Feature 3 | CAD/CAM Big Data in Shipbuilding
Patrick David, R&D engineer and Patrick Roberts, director of operations, SSI USA discuss the impact of big data in shipbuilding
T
here is a massive amount of data generated during shipbuilding. Due to the complexity of vessel construction,
it is hard to ignore the possibility that there might be relationships buried in this data that could lead to enhancements in business decision making regarding efficient resource management, purchasing and other critical factors influencing profitability. Teasing out those relationships is a job
for “Big Data” analysis. To explore this topic, an ad-hoc working group has been formed consisting of soſtware makers SSI and Praeses LLC., along with multiple US shipyards across the NSRP (National Shipbuilding Research Program) such as General Dynamics and Huntington Ingalls Inc. The group has identified several potential opportunities along with various challenges to implementing Big Data analysis.
What is Big Data? For clarity, the definition proposed by Gartner Inc. is oſten used: “Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” Tis definition identifies three key traits
pertaining to the data under consideration for analytics: Volume, Velocity, and Variety. Each of these traits applies to shipbuilding.
Volume Across the shipbuilding enterprise there is a large amount of data being generated. Schedules for operations are built around the many constraints that a shipyard has to contend with. Collaboration with vendors and lead-time materials has to be coordinated. Ship design and production information needs to be created and resources must be planned against the schedule and design as well. Tese are just a few of the global examples of data creation. Looking further into the shipbuilding process reveals multiple layers of data effecting the entire organisation. Shipyards recognise that there is a wealth
of information being generated at every level of the enterprise, but to date have only been
38 SSI looks at the bigger picture of handling ‘Big Data’ in shipbuilding
able to focus on the elements that directly impact efficiency and production schedules. Tere is far more data than might have been anticipated initially and the industry as a whole lacks a cohesive method of cataloguing and utilising this data beyond simple planning and scheduling. Te ability to consider this volume of data in the context of all the data being generated across the enterprise holds the possibility of greatly enhancing insights to make better business decisions.
Velocity Though a normal ship production cycle can seem quite long compared to other industries, within the shipbuilding enterprise the actual time constraints and requirements are very demanding. Multiple entities must coordinate very large capital across several disciplines in order to maintain a smooth construction schedule. Resources must be carefully planned
and scheduled from the beginning of a ship construction project, materials must be ordered and delivery schedules coordinated with just-in-time practices in some cases, and design information needs to be created and generated for directing production. Small changes in any of those resources can disturb the entire production schedule downstream leading to very costly schedule over-runs or missed deliveries. Te ability to quickly respond to changes in the production environment with the best course of action is essential to minimising the risks and costs for the shipyards.
Variety One of the largest obstacles to any Big Data Analytics implementation is the various forms that the data may take. In higher-tech industries there is oſten a mismatch in data types across existing systems. In shipbuilding this is more acutely felt due to the relative lack of data collection methods. Te organic way in which shipyards operate at the lowest levels means that methods of data collection and verification can be as complex as full computer soſtware tracking and cataloguing or as low-tech as a clipboard hanging in a warehouse. Te challenge is finding a method of accounting for the various types and formats of data that can be tightly integrated into a Big Data analytics workflow. Of the three main points in the definition
of Big Data, Variety may be the most challenging for shipbuilders. Combining relevant data from across the enterprise, often stored in disparate formats and systems, is a vital component in being able to leverage data analytics.
Challenges Shipyards, by nature, lack the computing infrastructure and toolsets for conducting Big Data analytics. Due to the volume of data that is created in a shipbuilding environment, this could become an issue of storage and management. In addition to the storage requirements, there are also requirements for soſtware systems for managing the data (i.e.: databases and tools). Tere are oſten other
The Naval Architect January 2015
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