sirris manufacturingday2011 the-future-of-smart-manufacturing-execution-de-clercq
DESCRIPTION
A case illustrates how Manufacturing Execution Systems enable a manufacturer to keep track of the production progress and contribute to an optimized execution on the workfloor. A vision is posted on two possible future orientations of MES systems which lead to optimized and self-organizing execution on the shop floor.TRANSCRIPT
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S ir r is M anuf act ur ing Day
The future of smart manufacturing execution
Erwin VervondelDe Clercq Solutions
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© 10 – 05 – 2011
S ir r is M anuf act ur ing Day
part 2 : MES functionalities by example
part 3 : Next steps, Innovations
part 1 : Challenges & Goals
Agenda
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• Flexibility – Everything and everyone needs to be more ‘flexible’• Company: production demands (make to stock make to order)• Customer specific demands• Workforce optimization
• Complexity• Product variance• Laws / regulations strictness• Supply Chain integration
• Focus on all costs• Cost control = control of Machine + Labour hours• Material cost: focus on Scrap and Rework• Be competitive across borders
Challenges
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S ir r is M anuf act ur ing Day
There is a clear need for• Integration of several information sources regarding “business intelligence,
manufacturing, logistics, reporting, alerts, analytical tools”
• Determining company’s key performance indicators (KPI’s) ? Production quota, how much, when
? Production interrupts
? Tracking en tracing requirements/regulations
? Key quality indicators
? Root cause analysis, most occuring scrap reason
? Control WIP stock
? Material consumption
? Setup / Change over Costs
“you cannot manage what you cannot measure”
Challenges
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• Setting application domain scope• First determine scope, then look at technical concepts
Determine need for integration of several application domains ?• HR (T&A)• Production (MES)• Logistics (WMS)
• Defining Information flows• One way versus two way• On-line integrated environment – is this possible ? Wanted ? Required ?
• Determine level of detail• Detailed info
• A lot of input / interfaces• Much data available• Easier to analyse, more flexible reporting
• Less Detailed info• Basic Input• Crucial data available (? What is crucial)• Analysis only based on crucial data
Setting goals
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© 10 – 05 – 2011
S ir r is M anuf act ur ing Day
part 2 : MES functionalities by example
part 3 : Next steps, Innovations
part 1 : Challenges & Goals
Agenda
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© 10 – 05 – 2011
S ir r is M anuf act ur ing Day
MES
ERP
MES functional model
P(roduction)
OperatorData Collection &Process Integration
SupervisorExecution Tracking
OperationsManagementPlanning & ReportingQ(uality) L(ogistic)
Infrastructure
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S ir r is M anuf act ur ing Day MES – ERP Interaction
Enterprise InformationPlant Production Scheduling,
Operational Management, etc
ManufacturingControl Information
Area Supervision, Production Planning,
Reliability, Assurance, etc
ProductDefinition
Management(How to make
a product)
ProductionResource
Management(What is
available for use)
ProductionScheduling &
Execution(What to
make and use)
ProductionData Collection
& Analysis(What was
made and used)
ISA 95 model based
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MES
ERP
MES functional model
P(roduction) Q(uality) L(ogistics)
Infrastructure
Integration ERP/MES ERP is master MES is slave/master(WMS is slave/master)
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•© De Clercq Solutions 2007
MES – Infrastructure interaction
Host
Data CollectionSystems
Mobile DCRF - Batch - Tag
IntelligentWorkstations
WeighingMeasuringMachines
Process ControlSCADA
PrintingLabeling
MES
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MES
ERP
MES functional model
P(roduction) Q(uality) L(ogistic)
Infrastructure
Production Scheduling/dispatching• Resource allocation• Realtime Planboard
Time and Quantities• Labor : order/indirect• Machine : Up/Downtime• Order : costing – planning• Quantities : Good/Scrap/rework
Dashboard on production• OEE on all levels• Realtime views• Actual/historical analysis
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MES
ERP
MES functional model
P(roduction) Q(uality) L(ogistic)
Infrastructure
Inline production QC registration Offline production QC registration (Spec) Proces integration/monitoring SPC
Operator instructions Document control
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MES
ERP
MES functional model
P(roduction) Q(uality) L(ogistic)
Infrastructure
Product Identification• Dynamic modelling• Inline labeling/printing• SSCC
Tracking and Tracing• Lot registrations / creation
WIPWMS or WM Middleware
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Integration at different levels of the organization
Responsible Ideal platform for production monitoring and steering The perfect tool for evaluation and analysis from efficiency through quality
OperatorsOptimized support for the workfloor employees (empowerment) Steering Registrations
Production Lines/Machines Technical integration with the processes Automate data input & Increase data quality
MES platform
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Easy production order creation with wizard
Overview of open orders / dashboard
MES by examplePlanner: Production dispatching
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S ir r is M anuf act ur ing Day MES by examplePlanner: Production dispatching
Running production orders& order queue
Releasing ordersto production
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S ir r is M anuf act ur ing Day MES by example
Intuitive view onproduction planning
Compact production order information
Indication of production statuse.g. “late start”
Planner: Realtime planboard
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S ir r is M anuf act ur ing Day MES by example
Real-time view on production progress
Operator: Operator Dashboard
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S ir r is M anuf act ur ing Day MES by exampleOperator: Operator Dashboard
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S ir r is M anuf act ur ing Day MES by example
Real-timedata collection
Registration of machine interrupts
Operator: Process info
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S ir r is M anuf act ur ing Day MES by exampleOperator: quality control
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S ir r is M anuf act ur ing Day MES by example
OEE reporting (real-time & historical)
Supervisor: OEE analysis
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S ir r is M anuf act ur ing Day MES by example
Machine-based Pareto
Reason-based Paretoon selected machine
Easy drill-down
Supervisor: Pareto analysis
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S ir r is M anuf act ur ing Day Production Logistics / WIP
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S ir r is M anuf act ur ing Day MES by example
Identification of material& characteristics
Operator Material identification
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S ir r is M anuf act ur ing Day MES by Example
Produced Logistic Units to be put away
Operator: Material output
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S ir r is M anuf act ur ing Day MES by example
Backward/forwardproduct genealogy
Tracking & tracingdrill-down
Supervisor: Traceability
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S ir r is M anuf act ur ing Day
part 2 : MES functionalities by example
part 3 : Next Steps, Innovations
part 1 : Challenges & Goals
Agenda
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Next Step : Intelligent Manufacturing
• Need for significant improvement of various Key Performance Indicators:Minimizing Cycle Time;Reducing Work in Progress;Better Delivery on Time;Higher Throughput
• Better use of resources
• How? Using Intelligent Manufacturing Methods and Systems
Next Steps
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Methodologies Decision Support
Operations Research
Manufacturing Science
Factory Physics
Production Methods
Supply Chain Management
Mathematical Optimization
Modeling & Simulation
Advanced Planning & Scheduling
Next Step: Intelligent Manufacturing
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• Scheduling / Release management• The heart of intelligent manufacturing
• ‘Typical’ Scheduling Methods• Dispatch Rules, User Defined & Dynamic Rules• Discrete Event Simulation• Branch & bound• Simulated annealing• Tabu search• Genetic Algorithms• Neural Nets• Mathematical Programming (LP, IP, etc)• Heuristics• Hybrid
• …
Scheduling Challenges
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• ‘Classic’ Scheduling techniques not always so obvious …
• Cannot handle job dispatch very well Not reactive enough
• In scheduling, the gap from theory to practice is HUGE ! Availability of correct dataMultiple criteria You cannot model everything ! Variability, variability, variability Handling huge problems
Validity of the schedules often problematic Academic methods do not translate as is to industry
• Many scheduling projects … Fail …
Scheduling Challenges
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• Industry faces a “scheduling crisis”
New approaches are required
New approach: self organizing/adaptive systems
DCS is currently building a new scheduling system called ALDISS …
ALDISS
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Our Way Forward: ALDISS
•Adaptive •Lean •DIstributed•Scheduling•System
ALDISS
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S ir r is M anuf act ur ing Day ALDISS
Adaptive Lean DIstributed Scheduling System
• Lean scheduling system
• Using local rules and methods, adaptive, intelligent, flexible, self-
organizing
• Distributed
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Goal Develop a scheduling system, able to be easily implemented & integrated in already
existing MES system.
A dynamic system that reacts to real-time situations.
A distributed system that relies on local information and uses distributed emergingintelligence to make scheduling decisions.
A system built upon Basic Principles of Self-Organizing Systems:• Local decisions
• Local rules
• Self-adaptive
• Flexible
• Resists disruptions
ALDISS
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• Two year R&D project sponsored by IWT .
• Collaboration between De Clercq Solutions, UEG and SirrisAdvanced Manufacturing group.
• Project covers system specification, development, piloting and benchmarking
• Implementable in a wide variety of industries.• Applicable to large firms as well as to SMEs.
• The system will be created, developed, piloted and benchmarked, and of course fully integrated with the Objective Manufacturing Framework.
The project
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• Specific focus on Release Management
Dispatching rules
• Modelling• E.g. Provide modelling of ‘control loops’
– Kanban
– Polca loops
– Nr of ‘virtual’ cards in the system
ALDISS
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• Dispatch Release jobs filter
Only allow start of ‘released’ job when allowed• E.g. Card present
Operator support for starting the correct job• Is not always the first job in the ‘classic’ queue
Dispatch verifier has knowledge of activities/jobs upstream / downstream allowing balanced job releases.
ALDISS
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Questions ?
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