Distribution Fitting and Parameterization of Individual Operator Work Routine Times for Small Dairy Parlors

A time and motion study was conducted at 13 small dairy farms with average herd sizes less than 100 cows. Parlors were configured with 3 to 6 stalls per side. A data acquisition methodology was developed using a video camera to gather work routine time data in the parlors. A computer-based data logger was used to extract individual event durations during video playback. Each parlor's video record was reviewed in the laboratory so that work routine times across all parlors and operators could be pooled to estimate typical operator performance. There were 34 operator work routine times associated with various procedures in milking parlors that were evaluated in this study. Individual times were compiled for each work routine and a data-fitting program called UNIFIT was used to fit the data to 1 of 4 data models: gamma, lognormal, Weibull, and Pearson #5. Each of 34 work routine variables was fitted, tested, and plotted to determine how well each of those models fit the actual data. Distributions for Pearson #5, lognormal, gamma, and Weibull models were best fitted to 12, 10, 8, and 4 work routine times, respectively. More common tasks such as attaching the milker, grabbing a towel, and drying the udder were more consistently executed and had smaller variances than routines in which the operator would leave the pit to go to the milk room or disassembled the milk collector after milking. One of the better fitting models was the lognormal distribution for the time to “attach milker,” which had a low relative discrepancy to the P-P plot (model probability vs. data probability) of 0.019 and a moderate χ2 test value of 0.358, thus demonstrating a good fit of the model to the data. Simulation tests were compared with observed data to validate models for work routine times and demonstrated that the models accurately predict parlor throughput in small- to medium-sized parlors.