Combining Qualitative and Time Series Forecasting to Increase the Forecasting Accuracy for Instant Noodle Sales in Thailand
Abstract
Demand forecasting is very important for the planning process. The forecast accuracy affects the efficiency of the procurement, production and delivery processes. Our research has the objective of increasing the sales forecasting accuracy of instant noodles for a case study company in Thailand. Many factors affect the sales of instant noodles, such as promotion, other commodities’ prices, national disaster and production capacity. Thus, we collected historical monthly sales data, analysed the data and their pattern and considered whether the data were irregular due to those factors. After obtaining the forecast data, data intervention by adjustment of the irregular effects was performed in accordance with our experience and judgement. Next, we used the predictor function in the Crystal Ball software to determine the best time series forecasting method for actual and adjusted sales data. Then, we verified the result with the actual sales data for one year. The result showed that the adjustment could increase the sales forecast accuracy by 46.14%, 22.53% and 56.42% for products A, B and C, respectively. In summary, the mean average percentage sales forecast error after adjustment was 6.48%–11.62%, which is better than the current method of forecasting based on experts.
Keywords: Instant Noodle; Intervention; Qualitative Forecasting; Sales Adjustment; Time Ser ies Forecasting
References
Barlas, Y., Gunduz, B., 2011. Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains. Journal of the Operational Research Society 62, 458473.
Boute, R.N., Lambrecht, M.R. 2009. Exploring the bullwhip effect by means of spreadsheet simulation. INFORMS Transactions on Education 10(1), 1-9.
Divakar, S., Ratchford, B.T., Shankar, V., 2005. CHAN4CAST: a multichannel, multiregion sales forecasting model and decision support system for consumer pakaged goods. MArketing Science 24(3),334-350.
Doganis, P., Alexandridis, A., Patrinos, P., Sarimveis, H., 2006. Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. Journal of Food Engineering 75, 196-204.
Goh, C., Law, R., 2002. Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management 23, 499-510.
Khamphinit, T., 2014. Demand forecasting and inventory management under demand uncertainty: a case study of instant noodles. Independent Study, Department of Agro-Industrial Technology, Kasetsart University, Thailand.
Lee, H.L., Padmanabhan, V., Whang, S., 2006. Information distortion in a supply chain: the bullwhip effect. Management Science 50(12), 1875-1886.
Leone, R. P., 1987. Forecasting the effect of an environmental change on market performance: An intervention time-series approach. International Journal of Forecasting 3(3–4), 463–478.
Mathews, B. P., Diamantopoulos, A., 1986. Managerial intervention in forecasting: an empirical investigation of forecast manipulation. International Journal of Research in Marketing 3(1), 3-10.
Montgomery, D.C., Weatherby, G., 1980. Modeling and Forecasting Time Series Using Transfer Function and Intervention Methods. IIE Transactions 12(4), 289307.