . D., C h . Eng. (June 15, 1959). V O L 5 4 NO. 3
MARCH 1962
51
