Eindhoven University of Technology MASTER Improving the promotion forecasting accuracy at Unilever Netherlands van der Poel, M.J. Award date: 2010 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. 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P.D.J. van Balkom Improving the promotion forecasting accuracy at Unilever Netherlands TUE. School of Industrial Engineering. Series Master Theses Operations Management and Logistics Subject headings: sales forecasting, promotions, retail trade, consumer goods Page II Improving the promotion forecasting accuracy at Unilever Netherlands Abstract This master thesis describes how the forecasting accuracy of promotions can be improved at Unilever Netherlands. Currently, a very judgemental way of forecasting is applied by employees within the organization. This research will develop the forecasting process by using a more mathematical forecasting model. With multiple linear regression the consumer demand and retailer orders are forecasted and an analysis is made between the difference of forecasting consumer demand and forecasting retailer orders. The effect size of the 21 dependent variables on the promotional demand are discussed and the most important are used to formulate a reduced model. It is concluded that the consumer demand can be forecasted quite accurate; however, the forecasting accuracy drops substantial for retailer orders. Multiple disturbing factors on consumer demand apparently increase the variability of the retailer orders. Therefore, this research advices Unilever to cooperate more extensively with their retailers to investigate the disturbing factors and develop a integrated forecasting approach. Page III Improving the promotion forecasting accuracy at Unilever Netherlands Management summary Problem introduction This research is performed at Unilever Netherlands in Rotterdam and is directed at the forecasting process for promotions. In the last 2 decades the promotional pressure has increased in the Fast Moving Consumer Market where Unilever operates in. This holds especially in the Netherlands, where the competition is fierce and multiple price wars have decreased the price level. Therefore, with a current promotional pressure of around 40%, Unilever has indicated the forecasting process of these promotions as a developmental area. An earlier internal project indicated that the forecast accuracy on promotion or range level is quite good; however, on product level the promotion accuracy drops dramatically. And since the Unilever plants have to produce product specific items and the stock levels are product specific, the goal is to increase the forecasting accuracy on SKU level. Problem definition The main research question is: What are the causes for the low forecasting accuracy of the promotion forecasting process and how can the forecasting accuracy be improved? A first analysis of the problem resulted in five problem areas. This research mainly focussed on the problem area Poor database usage: Within Unilever different data sources have to be consulted manually for each promotion. This is a time consuming user unfriendly process, which does not enhance the usage of data and thus the forecast accuracy. Furthermore, no model is provided to calculate the sales of a new promotion. Therefore, an employee has to search and analyze all the information him or herself. The research is performed at four retailers in the Netherlands (Albert Heijn, C1000, Kruidvat and Plus) and 86 different products. The promotions of these products are analyzed for the period January 2009 upto march 2010. Also, some practical requirements to make a forecasting model work in practice are defined: The forecasting model should be easy to use for Unilever employees, it should work with data which is available within the organization and it should forecast the consumer demand and use this as a basis to come to a retailer order forecast to enhance the usability of the model. Research design The research design depicts which method should be used and which variables are included in the model. Multiple linear regression is chosen as the most suitable method for a forecasting model. In this method one dependent variable is predicted with multiple independent variables. As dependent variable the Lift Factor of a promotion is forecasted. This is the promotional sales divided by the Page IV Improving the promotion forecasting accuracy at Unilever Netherlands weekly base line sales of a product. The independent variables are divided among the groups promotion, retailer and brand as depicted in the underlying figure. The research will test the effect of the different independent variables on the dependent variable, will reduce the number of variables and correct for data availability. Display Folder Advertising Promotion TV variables Holiday Length promo Retailer Absolute # of selling Retailer Promotional Price decrease variables sales Percentual points Promo mechanism Repeat buyers # of products in promotions Promotion Brand pressure variables Lift factor former promotions Market Preservability penetration Size of product Susceptibility to stockpiling Frequency of purchase Product category Summer products Weather Winter products Results The first step in developing a forecasting model for Unilever is to test the model performance of the full model, where all variables in above figure are included, on the consumer demand. The consumer demand is the actual number of products which are scanned at the registers of the retailer stores during a promotion. The effect size and direction of the independent variables are depicted in the table below, where two plus or minus signs indicate a strong effect of the variable on the promotional sales. The Adjusted R-square of the model is quite high with a value of 0.700. This indicates a good model fit where 70% of the variance of the promotional demand is explained by the model. Furthermore, the model results are robust when used for other promotions than the promotions with which the model is calibrate. Besides the fact that the variables with a large effect size are more important to inherit in a forecasting model, the effect size of a variable can also be used to drive marketing decisions. The first marketing implication is that a display (second placement) of a promotion in a retailer store is far more important than folder advertisement and TV advertisement. Hence, when the marketing budget should be allocated, investments in display should have priority above investments in folder advertisement and both should have priority on investments in TV advertisement. The second implication is that the promotion mechanism where a consumer has to buy four or more products to get the promotional discount results in the highest promotional demand. Surprisingly, a Single Price Page V Improving the promotion forecasting accuracy at Unilever Netherlands Off (SPO), where a consumer only has to buy one product, leads to a highger promotional demand than a promotion where a consumer has to buy two or three products. A promotion where the consumer gets a free product or premiaat has the lowest promotional demand, although the success of such a promotion really depends on the type of free product or premiaat. The last important implication is that marketing can increase the promotional sales by making sure that the promotion is sold in all stores of a retailer. This variable is especially important if the product is not sold in (almost) all stores in base line sales. For these products there is a lot of extra promotional sales to gain. One way of boosting the number of stores is by advertising the promotion in the folder, since all stores are expected to have the folder promotions available. So when a product is not sold in all stores it is more interesting for Unilever to invest in folder advertisement. Variable Effect size Variable Effect size Display ++ log_growth_number_selling_points ++ Folder + Percentage_repeat_buyers n.e. TV_support n.e. / + Promotion_pressure n.e. Holiday_products n.e. ln_LF_former_promotions_EAN ++ Promo_length ++ Market_penetration n.e. Percentual_discount ++ Preservability + a SPO - log_size_of_product n.e. a Two_for - Frequency_of_purchase - a c Three_for