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_aQuimora, Rafaelito S.
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_aReceivable forecasting :
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_ba Markov modeling approach /
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_cRafaelito S. Quimora.
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_ax, 80 pages
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_aThesis (M.A.) -- Pamantasan ng Lungsod ng Maynila, 2011.;A directed study presented to the faculty of Graduate School of Engineering in partial fulfillment of the requirements for the degree Master of Engineering Management (MEM) with specialization in Systems Management.
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_aSTATEMENT OF THE PROBLEM: To fine-tune the budget system by applying Markov Chain modeling that will produce more realistic and accurate forecast based from the current receivables and historical movement of payments that will yield to fairer evaluation of the operations personnel with respect to the budget system. And test the accuracy of the model against the historical data with the use of Goodness of Fit Test. RESEARCH METHODOLOGY: An operations research methodology was in the study using the Markov Chain model. The collection data were extracted from the database of the company under study with the help of SQL procedures using a 4-year historical data of loan receivables from 2006 to 2009 where the 2009accounts were limited to those accounts already matured or fully paid in a certain branch to establish consistency and accuracy on the results. The data used pertains only to one branch to establish consistency. STATISTICAL METHODS: The proponent used conversion factor to convert non 5000 loan amount to statistical 5000 amount to aggregate and have a uniform loan basis view of data. The cumulative weekly average was used to have a smoothing of the weekly view since the cumulative payment are distributed equally per week to determine overpayment/short payment of the current week due to an advance made in the last week collection or no payment at all. Lastly, the use of index in the actual amount derived from the historical data as against the forecasted amount from the model. A Goodness of Fit (Chi-Square) test was used to determine the significance in the different between actual and the forecast data. SUMMARY OF FINDINGS: The proponent determined the possible states that a certain weekly installment could be on the following week. He defined the transition probabilities based on the payment behavior of the member and built Markov Chain model. He solved the caseload and probabilities and as well as the collection forecast and compared it with the actual values from the historical data. The variances of the actual data versus forecast have shown small variances indicating realistic results were achieved. Variances were due to unexpected payments by the members which were part of the limitations of the study. Minimal probabilities of having 4 weeks and over past due were found indicating a health collection as per record. Weeks 15 up to 18 show the critical periods wherein delinquencies increase and must be monitored. These are due to offsetting made after the maturity of the accounts since the members have savings they can use as replacement for the infractions while others decided not to continue to get another loan or resigned. CONCLUSIONS: With respect to the Goodness of Fit Test result, statistically, this showed that there is no significant difference between the actual values and forecasted values using the Markov Chain Model method. Hence, it only proved that the new forecast method gives more realistic result than the previous forecast method. And so, by implementing the new model in the budget system, truly it will improve forecast projection for optimal result and have a fairer and better evaluation results to operations personnel. RECOMMENDATIONS: The proponent recommends the mew model be implemented in the company as standard method for forecasting receivables and as a gauge in evaluating the performance of the operations personnel by integrating in the budget system to forecast more realistic and accurate and have fairer operations performance evaluation reports.
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