Application of stochastic risk simulation to increase depth of production planning

Abstract

This paper presents a procedure model that allows for a systematic analysis of execution risk in ship production by using stochastic risk simulation. Hence, planners can increase the depth of production planning to reduce disruptions and delays even with insufficient information density. The derived four-step model was then applied to the planning process at a German shipyard. Effects and probabilities of risks were estimated using stochastic distribution functions for two exemplary work packages in outfitting. Simulating the risk profiles for all work steps, the critical work steps and accordingly proposed planning tasks to increase the depth of production planning were identified. The application showed altogether that the Monte Carlo method can be used to mitigate the overall execution risk. In addition to increasing objectivity in the production planning process, the approach offers automation possibilities for future use cases and integration into planning software.