Configuration and evaluation of an integrated demand management process using a space-filling design and Kriging metamodeling. (2018)

• Record Type: Journal Article
• Authors: Ben Ali, M.; D'Amours, S.; Gaudreault, J.; Carle, M-A.
• Is Part Of: Operations research perspectives. Volume 5(2018)
• Page Start: 45
• Page End: 58
• Publication Date: 2018
• Subjects: Integrated demand management process -- Sales and operations planning -- Revenue management -- Space-filling design -- Kriging -- Softwood lumber industry; Operations research -- Periodicals; Management science -- Periodicals; 658.403405
• DOI: 10.1016/j.orp.2018.01.002
• Languages: English
• ISSNs: 2214-7160

Abstract

Abstract: Objective: This research aims to develop a basic understanding of a demand management process integrating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies with limited capacity. Contribution: Typical researches about demand management processes analyze few system specifications or vary few potential factors one at a time. Yet, additional insights can be obtained by employing a space-filling design and Kriging metamodeling for analysis. Methodology: We compare two configurations of the integrated demand management process. While the First-Come First-Served concept is used at the order promising level for the first configuration, the second configuration uses nested booking limits and gives advantage to profitable customers and attractive periods. Considering various order arrival sequences, we generate Kriging metamodels that best describe the nonlinear relationships between four environmental factors (demand intensity, demand forecast error, customer heterogeneity and coefficient of variation) and three performance measures (yearly profit margin, yearly sales and high-priority fill rate) for Canadian softwood lumber firms. Since our simulation experiments are time-consuming, we employ a Latin hypercube design to efficiently take into account different market situations. Results: Our analysis reveals the potential to improve the performance of the demand management process if we know high-priority customers needs before fulfilling low-priority orders and if we use nested booking limits concept.