1. Wim De Bruyn lecturer ICT, R&D FBO, UCG product mgt. Inxites
The control of today and prediction of the future using predictive production scheduling
Wim De Bruyn lecturer ICT, R&D FBO, UCG product mgt. Inxites
Bert Van Vreckem Lecturer ICT, FBO, UCG Researcher Prinsyslab, UCG
SAPience.be User Day 2012

2. Agenda Introduction ISA-95: current and future production management
Production Scheduling
ISA-95 and Scheduling: Are they a lovely couple?
ISA-95+Scheduling+SAP = ?
Optimal Schedule and Algorithms
The power of Algorithms on a Real Production Case
From Algorithms to Management View: KPI dashboard
Integration from Shopfloor to Topfloor
Conclusions
SAPience.be User Day 2012

3. Introduction Predictive = What if we try ..... Supply Chain
Production Process
Customer, Market and Environment Demand
Visible, transparent and fully controlled?
Predictive = What if we try .....
Why do we need it?
Better Control of Daily Reality
10% Reduction in Energy Billing
Full control of waste treatment
Total Avoidance of Ecological Disasters
Full Customer & Citizen Satisfaction!
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4. ISA-95 Functional enterprise control model
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5. ISA-95 Production Information Overlap
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6. ISA-95 Production Segment Capabilities
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7. ISA-95 Product Definition Model
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8. ISA-95 Process segment relations Connecting a production request with a routing
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9. Current and future Production capabilities
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10. ISA-95 Production Schedule Model
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11. Short time Planning overview
GANTT chart, connecting resources with production orders (same colour)
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12. Production Scheduling is …
telling a production facility when to make, with which staff, and on which equipment.
allocation of jobs to scarce resources
a combinatorial optimization problem
maximize and/or minimize objective(s)
subject to constraints
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13. Production Scheduling
shorten delivery times
increase variety in end-products
shorten production lead times
increase resource utilization
improve quality, reduce WIP
prevent production disturbances (machine breakdowns)
More products in less time!
Less cost!
More profit!
Lower ecological impact!
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14. Production Scheduling in Manufacturing Planning Framework
Long range prediction and sales planning
Facility and resources planning
Demand management, aggregate and workforce planning
Order acceptance and resource loading
Shop floor scheduling, workforce scheduling
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15. Structure of APS
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16. Consumer goods planning
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17. Agenda Introduction ISA-95: current and future production management
Production Scheduling
ISA-95 and Scheduling: Are they a lovely couple?
ISA-95+Scheduling+SAP = ?
Optimal Schedule and Algorithms
The power of Algorithms on a Real Production Case
From Algorithms to Management View: KPI dashboard
Integration from Shopfloor to Topfloor
Conclusions
SAPience.be User Day 2012

18. Production Process Description Numerical Data Efficiency Criteria
ERP-system
Planner/
Decision Maker
Ok!
Production Schedule
Production Process Description
Numerical Data
Efficiency Criteria
𝐹=150
Information Transfer
Information Transfer
𝐹=180
Automatic
Parameter Adjustment
Optimization Algorithm
Good Schedule
Database
ISA-88 or ISA-95
Compliant and Complete
Compliant? Complete?
Constraints
Variable Values
Optimization Objective(s)
𝑃𝑟𝑜𝑓𝑖𝑡=𝐶𝑥→𝑚𝑎𝑥
𝐶𝑜𝑠𝑡=𝐷𝑥→𝑚𝑖𝑛
𝐸𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝐼𝑚𝑝𝑎𝑐𝑡=𝐸𝑥→𝑚𝑖𝑛
subject to
𝐴𝑥≤𝑏
Scheduler/
Decision Maker
Automatic
Information Transfer
Information Transfer
Information Transfer
Manual
Parameter Adjustment
Constraints Consistency Check
Inconsistent?
𝑖=0 𝑛 𝑎 𝑖 𝑥 𝑖 ≤𝑏→∅
22 March 2012
UCG - INXITES R&D

19. Agenda Introduction ISA-95: current and future production management
Production Scheduling
ISA-95 and Scheduling: Are they a lovely couple?
ISA-95+Scheduling+SAP = ?
Optimal Schedule and Algorithms
The power of Algorithms on a Real Production Case
From Algorithms to Management View: KPI dashboard
Integration from Shopfloor to Topfloor
Conclusions
SAPience.be User Day 2012

20. Perfect Plant implementation (B2MML to SAP at Polar © 2004 World Batch Forum)
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21. Mapping SAP PP-PI, ISA95 Production Schedule, ISA 88 and the Physical Model B2MML to SAP at Polar © 2004 World Batch Forum
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22. Simplified Schedule Request and Reponse Example
MES SAP ME SAP MII
SAP BC
SAP PP PI
B2MML Production Schedule XML (Request)
B2MML Production Schedule XML (Response)
The schedule can be refined and adapted in the MES execution part
Netweaver interface Web service
SAPience.be User Day 2012

23. Agenda Introduction ISA-95: current and future production management
Production Scheduling
ISA-95 and Scheduling: Are they a lovely couple?
ISA-95+Scheduling+SAP = ?
Optimal Schedule and Algorithms
The power of Algorithms on a Real Production Case
From Algorithms to Management View: KPI dashboard
Integration from Shopfloor to Topfloor
Conclusions
SAPience.be User Day 2012

24. Multiobjective Scheduling
𝑃𝑟𝑜𝑓𝑖𝑡=𝐶𝑥→𝑚𝑎𝑥
𝐶𝑜𝑠𝑡=𝐷𝑥→𝑚𝑖𝑛
𝐸𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝐼𝑚𝑝𝑎𝑐𝑡=𝐸𝑥→𝑚𝑖𝑛
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25. Multiobjective Scheduling subject to constraints
𝐴𝑥≤𝑏
Capacity
Processing
Times
Man Power
Idle Times
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26. Many models, suitable for specific production processes
Scheduling models
Many models, suitable for specific production processes
Continuous: Flow Shop Scheduling
Discrete: Open/Job Shop Scheduling
Batch: complex, several models depending on characteristics
Not for production scheduling: project scheduling, timetabling, ...
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27. Scheduling Algorithms
General Techniques:
Mathematical programming
linear, non-linear, (mixed) integer programming
Analytical (Exact) methods (enumeration)
branch-and-bound, branch-and-cut
dynamic programming
constraint satisfaction
Heuristics and meta-heuristics
genetic algorithm
tabu search
Artificial Intelligence
Reinforcement Learning
Hybrid Algorithms
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28. Scheduling Algorithms (cont.)
Decomposition Techniques
Temporal decomposition (rolling horizon approach)
Machine decomposition (Shifting Bottleneck)
Dantzig-Wolf
MILP-decomposition
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29. Scheduling Algorithms: Complexity
Analytical techniques
= algorithms that guarantee optimal solution
often infeasible
too many solutions (“NP-hard”)
mostly suitable for theoretical study of scheduling problems
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30. Scheduling Algorithms: Complexity
“Non-analytical” techniques
= no guaranteed optimum, but feasible in time
paradigms
heuristics: use expert knowledge (“rules of thumb”) to create good schedules
meta-heuristics: simulated annealing, tabu search, genetic algorithms (cfr. local search)
artificial intelligence: rule-based, agent-based, expert systems
hybrid: combination of paradigms
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31. Optimization Techniques: properties
Quality of Solutions Obtained (How Close to Optimal?)
Amount of CPU-Time Needed (Real-Time on a PC?)
Ease of Development and Implementation (How much time needed to code, test, adjust and modify)
Implementation costs
(Expensive third-party components required?)
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32. Software Solutions w.r.t. Optimization Techniques
Implementation costs
(Expensive LP-solvers required? Easy to implement?)
Required solution quality?
(Is an immediate answer required, or are long calculations allowed? Does customer accept complex solutions?)
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33. How to reduce # searches?
Dispatching
Rules
Value
Objective
Function
Local
Search
Beam Search
Branch and Bound
CPU - Time
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34. Decision Support Systems
Important issues in design of DSS:
Database design and management
Data collection (e.g. barcoding system)
Module Design and Interfacing
GUI Design (Gantt-charts, etc.)
Design of link between GUI and algorithm library (data organization before transfer)
Internal Re-optimization
External Re-optimization
SAPience.be User Day 2012

35. Agenda Introduction ISA-95: current and future production management
Production Scheduling
ISA-95 and Scheduling: Are they a lovely couple?
ISA-95+Scheduling+SAP = ?
Optimal Schedule and Algorithms
The power of Algorithms on a Real Production Case
From Algorithms to Management View: KPI dashboard
Integration from Shopfloor to Topfloor
Conclusions
SAPience.be User Day 2012

36. Real Case Chemical Batch Production Process
(D.Borodin, B. Van Vreckem, W. De Bruyn, MISTA 2011 Scheduling Conference Proceedings)
Big
Seed
Fermentation(2)
Main
Fermentation(5)
Buffer tanks (4)
Recovery (1)
Optimize the Production Process
Task
Minimize Total Tardiness (Customer Due-Dates)
Objective
Exact Optimal Solutions vs. Two Heuristic Methods
Solution Approach
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37. Real Case Results Comparison
Problem
Instance
Exact Solution
KL best
KL time
GA best
GA time
N10_1 90 91 7 93 5
N10_2 30 35 16 17
N10_3 42 56 10 44 6
N10_4 49 52 14 50
N10_5 43 48 45 12
N15_1 73 77 80 76 25
N15_2
112 34
N15_3 57 70 39 66 75
N20_1 54
490
180
N20_2 58
260 64
194
N30_1 _
1304
186
1560
SAPience.be User Day 2012

38. Agenda Introduction ISA-95: current and future production management
Production Scheduling
ISA-95 and Scheduling: Are they a lovely couple?
ISA-95+Scheduling+SAP = ?
Optimal Schedule and Algorithms
The power of Algorithms on a Real Production Case
From Algorithms to Management View: KPI dashboard
Integration from Shopfloor to Topfloor
Conclusions
SAPience.be User Day 2012

39. GUI’S should allow: Interactive Optimization
Freezing Jobs and Re-optimizing
Creating New Schedules by Combining Different Parts from Different Schedules
Cascading and Propagation Effects
After a Change or Mutation by the User, the system:
does Feasibility Analysis
takes care of Cascading and Propagation Effects,
does Internal Re-optimization
SAPience.be User Day 2012

40. GUI for Production Scheduling
Gantt Chart Interface
Dispatch List Interface
Time Buckets (resource capacity loading)
KPI dashboards
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41. GUI: KPI dashboard
Dashboard provides at-a-glance views of key performance indicators (KPIs) relevant to a particular objective, production or business process: capacities load, costs, profit, ecological impact, sales, marketing, human resources, etc.
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42. GUI Important Objectives = KPIs?
Yes and No!
Due Dates (KPI or objective?)
Late orders
Maximum lateness
Average lateness, tardiness, earliness-tardiness, makespan
Productivity and Inventory Related (KPI or objective?)
Total Setup Time
Total Machine Idle Time
Resource usage (KPI or objective?)
Resource Shortage
SAPience.be User Day 2012

43. KPI-driven Production
Overall KPI concept
KPI-driven Production
Operations Research Approach: KPI-driven factory = KPIs as objective functions
Overall KPI:
𝑂𝑣𝑒𝑟𝑎𝑙 𝑙 𝐾𝑃𝐼 =𝐶𝑥+𝐷𝑥−𝐸𝑥+…−𝑍𝑥→ min 𝑜𝑟 𝑡𝑎𝑟𝑔𝑒𝑡 𝑣𝑎𝑙𝑢𝑒 where 𝐶𝑥,𝐷𝑥,… are various KPIs, + 𝑎𝑛𝑑− mean maximization and minimization of a certain KPI
Goal: achieve production predictability, lean manufacturing
SAPience.be User Day 2012

44. Agenda Introduction ISA-95: current and future production management
Production Scheduling
ISA-95 and Scheduling: Are they a lovely couple?
ISA-95+Scheduling+SAP = ?
Optimal Schedule and Algorithms
The power of Algorithms on a Real Production Case
From Algorithms to Management View: KPI dashboard
Integration from Shopfloor to Topfloor
Conclusions
SAPience.be User Day 2012

45. Integration from shopfloor to topfloor
LIMS/ Inspection / Equipment Testing
MES
SCADA / HMI
Plant Data Collection
Wireless Integration
Plant Historian
Plant DB
DCS / PLC
MANUFACTURING
PLANT
Mgr.
SAP MII
Environmental Building Management
CORPORATE HQ
V.P.
Mfg
CUSTOMER
SAPience.be User Day 2012

46. SAP MII as KPI dashboard and and production schedule interface
SAP MII extracts data from SAP ERP and provides real-time visibility and distribution to Plant Floor Systems of:
SAP MII’s ability to perform transaction execution into SAP also enables automated, plant-level creation of:
ERP
SCM
PLM
Global Coordination
PLAN
MAKE
DELIVER
Production Confirmations
Process Messages
Material Receipts
Material Consumptions
Material transfers
Inspection results recording
Quality Notifications
Batch Characteristic recording
Work Orders & results recording
Maintenance Notifications
Planned Orders
Bills of Material
Production & Process Orders
Material Inventory Levels
Inspection Lots Data
Master Recipes
Material Details
Batch Details
Resources & Functional Locations
Maintenance Work Order & Notification details
Material & Order Costs
Manufacturing Integration & Intelligence
Composition Environment
Data Services
Visualization
Business Logic Services
Quality Engine
KPIs / Metrics / Alerts
DCS / PLC via OPC
Plant Historian
Simplified Execution
MAKE
SCADA / HMI
MES
LIMS/ Inspection / Equipment Testing
Environmental Building Management
Wireless Integration
SAPience.be User Day 2012
Plant DB
22 March 2012

47. Better Asset usage, right product at the right time, less inventory, less waste“
Reduce throughput times 30%
Reduce inventory 15 – 20%
14% less errors in production
Reduce data capture efforts 65%
Source: MESA International
CORPORATE HQ
VP Mfg
Quartile 1: 95% OEE vs. 78% Average*
MANUFACTURING
PLANT !
Plant Mgr.
Machine Uptime 99.5% Qtr 1
vs. 93.6% Average*
Qtr. 1: 98% First pass quality vs. 75% Avg.*
CUSTOMER
Qtr. 1: 98.5% On-Time Delivery vs. 89.1% Avg.*
* Benchmarks from ASUG Manufacturing Benchmarking Study
SAPience.be User Day 2012

48. Interaction between SAP APO and Workcenter schedule
Interactive Workcenter Schedule: Production Schedule updated every 30 minutes.
Double Clicking on a production order provide the confirmation screen to enter new production.
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49. Manufacturing Intelligence Manufacturing Integration
More control – more interactivity – More planner and operator responsibility
Alert !
Invoking of scheduling solution (SAP SCM APO).
Machine disruption is considered as machine-breakdown in scheduling board.
Finding an alternative capacity (manually or through rescheduling run).
SAP NETWEAVER
SAP Manufacturing
(mySAP ERP)
Dashboards für die intelligente Fertigung
Weitere SAP-Lösungen
SAP BI
SAP MII
Manufacturing Intelligence
Manufacturing Integration
Planner is able to respond to disruption in realtime and to resolve the conflict.
SAPience.be User Day 2012

50. Agenda Introduction ISA-95: current and future production management
Production Scheduling
ISA-95 and Scheduling: Are they a lovely couple?
ISA-95+Scheduling+SAP = ?
Optimal Schedule and Algorithms
The power of Algorithms on a Real Production Case
From Algorithms to Management View: KPI dashboard
Integration from Shopfloor to Topfloor
Conclusions
SAPience.be User Day 2012

51. Conclusions Respect the Environment = Optimize Ecological Footprint
Optimization of Production Scheduling (Algorithms) implemented in the Standardized Environment (ISA-95-compliant) and Incorporated in the Production Automation System (SAP) that allows visibility and transparency for all stakeholders involved in Production Process (KPI dashboard), will enable to:
Reduce Cost
Increase Profit
Respect the Environment = Optimize Ecological Footprint
Realise Lean Production: avoid waste and time-loss
SAPience.be User Day 2012

52. SAPience.be User Day 2012