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 - forecasts were made for a nonperishable product (shelf life of 9 months), with no service level information.
• T2 - forecasts were made for a perishable product (shelf life 1 day), with no service level information.
• T3 - forecasts were made for a nonperishable product, with a high service level information.
• T4 - the forecasts were still for a nonperishable product, with a lower service level information.
• T5 - forecasts were made for a perishable product, with high service level information.
• T6 - forecasts were made for a perishable product, with low service level information.

A total of 368 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 dataset includes four Excel files, two code script files, and a README file:
1. Excel files 1 and 2:
 "database_analysis.xlsx": Contains average adjusted forecasts for each subject during both promotional and non-promotional periods, along with demographic information, calculated MAPE, forecast bias, service level, and product perishability. This file is used as input data in the "data_cleaning.R" script.
"database_plot.xlsx": This Excel file contains a compact and cleaned version of the data from the first file, excluding outliers, and was used to create visuals such as boxplots.
2. Excel Files 3 and 4:
"Pool_1_Perishable.xlsx" and "Pool_2_Non_Perishable.xlsx": Contain real datasets for perishable and non-perishable products used in the experiment.
3. Code Script File:
"data_cleaning.R": This script performs data pre-processing by cleaning and transforming the dataset.
"analysis.R": This script loads the cleaned data and performs statistical analysis, including ANOVA and hypothesis testing.