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PC to establish manning levels of a proposed just-in-time production facility
USE OF GPSS/PC TO ESTABLISE MA~VING LEVELS OF A PROPOSED JUST-IN-TIME
PRCDUCPION FACILITY Dr. Gary Fellers Augusta College, Augusta, Georgia ABSTRACT A short explanation of Monte Carlo simulation is provided. Special emphasis is placed upon the GPSS/PC language and to an application of manning-level determinations of a proposed drill-bit manufacturing department. The optimum number of operators was determined in advance of construction. The objective was to hire the exact number of workers to provide zero in-proceas inventories to he consistent with the new management policy of "just-in-time" production plarming. The stochastic behavior of arrival times and processing times made Monte Carlo simulation a viable modeling tool. ~ S Monte Carlo simulation; GPSS/PC; optimum manning levels; just-in-time manufacture. INTRCDSL'TION Arrival times of orders and operator/machine processing times are seldom deterministic. Typical random variabilities of such times appear as shown in Fig. I. Note that the average time is thirty minutes. The existence of many random processes like the one drawn in Fig. I can make decision ~aking very difficult. Prob. of Occurrence
Fig i.
Typical random variabilities.
Note in Fig. 2, for the same rand~ variable, that the vertical axis represents cumulative probability. In other words, there is a ninety percent chance that the processing time will be less than or equal to thirty-two minutes. CUR.
Prob. 1.00.90.6-
0.1-
~
f
32 Fig. 2.
Ctmulative probabilities.
To simulate this process in a Monte Carlo fashion, one can create random ~ r s between zero ~md one for the vertical axis of Fig. 2. Then the corresponding values of the random variables on the
382
FELLERS: Use of GPSS/PC to Establish MannlnE Levels
383
horizontal axis are the simulated processing times. The GPSS/PC computer program performs the simulation in this Monte Carlo fashion. So when looking at a complicated manufacturing process with many random variables, through many runs representing years of actual operating time, the computer simulates all possible cx~bi~mtions of occurrences that might happen. Then by varying such items as ~ r of operators or machines, equipment layout, or inventory levels, the manager can see in advance the likely economic implications of many possible solutions. G~SS/PC COMP~ixa Writing computer code for simulations can be very time c(~suming. Consequently, several software vendors have developed products that circm~vent this problem. The product used in this study was GPSS/PC (Minuteman, 1984) that was created specifically for I~4/PC compatible personal computers. This program, like most, requires that the analyst establish a block diagram of the production process (or whatever is being simulated). Only a finite number of block types are available (Solomon, 1983). The analyst has to learn which types of blocks represent various categories of occurrences° Then the flow chart is assembled to represent the sequential relationship of the process to be simulated. Then the GPSS code is written to communicate the flowchart to the computer. Figure 3 shews the flowchart for the production process to be discussed in the next section of this paper. The PC version of (~SS will likely enhance the adoption of simulation techniques in manufacturing operations because of its ccmvenience. It was found that the PC version was comparable to the mainframe variety°
I
I
I Fig. 3.
Flowchart for drill-bit department
384
PROCEEDINGS OF THE 8TH ANNUAL CONFERENCE.ON COMPUTERS AND INDUSTRIAL ENGINEERING
SAMPLE PROJECT A drill-bit manufacturing facility had plans to build an expansion department for a new product. To stay in agreement with the new upper management policy, the zero inventory levels of the justin-time production scheduling philoeuphy was to be employed. Sales forecasts necessitated an estimated thirty-six machines; however, the number of operators had to be established by plant management. Machine efficiency would increase with a larger number of operators. Therefore, the queue lengths of parts waiting to be processed would be shorter for increasing numbers of aperators. After start up, a likely trial-and-error procedure would be to keep incrementing the nm,ber of operators until the typical in-process inventory level is zero. HOwever, in this case, management needed to know the optimum manning levels weeks in advance of start up because of training requirements. The arrival rates of orders wore random as in Figs. 1 and 2. The operator-machine processing cycle time was also random in nature. The shapes and average values of the statistical distributions wore known. Monte Carlo simulation was used to simulate several years of operation for many different manning levels. Bottlenecks wore identified. Also the minimmn manning level was established which would yield zero inventories for beth in-process and finished goods. Machine idle time cost was considered to be negligible when c(mpared to inventory holding costs and labor costs. It was shown that the manning level yielding a typical level of zero inventories was also the number of workers that minimized total costs. In summary, the GPSS/PC software and Monte Carlo simulation enabled the managers to hire and train the optimum number of operators in advance of production start-up. This solution also enabled mnagement to predict labor costs per unit in advance of start-up. After eighteen months of operation, the simulation solution has been proven to be correct. REFERENCES Minuteman Software (1984); Stow, Massachusetts. Solcman, Susan L. (1983). ~ of ~ Prentice-Hall, Englewood Cliffs, New Jersey.
Systems.
PRCDUCPION FACILITY Dr. Gary Fellers Augusta College, Augusta, Georgia ABSTRACT A short explanation of Monte Carlo simulation is provided. Special emphasis is placed upon the GPSS/PC language and to an application of manning-level determinations of a proposed drill-bit manufacturing department. The optimum number of operators was determined in advance of construction. The objective was to hire the exact number of workers to provide zero in-proceas inventories to he consistent with the new management policy of "just-in-time" production plarming. The stochastic behavior of arrival times and processing times made Monte Carlo simulation a viable modeling tool. ~ S Monte Carlo simulation; GPSS/PC; optimum manning levels; just-in-time manufacture. INTRCDSL'TION Arrival times of orders and operator/machine processing times are seldom deterministic. Typical random variabilities of such times appear as shown in Fig. I. Note that the average time is thirty minutes. The existence of many random processes like the one drawn in Fig. I can make decision ~aking very difficult. Prob. of Occurrence
Fig i.
Typical random variabilities.
Note in Fig. 2, for the same rand~ variable, that the vertical axis represents cumulative probability. In other words, there is a ninety percent chance that the processing time will be less than or equal to thirty-two minutes. CUR.
Prob. 1.00.90.6-
0.1-
~
f
32 Fig. 2.
Ctmulative probabilities.
To simulate this process in a Monte Carlo fashion, one can create random ~ r s between zero ~md one for the vertical axis of Fig. 2. Then the corresponding values of the random variables on the
382
FELLERS: Use of GPSS/PC to Establish MannlnE Levels
383
horizontal axis are the simulated processing times. The GPSS/PC computer program performs the simulation in this Monte Carlo fashion. So when looking at a complicated manufacturing process with many random variables, through many runs representing years of actual operating time, the computer simulates all possible cx~bi~mtions of occurrences that might happen. Then by varying such items as ~ r of operators or machines, equipment layout, or inventory levels, the manager can see in advance the likely economic implications of many possible solutions. G~SS/PC COMP~ixa Writing computer code for simulations can be very time c(~suming. Consequently, several software vendors have developed products that circm~vent this problem. The product used in this study was GPSS/PC (Minuteman, 1984) that was created specifically for I~4/PC compatible personal computers. This program, like most, requires that the analyst establish a block diagram of the production process (or whatever is being simulated). Only a finite number of block types are available (Solomon, 1983). The analyst has to learn which types of blocks represent various categories of occurrences° Then the flow chart is assembled to represent the sequential relationship of the process to be simulated. Then the GPSS code is written to communicate the flowchart to the computer. Figure 3 shews the flowchart for the production process to be discussed in the next section of this paper. The PC version of (~SS will likely enhance the adoption of simulation techniques in manufacturing operations because of its ccmvenience. It was found that the PC version was comparable to the mainframe variety°
I
I
I Fig. 3.
Flowchart for drill-bit department
384
PROCEEDINGS OF THE 8TH ANNUAL CONFERENCE.ON COMPUTERS AND INDUSTRIAL ENGINEERING
SAMPLE PROJECT A drill-bit manufacturing facility had plans to build an expansion department for a new product. To stay in agreement with the new upper management policy, the zero inventory levels of the justin-time production scheduling philoeuphy was to be employed. Sales forecasts necessitated an estimated thirty-six machines; however, the number of operators had to be established by plant management. Machine efficiency would increase with a larger number of operators. Therefore, the queue lengths of parts waiting to be processed would be shorter for increasing numbers of aperators. After start up, a likely trial-and-error procedure would be to keep incrementing the nm,ber of operators until the typical in-process inventory level is zero. HOwever, in this case, management needed to know the optimum manning levels weeks in advance of start up because of training requirements. The arrival rates of orders wore random as in Figs. 1 and 2. The operator-machine processing cycle time was also random in nature. The shapes and average values of the statistical distributions wore known. Monte Carlo simulation was used to simulate several years of operation for many different manning levels. Bottlenecks wore identified. Also the minimmn manning level was established which would yield zero inventories for beth in-process and finished goods. Machine idle time cost was considered to be negligible when c(mpared to inventory holding costs and labor costs. It was shown that the manning level yielding a typical level of zero inventories was also the number of workers that minimized total costs. In summary, the GPSS/PC software and Monte Carlo simulation enabled the managers to hire and train the optimum number of operators in advance of production start-up. This solution also enabled mnagement to predict labor costs per unit in advance of start-up. After eighteen months of operation, the simulation solution has been proven to be correct. REFERENCES Minuteman Software (1984); Stow, Massachusetts. Solcman, Susan L. (1983). ~ of ~ Prentice-Hall, Englewood Cliffs, New Jersey.
Systems.