THE FUTURE REALITY OF THE DIGITAL TWIN AS A CROSS-ENTERPRISE MARINE ASSET Denis Morais, SSI, co-CEO & CTO Greg Goulanian, SSI, Marketing Specialist Nick Danese, NDAR, CEO
The Digital Twin - the Digital Representation of a Physical Object Digital Twins: there's one for everyone • Geometrical: • drawings, 3D model • Process support: • procurement, production planning & management, • operations (monitoring, repairs, refits, etc.) • Simulation, predictive: • numerical digital twin: simulation by behavioral algorithms, • physical digital twin: simulation including physical characteristics • drawings, 3D model, etc. Digital Twins: where do they come from • Software: • drawings, 3D model, analytical, etc. • Big Data: • field data, post-processed data, derived data, statistics, etc. • etc.
TODAY - What Does Which Digital Twin Do Why do we have Digital Twins • Lots of good reasons: • Representation (geometry, functionality, etc.), • Monitoring (operation) • Tracking (KPIs), • Planning • Decision making support, etc. Many distinct Digital Twins • Geometrical Digital Twins: • never complete, generally incompatible, meta-data poor (especially process and functional meta-data) • Process Digital Twins: • design, procurement, reception, storage, etc., • production planning, production engineering, operations: monitoring, repairs, refits, etc. • Simulation and predictive: • numerical digital twin: simulation by behavioral algorithms, • physical digital twin: simulation including physical characteristics • drawings, 3D model, etc.
TODAY - Wish There Were (just) One Digital Twin: in reality there are a lot of objective constraints (& subjective) • Diversity of components: • by nature, by purpose, by digital definition, etc. • Diversity of platforms: • software, processes, data formatting, data set architecture, unique identification of components (e.g. definition), multiple-source reconciliation, IIoT (Industrial Internet of Things), etc. • Data set size: • a large ship is made of 1 E09+ parts, validity, legibility, etc. • Critical path definition: • linear and looping dependencies, critical data and patterns, etc. Digital Twin: ideally • One Digital Twin to Rule Them All: the Platform of Platforms
Shipbuilding : a Different (Digital) Beast Shipbuilding: evolution, fast-forward • Shipbuilders: • excel at shipbuilding • are forced into high-power IT • used to "do everything under one roof“ • must orchestrate a variety of specialists (mostly third parties), • are no longer experts in and can't control all domains Silos • Disciplines, processes, people, divisions, contractors, cultures, etc.: • Digital isolation: fragmented, disconnected, concurrent, interdependent processes, one-offs or prototypes ?, anti-synergetic Connectivity • Difficult and complex: • inadequate know-how, inadequate means, limited vision . . . overwhelming Pitfalls • Unrealistic • assumptions, goals, calendars, etc.
Digital Twins: Why Now ? We want more ! Who benefits • Shipbuilders: • always did, always will • Ship Owners • always would have, always will The drivers behind the acceleration • Technology • more and more accessible, still not very available, • cultural misfit between beneficiaries, system designers and IT providers • Consumers: • designers, builders, operators, owners The boosters behind the drivers • Technology: • PLM, IIoT, Business Intelligence, Data Mining, etc. • Operational requirements • various forms of drones and autonomous shipping, • classification, insurance, owners and operators
Digital Twin - Steady As She Goes (Production is King) A Digital Twin is Born • Design mission: • time and budget • geometry, function • interacting factors: ex. non-negotiable parameters such as ship evacuation and ventilation/comfort • advanced proof of concept The Digital Twin's growth is ballasted by constraints • Production: time and budget = no risk • geometry, function, production planning and execution, trials, delivery The Digital Twin shapes up but remains fragmented • Trials, adjustments and delivery: • changes The Ship goes to sea, the Digital Twin stays home (for now) • Lots of big data: • from the ship, from other relevant and related non-ship sources, . . . used less than one would like to
Digital Twin - Why So Slow Out of The Blocks: a Fundamental Challenge The fragmented Digital Twin • " CAD " data • meta-CAD data: • design (ascertain functional aspects) • shipyard (production) • non-CAD data: • shipyard (production) • owner (functional) • etc. The missing link • cultural shift: • synergize apparently conflicting requirements (time, cost, function, etc.), • develop a (quasi-)zero risk evolutionary path • Objectivity: • regroup and progress
A Disruptive Shift In The Explanatory Paradigm The Business Transformation • A considerable amount of useful data & information sits idle: • difficult to mine, not sure how to use it, not aware of its relevance , etc.) • The Digital Transformation • " digital" is a good start but not the whole answer • the road map analogy: pdf or Google Maps et al. ? • Available vs just Accessible: • the difference between can and do, theory and practice, • the subscription service vs search engine analogy The Digital Twin is here already • Some assembly required: • please welcome . . . the Business Transformation ! • Batteries not included: • add vision and establish short-term goals
The Cross-Enterprise Digital Twin Sine-qua-non • Strategy: • LEAN, AGILE, • small steps must generate immediate ROI • the scenario changes faster than development, • adjust course Implementation • Take a step shorter than the leg: • exploit & optimize what is already there, • incremental vs disruptive, • progressive vs aggressive, • the steam roller analogy Benefits • Cross-Enterprise • Lifelong cycle: • remove silos, • easier discovery and re-use of relevant data & information, • promote cross-enterprise collaboration, • favour and support the inception and implementation of new plans, strategies, workflows, technologies, etc.)
Digital Twin - Design More than just a growing CAD model • CAD: • evolutive modelling: design is just the start, • evolutive model: include as much meta- data as possible, • unique identification of defining components and defined components • non-CAD: all other meta-data (plenty) • Process: • connect CAD and non-CAD data & information, • define & start populating & enriching the PLM data & information model, • capture, benchmark and improve upon past processes and ship performances Benefits • Cross-enterprise: • improved performance of processes and ship, lower design, production and overall lifetime costs, reduced expected downtime thanks to predictive strategies, preventive maintenance, etc., compliance with the latest regulatory requirements, improved role-specific capabilities, etc.
Digital Twin - Building Can the future really be paperless ? Mobile information and guidance: • check the use by date ☺ , • tablets, • heads up displays in Augmented Reality: training, simulating, building, maintaining & repairing Timeliness compliance: • connect suppliers and third parties: availability, deliveries, etc., • connect yard machinery • measure and track performance with IIoT (off the shelf plug & play ), • connect yard management: warehousing, movement, special requirements, approval, supervision, etc.
Digital Twin – MRO : Converting Down Time to Service Time (1/2) Repairs • down time is inevitable but can be minimized , • average repair time 12-14 days /repair = 3.7% of service time /repair /year Refits • the ability to better and more accurately design ships has extended de-facto their life, • during the longer life period, regulation changes and/or changes of mission profile are far more likely to happen at least once, • refits take place, • the more complete the Digital Twin, the easier it is to ascertain the refit's ROI and the more effective the refit
Digital Twin – MRO : Converting Down Time to Service Time (2/2) Digital Twin: key • On-board collection of data & information becomes crucial: • mechanical, operational, etc. • Pervasive IIoT systems and sensors are readily available and inexpensive: • some are plug & play and fully connected out-of-the-box [Alleantia] • Decision making support: • what to do when, how to do it exactly • The Ulstein IAS example: • combine data already present to compound operational effectivity & ROI Who pays ? Who benefits ? • Incremental total cost of ship: absolutely need not be millions • ship owners pay and reap ROI • has the coin dropped ? • lack of case study / evidence / documented business cases . . . • How much IIoT is required to break even: • daily rates vary greatly (15k - 150k $ w/out fuel), • even at the lowest daily rates a few days in the dock will quickly cost more than a comprehensive IIoT installation
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