Presented By: John B. Whitmire Bentley Systems, Inc. Data Quality Management: Successes in Transferring Configuration Data from Suppliers to Owner Operators Presented By: John B. Whitmire Bentley Systems, Inc. Successes in Transferring Configuration Data from Supplier to Owner/Operator Presenter John Whitmire, Bentley Systems, Inc. The presenter will describe successes in techniques used to transfer configuration information from supplier to owner/operator in other industries. Challenges to the data transfer will be identified and the solutions employed to overcome them explained. The presentation will provide an overview of the data transfer process and will prompt discussion in the afternoon breakout of how these processes could be adapted to nuclear plant information.

Process Industry Drivers Data Handover – typically inefficient and costly Proprietary data systems no longer acceptable Operations demands clean, consistent and complete information Having intelligent data is not enough, we must be able to support our business processes with it or it becomes ‘just another database’.

Asset Lifecycle Information Management Why Invest? ‘An average of 40% of engineering time is dedicated to finding and validating information from disparate systems’ – “Cost Analysis of Inadequate Interoperability in the U.S. Capital Facilities Industry”, National Institute of Standards and Technology, Office of Applied Economics. Avoidance Costs – money spent to prevent interoperability problems from occurring. Often realized in redundant systems and increased use of bandwidth and training costs to maintain legacy systems. Mitigation Costs – money spent to correct problems once they occur. Primarily visible in the data reentry and validation requirements that results in redundant labor. As a consequence, no matter the phase of the project, time must be spent ensuring all parties have the same information via manual intervention. Delay Costs – money lost due to schedule slippage . The consequence of the impacts of avoidance and mitigation costs.

Why Data Quality Management? Defined ontology Specifies expected Objects, Attributes and Associations for each class of information Defined requirements for Suppliers Progress Measurement at a granular level Measurable compliance with the data needs of our organization Measurable completeness to the data handover specification

Interfacing with Client’s Environment Data Quality Management Allows clients to use tools of choice Can receive data from any application Can publish data to any accepting application Data qualified at time of receipt Qualification rules driven by client’s business requirements – not software limitations Enables the evaluation of data prior to publishing the information to the user community (Management of Change) AutoPLANT Consistent Complete Clean PlantSpace Smartplant® Story line When an owner operator is using eWarehouse in a project, the impact on the EPC should be as minimal as possible in regard to their work process. The key for the OO is when the receive data submittals, the information is validated against the requirements of the client – Data Quality Management The requirements are established in eWarehouse by the reference data library and the additional business rules the client wishes to enforce. Ex. Pumps coming from the design environment must supply a Design Pressure in PSI. If the item is not given or is given in a different unit of measure then that data is rejected. To get the biggest benefit of this environment, a process of ‘continual data handover’ is preferred over the traditional ‘dump truck’ approach at the end of the project. Continual data handover requires the OO establishes the contract (i.e. payments dependant upon successful submission of data) to require delivery of documents and data at milestones throughout the engineering process. Through this continual data handover, data quality management takes the form of a series of repeatable processes that evaluate and validate data from each data source from each contractor. The process enforces things such as identification rules (Tagging schemes), the existence of ‘required data’ (i.e. Maximum Pressure must be given for all Pumps), and client specific rules (all tags must have associations to 1. P&ID, 2. Datasheet, 3. PFD or it is considered to be incomplete)

Configurable Business Process Automation Example of configurable workflow and business process automation

Example System Map Key Data Exist and has been successfully loaded   Data Exist and has been successfully loaded Data exists but is not loaded Data exists but has issues Data not provided Data not required PR Document Tags PR Tag Fire and Gas PR Tag Cable Cables   Tags Doc References Attributes Connections Parent Tag Valves   Tags Doc References Attributes Connections Parent Tag Fire & Gas   Tags Doc References Attributes Connections Parent Tag Telecomms   Tags Doc References Attributes Connections Parent Tag Equipment Numbering Scheme Codings   Instruments Fire & Gas Telecomms Electrical Mechanical Lines Cables Valves Documents Facilities Systems Sub Systems Locations Sub Locations PR Tag Lighting and Small Power Electrical   Tags Doc References Attributes Connections Parent Tag Document Numbering Scheme PR Document Lines PR Tag Junction Box PR :Lines Mechanical   Tags Doc References Attributes Connections Parent Tag Instrumentation   Tags Doc References Attributes Connections Parent Tag PR Tag Instrumentation PR Line Connections Lines   Tags Doc References Attributes Connections Parent Tag Pr Tag Equipment List PR Document Tags PR Document Tags PR Assembly Tagged Items

Consolidated Master Tag List Tag Parsing Split tag into specific parts based on Tagging Scheme definition Tag validation Lookups on Facility, Class, System Back

Your QA criteria…

Data Analysis Data Source Number of Tags Number of Attributes Unpopulated Attributes % Populated Alstom 1,013 72,619 19,273 26.54% Boustead 269 12,540 4,009 31.97% Burgess Manning 10 252 90 35.71% Copa 1,291 60,681 13,635 22.47% Genergy 63,913 4,793,484 880,563 18.37% Nuovo Pignone 578 14,471 6,632 45.83% Prosernat 8,045 370,100 70,615 19.08% Rolls-Royce 16,002 736,126 147,078 19.98% SAFT-AEG Industrial 369 9,231 3,649 39.53% Wellinan 478 10,532 2,773 26.33% Flowserv Pumps Limited 1,328 55,804 21,267 38.11% Attribute Name Attribute Value Count Percentage Cross Sectional Area 0.58 mm 203 10.00%   0.5 mm 475 23.41% 0.75 mm 781 38.49% 0.8 mm 202 9.96% 1.5 mm 349 17.20% TBA 15 0.74% 15 mm 4 0.20% Total 2029 100% Low occurrence of a particular value may indicate erroneous data Low occurrence of a particular value may indicate erroneous data

Deployments in the Process Industry A Chevron/ConocoPhillips Joint Venture BritSats Platforms In Salah Gas In Amenas Gas Azerbaijan ACG1 Azerbaijan Gunashli Azerbaijan Shah Deniz Clair (North Sea) CIS Gas (ETAP) BP Harding Area Gas BP Skarv (Norway) BP Alaska North Slope BP Tangguh Indonesia BP BTC Pipeline BP South Caucasus Gas Pipeline SCP Houston Aberdeen Singapore Agbami Project Nigeria Gulf of Mexico (ACES) Tahiti Project Angola LNG Project Gorgon Project Wheatstone Project Sanhua / Nemba Projects Blind Faith Platform TengizChevroil(TCO) Standard AutoPLANT environment Lifecycle Server Base Configuration Houston Singapore Leatherhead (UK) Agbami FPSO Kashagan Project Shell GTL Project Gorgon Global rollout London (2010) Alaska North Slope Power Division – ISO 15926 / DQS Integration with PowerTrak London Perth Edmonton Refinery Next Presentation