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


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

WHITE PAPER 24 A MESA International, ISA and Invensys / Wonderware co-branded white paper. 07.10.07

ISA-95-BASEd oPERATIonS And kPI mETRIcS ASSESSmEnT And AnAlySIS Table of contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 defining operations metrics and kPIs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 1. Value Proposition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 2. Statement of need (Son) definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5 3. critical Success Factors (cSFs). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6 4. Prioritizing options using the Value chart . . . . . . . . . . . . . . . . . . . . . . . . . .7 5. defining and developing key Performance Indicators . . . . . . . . . . . . . . . .9 6. collecting data and normalizing manufacturing Information . . . . . . . . .11 7. Periodic Review and Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 8. conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13 Appendix 7-A: ScoR Performance Attributes and level 1 metrics . . . . . . . .14 Appendix 7-B: Asset Utilization Functional Analysis Example . . . . . . . . . . .15 Appendix 7-c: kPI Priorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 Appendix 7-d: object model Inter-Relations . . . . . . . . . . . . . . . . . . . . . . . . .17 Appendix 7-E: Standardized data definition Framework . . . . . . . . . . . . . . .18 Appendix 7-F: Actual versus Planned Production Volume: ISA-95 kPI Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19 Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20 Reviewers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

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

Abstract
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 supply chain scoreboard systems. An example is Production KPI inputs into a MAKE process element of the Supply Chain Operations Reference Model (SCOR) by Supply Chain Council. Designing and implementing these systems require using a process that ensures information alignment with business strategy through construction of financial metrics from operations metrics. Key points necessary to building successful supply chain scoreboard systems include: 1. Understand key stakeholders needs and expectations 2. Summarize and document those needs and expectations through a Statement of Needs (SON) document 3. Identify critical success factors (CSFs) and relevant metrics that align with the SON 4. Prioritize options (or projects) through the use of a value chart. The value chart quantifies benefits versus risk. 5. Derive appropriate operations KPIs, establish a baseline, and periodically measure identified KPIs based on operational priorities 6.Define the Data Standard Information Layer to normalize manufacturing information and align with Supply Chain metrics 7. Measure, visualize and analyze operations KPIs against baseline 8. Review SON periodically with key stakeholders and adjust operations KPIs based on evolving needs or when the corporate business models or markets change.

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 supply chain scoreboard systems. An example is Production KPI inputs into a MAKE process element of the Supply Chain Operations Reference Model (SCOR) by Supply Chain Council. Designing and implementing these systems require using a process that ensures information alignment with business strategy through construction of financial metrics from operations metrics. Key points necessary to building successful supply chain scoreboard systems include the following:

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

1. Understand key stakeholders needs and expectations 2. Summarize and document those needs and expectations through a Statement of Needs (SON) document 3. Identify critical success factors (CSFs) and relevant metrics that align with the SON 4. Prioritize options (or projects) through the use of a value chart. The value chart quantifies benefits versus risk. 5. Derive appropriate operations KPIs, establish a baseline, and periodically measure identified KPIs based on operational priorities 6. Define the Data Standard Information Layer to normalize manufacturing information and align with Supply Chain metrics 7. Measure, visualize, and analyze operations KPIs against baseline 8. Review SON periodically with key stakeholders and adjust operations KPIs based on evolving 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 align between strategic expectations, capital expenditure spending and expected operations KPI measurement. ISA-95 information exchanges aggregate key metrics from operations and production into enterprise planning and supply chain algorithms and data models. Leveraging KPIs derived from ISA-95 Part 3 manufacturing operations exchanges, the resulting operational metrics measure and then align true operational and financial benefits sought tactically by the organization. Relevant production measures for KPI construction are defined from manufacturing processes. The “high-performance organization” aims at achieving operational excellence by minimizing low-level process variability through Part 3 manufacturing operations analytics. Manufacturing analytics aggregate the large number and quantity of low-level, real-time I/O measures. Several organizations like Supply Chain Council and its SCOR Model; the Project Management Institute (PMI?); authors like Kaplan and Norton (Balance Scoreboard); and business/manufacturing intelligence software companies are all involved in defining and using enterprise-level KPIs. This ISA-95 best practice document intends to facilitate using ISA-95 standard when applying and constructing these enterprise-level KPIs and operations metrics.

1. Value Proposition
For companies strategically investing in operational excellence, critical success factor (CSF) choices are based on discipline, repeatability, and efficiency. Business managers must make logical and educated decisions when putting in place

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technology to achieve a high-degree of consistency and response. For example, manufacturing reduces material variability to increase 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. For manufacturing systems, the roadmap to align the manufacturing IT solution options (or projects) to the company’s business strategy follows the material flow from raw material through work-in-process intermediate material stages to finished goods. For example in the 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 used on the process of defining and implementing KPIs using ISA-95. The first step is to chart the business or its constituents as a series of work flows 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 work flows all have a subset of underlying “services” (or added value) 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)

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Figure 1: Typical Manufacturing Materials Costing Work Flow

When looking at the different processes that define a given process, the opportunities to improve processes get clearer as granularity increases and SixSigma or Lean analysis is applied. ISA-95 supports all levels of granularity through its “recursive” data model models (segments, equipment, and so on). In the example above, one may find an opportunity to improve packaging operations to increase customer satisfaction. Customer satisfaction should be a clear stakeholder need as defined in the following section. Many different standard bodies present different models to define enterprise business processes. ISA-95 is based on the Purdue Reference Model1 , which defines different levels of manufacturing activities. Scheduling production work takes place at Level 4, manufacturing operations management (MOM) and executing work take place at Level 3 and the physical work takes place at Levels 2, 1, and 0. In using ISA-95 to construct 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 four Levels where Level 1 is the highest Level of abstraction with nine supply chain benchmark metrics defined in Appendix 7-A, SCOR Performance Attributes and Level 1 Metrics. The SCOR Level 1 metrics are constructed through the configuration of Level 2, which defines supply business processes and through the 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.
1 Williams, T.J., ed., A Reference Model for Computer Integrated Manufacturing (CIM), A Description From the Viewpoint of Industrial Automation, Minutes, CIM Reference Model Committee, International Purdue Workshop on Industrial Computer Systems, Purdue University, West Lafayette, IN (1988) ISA, Research Triangle Park, NC (1989).

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2. Statement of need (Son) definition
A series of different techniques exist to define key stakeholders needs1. Performing a SON functional analysis based on stakeholder’s needs and expectations greatly helps to prioritize capital spending. The functional analysis method maximizes alignment between stakeholders’ objectives and relevant KPIs. The outcome is usually a SON document, which prioritizes capital spending (options or projects) and its associated relevant measurement. Figure 2 shows the functional analysis technique that decomposes a goal (or an objective) into a series of more detailed functions.

Figure 2: SON Functional Breakdown Structure

Constructing of the functional breakdown structure (FBS) is done by starting with the high-level functional expectation or need (outmost left). The hierarchy is then defined by asking “how” the highest level’s functional expectation should be fulfilled. The process then continues on until the proper level of granularity is obtained. Once this is done, stakeholders then agree on which lower level functions are the critical (or critical success factors) ones to the fulfillment of their expectation, need, or goal. Stakeholders brainstorm on which KPIs are relevant, and how frequently they need to be measured to determine whether or not the expectation or need is met (and strategic alignment obtained).

1 Project Management Institute Program Management, “Balanced Scoreboard, Kaplan, Norton ISBN 0,87564,651,3”

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3. critical Success Factors (cSFs)
Using the PMI? technique, once the functional analysis is completed, CSFs must be identified to ensure the overall stakeholder needs, or goal will be attained. An example is shown in Appendix 7-B, Asset Utilization Functional Analysis Example. Using a “functional breakdown structure” (FBS) separates the need from the actual solution used to fulfill this need. Appendix 7-B identifies specific functions, which are considered CSFs. Those CSFs are italicized, and they are: 1. 2. 3. 4. 5. 6. Resources tracking Measure failures Personnel training Share production forecasting Electronic data collection Tracking against production work order

The example shown in Appendix 7-B illustrates how a given stakeholder need or expectation can be broken down into a hierarchy of nested functions. The example is based on analyzing functions required to improve asset utilization from an executive standpoint. In Appendix 7-B, the following CSFs have been identified (under A through F) and weighed using the double-weighing method with the results shown in Figure 3.

Figure 3: Critical Success Factors for Appendix 7-B Example Based on the feedback from stakeholders, we find the following in Table 1.

Paired comparison criteria Evaluation: critical Success Factor A B c Product Genealogy minimize downtime Track Intermediates d E F

1: minor, 2: Significant

measure Availability Spare Parts Product SPc

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Table 1: Stakeholders Feedback

Functional Breakdown

Priority (from double weighing method)

Product Genealogy Minimize Downtime Track Intermediates Measure Availability Spare Parts Product SPC

7 2 5 2 1 4

After the functions have been prioritized, a list of options or projects is defined that will support critical success factor functions. Options (Project) List used in the example: 1. 2. 3. 4. 5. 6. Electronic data collection Track against production order/work center Personnel training Measure failures Resource tracking Share production forecast

These opinions then need 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.

4. 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 identified options (or projects) against two (2) factors: achievability and benefit contribution. Achievability takes into account project risks like financial, project, people, complexity, etc.

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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 projects that provide the highest benefit contribution as well as the highest achievability. These options are located in the upper right quadrant. Since CSFs are identified, weighed and prioritized by stakeholders, their use is critical in determining the overall value of alternative or options (or projects).

Table 2: Measurement of Identified Options (or Projects) against Two (2) Factors: Achievability and Benefit Contribution

Project option 1 2 3 4 5 6

Function name Electronic data collection Track against production order Personnel training Measure failures Resource tracking

Basic Function Measure Performance Quality available Personnel available Equipment available Balance capability/plan

Benefit Value 662 648 443 181 343 238

Achievable Risk 768 500 500 294 176 783

Share production forecasts Supplier management

Figure 4: Value Risk Index Chart with KPI Weight

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5. defining and developing key Performance Indicators
Based on the example shown in Appendix 7-C, KPI Priorities are appropriate in ensuring a stakeholder’s SON is being monitored and evaluated with the right metrics. Appendix 7-C also 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-95 Object Model and Attributes of a Level 3 MOM activity function and the corresponding data exchanges, transaction sequences, and work flow uses. These are used to define, track, measure, analyze, interface, and report metrics to enterprise and supply chain functions (SCOR) and system with Level 4, as well as to other MOM 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 KPIs must be aligned with stakeholders’ functional breakdown SON (Appendix 7-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 summaries of past performance, indications of future performance, or indicators of potential future problems (leading indicators). Collectively, this information is defined as “Production Indicators”. Examples are listed in Table 3, Examples of Production Indicators. One of the activities within production performance analysis is generating production indicators. This information typically is used internally within manufacturing operations for improvements and optimization. For instance, if receiving Level 4 business process (production scheduling and logistics), it requires Level 3 production indicators or production performance information, then it may also be sent to higher-level supply chain management business processes for KPI construction, further analysis and supply chain decisions (typically SCOR, Appendix 7-A). Production indicators can be as simple as values of process tags used as inputs to complex process models. There is a core set of values related to production output, but there can be a significant variation in the core set based on the vertical industry.

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Production indicators are often combined at Level 4 functions such as procurement with financial information, or at Level 3 functions, such as performance analysis (using Level 4 activity based costing standards) to provide cost-based indicators to trigger decisions. As described in Appendix 7-B, breaking up the desired high-level manufacturing operational expectation into its functional constituents allows the prioritization of capital spending. KPI’s typically feed into aggregated overall metrics (like SCOR, Appendix 7-A).

Table 3: Examples of Production Indicators3

category
order Fulfillment

kPI
Actual production rate as a percentage of the maximum capable production rate Percentage of lots or jobs expedited by bumping other lots or jobs from schedule Production and test equipment set-up time Production schedules met (percentage of time) Actual versus planned volume

comment

Asset Utilization

Average machine availability rate or machine uptime Percentage of tools that fail certification Hours lost due to equipment downtime Cumulative count of machine breakdown

Quality

Major component first-pass yield First product, first pass quality yield Reject or return rate on finished products Reject-rate reduction Rework-repair hours compared to direct mfg. hours Scrap and rework as a percentage of sales Scrap and rework percentage reduction Rework and repair labor cost compared to total manufacturing labor cost Number of process changes per operation due to errors Number of training days Yield improvement

Personnel

Percentage increase in output per employee Percentage unplanned overtime Safety and Security incidents Percentage of operators with expired certifications

Productivity

Percentage of assembly steps automated Percentage reduction in manufacturing cycle time Productivity: units per labor hour

3 APQC; ? APQC 2005, www.apqc.org

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Engineering material

HMI data entry count Percentage of alarm reduction Time line is down due to sub-assembly shortage Count of supplier shortages per period Material consumption variances from standards

Planning

Percentage reduction in component lot sizes Manufacturing cycle time for a typical product Percentage error in yield projections Standard order-to-shipment lead time for major products Time required to incorporate engineering changes

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.

6. collecting data and normalizing manufacturing Information
Logical and educated business decisions can be made when putting in place manufacturing operations management (MOM) technology to achieve a highdegree of manufacturing and supply chain responsiveness and flexibility. ISA-95 offers a normalized data definition framework to manage KPIs and construct them based on operations metrics particularly Part 2, Object attributes, and Part 3, Activity and Object Models and Attributes of MOM. When using the standard, companies solve one of the challenges data normalization brings, which is the agreement on metric definition across the business. The operations metrics are the derived analysis for resources (material, personnel, and equipment/work unit) at the product and process segment level used in the macro form for scheduling the micro form for dispatching and execution. The result of analysis and aggregation of operations metrics are used to construct KPIs for performance analysis in product definition, production and process capabilities and requested schedule (performance). Appendix 7-D illustrates the Part 2 object model inter-relations for MOM data aggregation and analytics for KPI construction. Appendix 7-E is an example of using ISA-95 as a unified data definition framework in defining KPIs. 1. Appendix 7-E shows how “planned” production is measured against “actual” 1.1. The variance provides with a typical Production-related KPI metric 1.2. In this case, an “Actual versus planned volume” KPI is derived from the model

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

2. Example #2 is to measure equipment capability versus equipment actual Use 2.1. The variance provides with a typical Asset Utilization KPI metric 2.2. In this case, an “Hours lost due to equipment downtime” KPI is derived from the model 3. Example #3 is to measure material consumption variance 3.1. This variance provides with a typical Material-related KPI metric 3.2. In this case, a “First product, first pass quality yield” KPI is derived from the model 4. Example #4 is to measure operator certification 4.1. 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 7-F, Actual versus Planned Production Volume: ISA-95 KPI Examples, shows how an ISA-95 KPI is used to track manufacturing cycle variances. It also highlights how this particular KPI can be aggregated and rolls into a supply chain responsiveness metric. In our example, the SCOR “Order Fulfillment Lead Time” metric (sum of procurement cycle, manufacturing cycle and replenishment cycle) uses the ISA-95 KPI to measure time variances between planned and actual manufacturing time for a particular product being manufactured. “Example A” in Appendix 7-F also shows a ISA-95 Level 3-4 transaction where variance is measured 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 include process variability constraints). The averaged KPI then is used to feed a Level 1 SCOR supply chain operation metric (Appendix 7-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-95 addresses the PRM Level 3 to 4 interface and the Level 3 MOM activities.

7. Periodic Review and Adjustment
Companies must keep in mind the fluid nature of KPIs as their importance shift over time depending on the SON and market and technology trends. These needs are influenced by the company’s external and internal environment like: ? ? ? ? ? Competitive threat New entrant Customer changing needs Regulatory compliance Mergers and Acquisitions, etc.

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Also, CSFs affecting the business today are most likely to change as time goes by. The need to periodically conduct SON meetings, evaluate business manufacturing strategy’s effectiveness and the capital productivity return is necessary. Measuring and monitoring relevant KPIs against a baseline supports ensuring the business strategy is met.

8. 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 be measured and monitored in order to ensure operational alignment. ISA-95 standards provide a highly efficient way to leverage shop-floor information. This is especially true for companies dealing with multiple manufacturing sites. Not only does ISA-95 provide a contextualized and normalized data model, it also offers vocabulary used by business managers and decision makers. Finally, it also offers the right granularity necessary to measure actionable KPIs, which can further be aggregated to service supply chain models.

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

Appendix 7-A: ScoR Performance Attributes and level 1 metrics
Table 7-4: SCOR Level 1 Benchmark Metrics

Customer Facing Activities ScoR ScoR metric definition Performance Attribute
A. Supply Chain Delivery Reliability The performance of the supply chain in delivering: 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. The velocity at which a supply chain provides products to the customer. The agility of a supply chain in responding to marketplace changes to gain or maintain competitive advantage.

lEVEl 1 ScoR metrics

A1. Perfect Order Fulfillment

B. Supply Chain Responsiveness C. Supply Chain Flexibility

B1. Order Fulfillment Cycle Time C1. Upside Supply Chain Flexibility C2. Upside Supply Chain Adaptability C3. Downside Supply Chain Adaptability

Internal Facing Activities ScoR ScoR metric definition Performance Attribute
D. Supply Chain Costs The costs associated with operating the supply chain.

lEVEl 1 ScoR metrics

D1. Cost of Goods Sold D2. Total Supply Chain Management Costs

E. Supply Chain Asset Management Efficiency

The effectiveness of an organization in managing assets to support demand satisfaction. This includes the management of all assets: fixed and working capital.

E1. Return on Supply Chain Fixed Assets E2. Cash-to-cash Cycle Time

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

Appendix 7-B: Asset Utilization Functional Analysis Example
needs
Asset Utilization

level 1
Fulfill Production Order

level 2
Balance Capability /Plan

level 3
Schedule Production

level 4
Issue to Shop Floor

level 5
Electronic Production Dispatch

level 6
Integrate Shop Floor /ERP

Measure Availability Schedule Procurement

Resource Tracking Issue to Suppliers EDI Portal

Define Capability

Define Master Recipes Define Equipment

Product SOPs Equipment Layout Measure Availability Rate measure Failures Integrate Shop Floor /Recipe Mgmt.

Equipment Available

Minimize Downtime

Minimize Equipment Setup

Download Setpoints Schedule Product Families Schedule Long Runs

Define Formula parameters

Maintain

Preventative Maintenance Corrective Maintenance Spare Parts SOPs

Condition-based Monitoring Issue Maintenance Orders Separate Inventory

Personnel Available

Certify Safety

Personnel Training MSDN Environment Locking Procedure PPE

Material Available

Receive Material Stage Material Track Intermediates Move Materials BOM Bar Coding Issue Movement requests ABC Classification Container Qty. Mgmt. Rework Material Reduce Suppliers Product BOM Usage/WC Define Product Structures

Quality Available

Define SOP Available Product Variability

Electronic SOP Product SPC Sampling Product Specification

Product Genealogy

Track against Production order Store Results

Record Material Constraints

Regulation Compliance

SOPs

Document Management

Version Control Master Records

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needs

level 1

level 2

level 3

level 4
Train Personnel Audit

level 5

level 6
Repository

Data Collection Action Plan

Supplier Management Maintain Assets Utility Services Engineering Support Mechanical Process It Support Systems Help Desk Measure Performance Schedule Response Actual Costs Energy Costs Electronics data collection Lean Mfg. FG Inventory Levels Physical Count Query System EOQ Guidelines JIT Delivery Logistics Minimize Movements Maximize Truck Loadings Research New Products Design Test Continuous Improvement Measure Key Processes CAPA Schedule Test Production Layout/Space Usage Load Sequence/ Customer Route WIP Tracking Cost Accounting Electrical

Long Lead Time Scheduling

Share Production Forecasts

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Appendix 7-c: kPI Priorities

Appendix 7-d: object model Inter-Relations

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Appendix 7-E: Standardized data definition Framework

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Appendix 7-F: Actual versus Planned Production Volume: ISA-95 kPI Examples
Example A. PRodUcTIon ScHEdUlE, PlAnnEd Id A unique identification of the production schedule and could include version and revision identification. The ID shall be used in other parts of the model when the production schedule needs to be identified. Example: 1999-10-27-A15 description Contains additional information and descriptions of the production schedule. Example: “Widget manufacturing schedule.” Production Schedule Identification of the associated production schedule Example A. PRodUcTIon PERFoRmAncE, AcTUAlS Id A unique identification of the production performance and could include version and revision identification. The ID shall be used in other parts of the model when the production performance needs to be identified. Example: 1999-10-27-A15 description Contains additional information and descriptions of the production performance. “Production performance report on October 27, 1999 production schedule.” 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 Start Time Start time of the associated production performance, if applicable. Example: 10-28-1999 End Time End time of the associated production performance, if applicable. Example: 10-30-1999 Published date Date/time on which the production performance was published/ generated. Example: 10-27-1999 13:42 EST location

Example: 1999-10-27-A15 Start Time Start time for the associated production schedule, if applicable. Example: 10-28-1999 End Time End time for the associated production schedule, if applicable. Example: 10-30-1999 Published date Date/time on which the production schedule was published/generated. Example: 12-30-1951 18:30 UTC location

Identification of the associated element of equip- Identification of the associated element of the ment hierarchy model. equipment hierarchy model. Example: East Wing Manufacturing Line #2 Element Type A definition of the type of the associated element of the equipment hierarchy model. Example: Enterprise, Site, Area, Production Line Example: East Wing Manufacturing Line #2 Element Type A definition of the type of associated element of the equipment hierarchy model. For example: enterprise, site, area. Example: Production Line

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Authors
yves c. dufort Eng, MBA; Invensys / Wonderware

Reviewers
charlie Gifford Director of Lean Production Management GE Fanuc Automation clifford lichkowski Prairie Malt Limited (a Cargil Inc. Joint Venture) Jim Strothman Editorial Consultant

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About Invensys / Wonderware: Wonderware is a business unit of Invensys plc. Wonderware is the world’s leading supplier of industrial automation and information software. Founded in1987, Wonderware pioneered the use of the Microsoft Windows operating system in HMI software for manufacturing operations. Today Wonderware’s leading software products and solutions for Production & Performance Management, Supervisory HMI and SCADA applications are “Powering Intelligent Plant Decisions, In Real-Time”, enabling customers to improve profitability across a wide range of discrete, process and hybrid manufacturing industries. Wonderware’s software products and solutions are based on the ArchestrA architecture from Invensys. Based in Lake Forest, California, Wonderware has regional sales and development offices throughout the North American, European, Latin American and Asia-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 30 percent 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, nonprofit organization that is setting the standard for automation by helping over 30,000 worldwide members and other professionals solve difficult technical problems, while enhancing their leadership and personal career capabilities. Based in Research Triangle Park, North Carolina, ISA develops standards; certifies industry professionals; provides education and training; publishes books and technical articles; and hosts the largest conference and exhibition for automation professionals in the Western Hemisphere. ISA is the founding sponsor of The Automation Federation (www.automationfederation.org).

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, systems integrators and vendors achieve manufacturing leadership by deploying practical solutions that combine information, business, manufacturing and supply chain processes and technologies. Visit us online at http://www.mesa.org.

?2007 MESA International

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