THE ROLE OF WOMEN IN ENVIRONMENTAL PROTECTION IN IGABI LOCAL GOVERNMENT, KADUNA STATE, NIGERIA
Hypothesis 2: Ineffective Utilization of Resources has no impact on sub-optimality in production in
3. Identification of metrics
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Model Stock forecasting error, variance of the demand and the number of periods constrained in lead time
safety stock when the variance of forecast error was large relative to the variance of demand.
Otherwise, push is more effective.
Hurley and Whybark (1999)
Engine assembly in a manufacturing
cell
Simulation Output Rate;
Cycle Time;
Utilization
Average WIP;
Protective capacity;
Variance reduction
Indicate the trade-off between inventory
buffers and capacity buffers and conclude that variance reduction and protective capacity are good alternatives to inventory.
Kelle and Peak (1996) Chemical Simulation Average Annual Cost; Service Level; Fill Rate;
Backorder Rate
Required service level;
Ratio of setup to holding cost; Variance of forecasted demand
Switching from a fixed schedule to an adaptive schedule decreases inventory holding costs and increases customer service while maintaining the same setup costs.
Kher et al.,(2000) Lot splitting in Flow shop
Simulation Lot Traceability;
Material Handling Costs
Number of batches;
Machine utilization;
Setup-to- processing time ratio
Pull lot splitting helps significantly in reducing the number of transfers incurred and in maintaining a greater degree of physical lot integrity.
Kim et al.,(2002) Production with emergency orders
Simulation Service Level;
Operating Costs;
Delivery Time for Late Orders
Proportion of emergency orders
With an option of safety stock, the pull system outperforms the push system in terms of throughput time, delay time of regular and emergency orders, and total cost when demand variation is high.
Krishnamurthy et al.,(2000)
Flexible Manufacturing
Simulation Throughput;
Inventory
Product mix changes, Demand and processing times
The pure push strategy has a higher
throughput for a given level of inventory than the pure pull strategy in some systems.
2.5.Simulation model
Digital simulation was utilized to model the actual production system. Although a brute force method and worker intensive, simulation supplies a feasible mechanism to analyze complex simultaneous activity. The simulation model employed standard probability distributions for input data. A run time period of 40 h, after steady-state, was observed. If the output change was statistically equivalent to zero, steady-state was said to have been reached Input data were verified by comparison to actual system process standards using derived probability distributions and the chi-squared goodness of fit test. Simulation model validation included internal mathematical consistency checks and comparing simulation model output to the physical system metrics.
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search (Galbraith et al.,1991). The six factors were: mean flow time (MFT), quality, flexibility, material flow, process utilization and mean queue time. Three levels of each factor tested were represented by three critical processes of the real system under study. The three levels (processes) for each factor were: component placement, wave solder and solder reflow. This method could be applied to other discrete parts manufacturing systems by selecting three critical processes for the system of interest.MFT was measured as the time spent in the system by a unit of product (Baker,1974). A direct output value from the simulation model, MFT was measured in hours, with lower values being desirable. Quality was calculated as the percentage of defects:
the ratio of inspection failures to the total number of product units flowing through a process block. Quality control included 100% inspection of product. A lower non-conformance ratio indicated better quality Flexibility was calculated as the mean number of available workstations able to process an operation (Chang,1986), where: flexibility= 1-process utilization. Material flow smoothness was measured by the level of congestion. Congestion was defined as the ratio of time spent in queue to time spent in process (Allen& Chavous,1989 ; Spearman, 1988).
Process utilization and mean queue time were obtained directly from simulation results.
It may appear that MFT and mean queue time would be co-dependent. Good material flow with low congestion would be expected to result in a low mean queue time, high or low flexibility depending on process utilization, and low MFT. On the other hand, poor material flow with high congestion centred around one or two large queues associated with bottleneck processes near the beginning of the line can result in downstream processes being starved. These downstream processes will have low mean queue time and high flexibility. Overall, the system may have a high MFT. These two examples show that MFT and mean queue time should not be assumed to be co-dependent based on definition From a multicollinearity study, it was concluded that multicollinearity was present for process utilization and flexibility, and MFT, material flow and mean queue time Figure 2 graphically portrays the factor dependencies.
System product flow was evaluated using the metrics of MFT, material flow congestion and mean queue time. The material flow measure of congestion is a direct indication of mean queue time and a significant influence on MFT Flexibility was thought to give a more direct indication than process utilization of overall system efficiency and capacity for meeting changes in demand.
Quality, an independent factor, was also used in the regression. The multicollinearity analysis was continued using the metrics of quality, flexibility and material flow. The results for placement and IR reflow processes are printed above wave solder for each matrix cell of Table 2.
Table 3 shows the eigenvalues of the correlation matrix for quality, flexibility and material flow, and further substantiates the relative independence of these three metrics. The correlation matrix eigenvalues were not very close or equal to zero.
Step-wise regression was then performed using these three metrics and their two-way interactions. Three way interactions were assumed to be negligible. If three-way interactions were in fact significant, it would be very difficult to interpret for an actual system (Hicks,1982 ; Law& Kelton,1986 ; Taguchi,1986).The response variable was total production cost as a function of simulation system run time. A constant cost of $1000 per run hour was assumed to relate system run time to production cost. Other cost per run hour values could be chosen to reflect a particular system under study.
Table 4 presents the best model of the step-wise multiple regression results. Those factors and factor interactions that were not shown to be significant influences (α= 0.05) on run time were not included in the model. The best model was defined in terms of coefficient of determination
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closest to 1.0, lowest standard error and lowest residual variance .The coefficient of determination indicated that 90.34% of the variation in the response data was explained by the model, and hence was considered a favorable indicator of model fit for these data. The input data did not contain gaps and the input data magnitudes were in the hundred thousands. The standard error of prediction indicated that the estimator of mean response had moderately low variance.
The balanced spread of the residuals about zero was indicated by the low coefficient of skewness.
Figure 2. Performance measure dependence
Table 2. Quality, flexibility and material flow correlation coefficients
Quality Flexibility Material flow
Quality:
Placement 1.0000, -0.0513 -0.1539, -0.3526 -0.2052, 0.2056
-0.1026 -0.2052 -0.3156
IR reflow -0.0513, 1.0000 -0.30, -0.1026 -0.1615, 0.2183
0.3000 - 0.3000 0.1625
Wave -0.1026, 0.3000 -0.30, 0.1823 0.30, 0.3218
1.0000 -0.2000 0.2000
Flexibility:
Placement -0.1539, -0.2301 1.0000, 0.0513 0.10, 0.2301 - 0.4000 0.0719 - 0.4000 IR reflow -0.2690, -0.1026 0.0513, 1.00 -0.3078, 0.3558
-0.1823 0.3591 0.3617
Wave 0.2052, -0.3173 0.0719,-0.3591 0.20, 0,1096
-0.2000 1.0000 -0.2000
Material flow:
Placement -0.2052, 0.1615 0.2615, -0.3078 1.00, 0.3485
0.3000 0.2000 0.3000
IR reflow 0.3862, 0.30 0.3382, 0.2892 0.3485, 1.00
0.2999 0.2000 0.3000
Material flow Flexibility
Quality
Mean queue time Process
utilizationn Mean
flow time
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JUNE 2011 VOL 3,NO 2 Table 3. Eigenvalues of the correlation matrix for quality, flexibility and material flow
Performance Critical process
measure Placement IR reflow Wave solder
Quality 3.2500 2.1047 1.2297
Flexibility -2.2715 -2.1663 5.0176
Material flow 0.7701 3.8120 1.4997
Table 4. Multiple regression results
Regressor Coefficient Constant 2.0486 Quality - wave solder 29889.9264
Material flow - placement 670.8824 Material flow - IR reflow 2.4639
Quality - placement 1.0519 Flexibility - IR reflow -1.7091 Quality - IR reflow -2.7817 Flexibility - wave solder -4.1799 Model fitting results 0.9034 Coefficient of determination 68.8858
Standard error of prediction 0.3829 Residual mean 28.7452 Residual variance -7.5326 Minimum residual 8.4147 Maximum residual -0.2778 Residuals standardized skewness 0.9034
Two-way interactions were not shown to be significant for this system model. The three critical processes were separated by several other processes in each case, so that the model indicating no significant interaction between critical processes' metrics seemed to be a reasonable result. The regression model results suggested that this system was heavily influenced by quality and material flow. Two disadvantages associated with the traditional push system design are complex material flow and poor quality.