Valmistusjärjestelmien mallinnus ja simulointi arvoverkoissa Juhani Heilala VTT Tuotteet ja tuotanto PL 1000, FI VTT, Finland

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1 Valmistusjärjestelmien mallinnus ja simulointi arvoverkoissa Juhani Heilala VTT Tuotteet ja tuotanto PL 1000, FI VTT, Finland Esitelmän aiheena on operatiivinen simulointi asiakasohjautuvan verkostoituneen tuotannon suunnittelussa ja hallinnassa. Esitelmän materiaali pohjautuu kirjoittajan omiin aikaisempiin kokemuksiin ja havaintoihin teollisuuden kanssa toteutetuista tavoitetutkimushankkeista sekä aihepiiriin tutkimuksiin muualla viimeisen kymmenen vuoden ajalta. Muutamalla esimerkillä havainnollistetaan aikaisempia kappaletavaratuotannon operatiivisen simuloinnin tutkimushankkeita (INDY, INDYNET ja INEL). Lisäksi esitellään juuri käynnistyneen MASI (Mallinnus ja simulointi) Tekes teknologiaohjelman hankkeen MS2Value tavoitteita. "Modeling and Simulation of Manufacturing Systems for Value Networks" - MS2Value Tuotantojärjestelmien simulointi on perinteisesti ollut työväline järjestelmäsuunnitteluun, keino varmistaa järjestelmän toimivuus. Tapahtumapohjaista simulointia voidaan hyödyntää myös tuotantoverkoston hallinnassa, tuotantokapasiteetin suunnittelussa ja asiakastilauksien ajoituksessa kuten esitelmän esimerkit osoittavat. Tekniikkaa ja menetelmiä tuotantotoiminnan operatiiviseen simulointiin on olemassa, mutta todelliset laajat toteutukset ovat edelleen harvinaisia. Ohjelmistoa ei vielä voi ostaa kaupan hyllyltä ja sovellutuskohtaisen räätälöinnin osuus on merkittävä kustannus. Tietojärjestelmien integroinnin haasteet, mukautuvien simulointisovellutuksien ja helppokäyttöisen käyttöliittymän toteuttamisen hankaluus on vaikeuttanut teknologian leviämistä. Kirjoittajan mielessä kuvatun teknologian hyödyntäminen tulee leviämään. Sillä myös EU on linjannut toimitusketjun hallinnan sekä mallinnuksen ja simuloinnin teknologiat eräiksi keinoiksi säilyttää eurooppalaisen valmistavan teollisuuden kilpailukyky. Tämä kävi ilmi IMS Conference on Advanced Manufacturing (Leuven November 2005) pidetyissä esitelmissä sekä "ICT in manufacturing" teeman raporteissa (www.ims-noe.org). Julkiset referenssi projektit, suluissa mainittu TEKES teknologiaohjelma - INDY , Integroitu dynaaminen verkostosimulointimalli operatiivisen toiminnan hallintaan (MALLITEHDAS KONSEPTI) - INDYNET , Integroidun dynaamisen asiakasohjautuvan tuotantoverkoston hallinta operatiivisen simuloinnin avulla (RASKO) - INEL 1 & 2, , Integroitu dynaaminen elektroniikkatuotannon ja alihankinnan ohjaus ja resurssien suunnittelu (ETX, Kyvykäs tuotanto)

2 Valmistusjärjestelmien mallinnus ja simulointi arvoverkoissa "Modeling and Simulation Aided Decision Support for Customer Driven Manufacturing Networks" Juhani Heilala, Jari Montonen, Tapio Salonen, Otso Väätäinen, Kaj Helin, Paavo Voho VTT Industrial Systems Past industry driven R&D and the future Outline VTT Industrial Systems - briefly Dynamic market - manufacturing challenges Manufacturing Enterprise M & S Simulation vs other methods VTT past R&D with industry VTT system concept INDY-INDYNET-INEL Future - MS2Value project Questions Juhani Heilala, VTT 1

3 VTT INDUSTRIAL SYSTEMS STRATEGIC FOCUS AREAS Risk Management and Systems Safety Future Machines and Transport Media Factory of the Future Structural Integrity Managing the value chain of products, production systems, and services Human - Technology Interaction Intelligent Systems and Services Industrial Management Best Before End of Year 2005 Best Before End of Year 2005 Research group 'Production Development' Product and production processes Assembly technology Environmental effects Process and innovation management Digital manufacturing Dynamic production management Step 1 Simulation Game I Idea Generation and Simulation Game II Implementation Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Step 8 Step 9 Step 10 Step 11 Step 12 Step 13 Step 14 F F F F F Juhani Heilala, VTT 2

4 Orderbook Trend -dynamic world market Chinamation! Customer driven manufacturing - customisation Global competition of supply chains Shrinking delivery times and orderbook, fluctuations Delivery time Market force! Faster reaction is needed - traditional production planning methods are not adequate (ERP, MRP) Availability to promise & Late order freeze = Horisontal and vertical visibility needed Business Process SCM, PLM,. Production Planning Management Manufacturing Process Control Monitoring Diagnostic Manufacturing Procesess Machine, Factory, Supply Chain Juhani Heilala, VTT 3

5 Customer Driven Production Order entry point Assembly or Build to Order : ATO Make or Product to Order : MTO Engineer to Order : ETO ACCURATE DELIVERY IS A MUST Every new product can be different and new order disturbs the current balance in production Production capacity management is the key factor: resources, materials, order scheduling Production networks makes it more complicated Production balancing New orders LOAD vs. CAPACITY HOW TO DO IT? Late orders Delivery time Capacity New scheduling of old orders Customer orders Limited Resources Subcontracting overtime/shifts Adapt production to the changing situations: management and synchronisation, orchestration minimise waiting time and improve delivery accuracy Juhani Heilala, VTT 4

6 Get it right, information, material, resources Material Flow The right amount of the right material to the right place at the right time under the right condition Information flow The right information in the right format at the right time to the right people at the right place Resource The right manufacturing resource, right fixtures & jigs (configuration), right programs or process, right tools, => Right Cost, Quality, Time Manufacturing Enterprise Modeling & Simulation Integrated Manufacturing Technology Roadmapping Initiative, IMTR, USA Juhani Heilala, VTT 5

7 Classification of models Static Models Deterministic Models Spreadsheet models, analytic calculation Stochastic Models Analytic queuing models Note also: V.Hyvönen, EP-Logistics, presentation in the morning session Dynamic Models Deterministic simulation models Stochastic simulation models Many planning concepts are based on static, average and historical data. Such planning methods are typically used for: Optimization of facility locations Cost analysis Forecasting and capacity planning Simulation vs Excel Spreadsheet and analytical solutions: Cannot handle dynamic behavior Are relatively customized solutions and can thus contain serious errors Can be maintained only by the person who originally created them May appear at the end to be very complex and contain serious simplifications Have limited scope due to the inability to model realistic complexities In Manufacturing Enterprise domain Juhani Heilala, VTT 6

8 Optimization vs Simulation Optimization produces only one solution and it can do so, if: There is limited numbers of variables over which an optimization can be done Linear or integer variables, normally All other output variables are fixed (like assets) The problem is not too complex Optimization tends to be applied to solve tactical/operational issues when simulation can resolve more complex and realistic supply chain issues. (Multiobjective optimization needed) Hybrid methods OPT + SIM Ref. Simflex 2002 Note also: V.Hyvönen, EP-Logistics, presentation in the morning session Benefits of simulation The benefits are numerous and generally include: Longer supply chain scope & stronger analysis False input becomes obvious during simulations whereas in optimization, it remains hidden in the optimization algorithms Simulation can handle: Conditional inputs (exceptions like stock-outs, shortages, etc.) Real-world realities: like departure frequencies, inventory policies, variation of transport times Logistics control policies: like inventory, consolidation and production policies Wide number of true variables Time related issues: like delivery times distribution and cash flows Ref. Simflex 2002 Note also: V.Hyvönen, EP-Logistics, presentation in the morning session Juhani Heilala, VTT 7

9 Operational use SIMULATION BASED PRODUCTION PLANNING Future "to plan" Tactical Operational Present "to do" Strategical Years Quarters Months Weeks Days/Shifts Minutes/Seconds Strategic Business Evaluation Create Order plan Order Acknowledgement Scheduling and Control Order Book Construction Order Negotiation Process Control Order Entry INDY Machine Cell/Line Factory Enterprise Virtual Enterprise ON-LINE OFF-LINE Off-line Modelling, Simulation, Strategical Planning and Tactical Control Reuse of Models On-line Simulation, Planning, Scheduling, Exception Handling, Forecasting, Monitoring and Control VTT Manufacturing Technology Juhani Heilala Multiple users in manufacturing Different time frames, strategic, tactical and operative, near real-time, on-line / off-line, Organizational and operational level Manufacturing Cell/Line: Production Planners Business Unit/Plant: Plant Manager Customer Service Enterprise: Upper Management Extended Enterprise Suppliers and Partners Time frame Minutes/Hours/ Shifts Shifts/Days/ Weeks Days/Weeks/ Months Days/Weeks/ Months Decision orders to each line/machine, scheduling of orders order list: workload Scheduling of orderbook and forecasts Material purchase Make or buy, production mix, budget and strategic planning Production planning Many decision hierarchy levels - different aims - needs - users Juhani Heilala, VTT 8

10 PAST Quick response planning needed???? FUTURE Multiple sources of information, use of own intelligence and judgement Decision support system, single source of information, visualisation and modelling of scenarios,... Decision Support Systems - DSS Interactive computer-based systems intended to help decision makers use data and models to identify and solve problems and make decisions: - Communications-Driven, - Data-Driven, - Document-Driven, - Knowledge-Driven and - Model-Driven DSS. Disctete Event Simulation (DES) + Real Production Data, Orders + Factory, Resource Model + Workflow Process Model + Scheduling Rules + Interactive Graphical User Interface = VTT IN(DYNET)EL Juhani Heilala, VTT 9

11 SCENARIO TRADE- OFF ANALYSIS WITH ERP REAL DATA Resource Review - engineering, - manufacturing, - maintenance, Supplier network Production data navigation - Load (suppliers), delivery times - weak capacity link there Production Review Customer orders, forecasts, VTT Decision support for production planning DATA MINING Material Review Budget Review - strategic, tactical aims - make or buy, - investment planning Planning and management of customer driven production capacity Proactive planning, less reactive corrections in present time Input data need spex Order Engineering Production scheduling chain Component need for engineering Component delivery Manufacturing need for Engineering Module production Component need Module need Assembly Subcontracting or own manufacturing Purchasing of materials, components and subcontracted modules Potential use of the planning tool, scheduling of operations, Test run Finishing Packing & delivery Juhani Heilala, VTT 10

12 Ref. Projects at VTT Dynamic production capacity management in customer driven manufacturing. Funded by TEKES, industry and VTT Period , several national projects (INDY, INDYNET, INEL) Content: Concept and tools for production operations planning, decision support and scenario evaluation in customer driven manufacturing, integration to legacy systems (ERP, MRP) Contribution: New methods for manufacturing operation planning, graphical user interface for production data and parametric simulation modeling methodology Case studies: heavy machinery, component or subsystems manufacturing for building industry, components manufacturing for process industry, electronic industry And some confidential contract work to industry BUT STILL MISSING LINKS FOR VALUE NETWORK PAST R&D VTT System concept Planning with VTT-system interface ERP/MRP Graphical user interface Visual Basic, Excel Production simulation model Automod 2 runtime VTT GeSIM new solution interactive simulation in user interface DB Parameterfiles Materials, components Subcontracting DB Transfer files (ASCII) load, orders, ordebook resources, calendar product structure, BoM routing and process times materials reservations, forecasts and quotations Order engineering Own Production Juhani Heilala, VTT 11

13 ERP/MRP Transfer file ASCII TEXT Other Input files READ THE INPUT FILES TO DATABASE work orders, product structure and routing, workcenters, factories and factory calendar, optionally quotations (even individual project or product) CALCULATION AND SIMULATION (optionally with animation) ANALYSIS OF SIMULATION RESULTS Bar charts: analysis of resources (utilisation, overload,...) Gantt charts: analysis of order batch/resources (batch or component scheduling, late/early deliveries ) Parameters Reports warnings CHANGE batch scheduling or batch size resource capacity etc. NO USER ACCEPTS RESULTS? Use the results and update changes to the ERP if needed YES INDY NET All orders Example of User Interface Tool bar and drop down menus Material profile Resource load % Late orders or overload in resources? Work in the selected resource Selected order work phases Juhani Heilala, VTT 12

14 PAST R&D Sales Companies and Offices DB Order negotiations DB DB INDY - Integrated dynamic simulation model of enterprise for planning of operations Research partners DB DB intrane t DB Order and order confirmation Customer Capacity management and simulation Customer Service Center Delivery Factory simulation Order book DB Visualisation of order and factory status Order rescheduling Management and planning of operations Delivery center Orders Make-to-order Component production Order engineering and customisation Component factories Delivery VTT Manufacturing Technology J.Heilala, J.Montonen, T.Salonen, M.Hentula, Has been updated,. Juhani Heilala, VTT 13

15 Summary of the past applications Indepenent add-on to ERP for scenario planning with real production data and customer orders - but not crossing enterprise boarders Capacity and load management of production lines or network, order scheduling, planning and control data analysis and visualization (bar and gantt charts). Flexible, parametric simulation modelling: End user can modify simulation model by changing parametric and input files, (some constrains). Cost effective solution - PC/ Windows platform Each application need engineering effort - customisation Not yet copy and paste software PAST R&D Summary of current features - Independent system, analysis is done off-line ERP/MRP database, scenario comparison - evaluation of alternatives, proactive planning - Batch process, no real time integration to ERP or MRP system or factory floor status - Traditional information transfer, ASCII-text, or SQL - Visualization of order book, resource status and materials - window to future events with given input - Generic, parametric factory model (some limitations) - Deterministic simulation/calculation model for speed - Decisions are made by the user - Sensitive to quality of data, garbage in garbage out Juhani Heilala, VTT 14

16 Potential future development As presented 2001 New applications, new features and functions, engineering project management tool, NPI phase planning Optimisation techniques; Rule based, Heuristics, GA, LP, Forecasting, Communication: SQL server, Agents, XML,... Own simulation/calculation engine; GeSIM From intranet to extranet and maybe to internet, Web browser interface Distributed parallel simulation; production network model in different computers Factory/shop floor status, MES integration OTHER MISSING LINKS MS2Value TEKES MASI Technology Program Modeling and Simulation of Manufacturing Systems for Value Networks Tuokko, Velez, Lanz, Lybeck, Voho, Heilala, Soini, Asmala Tampere University of Technology Visual Components Oy Digital Enterprise Simulation Framework VTT Industrial Systems Satakunta Polytechnic Mallinnuksen ja Simuloinnin Infrastruktuuri Juhani Heilala, VTT 15

17 Modeling and Simulation of Manufacturing Systems for Value Networks MS2Value The main goal of this project is to develop a generic modeling and simulation framework that would enable the modeling and analysis of manufacturing operations in value networks. The framework would include tools for optimization and analysis on different levels of abstraction, as well as filtering of information from one tier to the next, going from machine level to supply chain. Modeling and Simulation of Manufacturing Systems for Value Networks MS2Value Main Topics Multi-scale and distributed modeling and simulation framework Model life-cycle and time dependency Generic models for knowledge sharing Advanced modeling and simulation of manufacturing systems A holistic approach Model Validation on different levels of abstraction The role of manufacturing strategy and operations in the frame of value networks Theory of value networks and technology enablers Juhani Heilala, VTT 16

18 General Project Structure Responsible: TUT Partners: VTT, VC Responsible: VTT Partners: TUT, VC WP2. Modeling and Simulation Methods and Tools Specific modeling and Simulation methods and tools: - Advanced Cost Modeling - Supply Chain Optimization - Multi-scale/hierarchical Modeling - Distributed Modeling and Simulation Responsible: VC Partners: VTT, TUT, SAMK WP4. Modeling and Simulation Infrastructure Development/Enhancement of Infrastructure for modeling, simulation and visualization -Digital Plant Validator - Distributed Modeling and Simulation - Digital Enterprise Systems Connectivity WP1. MS2Value Modeling Framework Define and develop the generic modeling Framework Responsible: TUT Partners: Companies WP3. Value Networks Responsible: VTT Partners: TUT, VC, SAMK, Companies WP5. Case Studies and Pilot Implementations Value Networks from Finnish companies perspective Analysis of specific problems and implementation the methods developed in the project to solve problems in the end user s cases Finnish Global Network Value Roadmap Complete industrial partner use cases Disseminations Initial partner use cases Finalizing the simulation framework and M&S Methods Developing simulation framework, methods and applications Definition Use cases Development Results Disseminations Implementations specification Pilot project definitions Industrial use cases Initial results Juhani Heilala, VTT 17

19 Smart Factory Simulator in Value Network - information visibility, data transparency, decision support, modeling and simulation Source: J.Heilala, VTT Product, Process, Enterprize Use of Manufacturing Simulation and Analysis Tools DOMAIN Enterprize: Plant: Line: System Workcells: Individual operations: Product concept TARGET Supply Chain Mangement Scheduling Logistics Flow Analysis and Optimization Throughput, Capacity WIP Costs Flow Analysis and Optimization Feasibility Detailed Process Planning Cell Layout Optimization Ergonomics Cycle Time, assembly sequence Yield Prediction Product and Process analysis Tool and jig design Beginning-of-Life Middle-of-Life End-of-Life Source: J.Heilala, VTT. 1999/2000, modified 2005 Multi-scale/ Hierarchical Modeling Machine focus Factory focus Supply Chain Focus Life Cycle Focus Note also time domain Juhani Heilala, VTT 18

20 $ $ $ Distributed Modeling and Simulation Management International Marketing From shop floor to top floor Warehouse Reception Customer Service International Sales Inventory Manufacturing Packaging Packaging Inventory PIP Purchasing Manufacturing Packaging Warehouse Manufacturing Source: J.Heilala, VTT Quality Assurance Manufacturing Warehouse PIP, Partner Interface Processes, ref Internal manufacturing processes; modeling of information and material flows SCOR, Model is organized around the five primary management processes of PLAN, SOURCE, MAKE, DELIVER, and RETURN Juhani Heilala, VTT 19

21 THANK YOU ANY QUESTIONS? Needs and visions for future R & D? Customer driven manufacturing It is the customer who determines what a business is. Drucker, P.F. (1954), The Practice of Management, New York: Harper, Juhani Heilala, VTT 20

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