Document 3ezLb6v48mknmbOqvJbk8qpzJ

HBF&HT 10 OB-.. DUPONT ,3' ^ MARSHALL LABORATORY LIBRARY s, io m f&m m wmms * c o mpaiio imo FABPICS I FIHXSHIIS DPBASrMlMT BESEAfton i mmmfmm mw j s io k PHOCLESS SHOIOISESJ! BQBHE COPY SOI FOR jCBCULAIHW &< M7-*R0CB3SW COHTOSIOM. COgT MODEL By 2 P* j> Shanoahaii . Pate iQaaad: lfovarobex1. I:S 1969 Projects 4.1S5Z0* #" y m&mjm ,o .' A proposal, for- a. retrisf^e ''^"Pp c w b " eoffromioa sts 1 ifet sys&#isT mm& W :mM\tIfete t a b l e o p mmrms SUlWABY Introduction * * Ob.jete Conclusions * . Re ommendatteas DISCUSSION The Hanufaeturlngi Process Functions of a Cost System Cost Construction * * . , Initial,Investigations ,, Cost Allocations , . . , Basis for Comparisons ,* Conformance to Specifications Cost Calculations , . . .' Cost.Equation Comparison Cost Analysis Operational Analysis . : .* .a 6 . * *' ' :* - O * 9 9 APPENDIX DISTRIBUTION M 4s*u! O- Cft 'O* U*^ Page Bo, H ** H>`^ StlMARI Introduction For some time, Manufacturing Division ha# felt that the group cost system nm in use was not yielding the type or quality of informa tion that should be, available to aid in decision making., In particular, it was thought that intra-group difference#,; batch sia effects arid inter-plant variations were obscured. Pinpointing high cost factor# and the reasons for them was difficult to do. It was decided that a cost system should be developed that reflected the realities of paint production,, Objective Process .Engineering was assigned the .objective of devising the basic form of the expression for "^T'-Eroses#1' conversion costs which could then be used as a .model for other production operation conversion-,'. costs. She expression was to accurately reflect production process realities and be useful in identifying those, areas where cost improV#*- menis could be effected. * Conclusions. Bata should be gathered as set-up costs and running.costs for each, individual production batch. For reporting and prediction,, the, data can b# averaged only for each combination of product and pro duction areas further summing and averaging will result in unaccept able distortions., Mathematical forms of the expressions ar given- in the body of the report. Within a production area, equipment charges can be. averaged; i,,*. . the hourly charges for all machines can be said to be equal Without introducing unacceptable distortions, Tli way in which the data .are gathered can also permit evaluation of equipment utilisation, conformance to operating standards and useful cost ratios. Recommendations The, study should be taken over by the Business.Analysis Section of A & BA Division. A six-month operational trial should be initiated to evaluate the .overall utility1 of the system, ,io: establish the cost of installing and maintaining it and determine the modifica tions that may be,required to,blend the system into the Business Analysis Section1# overall.computerised cost analysis scheme. DUP030002490 2 in adaption # a study should t>' initiated `by A i BA Division on th manner in whiqh Indirect costs are allocated ars|>; and within the several plants * ms tih DUP030002491 -3- P1SCUSSX0H The Manufacturing Process From a process standpoint, paint manufacture can be thought of as a sequence of batch processes,, During actual processing, certain operations , such as sand grindings act as continuous operations* However, yen those operations are not truly continuous sine the Input materials are prepared as & batch . and when the batch is exhausted, processing ceases* .Even though the sand'grinder itself may operate without interruptions, it operates on sequenced batches* The situation -has long been recognised by Manufacturing and A & BA .Divisions/. Traditionally9 "batch cards" have been issued to authorise processing of materials * The production areas produce and record by batch* regardless of the operation* For this study,. m shall also use the batch approach. Functions of a Cost System . For the purposes of this study, a cost system Is viewed as a tool that will allow Management to: 1, determine the actual costs of operations that have been conducted. 2. identify high cost elements and indicate suitable areas: for cost reduction efforts. 3* predict costs f future .operations, t, determine the effectiveness of investment utilisation, : Cost Construction There are several ways of aceumu.lat.ing and presenting cost data,: There is no one "right" way,- For each instance, the proper system' to use is that system which accomplishes its defined objectives-: at the lowest system cost. Average Costs; Our traditional manner of cost thinking has been in terms of bulk gallon averages. Costs and production are gathered, and summed over the set in question, say a plant-.and ,v the mean obtained by simple division, Avg, Unit Cost {$/0al) * Total Cost ($)/Tot&l Prod. (Cal,) . This type of averaging can be applied to any accumulation Set ' desired. The. average obtained, whil# mathematically correct, may have no real significance,- .If the1' standard deviation -Of the population is large, the average will-provide little significant information about the action of the population. The average is, however, quite useful when plotted against time to show any tendency of the population toward drifting. DUP030002492 li it .is possible to fleam a bit more information ..from a cost average if the concept of batch operation is used*, Any batch process an be subdivided Into a set-up segment and a -inimlng segment. The set-up portion is generally relatively insensitive to the batch sine* with the rtnning segment proportional to the batch else except for start-up effects. The averages obtained are subject to the same significant* factors .as those mentioned above. However* the segmentation into set-up and running parts will Improve the analysis and prediction func tions of the system.. Specification. Cost.si it is possible to specify the rates at . whie!rall^hasiF'olf any operation are to be performed. Opera- ttonal costs an be constructed using' the specified rates and actual production volumes for the items in question, , This approach .recognises that there may be significant cost differences among products. However* accuracy of the .system is completely dependent on the actuality of conformance- to specifications,- Any deviations will result in distortions in stated costs,. In this ease-also* costs can be summed over any set desired and an average calculated. Since some information is available about the distribution of the set population* more meaningful analysis and. prediction are possible than' when gross averages aldne: are 'used. Detail Costs; It is possible to accumulate cost elements in-.-, almost infinite detail and to any accuracy and precision desired. The minutiae, can be summed over any set desireds and are avail able for any level of analysis or prediction desired. The. balance to be determined Is that between the value- of the detail and the cost' of obtaining and using' it,' initial Investigations Since the basic unit of production is the batch* it was decided to. accumulate costs on Individual batches. Data necessary fall .into, two categories.* the physical inputs and the dollar charges associated with those inputs. The complete costing of a batch would require , inputs to the equation where % * S X Cg * 0 X C0 gp total dollar cost of the batch $ ' ** set-up hours Cs * dollar charge per set-up hour O' operating.hours .: O0- * dollar charge per operating hour These .inputs,, were not routinely available and had to be developed.^Y; DUP030002493 5 Th Toledo Plant was selected fop pilot investigation, Toledo routinely maintains & detailed record of '<?4T-PrsessI! operations -in the form of a fear graph which .gives a continuous record of- the operational status of each sand mill, The inputs an fee constructed from the data on the fear chartsa batch cards3 and plant .accounting records* as shown in Table 1, TABLE 1 '^T-PROCESS" COSTS DATA SOURCES BATCH CARD bar c har t Line No, Line No, Code No. Cod No, .Batch No* ' Batch No, Lb, In Mill Deed - Lb. Out' Cleaning Time Specified Speed Loading Time Specified Mill Size Mixing Tim Specified Passes, PLANT RECORDS Milling,Time : ' Maint, Tim : Mill Sit ?s, 'No. . ` ' . Dept. Shift# Sehd> ' Investment' Detail PLANT ACCOUNTING MISCELLANEODS Dollars Charged Stay Tim Equiv. Man~Hr, Charged Analysis, of the data gathered at Toledo indicated that the approach chosen was valid ,and a. Divisional analysis should be done. Accordingly, all paint.manufacturing plants in the Division were ashed to keep fear chart records on sand mill operations- for the month of 3anuary, 1969* . A sample bar hart is shown in the Appendix in Figure 1. On the chart9 the different operational steps are drawn in different colored inks. The blank spaces show machine idle time. .(I DUP030002494 la addition t its. use 1b a cost program, the chart provides m excellent tool for production supervision, giving mi instant summary of a day*.a operation, current status, reasons .for delays in production and an aid to allocating equipment and manpower, *e fear charts were kept by the operators as a part of their daily duties. We believed that it was orueial for the operators to realise that the bar harts war a record of machine operations and not a cheek on the men. Plants5 supervision were instrumental in bringing about this realisation, and should be cograiended for a job well dona. Arrangements were made for Plant Accounting sections to record the information needed from the batch cards and provide the other data needed. Her .again, cooperation was .outstanding,. Cost Allocations Strictly,, each sand mill has a slightly different cost of operation .from very other sand mill. Obviously, units In one plant can ; have grossly.different costs from those in a second plant. Our . concern was'whether all units in a single location could be. treated alike or whether further subdivision was necessary -After consider ing factors such as operator attention required, depreciatloh:, ' repairs, power, floor space and the like, it was decided that the; error introduced by assuming: equivalence of .hourly'expense' was' small enough that`it would not affect the validity of the study. When, overall expenditures were compared, it was evident that, for Plants having .more than one production area involved with sahdjniXllng, each .area must b@ considered separately. rfhe total cost of operating a sandmilling area-including both-;. direct and. indirect costs had to b split between set-up., and: operating charges to provide inputs for the basic batch cost . equation. It was decided to allocate costs for operating and for .set-up on the basis of man-hours expended- .for each. Operating dollars' were then.reallocated to machine-hours operated* Admittedly* this distribution is more arbitrary than that regarding machine differences, It was felt* however, that most direct costs were associated more with time than other factors {such as volume) and that separate reallocation of the indirect costs was not Justified for this study. If it is decided to carry this work forward* a . reappraisal of the basis of cost allocation. .should assuredly. be an integral part of the work,' Basis for Comparisons The batch.cost as calculated was used as the -benchmerk* When ... groupings ' were made, and averages east, these were compared to the costs of the same batches calculated by batch, . DUP030002495 Before attempting; to construct this type of cost, a survey was made of the degree of conformance to running apeelfleatlons. The results are show In Table XX. It was immediately apparent that conformance was so poor that use f this model would be valueless* TABLE II Plant All .A B Ic Id . e :F Q H .% 3 : IS n47-PROCB$$'M COSTS CONFORMANCE TO FORMULA SPEED PERCENT OF BATCHES 100 tk up Percent of Formula Speed 100/80 80/60 60/40 40/2.0. 20 1 down ;s Percent of Batches 16.6 . 11.2 27*3 . 14.7 25.0 12,9 0 2 a, 2 2.9 10,5 10.7 5.n 8.3 XX o X 10.8 33,3 8.8 12.8 35.2 -15.0 9*1 5.4 10,3- - 7.8 18.2 23,8- 23.6 26,9 22,6 11.1. .14,5 20,2 16,2 14,7 38.3 40,2" 14.7 21,2 . 20.5 17,8 20.1 28.2 29.0 ' 25.7 25.7 .25.0 25.0 8.3 20,0. 27.7 26.9 13,7 21.6 19,3 12,4 : 18,5 30,0 12.1. . 31,9 32,8 -6,7 ' 1,1 . 11,3 . :2.9: 4,9 ' 12.1 5.5. 0 3*6: 1,1 18.6: 5*2.: Cost Calculations Eleven major permutations of cost were calculated, including the individual batch cost (On below) used as the benchmark* Thir variations were constructed to determine the amount of averaging... that could b tolerated without introducing unacceptable errors v in cost .statements and predictions * This, in turn, determines the degree of data detail that must be.developed. Mill the data used here are from sandmilling9 the equations as written can apply . to .any production operation or series of operations, The equation in general are sequenced in order .of increasing level f data detail, requirement. . DUP030002496 8- 1) Division overall average Cl * Dd * B aD" Dp * total dollars charged by the division % total gallons produced by the division B * gallons in the batch being costed .2) Plant overall average 02 * Dp x B GP . Dp total dollars charged by plant making batch Qp * total gallons produced by plant making batch B * gallons in the bateh being costed 3) Division overall average setup per batch and division - overall average running cost per gallon c3 * %D * %p * B JgT Ds d * total -dollars charged to setup by the division Nd 33 number of batches made by the division Dq d total dollars charged to running by the- division Gp total gallons produced by the division B- * gallons in the batch being costed t) Plant overall average setup per batch and plant overall, average running cost per gallon CIS Dgp + Dq p x B Np" Gp~ Dgp total .setup dollars charged by plant making batch Np m total number of batches made by plant making batch Dq p * total running- dollars charged by plant making batch Gp total gallons produced by plant making batch B gallons in the batch.being costed 5) -.Division average, cost for the product ' , 0;5 * DD0 X B %2 DpG total dollars, charged to the product by the division GDC total gallons of the product made by the division :' B ' ." - m' gallons' in . the- batch' being.. costed DUP030002497 6) Plant, average cost for the product Og 43 Dp0 * B ^ic Dpc * total dollars charged to the proemt by plant making batch PC * total gallons of the product made by plant making batch B gallons In the batch being costed 7) Division overall average setup cost per batch and division average running cost per gallon for the -product Cy a Dgp + Dq d 0 X B Hp 000 Ds b total dollars charged, to setup by the division '. No 83 total number of batches made by the division Dope** total running dollars charged by division to the preduet : 000 * total .gallons of product made by division B * gallons in the batch being costed > 8) Plant overall average setup cost per hatch and plant average. running cost per gallon .for the product . C8 Dgp .Dope X B % Dgp '* total dollars charged to setup by plant making batch Mp * total number of batches made by plant malting batch Dope** total running dollars charged to product by plant making batch. Gp0. * total gallons of produst made by plant .making' batch;' B * gallons, in the batch being costed 9) Division average setup cost per batch of the product and division average.running cost per gallon of the product 09 Dgo % ESDC * D0D X HODC X B hsd j bc Ho d d g D$d .total setijp dollars charged by the division Hgp total setup hours charged by the division. HsDra total setup hours charged, to the produet by the division. . N$C * total .number of batches of product mad by' the division Do b & total .running dollars charged by the division,. Hq d total running hours charged by the division Hq d o 53 total running .hours charged' to the product by the division. 0.00 .total gallons of the product.', made by the division , B gallons; In the. batch being.:.costed':','. .u:-. a m pi DUP030002498 * 10 - 10) Plant average setup Best- -per batch of the product and plant memm running cost .per gallon of the product. C10 * %f x HS?U * %P * H0PC * B h s p Npc~ So p ops'" BSP total setup dollars charged by plant making batch ESP * total setup hours barged by plant making batch Dq p " total running dollars charged by plant .making batch Hop * total .running hours charged by plant making batch HspC ** total setup hours charged to tbe product by plant making batch Hope ** total running hours charged to the product by plant making batch Npc * number of .batches of product made by plant making batch P0 * number of gallons of product made by plant making batch B gallons in the hatch being costed 11) Individual batch cost * 5 * Eg4 x S %P . %P : ' .Bsp. total.setup dollars charged by plant making batch' Hgp * total setup hours charged by plant making batch Dq p 88 total running dollars charged by plant making batch HOP* total running hours charged by plant making batch HgB * setup hours consumed by batch being costed Ho b * running hours consumed by batch being costed Cost .Equation Comparison Figures 2 and 3 in the Appendix are partial plots of C%3 2* 03 and Ci}. The lines are C3 (division) .and Cl? (plants)* and Ci (division) and 2 (plants) are shown as "X" on the lines. Partial plots are used to reduce clutter and increase the clarity of the illustration. Us of Ox implies that no plant or batch size cost differences exist. 2 implies that while there may be cost differences among plants 9 there are no batch size effects:. Us of 3 recognises that batch size affects costs but denies the existence of interplant differences. The effects of using Ux? 2 or Ca as a predictor are shown in Fig. 2 and 3? perhaps more compellingiy in'Fig, 3, the statement of per.gallon costs. It is immediately obvious that, there are . significant cost differences X), among the plants for a given batch . site* and 2) within each plant as a function of batch size... Ul* G2 tod Ujhave small mlua as cost predictors. DUP030002499 - ii - To the usefulness f C.jj, a least squares linear regression was rim tor each plant to heck the goodness f fit of the Cuts (the benchmark) and Olu Correlation coefficients were found to range from 0J6 to 0, . Coefficients greater than G5 were found only for. the two lowest volume production areas For illustrations the Cj l i scatter diagram for Plant A is shown in Fig. 4 in the Appendix together with the corresponding Ci| line. The implication la that product t product cost differences cannot be ignored, and 04 should not be used as a predictor, 05, while recognising inter-product differences ignores my cost effects of plant and batch sise differences and so suffers from the failings of Cj.,. Cg should show most of the failings of Cg in that batch -sir.: cost effects are not recpgnlted fern 06 was used to predict the Cu results, a surprisingly high degree of accuracy was attained. Further analysis revealed the reason. In general* each production area tends to run each produet in a particular piece of equipment and to use that equipment only for a particular, compatible group of products * This is done to minimise cleaning and contamination problems. -This leads toward a standard hatch sis for each product for the area. The batch site effect on the cost prediction then' becomes a lesser factor. However* there are times when smaller: or larger hatches are scheduled, the product i.s shifted to. a different site sanctoili or a saadmill with 'a different' site' premix tank is used, 'When this happens, the use of Cg will result in'' prediction errors. Because this type of operational pattern upset is difficult to predict. It is believed dangerous to as Cg as a predictor over any appreciable time span. If the study is extended by A 6 BA 'Division as proposed, the utility of Cg should be further evaluated. Of, eg, 9 and C10 can best be analysed by using a specifie .example. Figures 5, 6, 7 and 8 in the Appendix show the cost prediction plots for a particular mill base, 7*3-185* for the four production areas in which it ms manufactured during the data gathering period. Table III shows the batch costs (Oil) and the predicted costs using the ten cost constructs being'evaluated. Th table shows the magnitude of the errors introduced for each, cost construct .tor the batches produced. The figures show- the Variations in batch cost estimates that would come about if batch siises were changed. * ' '.' 7. From the table, we can see that %P C.g, O3, <?!>. Cf and 08 distort th# figures for the batch, plant totals and divisional totals. 0$ and 9 correctly assess divisional total costs but misstate batch costs and plant totals. Cg and Cio correctly state both.', divisional and plant totals, failing only to predict, individual .- batch cost#. The potential dangers in the us 'f: ;0g were ' discussedWe are'left .then with 0x0' as.'the most-'u#^l.'' C:dst7 construct* . . .' DUP030002500 !,%?-JPRO:CISS5r COSfS AMALYSXS OF ?% 3~l85 BATCH OOStS |T"9j ! to ** m * i ml of Of a\ H to ft! a* ,04 H oS* JffJ CM to H M 04 H i ol CM V HI m- arf^l o8& Wl in es cu Ml* CM M3 H to . AS | vof Ol to i H OS* IA CM H O to , H H[ " MU* O in H af HCM s> ; <# ar CM Hi .VJVi .: H . 9* H0 m CM H Hi OT CM hi CM <4 t** HI H Hi -w- M M3 40 **3 < 40 fif 40 sr 0 . o cm <SSf.. CM H *r wr HI H * r) SO r H e j sp H to to H 4 Hi Ol 4 8*4 08 HI OS s*"4 04 m CM 4 OS 9 -ef H Ol 0\ 08 ON 04 HI H Ol H CM ^** HI CO 5^ 8*~ <n <M CM .8*4 H H 04 0* 04 H H CM CM 04 MS ImSO in0 HHH 04 H H Hm* W 01 t* H 04 04 H 04 HHH CM CM ' . 01 CM Ol MS . 8"*** ft! to CM MS <* PH 03 C** Ol a* 04 H * 04 H if 04 H in 04 rt Sf H Ol ar H 01 t? H CM to 8*4 CM SM af w cm sr CM m cm CM r CM in Cm to cm to os. MS CM :T H1 as*. .8*4 Ol J0 H CM. m 8*1 CM to H sa H to 8*4 W H m H 'in. min H. m H Id a g jgj 0 m m .: rt H H. :H 01 n cn in- HCM CHM HCMS H CM ' Hin CM P0*4 H sH CM 4 004. > H mMS 40 MS a#* * . j s t a* *c- sn in in i mtmo *Cfr m in m r CM n to CVI CM H in 04 H. CM U4 in to to H p H 04 m s*** H to 4P` H #r to. is* H H *M* ' 04 to in CM. H H r to . 04 ' to 04 n CM H. to .to <41 *#> 04 04 to 80* - .04 to .CM ac to HH H CM 0* HI ^*- 01 40' H to m CM 60 to Hi. 4 CM: to O 4 .04 H MS*' H CM , 60 r 40 m H C9* to to - ; @4 . to Hi ,' #!; mto.' .. H' 04. 3 - 0V to: : 2r : .<M H : . MS- i*< to :04 to H to HH to . M9* O'' 04 to H > to MS cn vO H Hto. - ,f<*4M-'' :. SH . to ai .to: . H *8* ,-cO. 40 ' 04 to CM JSP CM H H to .to to to 'to. to H in 1 vo to. .a*CM H H atoir stoo' *# 04 to to -HS3- CM S04 - to' CM CM cn 80.. m40 *|P4**'; ' '.' . , ':> ' -O' ,SM$St:< 4. 3 m.9J- m fc a a a . & .s4 . &3'- fe SR H\ ' Hi'" a p. g H m M- cn &* . . MS' i; |0 .' 04 *. - H ;' to." : to- ' 04 to1 r*S iV":' DUP030002501 .1.3 Certainly, the us of C^q will not result in exactly predicting the opt of ash hatch made; Cio is ..an marage. However, as the data input grows, we can expect the Ox q estimates to become better and will, in fast, be able to apply reasonable confidence limits .t the estimate* Cost Analysis Comparison f the plant cost data shown in Fig.2 with the operating data shown in Table II .shows some apparent anomalies* For instance, .fro Table XI, Plant C would be expected t be a high cost pro duction site. However, Fig. 2 shows it to b a low east location. The whole explanation for cost differences cannot, then,, be only- in . physical factors. .Examination of any cost construct will show that it is the ..mathematical product of a physical event of observed magnitude and a dollar charge . assigned to the unit event. To analyse costs, both the physical vents and the charges must be investigated. Table I? lists a number of cost .analysis ratios for the four plants used in the earlier examples, When Plant C is compared to the divisional figures, it can be seen to be better in the physical > factors, setup hr,/batch and gal./maeh.oper. hr., but the greatest difference lies in the. dollar charges. The low .dollar charges' magnify the good performance, Hot that gel./mach, oper.hr,'is high .even though conformance to running specifications is poor, implying the use of larger machines or a., product mix based toward higher specification speeds. In the case .of Plant F, the' , salient figures appear to be setup hr./batch and dollars/mach. per. hr. Plant B has high dollars/dept, hr,., dollars/man .hr,, dollars/setup hr. and mn~hr/dept.hr.; low gallons/mach. per. hr* is balanced.by low dollars/mach. per, hr, and high maoh,.per, hr,/ dept, .hr* Plant A rides pretty close to average with highs balanc ing lows; ex,, high setup hr./batch vs ,- low dollars/setup hr. There are a -variety of cost ratios that can be calculated. Those shown here were useful in the situation at hand to determine reasons for cost behavior ..and, as important, to indicate areas that exhibit unexpected or unusual behavior (dollars/man-hr,, Plant C) or should be targets for cost reduction (setup hr./batch, Plant ?>* DUP030002502 TABLE Vf "87-FROCBSS" COSTS COST ANALYSIS RATIOS DIVISION 8/Dept..-Br. i/Man-HT, 53.15 18.89 f/Setup-Sr. 26.00 Settip-Br, /Batch 5*98 i/Maoh, Oper,-Hr 5.98 Oal./Maeh, Oper,-Hr. Mach. Oper .-Hr../Dept * -Hr, 31* If 3.00 Man-Sir */Dept,-Hr. 2.88 Man.-Hr./Mach. Oper.-Hr. 0,96 PLANT . A 51.73 15.0912.65 7*08 5.3% *13.. 10 2.8 3.88 1.22 PLANT PLANT PLANT 0FH 10,78 52.59 5,8.6 ' 17.18 91.30 22.12 5.15 3.65 31.28 68.58 9.88 '6.73 5,86 11,32 8,09 50.58' 32. Of-' 1936 i*0- - i.5 ' ; 5.8' .1.88 3.07 ^ 8.13 1.88 2,03 0.76 A plant~by~plant analysis of eenforrnanee to operating ttooughpot specifications an be made as was shown in Table IS, While there are differences in the product mix among the several plants s the analysis would appear to be a good Indicator of general plant effectiveness and if plotted on a tin basis Is an excellent Indicator of trends in efficiency. An analysis an. .also be made f the eonformanoe to operating speeds for each item* Two specific examples are shown in Table ? in- tine first example (910-1123)s there Is'a striking difference in performance between the two locations making the item* In the : second example (32-6157) overall conformance is poor and all loca tions are operating in the same range regardless of unit site or :.' batch sis*. To improve .ffeetiireness, the two situations should be handled, differently , a cirotmst.ance that is not obvious without, ; . this type of analysis, , ./;.A.vi.:: v Aa a -:x' DUP030002503 It should be noted also that variability in confossnane to operating specifications has implications for produst quality., Th data have been forwarded to' Divisional Alignment group to aid in their study of the effects of operating speeds on product uniformity in their effort to upgrade the quality of sandmlll operations, TABLE "^-PROCESS'* COSTS COIvF'OHMAWCE TO BHfCTO SPECIFICATIONS pl ant PERCENT OF SPECIFIED SPEED . mm GAL,PER Gils, PER 1D0^q 7W"~W?^T575F~0/3S SIZE BATCHES BATCH hour PERCENT Of BATCHES R0H 910-1123 All All 20 w 8o 50 0 15 ;25, ' 10 A All 11 447 123 91 0 0 0 9 B All 9 459 57 0 0 33 ,58 m A 30 11 mi. 123. 91 0 o' "0 ' : 4 B 30 9 459'. 57 : ' 0 . 0 33. ' $$ 11; 32-6157 - All All.. 9 312 . 27 0 .0 ; li. 78 A All: 3 . 4:21 B All 2 zm C Alt 2. n D All . 2 m 20 0 - o ' O :: $*' 68 0 0 O'" ' 50 5H 15 0 0 0 . -. 0 100' .35 0 0 0 .0 ,55- C3 A IS B 30 D 30- 2 71 15 3 421 20 2 248 68 2 455 35 0 0 0 0 100 0 0 o 0 67 0 0 :0 ' 50 50- 0 .0 ' 0 0 50 Haring data available a machine utilisation will allow analysis .'. of the way that equipment is used. Table VI shows the way in which, units were used in each location j and the way each Bm of unit was used 1ft ha opoeifie location* DUP030002504 ~ %& Plants A and. K have significantly higher setup percentages than; the others,, .Plant K also has. a ,higher operating percentage and s low idle percentage,, : Plant 3 has high maintenance time ill. generalr except .for Plant Ks Ml time is high. We might ih^ vesti'gate the maintenance time in Plant 4' fit was, in f&et^due- to an 'extended shutdown of one sandal'll)* We might also ask whether the high' setup time for Plant K was due to' frequent color change cleanups to meet production schedules, whether seasoml or chronic ? and whether it might be improved bp shifting work to other plants dr by adding capacity It is apparent that locations A 9 J and K could use further study and that.* for the data period $ excess capasify is. prevalent. ' Heed for Further... Work The analytical approach .taken here .would appear to supply, topis useful in costings predicting ancl control* The narrowness of . . the data base Cone month) makes, quantification dangerous.;; Without quantification,, the real utility- and s,ignificane;,.f,th,';. tools provided is obscured. Broadening the data base can only be done by taking data over a longer time span. Only If this is dene, can the utility of the system be realistically evaluated. DUP030002505  XT There A a cost associated with the gatherings ordering and analysis of data. This cost should he determined as a part of the valuation of the worth of the system.. This .can he done realistically only by setting the system into operation for a trial period since the various locations where the data will be gathered and ordered will doubtless operate in various manners. Analysis of the several cost ratios and dollar charges used as inputs to the cost calculations also indicates that a,look should be taken at the ways that dollar charges* direct and Indirect* are assigned and allocated. :vii SlIlIllPll : DUP030002506 DUP030002507 DUP030002508 f 4 DUP030002509 JO X lO T O T H E Vz IN C H K E U F F E L . Ot E S S E R C O . * 3 0t 58- t u 1 & 1 fc. DUP030002510 KEUFFEL ft ESSER CO. a 2 U z DUP030002511 DUP030002512 5-f s: $ DUP030002513 tK tU - F t' h i. & t.S S C R DUP030002514 Nt u ;S f\ 0- H t~ ' '* o": a fj a: 1 DUP030002515 DISTBXBimON E, S. Erehgl <J* 0. Sravoa Co W, Stahl ,R.* 3 Knke . < j, M. O&onoS' w, H; .'Edward. (3) 0. B* Sher34an ' . &ess Engineering File Library - Marshall .Laboratory. Copy No, 1 '2 3 ll ,5.. ' g~8 /. : ;' / ' 10 11