1. 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.

2. 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’.

3. 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.

4. 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

5. 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)

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

7. 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

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

11. Your QA criteria…

13. 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

14. 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 / DQS
Integration with PowerTrak
London
Perth
Edmonton Refinery
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