Le Club Francais du Vin Case Report
Group Members: Adrienne Durish-Christopher, Nichole Fortner, Matthew Moss, and Melissa
This case concerns Le Club Francais du Vin, The French Wine Club, which provides its
members extremely good quality wines at a great value. Le Club Francais du Vin creates their
wine catalog with a selection of wine from mid-size growers. Providing club members select
wine from mid-size growers allows them to capitalize on a niche market as most wine consumers
purchase their wine from local supermarkets. The issues in this case concern forecasting the
wines according to historical demands. Ordering optimal quantities to meet customer demand is
crucial to provide exceptional customer experience and to maximize profits while reducing
unnecessary losses due to overages.
Stephane Zanella, Directeur General of Le Club Francais must analyze historical forecast
and demand data to aid in the wine catalog orders for the upcoming season. This is not an easy
task as many variables are involved in making this important decision. If he orders too much of a
particular wine, then he will be faced with overage costs. If he orders too little of a particular
wine, then he will be faced with underage costs and dissatisfied customers. Both of these
scenarios incur negative consequences to the company and is the reason accurate, precise
analysis is required when selecting order quantities for the next season.
The first issue for consideration is the underage cost for Le Club Francais du Vin of
having one too few bottles of wine in their inventory. The calculation to determine the cost
underage (Cu) is found by subtracting the cost from the retail price. For example, a bottle of
wine with a retail price of 10 euro and cost of 5 euro has a cost underage (Cu) of 5 euro.
The second issue for consideration is the overage cost for Le Club Fracais du Vin of
having one too many bottles of wine in their inventory. The formula to calculate the cost overage
(Co) is found by subtracting the salvage value from the cost per bottle of wine. For example, an
overbought bottle of white wine sells at a 40% discount and on average costs the Club .80 euro
in indirect and direct warehouse operation costs. A bottle of red wine with a retail price of 10
euro has a cost overage of 5 euro (cost) minus 2 euro (salvage value) equals 3 euro. (The above
examples left out the shipping and handling costs for simplification. However, in the case excel
models the shipping cost has been added to the cost of each bottle of wine.)
The critical value is an important factor when calculating the optimal order quantity. The
critical value is determined by dividing the cost underage (Cu) by the sum of the cost underage
(Cu) and the cost overage (Co). The critical value is used with the mean and standard deviation
to calculate optimal order quantities based on historical forecasts and demands.
In the case excel file, the critical value, mean, and standard deviation was used to
calculate the optimal order quantity with a historical forecast of 2,000 bottles. The cost underage
(Cu) was set at 3 euro and the cost overage (Co) was set at 1 euro. The mean of demand
according to historical data was 1653 and the standard deviation of demand was 1886.03.
Inputting these values into the Newsvendor model with normally distributed demand calculated a
result of 2935.50. Based on this calculation, 2936 bottles of wine should be ordered.
The last consideration was to forecast how many bottles of each wine to order for the
upcoming season based on the historical data of the previous season. The previous season’s
forecast and demand quantities are used to calculate the error value and the A/F ratio. The A/F
ratio is used to calculate the mean and standard deviation. These values are then used to calculate
the expected actual demand. These calculations provide a good start for forecasting. However,
forecasting can be taken a step further by utilizing the excel function, norm.inv(CR,μ,σ), CR =
critical value, μ = mean, and σ = standard deviation.
All of the above mentioned techniques were utilized to calculate overage and underage
costs as well as to forecast optimal ordering quantities. These factors are critical when making
business decisions. An error in forecasting demand can be very costly to a company which is
evident by reviewing the overage and underage costs. Not only can poor forecasting negatively
affect a company by increasing costs, but it is bad for business. Meeting customer demand is a
part of providing a superior customer experience which is essential to business growth.