IGLC.net EXPORT DATE: 29 March 2024 @CONFERENCE{Gonzalez2009, author={Gonzalez, Vicente and Alarcon, Luis Fernando and Maturana, Sergio and Mundaca, Fernando and Bustamante, Jose Antonio }, editor={Cuperus, Ype and Hirota, Ercilia Hitomi }, title={Rational Commitment Model: Improving Planning Reliability and Project Performance}, journal={17th Annual Conference of the International Group for Lean Construction}, booktitle={17th Annual Conference of the International Group for Lean Construction}, year={2009}, pages={207-218}, url={http://www.iglc.net/papers/details/639}, affiliation={Postdoctoral Fellow, Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile. Lecturer, Escuela de Ingeniería de la Construcción, Universidad de Valparaíso, Chile. E-Mail: vagonzag@uc.cl ; Professor of Civil Engineering, Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile. E-Mail: lalarcon@ing.puc.cl ; Professor of Industrial Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile. E-Mail: smaturan@ing.puc.cl ; Well Engineer, Integrated Project Management - Schlumberger. E-Mail: mundaca@slb.com ; Planning and Development Engineer, Socovesa, Santiago, Chile. E-Mail: jabustamante@socovesa.cl }, abstract={Reliability of planning commitments at operational level is one of the key factors to improve project performance. The Last Planner System (LPSTM) is a tool designed to improve planning reliability in construction industry, however, the improvements in planning reliability are often limited due to the fact that the decision-making processes in construction, including those related to planning commitments, are mainly based on experience and intuition. The Rational Commitment Model (RCM) presented in this paper is a tool that helps to overcome this situation by introducing decision-making aids based on analysis of field data, which allows developing more reliable planning commitments using statistical models. RCM allows forecasting planning commitments for short term-periods using field production data such as labor available, buffer size, and planned progress. Several case studies have demonstrated the RCM forecasting capabilities and its practical use to improve reliability of planning commitments and project performance. The RCM also contributes to solve the well-known workloadcapacity problem and provides useful insight into lean production performance issues. }, author_keywords={Lean Production, Rational Commitment Model, Planning Reliability, Statistical Models. }, address={Taipei, Taiwan }, issn={ }, publisher={ }, language={English}, document_type={Conference Paper}, source={IGLC}, }