Data driven ship design: minimizing total life cycle cost

2018-09-28T18:37:11+01:00September 2018|Categories: Article|

How AI and data driven financial modelling at an early stage of ship design benefits the bottom line on the long run.

Designing a ship is an iterative process. At concept design level, during the tendering phase, few details of the vessel are known. The metrics used for estimating new building costs and future lifecycle cost are based on simplified estimation functions. An increasing number of cost and revenue drivers becomes known as the design process continues towards basic design. However, at this point, a large amount of the life cycle costs is already determined and the risk related to the design becomes a significant part of the total tendering costs. It is therefore, that shipyards and designers make trade-offs between further optimizing the total lifecycle cost of the vessel and lowering the cost of acquisition and concept design.

In contrast, for the future owners of the vessel the impact of relatively simple early design choices could make a giant difference in future revenues and total lifecycle cost. For example: What would be the effect on the total cost of ownership of a crew transfer vessel (CTV) in the offshore wind industry over the next 25-year lifespan if the speed of the vessel would be one knot faster? What would be the impact on the annual revenue of a cutter suction dredger (CSD) if the cutter power is increased with only a few kilowatts? What is the impact of additional bollard pull on a harbor tug or a few extra beds or restaurant seats on a ROPAX ferry? What is the impact on the future operational cost if the length/beam ratio of a trawler decreases a couple percent? What would be the reduction in annual depreciation and interest expenses if the new building price would be reduced a couple of million? And what would be the effect on the bottom line of a Heavy Lifting Vessel (HLV) if the weather window would be increased by a few hours per month?

In addition, the future ship owner should be aware of the exposure of the vessel to external forces. E.g.: fuel price hikes, interest rate spikes, charter rate fluctuations and more. For example: What is the impact of current design choices on the secondhand market value of the vessel?  Is it better to comply with future IMO regulations now, even though the regulation has not been ratified yet in order to increase the future residual value of the vessel in 25 years from now? These, and more questions are crucial during the early conceptual design stage and most of them are still relevant once the ship is already in operation. More and more ship design offices are experimenting with smart data driven estimation solutions and intelligent algorithms to calculate the effects of various design choices.

Designing models in which the different solutions are parametrized and the possible impact of changes in the economic and political environment are modelled, requires a parametric structure of the vessel new building and operational financials. Not only CAPEX and OPEX are relevant, also the potential revenue streams should be taken into account and modelled. These models produce quick and reliable assessments of potential profits and risks for design and operation. At early stages of the design, when many details of the design are not yet available, the models provide statistical values for missing parameters. If the vessel is already in operation, the models could help to make decisions on possible conversions. For example, whether or not to lengthen a cruise ship.

How ship design cost models work

Cost and revenue models typically return a full set of financial statements including balance sheet, profit & loss and cashflow statements forecasted over the complete lifecycle of the vessel. They provide a range of structural, powering and manning selections to predict weights, costs and various performance characteristics.

Models typically offer a wide variety of options: dimensional; cargo capacity; power and propulsion systems; crew and passenger size; structural materials, automation systems and equipment. Cost estimation parameters are based on estimated construction costs at different levels of detail and are not exclusively weight-based, but based on many different metrics including engine power, ranges and fuel type.

Separate reference financial models are be used for different ship types. For example, different models should be maintained for high-speed vessels, dredgers, cargo ships, tugs, crew transfer vessels and ferries.

The impact of each component of the vessel on future revenue, capital expenditure, operating expenditure and risk is entered into the general ledger of the financial forecasting model as a set of double entry accounting journals in the future. The model generates accounting postings over the lifecycle of the vessel. This way, different design choices can be stacked on top of each other as layers offering insight into the integrated effect of a composition of design choices and external forces. The models could for example show the bottom line impact of installing an Ampelmann motion compensator on a crew transfer vessel (CTV) or an additional barge loading installation on a trailing suction hopper dredger (TSHD).

Creating value

It will be these intelligent models that will make the difference in ship design in the coming years. Owners and financiers will demand these analyses and data driven design will become decisive in tendering procedures. Data driven intelligent models are typically not built in a spreadsheet environment, but are programmed in popular languages like Python or Java or maintained in engineering software packages like MATLAB. Machine learning algorithms source from data lakes that contain historical datasets of material prices, fuel cost and more. In order to increase the quality of the model and data over time, the models are stored in a safe, controlled and auditable environment.

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