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

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

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! SAPience.be User Day 2012

ISA-95 Functional enterprise control model SAPience.be User Day 2012

ISA-95 Production Information Overlap SAPience.be User Day 2012

ISA-95 Production Segment Capabilities SAPience.be User Day 2012

ISA-95 Product Definition Model SAPience.be User Day 2012

ISA-95 Process segment relations Connecting a production request with a routing SAPience.be User Day 2012

Current and future Production capabilities SAPience.be User Day 2012

ISA-95 Production Schedule Model SAPience.be User Day 2012

Short time Planning overview GANTT chart, connecting resources with production orders (same colour) SAPience.be User Day 2012

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 SAPience.be User Day 2012

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! SAPience.be User Day 2012

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 SAPience.be User Day 2012

Structure of APS SAPience.be User Day 2012

Consumer goods planning SAPience.be User Day 2012

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

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

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

Perfect Plant implementation (B2MML to SAP at Polar © 2004 World Batch Forum) SAPience.be User Day 2012

Mapping SAP PP-PI, ISA95 Production Schedule, ISA 88 and the Physical Model B2MML to SAP at Polar © 2004 World Batch Forum SAPience.be User Day 2012

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

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

Multiobjective Scheduling 𝑃𝑟𝑜𝑓𝑖𝑡=𝐶𝑥→𝑚𝑎𝑥 𝐶𝑜𝑠𝑡=𝐷𝑥→𝑚𝑖𝑛 𝐸𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝐼𝑚𝑝𝑎𝑐𝑡=𝐸𝑥→𝑚𝑖𝑛 SAPience.be User Day 2012

Multiobjective Scheduling subject to constraints 𝐴𝑥≤𝑏 Capacity Processing Times Man Power Idle Times SAPience.be User Day 2012

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, ... SAPience.be User Day 2012

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 SAPience.be User Day 2012

Scheduling Algorithms (cont.) Decomposition Techniques Temporal decomposition (rolling horizon approach) Machine decomposition (Shifting Bottleneck) Dantzig-Wolf MILP-decomposition SAPience.be User Day 2012

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 SAPience.be User Day 2012

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 SAPience.be User Day 2012

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?) SAPience.be User Day 2012

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?) SAPience.be User Day 2012

How to reduce # searches? Dispatching Rules Value Objective Function Local Search Beam Search Branch and Bound CPU - Time SAPience.be User Day 2012

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

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

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 SAPience.be User Day 2012

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

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

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

GUI for Production Scheduling Gantt Chart Interface Dispatch List Interface Time Buckets (resource capacity loading) KPI dashboards SAPience.be User Day 2012

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. SAPience.be User Day 2012

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

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

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

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

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

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

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. SAPience.be User Day 2012

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

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

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

SAPience.be User Day 2012