TY - JOUR

T1 - Use of contextual and model-based information in adjusting promotional forecasts

AU - Sroginis, Anna

AU - Fildes, Robert

AU - Kourentzes, Nikolaos

PY - 2023/6/16

Y1 - 2023/6/16

N2 - Despite improvements in statistical forecasting, human judgment remains fundamental to business forecasting and demand planning. Typically, forecasters do not rely solely on statistical forecasts; they also adjust forecasts according to their knowledge, experience, and information that is not available to statistical models. However, we have limited understanding of the adjustment mechanisms employed, particularly how people use additional information (e.g., special events and promotions, weather, holidays) and under which conditions this is beneficial. Using a multi-method approach, we first analyse a UK retailer case study exploring its operations and the forecasting process. The case study provides a contextual setting for the laboratory experiments that simulate a typical supply chain forecasting process. In the experimental study, we provide past sales, statistical forecasts (using baseline and promotional models) and qualitative information about past and future promotional periods. We include contextual information, with and without predictive value, that allows us to investigate whether forecasters can filter such information correctly. We find that when adjusting, forecasters tend to focus on model-based anchors, such as the last promotional uplift and the current statistical forecast, ignoring past baseline promotional values and additional information about previous promotions. The impact of contextual statements for the forecasting period depends on the type of statistical predictions provided: when a promotional forecasting model is presented, people tend to misinterpret the provided information and over-adjust, harming accuracy.

AB - Despite improvements in statistical forecasting, human judgment remains fundamental to business forecasting and demand planning. Typically, forecasters do not rely solely on statistical forecasts; they also adjust forecasts according to their knowledge, experience, and information that is not available to statistical models. However, we have limited understanding of the adjustment mechanisms employed, particularly how people use additional information (e.g., special events and promotions, weather, holidays) and under which conditions this is beneficial. Using a multi-method approach, we first analyse a UK retailer case study exploring its operations and the forecasting process. The case study provides a contextual setting for the laboratory experiments that simulate a typical supply chain forecasting process. In the experimental study, we provide past sales, statistical forecasts (using baseline and promotional models) and qualitative information about past and future promotional periods. We include contextual information, with and without predictive value, that allows us to investigate whether forecasters can filter such information correctly. We find that when adjusting, forecasters tend to focus on model-based anchors, such as the last promotional uplift and the current statistical forecast, ignoring past baseline promotional values and additional information about previous promotions. The impact of contextual statements for the forecasting period depends on the type of statistical predictions provided: when a promotional forecasting model is presented, people tend to misinterpret the provided information and over-adjust, harming accuracy.

KW - Information Systems and Management

KW - Management Science and Operations Research

KW - Modeling and Simulation

KW - General Computer Science

KW - Industrial and Manufacturing Engineering

U2 - 10.1016/j.ejor.2022.10.005

DO - 10.1016/j.ejor.2022.10.005

M3 - Journal article

VL - 307

SP - 1177

EP - 1191

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -