The control of today and prediction of the future using predictive production scheduling

Transcription

1 With support of Standards Certification Education & Training Publishing Conferences & Exhibits The control of today and prediction of the future using predictive production scheduling B. Van Vreckem, D. Borodin, W. De Bruyn, G. Rijcken, R. Rimkute, B. Scheers, C. Willems, P. Wynants Automation to assist a low carbon economy Brussels 15 & 16 September2011

2 Thanks to scheduling research group University College Ghent CAPSHED project, FP7 research project, UCG ISA-95 ; ISA-88 standards Presentations for students, Bianca Scholten Polar Breweries presentation, Francesco Ferrero, Alejandro Imass, WBF presentation 2004 MES & Value Stream Mapping, discussion about ecostream mapping, Johannes Cottyn INXITES SAP APO, MII, PP/PI, SAP ME expertise SAP business cases Scheduling overview, Meyr ISA Europe for the opportunity to speak to this audience

3 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

4 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

5 Introduction

6 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

7 ISA-95 Functional enterprise control model

8 ISA-95 Production Information Overlap

9 ISA-95 Production Segment Capabilities

10 ISA-95 Product Definition Model

11 ISA-95 Process segment relations Connecting a production request with a routing

12 Current and future Production capabilities

13 ISA-95 Production Schedule Model

14 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

15 Short time Planning overview GANTT chart, connecting resources with production orders (same colour)

16 Production Scheduling is 1) telling a production facility when to make, with which staff, and on which equipment. 2) allocation of jobs to scarce resources 3) a combinatorial optimization problem maximize and/or minimize objective(s) subject to constraints

17 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!

18 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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21 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

22 Information Transfer Information Transfer Information Transfer Information Transfer Information Transfer Production Schedule Production Process Description Database ISA-88 or ISA-95 Compliant and Complete Constraints ERP-system Numerical Data Variable Values Efficiency Criteria Optimization Objective(s) Good Schedule Ok! F = 150 Automatic Parameter Adjustment Compliant? Complete? Scheduler/ Decision Maker Planner/ Decision Maker F = 180 Optimization Algorithm Automatic Manual Parameter Adjustment subject to Constraints Consistency Check n i=0 Inconsistent? a i x i b

23 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

24 Perfect Plant implementation (B2MML to SAP at Polar 2004 World Batch Forum)

25 Mapping SAP PP-PI, ISA95 Production Schedule, ISA 88 and the Physical Model B2MML to SAP at Polar 2004 World Batch Forum

26 Simplified Schedule Request and Reponse Example MES SAP ME SAP MII B2MML Production Schedule XML (Request) SAP BC SAP PP PI B2MML Production Schedule XML (Response) The schedule can be refined and adapted in the MES execution part Netweaver interface Web service

27 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

28 Multiobjective Scheduling Profit = Cx max Cost = Dx min Ecological Impact = Ex min

29 Multiobjective Scheduling subject to constraints Capacity Ax b Processing Times Man Power Idle Times

30 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,...

31 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

32 Scheduling Algorithms (cont.) Decomposition Techniques Temporal decomposition (rolling horizon approach) Machine decomposition (Shifting Bottleneck) Dantzig-Wolf MILP-decomposition

33 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

34 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

35 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?)

36 Software Solutions w.r.t. Optimization Techniques Implementation costs vs. (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?)

37 Value Objective Function Dispatching Rules Local Search Beam Search Branch and Bound CPU - Time

38 Commercial Packages ERP-SYSTEMS SAP, Baan, JD Edwards, People Soft, Navision, MFG Pro GENERAL OPTIMIZATION IBM CPLEX, Dash, MINTO, XPRESS-MP, OML, XA GENERAL SCHEDULING I2, Cybertec, AutoSimulation, IDS Professor Scheer, ORTEC SCHEDULING OIL AND PROCESS INDUSTRIES Haverly Systems, Chesapeake, Finity, ORTEC

39 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

40 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

41 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) Task Optimize the Production Process Objective Minimize Total Tardiness (Customer Due- Dates) 41 Solution Approach Exact Optimal Solutions vs. Two Heuristic Methods

42 Real Case Results Comparison Problem Instance Exact Solution KL best KL time GA best GA time N10_ N10_ N10_ N10_ N10_ N15_ N15_ N15_ N20_ N20_ N30_1 _

43 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

44 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

45 GUI for Production Scheduling Gantt Chart Interface Dispatch List Interface Time Buckets (resource capacity loading) KPI dashboards

46 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.

47 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

48 Overall KPI concept KPI-driven Production Operations Research Approach: KPI-driven factory = KPIs as objective functions Overall KPI: Overall 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

49 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

50 Integration from shopfloor to topfloor V.P. Mfg Mgr. Mgr. Mgr. DCS / PLC SAP MII Plant Historian MES LIMS/ Inspection / Equipment Testing Plant DB Environmental Building Management Plant Data Collection Wireless Integration SCADA / HMI

51 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: ERP SCM PLM Global Coordination SAP MII s ability to perform transaction execution into SAP also enables automated, plant-level creation of: PLAN MAKE DELIVER 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 Business Logic Services Visualization Composition Environment Quality Engine KPIs / Metrics / Alerts Data Services Simplified Execution Production Confirmations Process Messages Material Receipts Material Consumptions Material transfers Inspection results recording Quality Notifications Batch Characteristic recording Work Orders & results recording Maintenance Notifications DCS / PLC via OPC MAKE Plant Historian MES LIMS/ Inspection / Equipment Testing Plant DB Environmental Building Management Wireless Integration SCADA / HMI

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

53 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.

54 Weitere SAP-Lösungen SAP BI More control more interactivity More planner and operator responsibility SAP NETWEAVER Dashboards für die intelligente Fertigung Alert! SAP Manufacturing (mysap ERP) 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 MII Manufacturing Intelligence Manufacturing Integration Planner is able to respond to disruption in realtime and to resolve the conflict.

55 Presentation Agenda 1. Introduction 2. ISA-95: current and future production 3. Production Scheduling 4. ISA-95 and Scheduling: Are they a lovely couple? 5. ISA-95+Scheduling+SAP =? 6. Optimal Schedule and Algorithms 7. The power of Algorithms on a Real Production Case 8. From Algorithms to Management View: KPI dashboard 9. Integration from Shopfloor to Topfloor 10.Conclusions

56 Conclusions Optimization of Production Scheduling (Algorithms) implemented in the Standardized Environment (ISA- 95-compliant) and Incorporated in the Production Automation System (SAP) allowing clear visibility and transparency for all parties involved in Production Process (KPI dashboard) enable to: Reduce Cost Increase Profit Respect the Environment = Avoid Ecological Disasters Realise Lean Production: avoid waste and timeloss

57 Merci de votre attention! Thank you for your interest! Thank you for your attention! 57