Research Information System
in: Artificial Intelligence for Business An Implementation Guide Containing Practical and Industry-Specific Case Studies, Hemachandran K Raul V. Rodriguez, Editor, CRC, New York , New York, pp.0-1, 2023
The oil and gas trade
involves highly complex procedures and relations through the downstream supply
chains. To ensure the real-time data flow, distributors, vendors, and customers
need to be well integrated. Coordination defects and disruptions occurring in
data flow lead to direct and indirect costs while reducing the supply chain
performance. The fourth industrial revolution (industry 4.0) promises new
technologies to diminish complexity and improve efficiency. However, it becomes
difficult to evaluate the effectiveness of these implementations when the
uncertainties arise regarding the performance outcomes. This study introduces a
business process reengineering (BPR) project that aims to improve the order
fulfillment performance of a large-scale oil and gas distribution network. We
introduce the shadow sell concept as a novel performance metric and use it to
evaluate the project success. In the case study, we assume that the daily count
data of the shadow sales convey a Poisson process. An eight-step Bayesian
methodology describes the performance density and explores a potential breakpoint
across the project timeline. The results provide insights into a successful
real-world industry 4.0 implementation and emphasize the merits of the Bayesian
methodology to overcome uncertainties when deciding on the success of
large-scale projects.