摘要：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.
关键词：focus forecasting; moving average; exponential smoothing; Holt's model; Winter's model; ; fast moving consumer goods (FMCG) ; * Corresponding author: zoran.rakicevic@fon.bg.ac.rs ; Serbian ; Journal ; of ; Management ; Serbian Journal of Management 10 (1) (2015) 3 - 17 ; www.sjm06.com ; DOI:10.5937/sjm10-7075 ; var currentpos;timer; function initialize() { timer=setInterval("scrollwindow()";10);} function sc(){clearInterval(timer); }function scrollwindow() { currentpos=document.body.scrollTop; window.scroll(0;++currentpos); if (currentpos != document.body.scrollTop) sc();} document.onmousedown=scdocument.ondblclick=initialize (Chopra & Meindl; 2007). The first step that ; managers must take is to forecast what ; customer demand will be. With adequate ; anticipation of sales; managers can plan the ; level of activities: production; capacity; ; inventory; transportation; distribution. ; Information on demand is one of the most ; important parts in the whole supply chain ; planning (Chen & Blue; 2010) and adequate ; sales forecast can prevent a bullwhip effect ; (Ramanathan & Muyldermans; 2010; ; Oyatoye & Fabson; 2011). It can be ; concluded that sale forecasting is: (1) an ; activity of Supply Chain Management-SCM ; (Warren Liao & Chang; 2010); (2) the main ; planning problem in SCM (Zamarripa et al.; ; 2012) and (3) the key success factor of the ; SCM (Thomassey; 2010). In this paper we ; applied several time-series forecasting ; methods through the focus forecasting ; system. The idea was to point out the ; importance of successful forecasting system ; in supply chains and to indicate the need for ; development of adequate forecasting system ; with plenty of forecasting methods and ; forecast accuracy indicators. ; This paper is organized as follows: ; Section 2 defines the term of forecasting and ; presents a group of forecasting methods that ; are based on time series. Section 3 presents ; and defines focus forecasting approach with ; measures of forecasting accuracy i.e. error; ; which is important element of this concept. ; Section 4 presents a case study on focus ; forecasting applied on sales of two types of ; FMCG; time-series forecasting methods are ; used on real data from practice and ; compared using different measures of ; forecasting errors. Section 4 also presents ; main results and discussion. Section 5 ; concludes the paper and discusses future ; research. ; 2. FORECASTING METHODS ; Forecasting is a process of building ; assumptions and estimates about future ; events that are generally unknown and ; uncertain (Vujo.evi.; 1997). Forecasting is ; the art and science of predicting future ; events. It implies taking historical data and ; projecting them into the future; using ; mathematical models and methods or ; intuition. Forecasts are used for many ; purposes: ; marketing; ; sales; ; finance/accounting; production/purchasing; ; and logistics (Kerkk.nen et al. 2009). ; Numerous factors are related to the sales ; forecast (Chopra & Meindl; 2007): past ; sales; product lead time; planned advertising ; or marketing efforts; state of economy; ; planned price discounts; competitors' ; actions. ; Generally; forecasting methods are ; classified into two groups: quantitative ; methods and qualitative methods (Chopra & ; Meindl; 2007; Vujo.evi.; 1997; Heizer & ; Render; 2011). Qualitative forecasting ; methods are based on experts' estimates and ; judgements as well as their experience. ; Quantitative forecasting methods use ; historical statistics and appropriate ; mathematical models to make future ; prediction. The most common categorisation ; of quantitative methods draws a distinction ; between projective and causal methods ; (Vujo.evi.; 1997) i.e. time-series methods ; and associative methods (Heizer & Render; ; 2011). Projective methods (time series ; methods) try to find rules in the data; where ; the forecast is a "picture" of history ; projected in future. Causal (associative) ; methods try to find and make causal ; relationship between variables (Vujo.evi.; ; 1997). ; 4 ; Z.Raki.evi. / SJM 10 (1) (2015) 3 - 17
