introduction to simio simulation-based...

10/13/2017 Copyright 2016 Simio LLC 1

Introduction to Simio

Simulation-based Scheduling


Agenda

�Simio Company Background

�What is Simio Risk-based Planning and Scheduling (RPS)?

�Simio Key Capabilities

�Scheduling Project Approaches

�Software Demonstration

�Simio Scheduling Workflows

�Case Studies

�Summary


1985 1996 2000 2005 2009 2010 2015 2017

C. Dennis Pegden, PhD.

Founder and Chief Executive Officer

1st

Generation

Simulation

invented

SLAM

3rd

Generation

Simulation

invented

ARENA(Market Leader)

2nd Generation

Simulation

invented

SIMAN/CINEMA

Develops

simulation-

based

scheduling

foundation for

Preactor 400

1st Scheduling

with

Simulation

invented

TEMPO

Rockwell

Automation

acquires

ARENA &

TEMPO

Dr. Pegden

founds Simio LLC

with the same

core engineering

team

4th Generation

simulation invented

(Patent for Flexible

Object Designing)

SIMIO

1st Scheduling

invented with

patented

simulation-based

risk analysis

(RPS)

Cloud-based

access for

simulation and

scheduling

execution

PORTAL EDITION

623

Universities

actively teach

simulation

worldwide

Simio Company Background

Systems Modeling Corp. Simio LLC


Simio Risk based Planning and Scheduling (RPS)

�Simulation• Visualize and understand the systems.

• Analyze system performance.

• Evaluate alternatives.

• Minimize risk of implementation.

�Planning and Scheduling• Deliver services on time and budget

in the presence of uncertainty.

• Add automated Risk Analysis to assess plan feasibility.

• Correctly allocate resources to improve daily operations.


Simio Key Capabilities

�Accurately captures Complexity

• Complex Material Handing and Secondary Resources� Cranes, robotic equipment, transporters, workers, etc.

• Specialized Operations / Resource Allocations� Changeovers, sequence dependent setups, etc.

• Experience-based Decision Logic and operating rules� Order priorities, work selection rules, buffering, etc.

1



�Effectively deals with Variability

• Breakdowns and Unplanned events

• Worker and Resource schedules

• Setup, Processing and Teardown times

• Material and Order arrivals

2



�Executes Concurrent planning and scheduling

• Simultaneous, in memory, solving for:

� Demand (sales order, stock orders, production orders, etc.)

� Capacity (workers, machines, transporters, cranes, etc.)

� Material (raw material, purchased material, intermediate material, etc.)

• The schedule generated through this process will fall within the

window of feasibility.

3

Demand Capacity

Material

Window of Feasibility



�Provides for Interoperability between systems• Manual data entry

• Excel and CSV table binding (Excel)

• Database table binding (SQL/Oracle, etc.)

• XML transformation (ERP/SAP/Oracle, etc.)

• Connecting to Wonderware MES using the standard API.

4

MES



�Facilitates Cloud based results distribution• Schedule Results

• Gantt charts

• Reports

• Dashboards

• Experimentation

5


Where does Simio RPS Fit?

�Typical Architecture

• ERP (SAP, Oracle, etc.)

• Master planning (SAP APO, etc.)

• Scheduling (Excel, Board, etc.)

• MES (Wonderware, etc.)

�Add Simio for Scheduling

ERP (SAP, Oracle, etc.)

Master Planning (APO, etc.)

Scheduling (Excel, etc.)

MES (Wonderware, etc.)

Simio RPS

Transactional

Operational

• Specified execution window

� Typical production campaign

� Multiple shifts

� Operational time fence


Scheduling Project Approaches

�A scheduling project can be approached in one of two basic ways (or hybrid) as follows:

• Model driven approach� Simulation design and/or optimization project as the initial

phase of the project

� New facility with no existing data (Greenfields project)

� Current facility but the MES that will contain the data is still in the implementation phase

� Current facility with no MES implemented and poor ERP/MRP data

• Data driven approach� Current facility with MES installed and operational (rich data)

� Current facility and Simio will integrate to the ERP/MRP system

� Current facility with all process, material and resource data in Excel data tables

� Complex systems with many process steps, parts, routings and final products


�To illustrate the process of building a simple scheduling model

using a “model driven approach” we are going to build the

process shown below in various Steps.

Simple Scheduling Model

Order

Arrivals

Cutting

Operation

Drilling

Operation

Painting

Operation

Shipping

Operation

Workers Forklift

Random

Distribution

Order Table


�To illustrate the process of building a data driven scheduling

model we are going to build the Order based process shown

below by using the Simio data add-in.

Data Driven Model

Filler 1

Tankfill 1

Packer 2

Mixing 2Order

Entry

Store Blue

Products

Workers

Mixing 1

Packer 1

Mixing 3

Store Red

Store Green

Filler 2Tankfill 2

Intermediate

Storage


Demonstration

�Let’s look at a more detail demonstration:


Simio Scheduling Workflow

�Manual Scheduling with MES integration

New Order DataProduction Schedule

Pro

du

ction

Master planning Operator View

MES

Operator

Screen

Ord

ers, W

IP, e

tc.

Sch

ed

ule

Pro

du

ctio

n D

ata

Order/Shop floor StatusActual Production Data

Production Floor

MES

Manual

Planning

Master Scheduler



�Automated Scheduling with MES integration (Solver mode)


Pro

du

ction


MES

Operator

Screen

Ord

ers, W

IP, e

tc.

Sch

ed

ule

Pro

du

ctio

n D

ata


Production Floor

Simio

Portal

View

Production Schedule

MES

Au

tom

ate

d



�Automated Scheduling with MES & Manual Intervention


Pro

du

ction


MES

Operator

Screen

Ord

ers, W

IP, e

tc.

Sch

ed

ule

Pro

du

ctio

n D

ata


Production Floor

MES

Manual Intervention

Au

tom

ate

d


�Global Manufacturer – saves $300 million per annum with vessel reduction

�Agricultural Equipment - $240,000 per annum less labor while trailer-to-door processing time drops 50%. Number of dock doors reduced with growth without more dock doors.

�Air Force – increased process output 30% that increased revenue $10.35 million

�John Deere - production scheduling tool was used to create a feasible schedule that stabilizes production operations from increased throughput

�Vancouver Airport – saved $100 million by avoiding unnecessary terminal expansion

�BAE Systems – helped meet production deadlines and decrease overtime

�Retirement Clearinghouse – shows Americans can add $115 billion to their savings with auto portability

�Cosan - predicted unnecessary sugar cane transport purchases that saved $500,000

�Virgin Airlines - showed that a new gate plan would decrease OTP by 50 points and drop them from 1st place market leader to 2nd

User Financial Impact Examples


Denmark Capitol Region

Challenges

Size of scope and complexity of the environment.

No compromise environment.

Sterile equipment availability.

Delivery performance to other hospitals.

Solution

Sequences that respect all process constraints.

Quick and efficient re-planning and scheduling.

Fully automated solution.

Awareness of all variability in the process.

Value

Meet committed performance targets.

Real-time integration to the execution systems.

Optimized resource schedules.

Forward visibility based on expected demand


Challenges

Ineffective planning in Excel.

Infeasible production schedules.

Schedules are disconnected from the MES and ERP.

Poor production performance and throughput.

Solution

Integrated scheduling and MES solution.



Schedule presented on the MES operator screens.

Value

Greater production stability.

Improved production throughput and efficiency .

Data integration and integrity.

Time feedback and re-planning.

John Deere

20


Shell Vessel Scheduling

21

Challenges

Size of scope and complexity of the environment.

Weather and delivery constraints.

Equipment availability.

Delivery performance to offshore facilities.

Solution



Discrete event simulation.

Awareness of all variability in the process.

Value

Measure the quality of the schedule.

Allow users to react to changes in the system.

Schedule visibility to all parties in the supply chain.

Visibility allows running tighter operation.

One tool for LT planning and ST scheduling


Steel Corp.

The Company: Produces build-to-specification rolled-

steel. Most important metrics: quality & on-time

delivery.

The Challenges: Current tools do not capture key

scheduling considerations.

� Worker schedules and qualifications

� Equipment availability

� Material allocation complexity

� Random aspects of the process

The Solution: A simulation model takes into account

all of the factory process complexity, production

order/material matching algorithms, and due date

considerations.

The Results: Additional market share expected with

existing capacity.


Problem Statement

� Ramping up production of the F-35 Joint Strike

Fighter from 1 aircraft per month to 1 per day.

� Response time and accuracy of decision support

techniques is insufficient to evaluate the outcomes

of complex and highly interconnected operations

environments.

Business Rationale

� Mitigates risk to production cost and schedule by

providing increased visibility of potential production

problems and the ability to judge the effectiveness

of possible solutions.

Expected Results

� Reduce lost days 50%

� Less workstation delays $3.67M/yr.

� Less rescheduling review $173K/yr.

� 5-Year NPV ROI 18.6 : 1

F35-Joint Strike Fighter

F-35 Joint Strike Fighter


Summary

�Provide both simulation and scheduling

�Unique capabilities• Captures and represents complex processes

• Manages process variability

• Concurrent planning creating feasible schedules

• Provides for an Integrated solution (WW MES)

�Creates unprecedented value

�Enables repeatability and corporate wide rollout

Благодарим за внимание!

www.wonderware.ru

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