Norm Staff Planning in Automotive Sector with Artificial Neural Networks

This study proposes an autocorrelated integrated moving average (ARIMA)-enhanced artificial neural network

(ANN) model in order to make norm staff forecasts more effectively within the scope of human resources planning

in the automotive sector. Accurate and timely determination of labour force requirements is one of the main factors

that directly affect productivity and cost factors in the sector. Although there are many approaches to norm staffing

calculations in the literature, it is rare to use time series and artificial intelligence based models together. In this

study, the data of the past years were analysed with the ARIMA model, which is widely used in time series analysis,

to reveal the time series components, and then the estimated workload was evaluated with ANN to predict the

future personnel needs. The model was trained with 60 periods of historical data and forecasting was performed

for the next 12 periods. According to the results obtained, this combined model outperformed both the classical

ARIMA model and the ANN model used alone in performance measures such as root mean square error (RMSE),

mean absolute percentage error (MAPE) and coefficient of variance (R2

).