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Designing Data Collection for Consistency that Improves Process Management

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Part 2 of the “Factors for Manufacturing Analytics Success” webinar series examines the underlying system and user interface design for successful data collection. The webinar will explain how to …

Part 2 of the “Factors for Manufacturing Analytics Success” webinar series examines the underlying system and user interface design for successful data collection. The webinar will explain how to define the process and identify the information flows for each operation. This includes how to conduct a value stream analysis to understand how waste streams and process variances impact quality.
Additionally, Charlie Gifford will discuss how to select and measure the appropriate parameters to deliver the data needed to understand and control the process. He will show how create a successful system by taking steps to:
• Define and maintain consistent data formats
• Maintain consistent variable naming across databases
• Design and implement data collection that delivers high quality data
• Design for compliance with industry standards and best practices
The webinar will provide a road map on how to deliver, role-specific reporting and analytics to everyone in operations and management. The final system will provide actionable feedback on both the measurement and manufacturing processes, thereby establishing a solid foundation for process management and continuous improvement.
Recording at (https://www1.gotomeeting.com/register/580933265 ).

NWA website http://www.nwasoft.com

Published in: Business, Technology

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  • 1. Factors forManufacturing Analytics Success - Part 2:Designing Data Collection for Consistency to Improve Process Management ©2011 21CMS All Rights Reserved
  • 2. Standards Liaisonfor Manufacturing OperationsCharlie Gifford• Thomas Fisher Award for Best Standards Book of Year 2010• MESA International Outstanding Contribution Award 2007• Chairman, ISA-95 Best Practices Working Group• Published over 45 papers and 4 books on Mfg Operations IT• Director, MESA Global Education Program• Certified TQM Facilitator / Process Action Team (PAT) Leader, 22 years• Voting Member, ISA-88 & ISA-95 Committee• ISA-95 Representative, ISA-95/SCOR Alignment Working Group• Information Member: ISA-99 (Security), ISA-100 (Wireless)• Director, ISA Computer Technology Division 96-99• Coauthor, SCOR MAKE Section• Chairman, Editorial Board, Industrial Computing Magazine 98-02• Standards Work: ISA-84, 88, 95, MESA, SCOR, Many DOD Standards ©2011 21CMS All Rights Reserved 2
  • 3. Agenda• The Data Collection Problem for Standards-based Manufacturing Intelligence• What Data to Collect• How to Collect for Data Integrity ©2011 21CMS All Rights Reserved 3
  • 4. Mfg 2.0 Requirement: Design forGlobal Manufacturing EnvironmentMfg 2.0: Evolve Demand-Driven Manufacturing asa Scalable Adaptable Business Model• Synchronize manufacturing and supply chain work processes• Dynamically reconfigurable global supply network to a known profit per order fulfillment path• Reuse of Model-based architecture provides scalable continuous improvement capability• Scalable Continuous Improvement “Network” ©2011 21CMS All Rights Reserved 4
  • 5. Mfg Operations Contribution Required • Right product • Right Quality • Right place Demand Forecast • Right time • Right profit margin Perfect SCM Order Cost Cash-to-Cash Inventory AP Total AR Supplier Supplier RM Purch Dir Mtl Quality On-Time Inv Costs Costs Production Order Perfect Cost Plant WIP + FG Sched Cycle Order Detail Utilization Inventory Variance Time Detail Enterprise Manufacturing IntelligenceCopyright © 2011 Gartner Group ©2011 21CMS All Rights Reserved 5
  • 6. Mfg Operations Contribution Required• Importance of Perfect Order Performance• 1/10 of the stockouts of their peers• 15% less inventory• 17% stronger perfect order fulfillment• 35% shorter cash-to-cash cycle times ©2011 21CMS All Rights Reserved
  • 7. Mfg 2.0 InnovatesOperations Process Effectiveness ©2011 21CMS All Rights Reserved
  • 8. Top Line Opportunities are Compelling, But More..Larger benefits from continuous 3. Increase Market Share improvement: And/Or Pursue New MarketsMOM is necessary to Supports collaboration achieve this level Supply chain visibility 10X Platform for continuous improvement $$ Value of Benefits 2. Increase Volume and /or Margins At Platform for Continuous Same Cost Improvement 3X Faster NPI cycle: Shorten innovation innovation Faster NPI cycle – shorten TTM for TTM for 1. Reduce MOM marketed as competitive tool Customer audit requirements: traceability and genealogy MES marketed as competitive tool traceability and genealogy Customer audit requirements: Operating Promotes flow manufacturing Costs Average 1X Lower WIP and FGI Reduce indirect labor costs Reduce waste/scrap/materials Shorten cycle/flow time payback Reduce cost of regulatory compliance Improve quality/ reduce process & product Reduce rework 12 Months on Reduce rework Reduce maintenance costs variability variability Reduce maintenance costs Reduce cost of regulatory compliance 1X Benefits 3 - 12 mos. 12 to 36 mos. 12 to 36 mos. 3 years +MOM Systems justified on cost reduction Project payback ranges 6 to 24 months Copyright © 2011 Gartner Group Report: MES Provides Long-Term Revenue and Market Benefits Beyond Easy-to-Quantify Operational Cost Savings ©2011 21CMS All Rights Reserved 8
  • 9. Mfg Data Sophistication Determined byMfg Work Processes ©2011 21CMS All Rights Reserved 9
  • 10. Required Agility Forces Change Corp Suppliers Systems Re Broadcast Sequence Data Collection Order Scheduling Logistics Mgmt WIP Quality eKanban Track ANDON Asset Production Error Monitoring Mgmt. SCADA Proof ©2011 21CMS All Rights Reserved 10
  • 11. Plant Data Collection Issues• Primary Plant Data Collections: • Process and Work Process Data • Operations KPIs and Metrics • Business Process Data • Business Process Metrics ©2011 21CMS All Rights Reserved 11
  • 12. Plant Data Collection Issues• Too many Shop Floor GUIs and Paper Forms from Manual Data collections for too many applications• Too many paper forms are manually transcribed into applications with point-to-point interfaces to other applicationsHas Led To…..• Non-value added activities• Large data translations error propagates poor data Integrity• No “same shift” feedback: • Operators and supervisors MUST CARE about their manual data collections ©2011 21CMS All Rights Reserved 12
  • 13. “Automate” Manual Data Collection Data Collection Mechanism for Metrics 40% 35% 30% 25% 20% 15% 10% 5% 0% Fully automated Partially automated Keyed into Manual recording spreadsheets Business movers Others Source: Correlating Plant Performance to Business Performance, © 2010 MESA International & Cambashi Inc. ©2011 21CMS All Rights Reserved
  • 14. Focus on Value-Add Data Collections• User-centric User Interfaces (UI) streamline activities by contextualizing all applications to a single UI for each operation• Orchestrated Manual Data Collection: Minimize Typing or Writing • Wireless UIs with Single action methods: Bar Code Sheets, Menus, Value Inputs, Error Proof ranges • Mobile Applications: MS OS, Apple IPAD, Android • RFID Mesh Networks• Contextualized Automated Equipment Data Collections • Standard OEM equipment interfaces • Rationalize equipment state models for OEE data integrity ©2011 21CMS All Rights Reserved
  • 15. Business Movers Show ImprovementRequires Rapid Feedback How rapidly operational KPIs showed to those managing the operations measured 100% 80% 60% 40% 20% 0% Within a shift Longer than a shift Business movers Others Source: Correlating Plant Performance to Business Performance, © 2010 MESA International & Cambashi Inc. ©2011 21CMS All Rights Reserved
  • 16. High Value Realized by Actionable Accurate Decisions© All rights reserved. Industrial Management Enhancement, 2011 ©2011 21CMS All Rights Reserved
  • 17. Competitive Framework for Process Capabilities Best-in-Class Average Laggards Standardize processes across the enterprise for optimizing manufacturing operations 64% 37% 30% Standardize measurements of KPIs across Process enterprise 68% 58% 51% Standardize processes for response to adverse events 64% 51% 19%Copyright @2008 Aberdeen Group, All rights reserved. ©2011 21CMS All Rights Reserved 17
  • 18. Agenda• The Data Collection Problem for Standards-based Manufacturing Intelligence• What Data to Collect• How to Collect for Data Integrity ©2011 21CMS All Rights Reserved 18
  • 19. Best-in-Class Focus on Perfect Order and NPI Supported by Actionable OEE© 2011, Aberdeen Group. All Rights Reserved. ©2011 21CMS All Rights Reserved 19
  • 20. Manufacturing Intelligence Foundation… CONTEXTUALIZE ANALYZEIncreasing Strategic Value to the Enterprise COST-BASED MODELS e.g. Capable/Profitable to Promise VISUALIZE BUSINESS Perform RULES To Demand ORDERS Performance SPECIFICATIONS to schedule MATERIAL & PRODUCT Overall process FLOWS performance metrics Copyright © 2011 Gartner Group PRODUCTION MODELS, RECIPES/ BOMS Correlate of work process data, & ROUTES equipment data and product data EQUIPMENT Operating data transformed into asset & ASSET performance KPIs INSTRUMENT Large volumes of extremely detailed production data from multiple back-end data sources. DEVICE I/O TAGS ©2011 21CMS All Rights Reserved 20
  • 21. …Enables Collaborative Manufacturing Management Enterprise Domain Business Business Scope FIN HR ERP Lifecycle Domain EAM APS/ PLM/S FCS Suppliers SRM CPS CRM Customers MOM MES TMS BPM GLS Value Chain Domain PLM/D APC Logistics RPO/ PSO Automation Collaborative InfrastructureSource: ARC Advisory Group Production ©2011 21CMS All Rights Reserved 21
  • 22. MESA Metrics Conceptual Framework Profitability Audience: • Focus on Actionable CFO, CEO metrics for improvement Increasing ability to take actionIncreasing aggregation Corporate Financials • Link metrics from Plant Accounting, External Internal Strategic Finance operations to finance Investors Business • Logical links do exist & Creditors Planning Plant Management, Aggregated Financial & Operations Metrics Operations • Focus on Financial drill Management downs to operations improvement efforts Operators, Supervisors, Operations-level KPIs & Quality, Engineers, Dynamic Performance Metrics Technicians Plant floor sensors, Operator, and Machine to machine to machine interface Machine ©2011 21CMS All Rights Reserved
  • 23. Structured Data ProvidesGreater UnderstandingKnowledge is a Key Enabler of the Knowledge Worker toSupport Problem Solving and Troubleshooting Understanding INDEPENDENCE Understanding Principles Knowledge CONTEXT Understanding Patterns Information Understanding Relationships Structure Data UNDERSTANDING ©2011 21CMS All Rights Reserved 23
  • 24. MfgMasterDataMgt.A RecipeManagementExample:Master Dataand its mMDM 24 ©2011 21CMS All Rights Reserved 24
  • 25. Align Mfg Ops Master Site Recipe Product Master Work Master Recipe Data Definition DefinitionProduct Related Definitions Production Work Batch List Output from Schedule Schedule Scheduling Control Work Control Recipe Definition Executable Elements Production Work Production Batch Production Execution Performance Record Record Results ©2011 21CMS All Rights Reserved 25
  • 26. Agenda• The Data Collection Problem for Standards-based Manufacturing Intelligence• What Data to Collect• How to Collect for Data Integrity ©2011 21CMS All Rights Reserved 26
  • 27. First, Top-down Orchestrated…Next, Bottom-up Optimized.Links Plant to Business Performance Links between operations and business KPIs are very effective 25% 20% 15% 10% 5% 0% Business Movers Others Source: Correlating Plant Performance to Business Performance, © 2010 MESA International & Cambashi Inc. ©2011 21CMS All Rights Reserved
  • 28. Understand Plant’s Role in Supply Chain: SCOR’s 5 Management Processes Plan Deliver Source Make Deliver Source Make Deliver Source Make Deliver Source Return Return Return Return Return Return Return Return Suppliers’ Supplier Your Company Customer Customer’s Supplier Customer Internal or External Internal or External SCOR Model Building Block Approach Processes Metrics Best Practice TechnologyCopyright © 2011 Supply-Chain Council All rights reserved. ©2011 21CMS All Rights Reserved
  • 29. Top-Up Metrics Based on Business Process Metrics Decomposition© All rights reserved. Industrial Management Enhancement, 2011 ©2011 21CMS All Rights Reserved
  • 30. Bottom-Up KPI Hierarchy based onVSM and URS Process Definitions © All rights reserved. Industrial Management Enhancement, 2011 ©2011 21CMS All Rights Reserved
  • 31. Data Collections fromValue Stream Maps and 6 Sigma…Enable Manufacturing Transformation Lead Time/Cycle Time Lean Attacks Waste Process Value Add Time Six Sigma Attacks Variation Non Value Add TimeFOCUS on continuous improvement data collections:• Lean … Cycle time reduction and waste elimination• Six Sigma … Defect reduction and variation control 31 ©2011 21CMS All Rights Reserved
  • 32. URS Defines Operations, Information Flows, Data Collections, and Timings Business Logistics Management Make Material Segment Common Material Final Material Final Product Segment Segment Segment Inventory Inventory Inventory Inventory Operations Management Deliver Deliver Deliver Production Operations Batch Batch Batch Mix Fill Cap Label Package Management Quality Test Test Operations Management Maintenance Setup/ Setup’ Operations Maintain Maintain Management LEVEL 2 Data Inputs and Outputs: Manual and AutomatedCopyright © 2011 ISA ©2011 21CMS All Rights Reserved 32
  • 33. ISA-95 Object Models DefineData Exchanges & Data Models4 Resource Categories 4 Information Categories Resources Product/Operations Capability Product & Operations ProductPeople Equipment Materials Definition Time Production & Operations Capability Structure / View Production Production & Operations Schedule Process & Operations Segments Production & Operations Performance ©2011 21CMS All Rights Reserved 33
  • 34. Correlate Data to Construct Metrics and Complete Production Genealogy Product Production Production Production Definition Capability Schedule Performance Detailed Metrics Categories Production Scheduling Three Types of MOM Production Analytics Resource Production for KPIs Management Tracking Production Product Dispatching Analysis Product Production Definition Data Production Management Collection AnalysisFrom ANSI/ISA-95.00.03-2007 Production ProcessCopyright © 2010 ISA. Used withpermission. www.isa.org Execution Analysis Level 2 Process Control / Plant Work ©2011 21CMS All Rights Reserved 34
  • 35. Production Operations Depends on Operations Data Response • Shaded elements define information flows within Level 3 areas to support Production • Some information may flow to other Level 4 systems Production Maintenance Quality Inventory Product Production Production Production Quality Quality test Quality test Quality test MaintenanceMaintenance MaintenanceMaintenance Inventory Inventory Inventory Inventory definition capability schedule performance definitions capability request response definitions capability request response definitions capability request response Detailed Detailed Detailed Detailed production inventory maintenance quality test scheduling scheduling scheduling scheduling Production Quality Inventory Production Maintenance Inventory resource Maintenance Quality test tracking resource test resource resource management tracking tracking tracking management management management Production Production Quality test Quality Inventory Inventory performance Maintenance Maintenance dispatching dispatching analysis dispatching analysis analysis dispatching analysis Product Production Maintenance Maintenance Quality Quality Inventory Inventory definition data definition data definition test data definition data management collection management collection management collection management collection Quality test Inventory Production Maintenance execution execution execution execution management management management management Level 2 Process Control:ANSI/ISA-95.00.03-2006 Inputs and Outputs are Bi-Directional Data CollectionsCopyright © ISA 2011. ©2011 21CMS All Rights Reserved 35
  • 36. Conclusion• Too much data…Poorly collected for today’s modern manufacturing environment…with predictably disastrous results1. Assess and Define processes for each operation to understand quality impact of product and processes: Their information flows, data, & timings2. Successful systems 1. Define and maintain consistent data formats 2. Design for compliance with industry standards for MDM governance 3. Design value-add data collections for Actionable control of process 4. Design data collection methods to deliver high integrity data3. Provide actionable “same shift” feedback on processes to establish a solid foundation for process management and continuous improvement ©2011 21CMS All Rights Reserved 36
  • 37. Question and Answer Charlie Gifford President 21st Century Manufacturing Solutions LLC charlie.gifford@cox.net 208-309-0990 ©2011 21CMS All Rights Reserved
  • 38. Working Notes • Problem • Too many GUIs, too many applications • Non-value added activity • Data translations, Data Integrity • Same shift feedback: Metrics that matters • What • VSM required • Metrics SCOR, MESA • How and Governance? Master Data • Mfg 2.0 User centric interfaces: Work and work cell specifics • RFID, Mesh Networks, Wireless, Automated, Paper, spreadsheet, HMI, IPAD • MS OS, Apple, Android • Bar Codes, Menus, Value inputs, Error Proofing ranges • Equipment interfaces • State models • Companies that fail to manage their data properly can’t remain competitive. Product data management, though, is only as effective as the quality of data being managed. Poor data quality can lead to endless headaches and poor decision making. ©2011 21CMS All Rights Reserved 38
  • 39. MOM User and Functional RequirementsDefine Data Structure for Mfg Intelligence 1. MOM URS: 2. MOM URS: 3. MOM FRS: Open Open O&M Open O&M O&M Data Definition, Process Model Information Flows Structure, Transactions & Rules• Manufacturing Intelligence Requirements: • URS define processes, resources, data, KPIs, and metrics• Governance, Definitions, and Structure of Manufacturing Data • Mfg Master Data Mgt: Mapping and Synchronization Processes • Metrics: Operations and Financial • KPIs: Quality and Work Processes • Align Master and Meta data for each application • Align Syntax data for each application • Mfg Integration Semantic Models (Processes and resources) • Systems of Record: Incidence and Historical Data ©2011 21CMS All Rights Reserved 39