your logo the control of today and prediction of the future using predictive production scheduling...

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

scheduling

Wim De Bruynlecturer ICT, R&D FBO, UCGproduct mgt. Inxites

Bert Van VreckemLecturer ICT, FBO, UCGResearcher Prinsyslab, UCG

SAPience.be User Day 2012

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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


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Introduction


Better Control of

Daily Reality

10% Reduction in Energy Billing

Full control of waste

treatment

Total Avoidance of

Ecological Disasters

Full Customer &

CitizenSatisfaction!

Supply Chain Production Process

Customer, Market and

Environment Demand

Visible, transparent

and fully controlled?

Why do we need it?

Predictive =What if we try .....

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SAPience.be User Day 2012 4

ISA-95 Functional enterprise control model

Your logoISA-95 Production Information Overlap

5SAPience.be User Day 2012

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ISA-95 Production Segment Capabilities

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ISA-95 Product Definition Model

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ISA-95 Process segment relationsConnecting a production request with a routing

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Current and future Production capabilities

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ISA-95 Production Schedule Model

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Short time Planning overview

• GANTT chart, connecting resources with production orders (same colour)

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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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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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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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Structure of APS

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Consumer goods planning


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Agenda



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Optim

ization

Objective(s)

Variable Values

Constraints

DatabaseISA-88 or ISA-95

Compliant and Complete

Production Process

Descriptio

n

Num

erical D

ata

Efficiency

Criteria

ERP-system

Production Schedule

Scheduler/Decision Maker

Constraints Consistency

Check

Compliant? Complete?

Inconsistent?

Automatic

Parameter Adjustment

Manual

Optimization Algorithm

𝐹=180

Good Schedule

Planner/Decision Maker𝐹=150

AutomaticParameter

Adjustment

Ok!

22 March 2012 UCG - INXITES R&D 18

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Agenda



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

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Mapping SAP PP-PI, ISA95 Production Schedule, ISA 88 and the Physical ModelB2MML to SAP at Polar © 2004 World Batch Forum

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Simplified Schedule Request and Reponse Example

B2MML Production Schedule XML (Request)

B2MML Production Schedule XML (Response)

SAP BC SAP PP PI

MESSAP MESAP MII

The schedule can be refinedand adapted in the MES execution part

Netweaver interfaceWeb service

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Agenda



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Multiobjective Scheduling

𝑃𝑟𝑜𝑓𝑖𝑡=𝐶𝑥→𝑚𝑎𝑥

𝐶𝑜𝑠𝑡=𝐷𝑥→𝑚𝑖𝑛

𝐸𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝐼𝑚𝑝𝑎𝑐𝑡=𝐸𝑥→𝑚𝑖𝑛

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Multiobjective Schedulingsubject to constraints

𝐴𝑥≤𝑏Capacity

Man Power

Processing Times

Idle Times

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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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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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Scheduling Algorithms (cont.)

• Decomposition Techniques

• Temporal decomposition (rolling horizon approach)

• Machine decomposition (Shifting Bottleneck)

• Dantzig-Wolf

• MILP-decomposition

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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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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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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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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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How to reduce # searches?

Local Search

ValueObjectiveFunction

DispatchingRules

Beam Search

Branch and Bound

CPU - Time

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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

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Agenda



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Real CaseChemical 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 ProcessTask

• Minimize Total Tardiness (Customer Due-Dates)Objective

• Exact Optimal Solutions vs. Two Heuristic MethodsSolution Approach

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Real CaseResults Comparison

ProblemInstance

Exact Solution

KLbest

KLtime

GAbest

GAtime

N10_1 90 91 7 93 5

N10_2 30 35 16 30 17

N10_3 42 56 10 44 6

N10_4 49 52 14 50 5

N10_5 43 48 6 45 12

N15_1 73 77 80 76 25

N15_2 43 45 112 45 34

N15_3 57 70 39 66 75

N20_1 52 54 490 54 180

N20_2 58 66 260 64 194

N30_1 _ 180 1304 186 1560

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Agenda



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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

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GUI for Production Scheduling

• Gantt Chart Interface• Dispatch List Interface• Time Buckets (resource capacity loading)• KPI dashboards

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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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GUIImportant 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

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Overall KPI concept

• KPI-driven Production• Operations Research Approach:

KPI-driven factory = KPIs as objective functions• Overall KPI:

where are various KPIs, mean maximization and minimization of a certain KPI• Goal: achieve production predictability, lean

manufacturing

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Agenda



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Integration from shopfloor to topfloor

LIMS/ Inspection /Equipment Testing

MESSCADA / HMI

Plant DataCollection

Wireless Integration

Plant Historian

Plant DB

DCS / PLC

Mgr.

Mgr.

Mgr.

SAP MII

EnvironmentalBuilding Management

V.P.Mfg

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SAP MII as KPI dashboard and and production schedule interface


Manufacturing Integration & Intelligence

Composition Environment

Data Services

Visualization

Business Logic Services

Quality Engine

KPIs / Metrics / Alerts

PLAN MAKE DELIVER

MAKE

Simplified Execution

Global Coordination

ERP SCM PLM

LIMS/ Inspection /Equipment Testing

MESSCADA / HMI

Wireless IntegrationEnvironmentalBuilding Management

Plant Historian

Plant DB

DCS / PLC via OPC

•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

SAP MII extracts data from SAP ERP and provides real-time visibility and distribution to Plant Floor Systems of:

•Production Confirmations•Process Messages•Material Receipts•Material Consumptions•Material transfers•Inspection results recording•Quality Notifications•Batch Characteristic recording•Work Orders & results recording•Maintenance Notifications

SAP MII’s ability to perform transaction execution into SAP also enables automated, plant-level creation of:

22 March 2012

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Better Asset usage, right product at the right time, less inventory, less waste

!

Plant Mgr.

VP Mfg

* Benchmarks from ASUG Manufacturing Benchmarking Study

Machine Uptime 99.5% Qtr 1 vs. 93.6% Average*

Qtr. 1: 98% First pass quality vs. 75% Avg.*

Reduce throughput times 30%

Reduce inventory 15 – 20%

14% less errors in production

Reduce data capture efforts 65%Source: MESA International

“

Quartile 1: 95% OEE vs. 78% Average*

Qtr. 1: 98.5% On-Time Delivery vs. 89.1% Avg.*

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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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More control – more interactivity – More planner and operator responsibility

SAP NETWEAVER

SAP MII

Manufacturing Integration

Manufacturing Intelligence

Wei

tere

SA

P-L

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ng

en

S

AP

BI

SAP Manufacturing (mySAP ERP)

Dashboards für die

intelligente Fertigung

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).

Planner is able to respond to disruption in realtime and to resolve the conflict.

Alert !

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Agenda



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Conclusions

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

Thank you!

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52SAPience.be User Day 2012

your logo the control of today and prediction of the future using predictive production scheduling...

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