Service Level Anchoring in Demand Forecasting: The Moderating Impact of Retail Promotions and Product Perishability

This dataset is used for the working paper 'Service Level Anchoring in Demand Forecasting: The Moderating Impact of Retail Promotions and Product Perishability,' authored by Fahimnia, Tan, and Tahirov. The data was collected during a laboratory experiment designed based on data from a real case in the fast-moving consumer goods (FMCG) industry. Each subject was assigned to one of the following treatment groups:

T1 (control group) - forecasts were made for a nonperishable product (shelf life of 9 months), with no service level information.

T2 - forecasts were made for a nonperishable product, with a high service level information.

T3 - the forecasts were still for a nonperishable product, with a lower service level information.

T4 - forecasts were made for a perishable product, with high service level information.

T5 - forecasts were made for a perishable product, with low service level information.

A total of 313 subjects prepared four forecasts each. For each forecast, a subject was provided with 30 weeks of sales data, including both normal and promotional weeks. The promotional weeks were highlighted as 'Promo.' The subjects were asked to provide their forecasts for week 31, basing their forecasts solely on historical data and potential sales promotions. Mean absolute percentage error (MAPE) was used to assess the accuracy of the forecasts. Percentage forecast bias was used to measure the deviation of adjusted forecasts from the normative benchmark forecast.

The new version of dataset includes three Excel files:

Excel file 1 (“DataSet.xlsx”) – This file contains the average adjusted forecast for each subject during both the promotional and non-promotional periods, along with other data such as demographic information, calculated MAPE, forecast bias, service level, and product perishability.

Excel file 2 and 3 (“Pool_1_Perishable” and “Pool_2_Non perishable”) - These files contain all the real datasets for perishable and non-perishable products used during the experiment.