As stated earlier many journal articles in the area of Operations Management have focused
on various supply chain processes and information exchange in supply chains. But not
many of these articles have discussed the relationship between demand and its influencing
factors to a great extent. This is evident from the recent review papers on supply chain
coordination (Arshinder et al., 2008; Bahinipati et al., 2009). Raju (1995) has attempted to
various types of promotions such as sales price discount with advertisement, on-shelf
discount without advertisement and coupon discount. The effect of promotions on
consumers‘ buying behaviour and consumption patterns are an interesting topic of research in the area of marketing and retailing (Sun, 2005; Chandon and Wansink, 2002; Wansink,
1996). With respect to the supply chain operations, every piece of information on
customers‘ demand and their preferences are important to plan and forecast. Accurate demand forecasts are important to avoid lost sales or excess inventory.
Forecasting is one of the most important topics in the field of supply chain management.
Generally, short term forecasts are used for production scheduling and inventory control
and long term forecasts are used for market planning (Wacker and Sprague, 1998; Sanders
and Ritzman, 1995). In the past three decades several quantitative and qualitative
forecasting techniques have been proposed in the literature. Quantitative forecasting
techniques include multivariate regressions, exponential smoothing, the Holt-Winter model,
the Box-Jenkins model, and many other time series models. Qualitative forecasting
techniques include sales force composites, customer surveys, jury of executive opinion, the
Delphi method and judgemental forecasting.
The 1980s literature on forecasting supported judgemental forecasts more than any single
forecast based on mathematical/statistical techniques (e.g. Mentzer and Cox, 1984; Sparkes
and McHugh, 1984, and Dalrymple, 1987). Later, combination forecasts based on two or
more techniques were supported. These procedures resulted in higher levels of forecast
accuracy than many other forecasts methods (Winkler and Makridakis, 1983; White and
Dattero 1992; Clemen, 1989; Sanders and Ritzman, 1990). It has been well understood by
improve forecast accuracy, but additional information and knowledge from different supply
chain partners is required to reach reliable forecasts (Makridakis et al., 1998; Ireland and
Crum, 2005). A recent study by Onkal et al. (2008) stressed the importance of judgemental
information to adjust forecasts. Some review articles discussed general issues related to
forecasting techniques (Clemen, 1989; Mentzer and Cox, 1984; Fildes et al., 2008).
Though the forecasting literature is vast and wealthy, to the best of my knowledge
promotional forecasting techniques have been reported in the literature only in the past two
decades. A recent review article of Fildes et al. (2008) discussed in detail the literature on
forecasting in the area of operational research. The authors have considered both qualitative
and quantitative approaches. This review paper has pointed that there were few articles on
promotional forecasting, mostly published in the marketing literature. This article refers to
very few 'event forecasting models' such as PromoCast™ and CHAN4CAST (see Table 2- 5).
Table 2-5 Some models on promotional forecasting
Authors Forecasting model Dependent variable
(Sales forecast) Independent variables
Divakar et al. (2005)
CHAN4CAST Regression Model
Group of products (pack size and category) - soft drinks and water
Display, advertisements Cooper et al. (1999) PromoCast™ Regression Model Individual products - detergents, food products etc.
67 variables used - region, price, seasons, special days and temperature
Rinne and Geurts (1988)
Regression Model Forecast of 3 individual functional products included to forecast total profit
Price, advertise medium, size of advertisement, type of
promotion and day of the week Dube (2004) Econometric Model Bundle of products (cereals,
soups, soft drinks)
Family size, time of buying, time of consumption, location, loyalty and advertisements, quality of product
Sales promotions are normally attached with a price discount (Raju, 1995; Sun, 2005).
example holidays, temperature and display locations (Cooper et al., 1999). However it is
important to recognise the impact of these factors individually on sales to measure the
effectiveness of promotion. The effectiveness of promotions is normally reflected in an
increase in the volume of sales during the period of promotion (Divakar et al., 2005).
Forecasting the promotional sales is a complicated task requiring a variety of information
from different supply chain partners. The promotional sales forecaster also needs a good
knowledge of the local market such as the behaviour of consumers and their buying habits
(Dube, 2004).
Some articles have proposed regression forecasting models with multiple independent
variables (Cooper et al., 1999 and Divakar et al., 2005). Cooper et al. (1999) have
developed a forecast model called PromoCast™ which was specific to a particular company. This model included 1.3 million promotional events of a retailer grocery chain
and 67 independent variables. The forecasts of new products were not included. In an
attempt to forecast the effect of promotional sales, Cooper et al. (1999) identified that the
display location of items in the store has a positive impact on sales. In addition longer
duration of promotions with major displays had mixed effects on sales especially for slow
moving items.
Several attempts have been made to estimate the effect of promotions and price discount on
sales using store level data (Abraham and Lodish, 1987; Blatteberg and Levin, 1987; Naik
et al., 2005) and market level data (Dube, 2004; Christen et al., 1997). However, these
articles have not suggested any method of forecasting to predict promotional sales. But,
these effects of promotions have been considered by other researchers to relate demand
to predict the consumers‘ purchase behaviour of bundles of products, such as cereals, soups, soft drinks etc., using the quality of the product. In Dube‘s econometric model, consumers‘ purchase pattern has been related to the time of buying and the time of consumption. The author has used several variables including price, location, loyalty and
advertisements. Sun (2005) has related the customers‘ behaviour with different types of promotions. McIntyre et al. (1993) have attached demand factors with judgemental
indexing. Rinne and Geurts (1988) have developed a forecast model with five variables
namely price, advertise medium, size of advertisement, type of promotion and day of the
week. The authors have related the forecast model with an evaluation of the profit of three
products. The model with more profit has been considered the best performing model.
Divakar et al. (2005) have developed a model called CHAN4CAST for a packaged goods
(food products) company with multiple products. CHAN4CAST, a sales forecasting
decision support model, incorporated a variety of variables such as trend, promotional
variables, seasonality, holidays and temperature. The authors have suggested judgemental
intervention for new products and trading day‘s adjustments. In the model, the authors considered channel level data combining a variety of products. Divakar et al. (2005)
concentrated more on forecasting rather than identifying the underlying demand variables
and their relationship to sales. Despite the previous research on promotional forecasting
methods, there is still a lack of a structured methodology which can guide and direct
practitioners (Fildes and Goodwin, 2007).