AN ICOR APPROACH TOWARDS SHIP MAINTENANCE SOFTWARE DEVELOPMENT

Ship maintenance is one of the key processes to improve system performance and reliability onboard. Maintenance onboard ships is required to be performed in a planned manner because safe and efficient operation of a ship is very much depending on equipment reliability in operational level. A planned maintenance system (PMS), mainly supported with software, is established to monitor the maintenance implementations on board ships. This study aims to assess the effectiveness of onboard maintenance and limited with the procedures and specific softwares employed for the maintenance on board. Specifically, the process analysis approach is developed to execute the existing ship PMS phases based on four dimensions such as input, control, output, resources (ICOR). Besides the critical points to be improved in ship maintenance, this research reveals recommendations to increase the functionality of selected software in terms of workload balance, smart scheduling and safe working environment. To demonstrate the functional improvements, an illustrative case study on an auxiliary diesel generator maintenance routines are conducted. The contributions of the study are expected in both ship operations management and marine software development.

Predicting Ship Maintenance and Repair Labor with Artificial Neural Networks

Ship maintenance and repair work cost estimation is often regarded as an “Art,” which may contribute to the financial success or distress of a shipyard. Regarded as experts by senior management, estimators are among the most valued resources, and nonetheless, human. Over time, estimators learn from mistakes, and get better with tenure at sharpening assessments. When estimators retire without having groomed an apprentice, shipyards may be at risk of losing a lot of know-how, all at once. These shipyards may well find very costly to experience, for a while, estimating skills stepping back on the learning curve. Yet, even shipyards relying on less advanced information technology may have unwittingly accumulated a lot of valuable data relevant to ship maintenance and repair works. These shipyards may overlook how easily accessible knowledge can be turned into a competitive advantage through predictive analytics. Not only can this data be literally mined, but machine learning algorithms, such as Artificial Neural Networks (ANN), can now process it for a speedy and preliminary estimate through faster and cheaper computing power. To be clear, the purpose is not to replace the human estimator but to help the expert quickly assess, when times are busy, whether to bid or not on a specific project opportunity. In the absence of The Master Estimator, an Apprentice may also look for a quick and cheap sanity check of the prepared estimate before submitting a bid. The study carried out in this article is based on all ship maintenance and repair data recorded at a single North American shipyard over the last 19 years since the current information systems were implemented. This raw data extract with all directly paid hours logged daily by workers on 1277 ship maintenance and repair projects was screened through advanced data cleansing. To enrich the cleansed data tables, additional independent variables were subsequently collected internally and externally to develop a training–testing data set. The final 657 projects represent 136 vessels regrouped in eight types, for which 28 other independent variables were all made available for training up to testing simple ANN models. The scope of this article is limited to the estimation of the direct labor required to complete ship maintenance and repair projects on a specific type of vessels for which workforce planning and tactical pricing was deemed the most relevant to keep the business afloat.

Estimation of the time of ship maintenance by the terminal in the transport hub system

It is proposed to evaluate various options for the mooring of a vessel at the berth of a cargo terminal by integer time intervals, which, when using Erlang's formulas, generalized to the case of the Markov process, can be used to solve the problems of planning loading and unloading operations on the company's ships, and should be presented in the form of a vector, the size of which is determined by the number of possible states of the "ship – berth" system. A method for assessing the time of servicing a loading and unloading operation by the terminal has been compiled, when observing a number of particulars corresponding to the usual histogram, and each value of the time interval corresponds to a certain bit of the histogram, a variant of evaluating the mathematical expectation of the time the terminal serves one loading and unloading operation. It was found that using the least squares method, it is possible to identify the value of the variance of the time of the loading and unloading operation, which, in turn, can be used as a point estimate when compiling the confidence interval for the mathematical expectation of this interval.

Quantitative analysis of influencing factors of single vessel maintenance standard cost

Ship equipment maintenance fee of the single standard funds under the influence of many factors, in order to analyse the ship maintenance fee standard for each single loading factors influence the size of paper from two aspects: the ship itself and using the environment of ship cost, displacement, use fixed number of year, the price level, service environment, the use frequency of six index quantification, the factors which influence on factors affecting the characteristics of the mutual influence and mutual restriction between the measures, using the index of conflict and contrast between the strength of give attention to two or more things CRITIC method for quantitative analysis. The results show that the six influencing factors are in order from large to small: price level, service life, service frequency, service environment, ship cost, displacement.

Combination Forecasting Method of Ship Maintenance Cost Based on Integrated Weighting

In view of the existing combination forecasting methods based on rough set theory that may not be able to weight individual individual forecasting models, the attribute importance in the original method is adjusted and combined with the root mean square error of the individual forecasting model to form a new attribute importance. The new attribute importance is used to determine the combination forecasting weight coefficients, which solves the problem that the original method cannot be weighted, and increases the consideration of forecasting accuracy. Weight coefficients are also determined according to the historical forecasting performance of the models, which reflects the forecasting stability of the models. The integrated weighting method is used to fuse the two kinds of weight coefficients. Based on a certain type of ship maintenance cost data example, the improved method is compared with the commonly used combined forecasting methods, and better results are obtained, the accuracy and stability of the forecasting are improved, and the effectiveness of the method is verified.

PT Pelindo Marine Service: An Alternative Strategy for Maintenance of the Indonesian Navy’s Warships

The sea defense strategy, and concepts related to maritime strategy, relating to the overall strategy for achieving victory at sea. In securing the sea, the navy is very dependent on the main weapons system, especially warships. The Indonesian Navy in carrying out its duties rests on the strength of the IFWS (Integrated Fleet Weapons System): Warship, Aircraft, Marines, and Base. The four components of the IFWS are always maintained in combat readiness. One of the ship maintenance facilities in Surabaya that has the potential to be able to carry out maintenance of Navy ships is PT Pelindo Marine Service in Surabaya. This research uses descriptive qualitative approach. Logistics management theory and SWOT analysis are used by researchers as an analysis tool. The results of this study are the logistics management of PT PMS has ability to maintain Navy ships to support sea defense. Constraints faced by PT PMS include: (1) High sedimentation; (2) Inlet flow is crowded, narrow and shallow; and (4) Small dock capacity. The strategies developed by PT PMS to overcome obstacles in the maintenance of the navy’s ships: (1) Implement efficiency in HR, methods, budgets, and infrastructure to maximize profits; (2) Cooperation with similar companies; (3) Investing: dredging, increasing the capacity of the dockyard, and firefighting equipment, and damage control (controlling losses) so it does not get worse than expected.