Applying Box-Jenkins methodology to forecast the monthly demand of customers in a branch of Caixa Econômica Federal
DOI:
https://doi.org/10.48017/dj.v8i4.2517Keywords:
Data analysis, Times series econometrics, ARIMA, Prediction models, Bank, Caixa Econômica Federal BankAbstract
Queue management in the banking service is a current and recurring challenge that service units face daily in modern times, and especially in Caixa Econômica Federal branches, due to their profile and social mission, this problem is quite relevant as it affects the quality of lives of customers and employees, leading to effects at scale where simple demands can result in hours of waiting, a situation even more critical considering the current pandemic and post-pandemic context. The present work aims to estimate the service demand in a typical branch of Caixa Econômica Federal (CEF) for strategic planning and distribution of resources over time and sectors of the bank. Data were collected on the times and quantities of service in the years 2019 and 2020. After collection, the data were processed and the Box -Jenkins methodology was applied to find the best forecast models for the data reported by the establishment. It was found that by using Box-Jenkins models, varying their parameters, it is possible to obtain important results when it comes to demand forecasting. The method has proven to be very effective, even with the limited amount of data.
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