Paint Data Management and Process Optimization

1. Digital Paint Tools & Process OptimizationNSRP All-Panel MeetingMarch 12, 2019 DCN# 43-5002-19 Jamie BreakfieldR&D Project Manager/Systems Engineer Prepared under ONR Contract N00014-14-D-0377 as part of the Navy ManTech Program

2. Project Team

3. What is the issue? Need to improve the creation, handling and dissemination of paint data Terminology across platforms not standard Multiple disparate databases Data format not standardized Separate database for each hull Difficult change management Engineering and planning NEED to provide best possible data downstream

4. Paint data quantified: What is a paint schedule?

5. Objectives Defined Create a new optimized process for paint data management; Create a unified data tool to support Streamlined process Consolidate paint data Standardize terminology & syntax across platforms Increase efficiency Reduce rework Easily rate and plan paint execution Manipulate paint data in conjunction with product model

6. What did we do? – Phase I • Conductedextensive assessment with stakeholders: • Discovery of all current paint data products and underlying process • Mapped current paint data process • Developed future process • Compiled detailedrequirements set for functional development • Defined architecture based on functional requirements Approved for public release (A); distribution unlimited

7. What did we do? – Phase I • Identify all current paint-data driven products, producers, and consumers Map the Extent of the Current Process

8. What did we do? – Phase I CURRENT ! Paint schedule and Deck Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Change process:Propagated manually per database Paint schedule and Deck Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover ! ERP ! ! Bills structured, estimated, created then bulk created in MARS via SQL, DWGs/aids manually attached individually Manualrollover, creation, or maintenance for each DB separately Unique MS Access Databases - Per hull(Deck Covering & Paint) Publish tabular products (DBs replicatedfor billing) Data calls, queries constructed as needed

9. What did we do? – Phase I Create New Process and Architecture

10. What did we do? – Phase I FUTURE Paint schedule and Deck Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover PLM Paint schedule and Deck Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover Paint schedule and Deck Cover ERP + Consolidated data set for all hulls & platforms (Deck Covering & Paint) Changes propagated automatically across centralized data set Bills structured, estimated (more accurately), created, transacted to ERP in bulk with automated attachments Publish (release) live data with extraction capability (no replication required) Automatedrollover, efficient maintenance for all data together + DESIGN MODEL Increased utilization of design data (sq. ft., compartments, etc) • More Data Automation, Improved Data Quality and Less Manual Replication • Reduces time to perform billing and change paper activities • Reduces rework by improving data accuracy • Quickly serves the latest enhanced quality data to the work site + Powerful queries easily built capable of searching all available data

11. Phase II - Approach Access Provisioning • Aras Innovator • Develop the Digital Paint Tool as a single repository for all paint data • Interface with work provisioning system • Create a final document of the full paint schedule • Thousands of pages of paint coat, square footage, and location information • Creating a workflow to track paint changes with work provisioned • Alert to user when changes to the schedule have already been worked, or when changes can be sent • ShipConstructor • Extracting necessary information from ShipConstructor using Enterprise Hub PublisherLTOperations • Convert ShipConstructor data for automated square footage calculation Legacy Data Status SSRS Reports Geometry

12. Phase II - Results Manage Paint Schedules and Locations

13. Phase II - Results Add Paint Coats and General Notes

14. Phase II - Results Manage Paint Work Orders

15. Phase II - Results Initiate Notifications (real-time notifications for state changes) New In Planning In Review Deleted

16. Phase II - Results Robust reporting through SQL Reporting Services

17. Phase II – Key Points • Aras Innovator provided an excellent foundation • Lifecycle, workflow, user management, data management, and email/notification management are already included and easy to configure • Allowed the project team to focus on the more complex problems with paint • Aras provided an API that allowed us to sync large amounts of external data sources into the Aras system • Utilizing the ShipConstructordatabase structure to pull part information by unit • Calculate percentages of square footage for those rooms split by unit • Autocorrecting room boundaries that overlap • Syncing large datasets of provisioning data to be worked on a nightly basis

18. Conclusions/Next Steps • Consolidated digital paint process utilizes a single data management tool across all ship programs • Effectively uses the prior investments in the PLM platform to drive additional process improvements • Leverages digital thread/digital twin concepts to integrate the product model into the downstream planning and production processes • Similar concepts can be employed for other processes to drive additional value

19. Questions?