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On Bayesian forecasting of procurement delays: a case study
Author(s) -
Palomo Jesus,
Ruggeri Fabrizio,
Rios Insua David,
Cagno Enrico,
Caron Franco,
Mancini Mauro
Publication year - 2006
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.627
Subject(s) - procurement , computer science , task (project management) , competition (biology) , operations research , order (exchange) , quality (philosophy) , service (business) , bayesian probability , industrial organization , risk analysis (engineering) , business , marketing , engineering , systems engineering , finance , artificial intelligence , ecology , philosophy , epistemology , biology
In the engineering and contracting sector, the on‐time availability of materials is a crucial element of any project. In recent years, there has been increasing competition in the supply of such components, as a result of market globalization. This has generated customer demand for higher performance, service, and quality—and, most of all, shorter delivery times. However, it has also forced suppliers to increase efforts to satisfy these requirements in order to remain competitive. Thus, contractors have moved to focus their attention on the task of more precisely modelling on‐time delivery risks. Historical data, expert opinion, and agreements between contractors and suppliers are some available sources of information that can be used to generate more accurate forecasts. We combine these various inputs in a Bayesian approach based on dynamic linear modelling. Our methodology has been implemented as a web‐based Decision Support System, and has been applied in a real case study from an oil sector engineering and contracting company. Copyright © 2006 John Wiley & Sons, Ltd.