Computational Shipyard Dynamics

Early simulations focused mainly on virtualizing the shipyard itself and the approach in terms of production management that focuses on improving productivity was hardly sufficient. For systematically approaching shipyard production problems, to which the dynamics between various factors are applied, this study uses the shipyard simulation information model to explain the six factors, which are the input variables of the shipyard production system simulation. Then, the objective or output variable is set up as the key performance index (KPI) by aligning the interests from an enterprise perspective. The output variable, KPI, is generally expressed as a function of functions of the input variables, namely a functional. To evaluate the value of the functional, a computational method in the form of a simulation is used. The process-centric simulation is adopted as it is appropriate for the shipyard production system simulation, which is an engineered-to-order industry, and it is easy to implement the concept of dynamic resolution. According to this methodology, we present a simulation application that focuses on the fabrication shop of a shipyard. 1. Introduction 1.1. Background The shipbuilding industry is suffering because of economic stagnation and the rapid growth of emerging shipbuilding nations. To survive the competition, it is necessary to increase productivity and secure the ability to build high value-added products. Small- and medium-scale shipyards in Korea, which are less competitive than the large-scale shipyards, are losing ground to preexisting shipyards with high productivity and to those of emerging shipbuilding countries that use cheap labor as most of their products overlap with those of their competitors. Large-scale shipyards attempting to build high value-added offshore plants are in an unprecedented crisis, although building a product that has never been built. Increasing attempts are being made to overcome this crisis by using simulation technology. Simulation uses a cyber-physical environment to attempt new processes and can be of considerable help in production management. It enables us not only to see if a new plan or a new method is executable even without preexisting records but also to investigate various means to establish and accomplish targets to improve productivity.