GEN-02 Operational Information Transformation: From Historian to Operational Information System

Transcription

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2 GEN-02 Operational Information Transformation: From Historian to Operational Information System Tim Sowell Stan DeVries 2013 Invensys. All Rights Reserved. The names, logos, and taglines identifying the products and services of Invensys are proprietary marks of Invensys or its subsidiaries. All third party trademarks and service marks are the proprietary marks of their respective owners.

3 Key Takeaways 1. Operational decision needs are changing to much earlier, more specific context, and much more trustworthy information 2. To support this change, information processing is changing to include more transformation 3. Operations decisions use the new information to make many more decisions, much earlier, including ahead of real-time Slide 3

4 What s Taking Place Today? The traditional historian and data capture systems are transforming to federated operational information systems, (PIMS or Plant Information Systems) which enable a unified information model across MES time series and plants, that enable consistency in structure and context and exception based notifications. This enables both operational information and knowledge for decisions in the NOW, as well as the operational analysis and performance teams required analysis. This session will outline the PIMS and Operational Information of today, and how they are different from the traditional historian approach. Slide 4

5 Changing the Experience Slide 5

6 Critical Project Trends We are Seeing! Slide 6

7 The Challenge of Alignment across a Dynamic Value Chain Evolution from Manned to Transition Existing control rooms Existing operational Kiosks Roaming/ Dynamic teams with Wireless Central Co ordination End to End View Information Tsunami Operator/ Controllers Product Management Planners Access to Expertise Anywhere Any device Expertise in Process Expertise in Equipment Within Company External to Company How to Achieve Trusted Common Information Real-time collaborations, Sharing Enable Timely Correct Operational Decisions / Actions Slide 7 Slide 7

8 Flexible Team Coordination (Operational Centers) Enterprise Control Game-changer: use people differently for efficiency or agility Operational Team works in coordinated fashion over multiple sites A community of Experts provide the knowledge/ experience real-time Business, Operations and Plant Floor personnel working together in new ways Move from individual Operational Control to running a flexible team across a network of operational interfaces to execute; usually an Operational center, virtual expert teams, roaming, working as one Built in work practices and operational task management so tasks can be passed between team members smoothly Slide 8

9 Concept: Virtual Situation Room Activities are Transformational across state, Collaboration is Natural, Awareness is native Slide 9

10 Rio Tinto Example: EOS The Operator in Control Slide 10

11 The Evolving Operational Landscape Trend Changing Requirement Operational Control Role-based to action-based Transformable Day in the Life Natural collaboration across roles Knowledge Workers Operational Decision Support Situational awareness Preemptive practices To be vs. as is Agility thru Empowerment Time to Experience Rotating Roles Access to experience Self training Understanding the future Task/ Work Mgmt Workforce Transition Digital Natives Slide 11 Work/Action Capture Work/ Action Planning Work/ Action Consistent with inbuilt SOPs

12 Operational Experience: Changing Face of User Base System is Aware Move to To Be State Awareness Condition vs. Alarm Access Experience Pre-Emptive/ Dynamic Auto Escalation Auto Investigation to determine condition Auto awareness of condition based on History pattern Built in Procedure Built in How to Access to Experience How to access experience How to locate correct people How to engage naturally How to empower and synch state How a use must be dynamic to become an agent for task Virtual teams Slide 12

13 Re-wiring Operational Information Boyd OODA Loop a way to organize how information is designed and used to act faster and earlier than events Design like a fighter aircraft engineer, operate like a fighter pilot Re-wire operational information Design for performance, establish harmony and ground truth Orient for normal and abnormal, economic performance Slide 13 Faster and earlier than situations evolve Balance targets and reality

14 2 Journeys in Operational Information A historian tag s journey An operations team s day Flow measurement (exiting a processing area) Manage a familiar situation where the range of quality has moved Used for: Calculating efficiency Recognizing event frames Material and energy balances Calculating production Respond to a request to share a production rate change with another site Investigate a material imbalance Find out why the night shift out-produces the day shift Slide 14

15 Historian tag journeys: not-so-good and good Not so good: Good: The tag data quality is poor because the system doesn t overcome outages The tag data quality is trusted because the system overcomes outages Data structures don t match the sources when the sources are reconfigured Data structures are living and are trusted; the operational information data often share the same structures as the sources Information is in 1 million tags instead of several thousand structures Slide 15 Information is in several thousand, humanunderstandable structures

16 Operations team s day: not-so-good and good Not so good: Good: Only one person has the operational information needed by many The right person has the right information in the right context at the right time, which includes ahead of real-time That person spends more than 50% finding and cleaning up information Information is processed only as projects or after significant events Slide 16 Knowledge workers spend less than 10% of their time on data quality management Teams are proactive, understand new normal and new abnormal

17 PIMS Definitions Gartner: Application for the acquisition, display, archiving and reporting of information from a wide variety of control, plant and business systems. A critical component in an enterprise s application architecture for creating a common repository of plant information that can be effectively leveraged in enterprise and supply chain management applications. Slide 17 Invensys: Architecture pattern for standardized decision support information, which is integrated and derived from a wide variety of control, plant and information systems.

18 Why Customers Choose Historian or PIMS? Historian Organization budget only for basic storage and minimum client function Centralized budget for enterprise historian De-centralized budget only for local historian Slide 18 PIMS Strong influence from Operations need highervalue information for plant architectures Strong influence from corporate IT need more trustworthy information for enterprise architectures

19 Historian vs. PIMS Historian PIMS Manage the data quality (disconnects, out of range, wrong type) Restructure the integrated data into historian tags Restructure the data into tags or add structure in objects (either tag-centric or model-centric) Derive information from historized data mainly with algebra and simple statistics (averages, totals, counts) Derive information from historized data mainly with algebra and simple statistics (averages, totals, counts) Derive information from online model-based applications mainly with calculus and advanced statistics applications are often focused on customer industries or equipment Slide 19

20 Traditional Historian Approach vs. Unified Information Approach Traditional Enterprise Historian Approach Collect data without structure or cleansing Put data into historian as early as possible Add cleansing and context after the historian Cleanse each data element in more than one place Context is reference information ( dead ) for data only Model-Centric Information Approach Collect data with structure Structure and cleanse as close to the sources as possible Cleanse each data once Context is used for user roles, graphics, workflow, calculations ( living ) Context is everywhere at each plant, both tiers Centralized Information Near real-time modeling Near Real-time analysis Tier 2 ONE Active Model

21 Changing the Game : The Invensys PIMS Approach Increase People & Asset Effectiveness Data Access ODBC API Web Service Model Driven Notifications Awareness Living Assets Model Living Assets Provides structure Provides validation Provides situational awareness Triggers events/ Workflows Inbuilt calculations Escalation Information Model Pump Pump Pump Storage: Tier 2 Historian (Near Real-time Analysis) One Namespace, Unified Managed Configuration Unified Real-time Asset/ Process Model built on Managed Standards Renormalizing of data into effective information/ intelligence Storage: Tier 1 Historian Each Site: Local AOS Local Historian Data Capture Data Validation Slide 21 Data Capture Data Validation Storage: Tier 1 Historian Data Capture Data Validation Storage: Tier 1 Historian Data Capture Data Validation Storage: Tier 1 Historian

22 Comparison Invensys Historian-based Workflows Alerts Derive information Derive information Push into the Historian (50 thousand objects) Re-structure the data and manage the quality Structure the data and manage the quality once Push into the historian (1 million tags) Integrate the data and keep the source data structure Integrate the data and discard the source data structure Slide 22

23 Changing from Reactive to Proactive Slide 23

24 The Changing Landscape to Enable Decisions Slide 24

25 PIMS Example 1 Performance PIMS summary: As a user, I want to compare KPI s across multiple assets & products, across multiple event frames, so that I can reduce the variation in performance (Centerlining) Small plant historian: self-service chart recorder Slide 25

26 PIMS Example 2 Performance PIMS summary: As a user, I want to compare KPIs across multiple assets & products, across multiple event frames, so that I can reduce the variation in performance (Centerlining) Small plant events: selfservice event recorder Slide 26

27 PIMS Example 3 Performance and Prescriptive PIMS summary: As a user, I want to compare KPIs across multiple assets & products, across multiple event frames, so that I can reduce the variation in performance (Centerlining) SimSci ARPM Slide 27 Plant event frames: selfservice integration of production frames (e.g. batches, shift, product change ) with exception event frames in context with process data

28 PIMS Example 4 Performance and Prescriptive PIMS summary: As a user, I want to compare KPIs across multiple assets & products, across multiple event frames, so that I can reduce the variation in performance (Centerlining) SimSci ARPM Slide 28 Multiple plant event frames: self-service multi-site event recorder to view plant exceptions across event frames in context with process data

29 PIMS Example 5 Performance and Prescriptive PIMS summary: As a user, I want to compare KPIs across multiple assets & products, across multiple event frames, so that I can reduce the variation in performance (Centerlining) SimSci ARPM Slide 29 Complex events: composite KPIs, complex events and event timeframes from my plant data

30 PIMS Example 6 Performance and Prescriptive PIMS summary: As a user, I want to compare KPIs across multiple assets & products, across multiple event frames, so that I can reduce the variation in performance (Centerlining) SimSci ARPM Slide 30 Multiple plant PIMS: calculate & compare KPIs, Events and frames for multiple plants, assets & products across multiple event frames

31 PIMS Example 7 Performance, Predictive and Prescriptive PIMS summary: As a user, I want to compare KPIs across multiple assets & products, across multiple event frames, so that I can reduce the variation in performance (Centerlining) SimSci ARPM Slide 31 Predictive PIMS: predictive trends/scenarios

32 Operational Excellence Journey Consistency, Repeatability, Predictability Where most organizations are today Where have we been? Departmental approach Function specific metrics Use of spreadsheets/ manual processes Scheduled/timed process Tools based Historical focus Where are we now? Spreading across depts. Processes fully defined but not streamlined or institutionalized Strategic & operational metrics defined but not aligned Technology in place to support data integration and dynamic reporting but there are gaps Tools and process based Near real time Where are we going? Clear financial and operational metrics mapped to strategy and goals Business processes streamlined and optimized Cause and effect fully understood Dashboards in full use Trends analysis Exception handling Collaboration and accountability across functions Near real time Where do we want to reach? Empowerment of operators Culture of performance and accountability Collaborative and dynamic decision making Performance information pinpoints everyone s contribution to goal attainment Clear transparency Wide use of analytics Sense & response Stage Stage Stage Stage Reacting to Business Improving Business Driving Business Driving the Market 1 2 Slide

33 Recommendations Review the changes in roles and tasks first, then design for the required functions and information Consider prescriptive and predictive interactions (i.e. more than performance) Design for trustworthy information (end-to-end) Design for higher availability distributed, multi-tier, redundancy, disaster recovery Slide 33

34 Key Takeaways 1. Operational decision needs are changing to much earlier, more specific context, and much more trustworthy information 2. To support this change, information processing is changing to include more transformation 3. Operations decisions use the new information to make many more decisions, much earlier, including ahead of real-time Slide 34

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