Focus forecasting in supply chain: The Case study of fast moving consumer goods company in Serbia

  • Zoran Rakićević Faculty of Organisational Sciences University of Belgrade
  • Mirko Vujošević Faculty of Organisational Sciences University of Belgrade
Keywords: Fast moving consumer goods, Winter’s model, Holt's model, moving average, Focus forecasting, Exponential Smoothing,

Abstract


This paper presents an application of focus forecasting in a fast moving consumer goods (FMCG) supply chain. Focus forecasting is tested in a real business case in a Serbian enterprise. The data used in the simulation refers to the historical sales of two types of FMCG with several different products. The data were collected and summarized across the whole distribution channel in the Serbian market from January 2012 to December 2013. We applied several well-known time series forecasting models using the focus forecasting approach, where for the future time period we used the method which had the best performances in the past. The focus forecasting approach mixes different standard forecasting methods on the data sets in order to find the one that was the most accurate during the past period. The accuracy of forecasting methods is defined through different measures of errors. In this paper we implemented the following forecasting models in Microsoft Excel: last period, all average, moving average, exponential smoothing with constant and variable parameter α, exponential smoothing with trend, exponential smoothing with trend and seasonality. The main purpose was not to evaluate different forecasting methods but to show a practical application of the focus forecasting approach in a real business case.

Author Biographies

Zoran Rakićević, Faculty of Organisational Sciences University of Belgrade
Departmant for Operations management
Mirko Vujošević, Faculty of Organisational Sciences University of Belgrade
Depatment for Operations research

References

Bratu-Simionescu, M. (2013). Improvements in assessing the forecasts accuracy: A case study for Romanian macroeconomic forecasts. Serbian Journal of Management, 8 (1), 53-65.

Chang, P.C., & Lin, Y.K. (2010). New challenges and opportunities in flexible and robust supply chain forecasting systems – Editorial. International Journal of Production Economics, 128 (2), 453-456.

Chen, A., & Blue, J. (2010). Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands. International Journal of Production Economics, 128 (2), 586-602.

Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. New Jersey: Pearson, Prentice Hall.

Davydenko, A., & Fildes, R. (2013). Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts. International Journal of Forecasting, 29 (3), 510-522.

Ferbar, L., Čreslovnik, D., Mojškerc, B., & Rajgelj, M. (2009). Demand forecasting methods in a supply chain: Smoothing and denoising. International Journal of Production Economics, 118 (1), 49-54.

Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25 (1), 3-23.

Gardner Jr, E.S., Anderson-Fletcher, E.A., & Wicks, A.M. (2001). Further results on focus forecasting vs. exponential smoothing. International Journal of Forecasting, 17 (2), 287-293.

Heizer, J., & Render, B. (2011). Operations Management. New Jersey: Prentice Hall.

Huang, T., Fildes, R., & Soopramanien, D. (2014). The value of competitive information in forecasting FMCG retail product sales and the variable selection problem. European Journal of Operational Research, 237 (2), 738-748.

Kerkkänen, A., Korpela, J., & Huiskonen, J. (2009). Demand forecasting errors in industrial context: Measurement and impacts. International Journal of Production Economics, 118 (1), 43-48.

Min, H., & Yu, W.B.V. (2008). Collaborative planning, forecasting and replenishment: demand planning in supply chain management. International Journal of Information Technology and Management, 7 (1), 4-20.

Oyatoye, E.O., & Fabson, T.V.O. (2011). A comparative study of simulation and time series model in quantifying bullwhip effect in supply chain. Serbian Journal of Management, 6 (2), 145-154.

Ramanathan, U., & Muyldermans, L. (2010). Identifying demand factors for promotional planning and forecasting: A case of a soft drink company in the UK. International Journal of Production Economics, 128 (2), 538-545.

Sayed, H.E., Gabbar, H.A., & Miyazaki, S. (2009). A hybrid statistical genetic-based demand forecasting expert system. Expert Systems with Applications, 36 (9), 11662-11670.

Thomassey, S. (2010). Sales forecasts in clothing industry: the key success factor of the supply chain management. International Journal of Production Economics, 128 (2), 470-483.

Yang, B., & Burns, N. (2003). Implications of postponement for the supply chain. International Journal of Production Research, 41 (9), 2075-2090.

Vayvay, O., Dogan, O., & Ozel, S. (2012). Forecasting techniques in fast moving consumer goods supply chain: a model proposal. International Journal of Information Technology and Business Management, 13 (1), 118-128.

Vujošević, M. (1997). Operational management: quantitative methods. Yugoslav Operational Research Society – DOPIS, Belgrade. (in Serbian)

Warren Liao, T., & Chang, P.C. (2010). Impacts of forecast, inventory policy, and lead time on supply chain inventory – a numerical study. International Journal of Production Economics, 128 (2), 527-537.

Wallström, P., & Segerstedt, A. (2010). Evaluation of forecasting error measurements and techniques for intermittent demand. International Journal of Production Economics, 128 (2), 625-636.

Zamarripa, M.A., Aguirre, A.M., Méndez, C.A., & Espuña, A. (2012). Improving supply chain planning in a competitive environment. Computers & Chemical Engineering, 42, 178-188.

Published
2014/09/26
Section
Original Scientific Paper