A first glance at the Verhouten-case
Case
The case is about a chocolate factory that markets several products. The goal is to
develop a sales forecasting model for one of the products. This happens in a number
of steps, linked to weekly assignments. We will use this case throughout the course.
Background
The chocolate factory is called Verhouten. It is a family company founded in 1899 by
Cornelis Verhouten in Haarlem. Since then, the company has grown continuously. In
1899 there were two employees and at this moment there are 125. The turnover was
6,647 guilders in the first year, whereas last year's turnover was 200 million guilders.
Currently, a grand-grand child of the founder, Frans Verhouten, is member of the
board of directors. In the first years Verhouten sold only chocolate bars of 75 grams.
Currently, the assortment consists of four product classes: chocolate bars, candy bars,
specialties, and seasonal products. The chocolate bars carry the brand name
Verhouten and they are available in three weight classes (100, 200, and 400 grams),
and four flavors: milk, pure, almond-milk, and white. The candy bars are sold under
the brand names Tiger and Dream. The specialties are Belgian bonbons and pastilles.
Seasonal products are sold in December (chocolate letters, and Christmas chocolates,
and at Easter (Chocolate Easter eggs and Chocolate Easter bunnies). In the beginning,
Verhouten products were distributed only by grocery stores in Haarlem and
Amsterdam. Today there are several distribution channels. The supermarket channel
distributes 75%, candy stores 10%, sport canteens 8%, gasoline stations 4%, and
tobacco stores 3%. The most important supermarkets are Albert Heijn (28% of the
supermarket channel), Super-De Boer (10%), and Edah (8%). In this case we focus on
the most important category for Verhouten: chocolate bars.
Consumer behavior for chocolate bars
There are several usage motives for chocolate bars:
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The functional motive: to satisfy appetite;
The treat-oneself motive;
The sharing-motive (family, friends);
The give-away motive (as a present).
Except for the last motive chocolate bars are often purchased by impulse. It is
typically not listed on the grocery list. Instead, while the consumer browses the
shelves, s/he notices chocolate and puts it into the shopping cart. Very often this
purchase behavior is influenced by sales promotions. A display containing chocolate
products with a temporary price decrease or an enlarged quantity for the same price
may be the incentive to purchase. Therefore, sales promotions are used frequently in
the chocolate category. Twenty percent of the annual chocolate turnover is being sold
accompanied by sales promotions. In the top 25 of most frequently promoted products
in 1997 there are five chocolate items (Source: AC Nielsen Yearbook 1998). Due to
frequent promotions consumers adapt their purchase behavior. More and more
chocolate products are purchased in promotional periods and fewer and fewer outside
these periods. As a consequence, the promotional sales spikes are very large. This has
consequences for production and logistics as well. The distribution channel must have
sufficient stocks to anticipate these promotion-induces spikes. On the other hand, too
many inventories cost much as well. To summarize, to anticipate the expected effects
of sales promotions, we need good sales predictions.
Goal
This assignment's goal is to develop a sales forecasting model for chocolate bars. The
predictions should be accurate enough to base production and logistic decisions on it.
More specifically: there are 68 weekly observations.
Model application
The model you are going to develop for Verhouten is a test case for this company.
Therefore, you will focus on one chocolate bar variety sold at one supermarket chain:
100 grams milk chocolate sold at Albert Heijn. This is the single most important
product for Verhouten since it has the largest turnover of all products in all channels.
This test case, if successful, is to be extended to all products in all chains. But before
management is willing to take this step, this test case should convince them of the
usefulness of a forecasting model. The model will be developed based on ‘chain-level
scanner data’ from Albert Heijn, contained in the SPSS-file chocolate.sav, that will
be provided to you. ‘Chain-level’ means that all variables are at the aggregate chain
level, instead at the individual supermarket level. ‘Scanner data’ are obtained by
‘scanning’ items at supermarket checkouts. In this case we have four 100 grain milk
chocolate brands: Verhouten (brand 1), Droste (brand 2), Baronie (brand 3), and
Delicata (brand 4, Albert Heijn's private label). We have 68 weekly observations.
Each row in the data set is a weekly observation. The first row is from week 1 of
December 1997. In the columns of the data set are the following variables:
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Sales (hundreds of kilos) of Verhouten;
Prices (guilders per unit) of each of the four brands;
Variable for ‘feature-only’: weighted distribution figure at the chain level for a
‘feature-only’ (no display, see below) in a certain week. A ‘feature-only’ means
special outside-store attention for the brand, either in the store flier or in an ad in a
newspaper or a magazine. At the individual store level this variable is measured as
a dummy: = 1 for feature-only, = 0 else. The weighted distribution figure means
that the store-level dummy variables have been weighed with the turnovers of
these stores. Hence, this variable varies between 0 (=no store has a feature-only)
and 1 (= all stores have a feature-only). This variable is available for all four
brands.
Variable for display-only: weighted distribution figure for display-only (no
feature) in a certain week. A ‘display-only’ means special inside-store attention
for a brand: a temporary shelf in the one of aisles, or a change in the brand's
regular shelf. At the store-level this variable is measured as a dummy: = 1 for
display-only, = 0 else. At the chain level it varies between 0 and 1. This variable is
also available for all brands.
Variable for combined feature and display: again a weighed distribution figure. It
is measured as a dummy at the store level: = 1 for combined use of feature and
display, 0 else. Hence, at the store-level, we have four situations: no promotion
(feature-only, display-only, feature & display all zero), feature-only promotion:
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only this variable is 1, others are zero, display-only promotion: only this variable
is 1, the others zero, and combined feature-and-display promotion: this variables
is one, the others zero. This feature-and-display variable is available for brands 1,
3, and 4 only;
Average weekly temperature in degrees Celsius.