Where demand planning meets revenue management: a research into forecasting baseline sales and forward buy at the commerce department of Grolsch

Groot Bluemink, Danique M. (2011) Where demand planning meets revenue management: a research into forecasting baseline sales and forward buy at the commerce department of Grolsch.

[img] PDF
Restricted to Restricted

1MB
Abstract:This research is accomplished within the Commercial department Retail of Grolsch, which is engaged in sales activities in the national Retail channel. The aim of this research is making forward buy comprehensible and presentable. From there the Revenue Manager can try to decrease forward buy and the Account Managers can discuss the forward buy in negotiations with the retailers. The link between forecasting and forward buy is difficult. To get a good forward buy overview, information about the accuracy of the forecast is needed; accurate baseline sales are needed, in order to define the incremental sales. Baseline sales are the regular sales in packages; the sales normalized for promotional activities and other outliers. At the moment the baseline sales forecast is calculated by an algorithm and manually adjusted by the Demand Planner (DP). Due to increased sequence of promotions, insufficient outlier cleaning and possibly a statistical forecast algorithm that does not meet the current market circumstances in the forecasting model, it is hard for Grolsch to define an accurate baseline sales forecast. For improvement on the forecast accuracy can be focused on outlier cleaning, the statistical forecast algorithm or scanner data. In this research is chosen to focus on scanner data. For calculating the baseline sales forecast the DP limitedly takes consumer data of ACNielsen into account, about what the retail customers sell in baseline and in incremental volume. The DP only checks ACNielsen data on an annual base, to compare the annual actual baseline sales according to ACNielsen with the annual actual baseline sales of Grolsch. ACNielsen data is scanner data which provides the complete view of what consumers buy. In this research Ex Factory and ACNielsen data are compared for the following Premium Pilsner products: 33cl. returnable bottles, 45cl. swingtop bottles, 33cl. cans and 50cl. cans for the retail customers Albert Heijn, C1000, Jumbo, Plus and Super de Boer, to verify if taking consumer data into account in the baseline sales forecast improves the baseline and thereby the total forecast. The research on the baseline, to meet a more accurate forecast, consists out of six steps briefly worded in the summary of chapter four. Based on the new baseline, the amount of forward buy can be calculated to solve the problem statement mentioned above. The research on identifying forward buy consists out of ten steps summarized in the conclusion in chapter six. Overall, Albert Heijn has the best position concerning forward buy seen from Grolsch. Grolsch uses different payoff methods, known as deducting the discount directly at the invoice and accruals. With Albert Heijn Grolsch uses a Vendor Managed Inventory (VMI) concept, a method to control forward buy, which is the best pay off method concerning forward buy from the currently used pay off methods.
Item Type:Essay (Bachelor)
Clients:
Royal Grolsch N.V., Enschede
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Industrial Engineering and Management BSc (56994)
Link to this item:http://purl.utwente.nl/essays/63077
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page