TY - CHAP

T1 - From Volatile Maintenance Data Forecasting to Reliable Capacity Planning

AU - Berkholz, Daniel

AU - Schmidt, Matthias

AU - Nyhuis, Peter

PY - 2008

Y1 - 2008

N2 - Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintainingvaluable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network.This paper outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning method. This is achieved by deriving order probabilities from Bayesian networks. In addition the residual wear margin isused to determine an appropriate part-dependent work content.

AB - Maintenance, repair and overhaul processes (MRO processes) are elaborate and complex. Rising demands on these after sales services require reliable production planning and control methods particularly for maintainingvaluable capital goods. Downtimes lead to high costs and an inability to meet delivery due dates results in severe contract penalties. Predicting the required capacities for maintenance orders in advance is often difficult due to unknown part conditions unless the goods are actually inspected. This planning uncertainty results in extensive capital tie-up by rising stock levels within the whole MRO network.This paper outlines an approach to planning capacities when maintenance data forecasting is volatile. It focuses on the development of prerequisites for a reliable capacity planning method. This is achieved by deriving order probabilities from Bayesian networks. In addition the residual wear margin isused to determine an appropriate part-dependent work content.

KW - Engineering

KW - Capacity Planning

KW - capital

KW - forecasting

KW - Maintenance

KW - Logistics

KW - Production and Operation Management

M3 - Article in conference proceedings

SN - 978-988-98671-0-2

T3 - Lecture Notes in Engineering and Computer Science

SP - 1118

EP - 1122

BT - Proceedings of the World Congress on Engineering and Computer Science 2008

A2 - Ao, S. I.

A2 - Douglas, Craig

A2 - Grundfest, W.S.

A2 - Schruben, LE

A2 - Borgstone, John

PB - Newswood Limited

T2 - World Congress on Engineering and Computer Science - 2008

Y2 - 22 October 2008 through 24 October 2008

ER -