isa-95-based operations and kpi metrics assessment...

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ISA-95-BasedOperations and KPIMetrics Assessment and Analysis

WHITE PAPER 24A Mesa International, ISA and Invensys Wonderwareco-branded white paper.11.28.06

ISA-95-BASED OPERATIONS AND KPI METRICS ASSESSMENT AND ANALYSIS

Table of Contents

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Defining Operations Metrics and KPIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Value Proposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Statement of Need (SON) Definition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Critical Success Factors (CSFs). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Prioritizing Options Using the Value Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Defining and Developing Key Performance Indicators. . . . . . . . . . . . . . . . . . . . . . 11

Collecting Data and Normalizing Manufacturing Information . . . . . . . . . . . . . . . 13

Periodic Review and Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Appendix A: SCOR Performance Attributes and Level 1 Metrics . . . . . . . . . . . . . . 16

Appendix B: Asset Utilization Functional Analysis Example . . . . . . . . . . . . . . . . . . 17

Appendix C: KPI Priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Appendix D: Object Model Inter-Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Appendix E: Standardized Data Definition Framework . . . . . . . . . . . . . . . . . . . . . 20

Appendix F: Actual versus Planned Production Volume: ISA-95 KPI Examples . . . . 21

Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Contributing Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Reviewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

©2006 MESA International 2

ISA-95-Based Operations and KPI Metrics Assessment and Analysis


Overview

ISA-95 Part 2 and Part 3 provide a valuable data definition framework when applying best practices for managing operations and related key performance indicators (KPIs). The data definition framework is able to serve as the KPI source of information for SupplyChain Scoreboard systems. An example is Production KPI inputs into a MAKE processelement of the Supply Chain Operations Reference Model (SCOR) by Supply Chain Council. Designing and implementing these systems require using a process that ensuresinformation alignment with business strategy through construction of financial metricsfrom operations metrics.

Key points necessary to building successful Supply Chain Scoreboard Systems include:

• Understanding key stakeholders needs and expectations.

• Summarize and document those needs and expectations through a Statement of Needs (SON) document.

• Identify critical success factors (CSFs) and relevant metrics that align with the SON.

• Prioritize options (or projects) through the use of a value chart. The value chart quantifiesbenefits versus risk.

• Derive appropriate operations KPIs, establish a baseline, and periodically measureidentified KPIs based on operational priorities.

• Define the Data Standard Information Layer to normalize manufacturing information and align with Supply Chain metrics.

• Measure, visualize and analyze operations KPIs against baseline.

• Review SON periodically with Key Stakeholders and adjust operations KPIs based onevolving needs or when the corporate business models or markets change.



Defining Operations Metrics and KPIs

Using the ISA-95 standard, operations and financial managers are able to achievealignment between strategic expectations, capital expenditure spending and expectedoperations KPI measurement. ISA-95 information exchanges aggregate key metrics fromoperations and production into enterprise planning and supply chain algorithms and data models. Leveraging KPIs derived from ISA-95 Part 3 manufacturing operationsexchanges, the resulting operational metrics measure and then align true operational and financial benefits sought tactically by the organization. Relevant production measuresfor KPI construction are defined from manufacturing processes. The “high-performanceorganization” aims at achieving operational excellence by minimizing low-level processvariability through the Part 3 manufacturing operations analytics. Manufacturing analyticsaggregate the large number and quantity of low-level, real-time I/O measures.

Several organizations like Supply Chain Council and it SCOR Model, the ProjectManagement Institute (PMI™), authors like Kaplan and Norton (Balance Scoreboard) and business/manufacturing intelligence software companies are all involved in definingand using enterprise-level KPIs. This ISA-95 best practice document intends to facilitate the use of ISA-95 standard when applying and constructing these enterprise-level KPIs and operations metrics.

Value Proposition

For companies strategically investing in operational excellence, Critical Success Factor (CSF)choices are based on discipline, repeatability and efficiency. Business managers must makelogical and educated decisions when putting in place technology to achieve a high-degreeof consistency and response. For example, manufacturing reduces material variability toincrease quality, lower unit costs, increase service levels and reduce risks of product recalls.For high performance organizations, the end game is about using information technology(IT) to provide solutions to increase material throughput by optimizing working (inventory)and physical capital (equipment) in combination with high product quality at the same time.

In short, this is the manufacturing IT value proposition to the manufacturing function. Formanufacturing systems, the roadmap to align the manufacturing IT solution options (orprojects) to the company’s business strategy follows the material flow from raw materialthrough working-in-process intermediate material stages to finish goods. For example inthe consumer packaged goods industry, manufacturing IT solutions usually typically include:

• Receiving and inspection of incoming raw ingredients

• Recipe preparation and material weigh and dispense

• Recipe batching

• Packaging and storing

• Shipping



Throughout this white paper, examples are utilized on the process of defining andimplementing KPIs using ISA-95. The first step is to chart the business or its constituents as a series of workflows and analyze and map the dependencies between them. The resulting business processes, along with their associated dependencies and timing,constitutes a working canvas or baseline. A simplified example for a classical Batching-Packaging is illustrated in Figure 1.

In this example, the general workflows all have a subset of underlying “services” (or addedvalue) in carrying product from one process area to the other. Figure 1 shows only one of the ‘views’ available (in this case the accounting view to measure costing variances).Other typical views include (but are not limited to):

• Product Specification

• Material Procurement

• Material Logistics (including storage)

• Standard Operating Procedures

• Employee Certification and Operation Training

• Production Scheduling

• Equipment Maintenance

• Regulatory Compliance, Quality Control and Continuous Improvement

• Product Costing

• Engineering (security, configuration)

• Planning (bill of resources, manufacturing bill, standard definition)

Figure 1: Typical

Manufacturing

Materials Costing

Workflow



When looking at the different processes that define a given process, the opportunities to improve processes get clearer as granularity increases and 6 Sigma or Lean analysis isapplied. ISA-95 supports all levels of granularity through its ‘recursive’ data model models(segments, equipment, etc.). In our example above, one may find an opportunity toimprove packaging operations to increase customer satisfaction. Customer satisfactionshould be a clear stakeholder need as defined in the following section.

Many different standard bodies present different models to define enterprise businessprocesses. ISA-95 is based on the Purdue Reference Model , which defines different levels of manufacturing activities. Scheduling of production work takes place at level 4,manufacturing operations management (MOM) and execution of work take place at Level 3 and the physical work takes place at levels 2, 1 and 0. In utilizing ISA-95 toconstruct KPIs in conjunction with the Supply Chain Operations Reference (SCOR) Model or similar enterprise models, system architects must map the enterprise model to ISA-95(Purdue) Levels. For instance, SCOR has 4 Levels where Level 1 is the highest Level ofabstraction with 9 supply chain benchmark metrics defined in Appendix A, SCORPerformance Attributes and Level 1 Metrics. The SCOR Level 1 metrics are constructedthrough the configuration of Level 2, which defines supply business processes and throughthe subsequent Level 3 metrics that define the performance of each process element in a SCOR business process. SCOR Level 3 processes are equivalent and can be mapped to ISA-95 Level 4 processes. Consequently, ISA-95 Level 3 KPI and information flows for manufacturing operations management are able to be mapped in support of SCOR Level 3 business processes.


Statement of Need (SON) Definition

A series of different techniques exist to define key stakeholders needs. Performing an SON functional analysis based on stakeholder’s needs and expectations greatly helps inprioritizing capital spending. The functional analysis method maximizes alignment betweenstakeholders’ objectives and relevant KPIs. The outcome is usually a SON document, which prioritizes capital spending (options or projects) and their associated relevantmeasurement. Figure 2 shows the functional analysis technique that decomposes a goal (or an objective) into a series of more detailed functions.

The construction of the Functional Breakdown Structure (FBS) is done by starting with the high level functional expectation or need (outmost left). The hierarchy is then definedby asking “how” the highest level’s functional expectation should be fulfilled. The processthen continues on until the proper level of granularity is obtained.

Once this is done, stakeholder then go on and agree on which lower level functions arethe critical (or critical success factors) ones to the fulfillment of their expectation, need or goal. Stakeholder brainstorm on which KPIs are relevant, how frequently they need tobe measured to determine whether or not the expectation or need is met (and strategicalignment obtained).


Figure 2: SON

Functional

Breakdown

Structure



Critical Success Factors (CSFs)

Using the PMI™ technique, once the functional analysis is completed, CSFs must byidentified to ensure the overall stakeholder needs, or goal will be attained. An example is shown in Appendix B, Asset Utilization Functional Analysis Example. Using a “functionalbreakdown structure” (FBS) separates the need from the actual solution used to fulfill this need.

Appendix B identifies specific functions, which are considered CSFs. Those CSFs are shown in red and they are:

i. Resources tracking

ii. Measure failures

iii. Personnel training

iv. Share production forecasting

v. Electronic Data Collection

vi. Tracking against Production Work Order

The Appendix B example illustrates how a given stakeholder need or expectation can be broken down into a hierarchy of nested functions. The example provided is based onanalyzing functions required to improve asset utilization from an executive standpoint.

In Appendix B example, the following CSFs have been identified (under A through F) and weighed using the double-weighing method with the results shown in Figure 3.

Paired Comparison Criteria Evaluation

1 Minor

2 Significant

Critical Success Factor

A Product Genealogy C Track Intermediates E Spare Parts

B Minimize Downtime D Measure Availability F Product SPC

Figure 3: Critical

Success Factors

for Appendix B

Example



Based on the feedback from stakeholders, we find the following in Table 1.

After the functions have been prioritized, a list of options or projects is defined that willsupport critical success factor functions.

Options (Project) List used in the example:

1. Electronic Data Collection

2. Track against Production Order / Work Center

3. Personnel Training

4. Measure Failures

5. Resource Tracking

6. Share Production Forecast

These opinions need then to be evaluated against two criteria:

• Benefits contribution or value in contributing to the overall need or goal

• Risk (from a project standpoint) or achievability

This is explained in the following section.

Functional Breakdown Priority (from double weighing method)Product Genealogy 7Minimize Downtime 2Track Intermediates 5Measure Availability 2Spare Parts 1Product SPC 4

Table 1: Stakeholders Feedback



Prioritizing Options Using the Value Chart

The previous section identified which function(s) are critical to achieving stakeholder(s)overall need(s) or goal. Figure 3 lists the priority under which options (or projects) are to be rated. Table 2 shows the measurement of the identified options (or projects) againsttwo (2) factors: achievability and benefit contribution.

Achievability takes into account project risks like financial, project, people, complexity, etc.Benefit contribution quantifies the function’s overall value to attaining the need or goal.Figure 4, Value Risk Index Chart with KPI Weight, shows the difference options (or projects)used in our example. The priority should always be given on the projects that provide withthe highest benefit contribution as well as the highest achievability. These options arelocated in the upper right quadrant. Since CSFs are identified, weighed and prioritized bystakeholders, their use is critical in determining the overall value of alternative or options(or projects).

Project Option Function Name Basic Function Benefit Value1 Electronic Data Collection Measure Performance 6622 Track against Prod. Order/WC Quality Available 6483 Personnel Training Personnel Available 4434 Measure Failures Equipment Available 1815 Resources Tracking Balance Capability/Plan 3436 Share Production Forecasts Supplier Management 238

Table 2: Measurement of Identified Options (or Projects) against Two (2) Factors:

Achievability and Benefit Contribution

Figure 4: Value Risk

Index Chart with KPI

Weight


Defining and Developing Key Performance Indicators

Based on our example, Appendix C, KPI Priorities, are appropriate in ensuring stakeholder’sStatement of Needs (SON) is being monitored and evaluated with the right metrics. Appendix Calso shows the options (or project) in order of importance (from option/project 1 to 6).

After having identified operations and key metrics, companies can then identify the ISA-95Object Model and Attributes of a Level 3 MOM activity function and the correspondingdata exchanges, transaction sequences and workflow uses.

These are utilized to define, track, measure, analyze, interface and report metrics toenterprise and supply chain functions (SCOR) and system with Level 4 as well as to otherMOM functions and systems within Level 3.

For each metric, the following step needs to take place:

• Define data source and format

• Assess ISA-95 readiness

• Define transformation requirement when applicable

• Measure KPI and operations metrics per product segments

• Visualize KPI and operation metrics per product segments

• Analyze and report KPI to extended enterprise

Grouping of KPIs must be aligned with stakeholders’ functional breakdown Statement ofNeeds (Appendix B Appendix B, Asset Utilization Functional Analysis).

In addition to the formally defined Production Performance data model defined in the ISA-95 standard, there is additional information about production that provides summariesof past performance, indications of future performance, or indicators of potential futureproblems (leading indicators). Collectively, this information is defined as "ProductionIndicators". Examples are listed in Table 3, Examples of Production Indicators.

One of the activities within production performance analysis is the generation ofProduction Indicators. This information typically is used internally within manufacturingoperations for improvements and optimization.

For instance, if receiving Level 4 business process (production scheduling and logistics)requires Level 3 Production Indicators or Production Performance information, then it mayalso be sent to higher-level supply chain management business processes for KPIconstruction, further analysis and supply chain decisions (typically SCOR, Appendix A).

Production indicators can be as simple as values of process tags used as inputs to complexprocess models. There is a core set of values related to production output, but there can bea significant variation in the core set based on the vertical industry.




Production indicators are often combined at Level 4 functions such procurement withfinancial information, or at Level 3 functions such as performance analysis (utilization Level 4 activity based costing standards) to provide cost based indicators to triggerdecisions. As described in the Appendix B, breaking up the desired high level manufacturingoperational expectation into its functional constituents allows the prioritization of capitalspending. KPI’s typically feed into aggregated overall metrics (like SCOR, Appendix A).

Category KPI CommentOrder Fulfillment Actual production rate as a percentage of the maximum

capable production ratePercentage of lots or jobs expedited by bumping other lots or jobs from scheduleProduction and test equipment set-up timeProduction schedules met (percentage of time)Actual versus planned volume

Asset Utilization Average machine availability rate or machine uptimePercentage of tools that fail certificationHours lost due to equipment downtimeCumulative count of machine breakdown

Quality Major component first-pass yieldFirst product, first pass quality yieldReject or return rate on finished productsReject-rate reductionRework-repair hours compared to direct mfg. hoursScrap and rework as a percentage of salesScrap and rework percentage reductionRework and repair labor cost compared to total manufacturing labor costNumber of process changes per operation due to errorsNumber of training daysYield improvement

Personnel Percentage increase in output per employeePercentage unplanned overtimeSafety and Security incidentsPercentage of operators with expired certifications

Productivity Percentage of assembly steps automatedPercentage reduction in manufacturing cycle timeProductivity: units per labor hour

Engineering HMI data entry countPercentage of alarm reduction

Material Time line is down due to sub-assembly shortageCount of supplier shortages per periodMaterial consumption variances from standards

Planning Percentage reduction in component lot sizesManufacturing cycle time for a typical productPercentage error in yield projectionsStandard order-to-shipment lead time for major productsTime required to incorporate engineering changes

Table 3: Examples of Production Indicators



Note: this list is non-exhaustive and is only provided to illustrate typical manufacturing KPIs and how ISA-95 provides with a data definition framework for these KPIs.

Collecting Data and Normalizing Manufacturing Information

Logical and educated business decisions can be made when putting in place manufacturingoperations management (MOM) technology to achieve a high-degree of manufacturingand supply chain Responsiveness and Flexibility.

ISA-95 offers a normalized data definition framework to manage KPIs and construct thembased on operations metrics particularly Part 2, Object Attributes, and Parts 3 Activity andObject Models and Attributes of MOM.

When using the standard, companies solve one of the challenges data normalizationbrings, which is the agreement on metric definition across the business. The operationsmetrics are the derived analysis for resources (material, personnel, and equipment/workunit) at the product and process segment level used in the macro form for scheduling themicro form for dispatching and execution.

The result of analysis and aggregation of the operations metrics are used to construct theKPIs for Performance Analysis in Product Definition, Production and Process Capabilitiesand Requested Schedule (Performance). Appendix D illustrates the Part 2 P object modelinter relations for MOM data aggregation and analytics for KPI construction.

Appendix E is an example of using ISA-95 as a unified data definition framework indefining KPIs.

1. The figure shows how “planned” production is measured against “actual”1.1. The variance provides with a typical Production-related KPI Metric1.2. In this case, an “Actual versus planned volume” KPI is derived from the model

2. Example #2 is to measure Equipment Capability versus Equipment Actual Use2.1. The variance provides with a typical Asset Utilization KPI Metric2.2. In this case, an “Hours lost due to equipment downtime” KPI is derived

from the model

3. Example #3 is to measure Material Consumption Variance3.1. This variance provides with a typical Material-related KPI Metric3.2. In this case, a “First product, first pass quality yield” KPI is derived from the model

4. Example #4 is to measure Operator Certification4.1. This exception count provides with a typical Quality-related KPI metric 4.2. In this case, a “Percentage of operators with expired certifications” KPI is derived

from the model



Appendix F, Actual versus Planned Production Volume: ISA-95 KPI Examples, provides an example of how an ISA-95 KPI is used to track manufacturing cycle variances. It alsohighlights how this particular KPI can be aggregated and rolls into a supply chainresponsiveness metric. In our example, the SCOR “Order Fulfillment Lead Time” metric(Sum of procurement cycle, manufacturing cycle and replenishment cycle) uses the ISA-95KPI to measure time variances between Planned and Actual manufacturing time for aparticular product being manufactured.

“Example A” in Appendix F also shows a ISA-95 level 3-4 transaction where variance ismeasured between scheduled start time, actual start and end time. This variance is used to measure the average manufacturing cycle (and possibly its standard deviation to includeprocess variability constraints). The averaged KPI then is used to feed a Level 1 SCORSupply Chain operation metric (Appendix A).

Note: Level 5 (Sales Order Management and Plant-to-Plant Communication and Assignment)and 6 (Supply Chain Management) are defined in the Purdue Reference Model (PRM).These levels help define data exchanges between functional entities. For example, ISA-95addresses the PRM Level 3 to 4 interface and the Level 3 MOM activities.

Periodic Review and Adjustment

Companies must keep in mind the fluid nature of KPIs as their importance shift over timedepending of the SON and market and technology trends. These needs are influenced bythe company’s external and internal environment like:

• Competitive threat

• New entrant

• Customer changing needs

• Regulatory compliance

• Mergers and Acquisitions, etc.

Also, critical success factors affecting the business today are most likely to change as timegoes by. The need to periodically conduct SON meetings, evaluate business manufacturingstrategy’s effectiveness and the return capital productivity return is necessary. Measuringand monitoring relevant KPIs against a baseline supports ensuring the business strategy is met.



Conclusion

Operational excellence in manufacturing is now required to support true Supply Chain Responsiveness and Flexibility. When planning and executing business strategies,stakeholders’ expectations and needs must by measured and monitored in order to ensure operational alignment. ISA-95 standard provide with a highly efficient way of leveraging shop floor information. This is especially true for companies dealing withmultiple manufacturing sites.

Not only does ISA-95 provide with contextualized and normalized data model, it also offersvocabulary that is used by business managers and decision makers. Finally, it also offers the right granularity that is necessary to measure actionable Key Performance Indicators,which can further be aggregated to service Supply Chain Models.



Appendix A: SCOR Performance Attributes and Level 1 Metrics

Customer Facing Activities

SCOR Performance Attribute SCOR Metric Definition LEVEL 1 SCOR MetricsA. Supply Chain Delivery Reliability The performance of the supply chain in delivering: A1. Perfect Order Fulfillment

the correct product, to the correct place, at the correct time, in the correct condition and packaging, in the correct quantity, with the correct documentation, to the correct customer.

B. Supply Chain Responsiveness The velocity at which a supply chain provides products B1. Order Fulfillment Cycle Timeto the customer.

C. Supply Chain Flexibility The agility of a supply chain in responding to marketplace C1. Upside Supply Chain Flexibilitychanges to gain or maintain competitive advantage. C2. Upside Supply Chain

AdaptabilityC3. Downside Supply Chain

Adaptability

Internal Facing Activities

SCOR Performance Attribute SCOR Metric Definition LEVEL 1 SCOR MetricsD. Supply Chain Costs The costs associated with operating the supply chain. D1. Cost of Goods Sold

D2. Total Supply Chain Management Costs

E. Supply Chain Asset The effectiveness of an organization in managing E1. Return on Supply Chain Fixed Management Efficiency assets to support demand satisfaction. This includes the Assets

management of all assets: fixed and working capital. E2. Cash-to-cash Cycle Time

Table 5: SCOR Level 1 Benchmark Metrics



Appendix B: Asset Utilization Functional Analysis Example



Appendix C: KPI Priorities



Appendix D: Object Model Inter-Relations



Appendix E: Standardized Data Definition Framework



Appendix F: Actual versus Planned Production Volume: ISA-95 KPI Examples

Example A. PRODUCTION SCHEDULE, PLANNED Example A. PRODUCTION PERFORMANCE, ACTUALS

ID IDA unique identification of the production schedule and could include A unique identification of the production performanceversion and revision identification. and could include version and revision identification.The ID shall be used in other parts of the model when the production The ID shall be used in other parts of the model when theschedule needs to be identified. Production performance needs to be identified.Example: 1999-10-27-A15 Example: 1999-10-27-A15

Description DescriptionContains additional information and descriptions of the production Contains additional information and descriptions of theschedule. production performance.Example: “Widget manufacturing schedule.” “Production performance report on Oct 27, 1999 production

schedule.”

Production Schedule Production ScheduleIdentification of the associated production schedule. Identification of the associated production schedule, if applicable.

Production performance may not relate to a production schedule,it may be a report on production for a specific time, or reported by plant floor events.

Example: 1999-10-27-A15 Example: 1999-10-27-A15

Start Time Start TimeStart time for the associated production schedule, if applicable. Start time of the associated production performance, if applicable.Example: 10-28-1999 Example: 10-28-1999

End Time End TimeEnd time for the associated production schedule, if applicable. End time of the associated production performance, if applicable.Example: 10-30-1999 Example: 10-30-1999

Published Date Published DateDate/time on which the production schedule was published/generated. Date/time on which the production performance was published/

generated.Example: 12-30-1951 18:30 UTC Example: 10-27-1999 13:42 ESTLocation LocationIdentification of the associated element of equipment hierarchy model. Identification of the associated element of the equipment

hierarchy model.Example: East Wing Manufacturing Line #2 Example: East Wing Manufacturing Line #2

Element Type Element TypeA definition of the type of the associated element of the equipment A definition of the type of associated element of the equipmenthierarchy model. hierarchy model. For example: enterprise, site, area.Example: Enterprise, Site, Area, Production Line Example: Production Line



Author

Yves C. DufortEng, MBAInvensys / Wonderware

Contributing Editor

Charlie GiffordDirector-Lean Production Mgt. GE Fanuc Automation [email protected]

Reviewer

Clifford LichkowskiPlant EngineerPrairie Malt Limited (a Cargill Inc. Joint Venture)[email protected]

About MESA: MESA promotes the exchange of best practices, strategies and innovation in managing manufacturing operations and in achieving plant-floor execution excellence.MESA’s industry events, symposiums, and publications help manufacturers, systemsintegrators and vendors achieve manufacturing leadership by deploying practical solutionsthat combine information, business, manufacturing and supply chain processes andtechnologies. Visit us online at http://www.mesa.org.

Wonderware Overview: Wonderware is a business unit of Invensys plc. Wonderware isthe world’s leading supplier of industrial automation and information software. Founded in1987, Wonderware pioneered the use of the Microsoft Windows operating system in HMIsoftware for manufacturing operations. Today Wonderware’s leading software productsand solutions for Production & Performance Management, Supervisory HMI and SCADAapplications are “Powering Intelligent Plant Decisions, In Real-Time”, enabling customersto improve profitability across a wide range of discrete, process and hybrid manufacturingindustries. Wonderware’s software products and solutions are based on the ArchestrAarchitecture from Invensys. Based in Lake Forest, California, Wonderware has regional salesand development offices throughout the North American, European, Latin American andAsia-Pacific regions to provide support to its network of more than 160 distributor offices.Wonderware has licenses in approximately 100,000 plants worldwide, which is about 30percent of the world’s 335,000 plants with 20 or more employees. For more information,visit www.wonderware.com.



About ISA: Founded in 1945, ISA (www.isa.org) is a leading, global, nonprofitorganization that is setting the standard for automation by helping over 30,000 worldwidemembers and other professionals solve difficult technical problems, while enhancing theirleadership and personal career capabilities. Based in Research Triangle Park, NorthCarolina, ISA develops standards; certifies industry professionals; provides education andtraining; publishes books and technical articles; and hosts the largest conference andexhibition for automation professionals in the Western Hemisphere. ISA is the foundingsponsor of The Automation Federation (www.automationfederation.org).

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