WITNESS Simulation and its role in Decision Support

EWG-DSS Liverpool 2012

12th April 2012

Tony Waller

Customer Development Manager

Lanner Group

Agenda

• Introduction – Lanner/WITNESS

• Why Simulate?

• Example models from around the world

• 2 in-depth projects

• 2 examples of current University research

• Optimization & Virtual Reality videos

Lanner Portfolio

Simulation

Driven Solutions

Tools for

Simulation Professionals

Embedded Simulation

Components

Provide apps that provide answers

Provide tools that provide

answers

Provide ability to provide answers

Desktop / Server / Web / Cloud

Introduction to Simulation

• A simulation model is a dynamic representation of some part of the real world sufficient to ensure that experiments using this model are adequately accurate predictors of reality.

Typical questions today…

• How do I deliver MORE with LESS?

• What impact will changes have on delivery & cost?

• What investments should we prioritise?

• Is this capital project justifiable?

• Will this change really work?

• What is the best resource balance?

• How to get buy-in to changes?

• What is the right level of inventory to hold?

• How to reduce risks of making key decisions?

Many are not easy to answer!

Advantages over Spreadsheet Analysis

• Greater accuracy of analysis

• Includes Variation in calculations where appropriate – E.g. Distributional timings

– E.g. Effect of work interruptions or machinery breakdowns

• Includes full complexity of real life operations where pure calculation difficult – or impossible!!

– Real world rules plugged in

– Real world layouts defined

– Real world control exerted

• Accurate resource modeling over time – limiting across peaks and troughs – full delay analysis

Reduce Operating Costs

Reduce Work in Progress

Improve Efficiency Reduce Lead-Time

Capital Justification /Avoidance

Quantitative Benefits

Increase Capacity

Enables Evaluation of How to:

Reduced Risk

Ask The Right Questions

Improved Confidence & Buy In

Cause & Effects Clear Team

Communications & Understanding

Assumptions Tested

Supports Decision Making &

Action Taking

Qualitative Benefits

Models from around the World

Airports – Air France

• Large model of baggage handling at CDG Paris

• Throughput achieved at the right level of resources and costs and the right layout design

• Small detail from overall model

Airports – British Airways

• Model Used for

– Clarification of Infrastructure Decisions – e.g. number of bag drop stations – must be able to cope with busy days – not just average

– Staffing Levels on all different types of day

– The identification of the queuing area required at each point

– A video of the model used for staff training e.g. the positive effects that “hosting” has on queue lengths

• Full Story in Journal of Simulation Volume 5 Number 2

Automotive - Chery

• Model created looks at the logistics from a preparation centre outside the factory to the assembly line.

Objectives

1. Establishing the buffer size at each dock

2. To establish the number of forklifts, labor and tuggers needed

3. To establish the best routing for the tugger (out of a small number of possible designs)

4. To look at the traffic load on the road (inner work shop) and each cross point

5. To find out the minimum inventory level for line station and also the preparation buffer

• New Nissan Battery Plant in UK

• Contractor Daifuku

• BOTH Nissan and Daifuku modelled the facility in WITNESS

• Large Model – many options

• Savings in equipment and operating costs of 2.5 million Euros

Automotive - Nissan

• The key question was whether to follow a proven existing design from Japan

• Nissan UK thought that a new design would use less area, less resources and require less capital investment – but needed proving to be accepted

• Further issue – key supplier proposing new delivery system – would it work.

Automotive - Nissan

• Nissan Bush Press Robot Cell Model

• Four types of part each with different build sequence and robot process durations

• WITNESS Optimizer used to optimize sequence of parts to minimize TACT time (cell cycle time)

• 3% better answer than previous best calculated solution

• Model constantly being re-used as designs change and the process evolves

Automotive - Nissan

Automotive – Additional Examples

• Ruhrbotics

– Robot Lines

– WITNESS used to optimize cycle times to maximize output

• Nemak

– Engine Production

– Mixed Fluid flow and discrete WITNESS elements used

Supply Chain – Coca-Cola

• Coca-Cola

– At start of 2010 Wakefield Factory in UK produced 30,000 bottles per hour#

– Planned increase by 20 to 30 percent in 2010

– Northampton distribution centre trailer loads to increase from 160 to 225 per day. Could the gatehouse / trailer parking / loading bays cope?

– WITNESS model showed second gatehouse lane needed – built and implemented by May 2010

– Picture shows double lane with zoom of single lane option

Supply Chain – Carrefour

• To Understand limitations of the system & Exploration of the options when critical levels reached,

• Sensitivity of parameters – e.g. schedules of lorry arrivals and departures, flow allocation,…

• Anticipation of problems in high-risk areas,

• Validation of stock capacity, especially for the main automated warehouse

Two Example Projects in a little more detail

• HRS – Liverpool John Lennon Airport

• Apache Corporation – Kitimat LNG Terminal, Canada

The value of a second the increasingly important role of Process

Simulation in Airport Planning by Simon Appleton 13.10.10

The Challenge at LJLA

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445464748

PAX processed per hour

0 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 1 0 11 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3

Q Times = £

The solution

• Liverpool John Lennon Airport (LJLA) invested £12M in new airport

operations

• Significant changes to airport layout

• Significant investment in new technology (X-Rays, WTMDs etc)

Operational Changes

New dedicated

Central Search

facility

Required Understanding

• How much space is required?

• How much equipment (number of lanes) is required?

• How efficient will the Search processes need to be?

• How long people will be waiting (standard/priority)?

• Impact of Staff using same channels?

• Impact on Retail spend?

• What if we introduced automated Ticket Presentation?

The Inputs

• Number of passengers – actual flight schedules & load factors

• Future uplifts in passenger numbers (ensuring future proofing of any

recommended configurations) – 7.5% annual growth forecast

• Check-in type ratios and earliness profile for each

• Number of search lanes open (Central Search & Ticket Presentation)

• Length of time taken to search passengers

• Baggage injection rates – KEY INPUT

• Ratio of passengers with 1 or 2 trays.

Ticket Presentation – 5 Barriers

Central Search – 6+1 Lanes

The value of a second – why it’s important

Driven by Baggage Injection Rate – time (secs) between bags entering

the X-Ray tunnel.

We know how extra infrastructure will affect the airport’s performance

but what about improvements in passenger throughput – delivered by

better training and/or procedural streamlining?

Assume ‘best in class’ and work backwards

Average Airside Dwell Time (Min.s)

Average Airside Dwell Time(Min.s)

Average Injection Rate (Seconds)

Best-in-Class + 3

Best-in-Class + 2

Best-in-Class + 1

Best-in-Class

Finding the value of a second

How does a 1 second improvement in Baggage Injection Rate improve

Retail Dwell Time?

Second less at X-Ray Seconds more in retail

22s 1s

What does this mean to the balance sheet?

It doesn’t sound like much, but…

Second less at X-Ray Increased Retail Revenue

£220k

Key Findings

• Best in class operation (15 seconds) will achieve queue times of 12

minutes

• Search performance will have to be best in class to run area in 2015

• ROI for 7th search lane within 6 months (Annual spend up by £730,000)

• Financial incentive to remove staff is £330,000 p.a.

• Financial impact of WBI = £700,000 p.a. in lost retail spend

• Priority lane provided no benefit at Ticket Presentation

The future – MFlow Forecast

• Generic airport model, built on the WITNESS PWE & WITNESS Server

platform

• Airports will be able to run their own data (flight schedules etc) through a

website and receive forecast reports via email

Effective Modelling of LNG Operations for Kitimat Terminal

Presented at SW12 – Redditch – March 28th 2012

Apache Corporation

Gretchen Fix – Supervisor, Risk Assessment and Special Projects Shahzaad Mohammed – Manager, LNG Storage Loading and Marine Facilities

Lanner Group

Tony Waller – Customer Development Manager Craig Clee – Senior Consultant

Forward-Looking Statements

Certain statements in this presentation contain "forward-looking statements" within the meaning of the "safe harbor" provisions of the Private Securities Litigation Reform Act of 1995 including, without limitation, expectations, beliefs, plans and objectives regarding production and exploration activities. Any matters that are not historical facts are forward-looking and, accordingly, involve estimates, assumptions, risks and uncertainties, including, without limitation, risks, uncertainties and other factors discussed in our most recently filed Annual Report on Form 10-K, recent Quarterly Reports on Form 10-Q, recent Current Reports on Form 8-K available on our website, http://www.apachecorp.com/, and in our other public filings and press releases. These forward-looking statements are based on Apache Corporation’s (Apache) current expectations, estimates and projections about the company, its industry, its management’s beliefs and certain assumptions made by management. No assurance can be given that such expectations, estimates or projections will prove to have been correct. Whenever possible, these “forward-looking statements” are identified by words such as “expects,” “believes,” “anticipates” and similar phrases.

Because such statements involve risks and uncertainties, Apache’s actual results and performance may differ materially from the results expressed or implied by such forward-looking statements. Given these risks and uncertainties, you are cautioned not to place undue reliance on such forward-looking statements. We assume no duty to update these statements as of any future date. However, you should review carefully reports and documents that Apache files periodically with the Securities and Exchange Commission.

Agenda

• Introduction

• Background to the Project

• Modeling Detail including Key Factors influencing Success

• Results and Conclusions

Introduction

Introduction • Apache and its partners, EOG and Encana, plan to build the Kitimat LNG

export facility in Bish Cove near the Port of Kitimat, 643 kilometers north of Vancouver in British Columbia

• The modeling described in this presentation is part of the Front-End Engineering and Design study which is currently taking place

• Main construction is expected to commence in 2012

• The simulation study was planned and carried out by Apache Corporation with assistance in model design and build from the Lanner Group, experts in LNG modeling with the WITNESS simulation package that they develop and market world-wide.

Project Background

• Kitimat in British Columbia in Canada offers a natural deepwater, ice-free harbor with no dredging or breakwater required

• It was originally conceived as a facility to import LNG, but plans changed due to changes in shale technologies opening up new basins

• These discoveries opened up the opportunity for a profitable export business to be established

Project Background

• The Kitimat LNG facility location is ideally located due to its close proximity to the Asian market.

• Measures to minimize environmental impacts include:

• Small facility footprint to minimize land use

• No harbor dredging minimizes impact on marine environment

Project Background

MILESTONES

• December 2008 – Canadian federal environmental assessment approval

• January 2009 – Canadian provincial environmental assessment approval

• January 2010 – KM LNG through its managing partner Apache Canada Ltd. purchased 51 per cent of the project and becomes operator

• December 2010 – EOG Resources Canada Inc. (EOG) closes agreement on purchase of 49 per cent of Kitimat LNG project

• December 2010 – FEED commences

• March 2011 – Kitimat LNG awards Front End Engineering and Design (FEED) contract to KBR

• March 2011 –Kitimat LNG partners acquire Pacific Trail Pipelines

• March 2011 – Kitimat LNG partners Apache Corporation and EOG Resources Inc. announce that Encana Corporation has agreed to acquire a 30-per cent, working-interest ownership

• July 2011 – Kitimat LNG purchases Eurocan industrial site

• October 2011 – Canada’s National Energy Board grants Kitimat LNG a 20 year Export Licence to serve international markets.

LNG business simulation applications

• Many simulation models useful across the supply chain and through the project life-cycle

– Pre – FEED study

– FEED study validation

– Terminal Use and shipping studies

– Pipeline & tank farm studies

– ADP calculation & testing

– SPA studies

– Project expansions

Library based on work for many clients

Inputs Process Outputs - WITNESS®

Solution Architecture: LNG business simulation

Production

Facilities

Liquefaction &

Loading

Fleet & Voyage

Receiving &

Regasification

Send Out

High Tide

Low Tide

Required height is

linearly interpolated

< Berthing Journey

= OK to Journey

Loading Receiving

From Simple To Complex

Customer1 Customer2

Pipeline1

Pipeline2

Customer3

Customer4 Customer5

Customer6 Customer7

Detailed Reports

Summary Reports

Utilisation

Trade-off analysis

Cause & Effect

Kitimat Model Scope

Inlet Pipeline

LNG Train

Fixed CapacityIncreased when 2nd Train comes online

1 in Phase 12 in Phase 2

1 initially with a 2nd phased in

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Voyage through Channel

Pilot Station

Not modelled in any detail.

Connect/Load/Disconnect

Inlet Pipeline

LNG Train

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Pilot Station

Inlet Pipeline

LNG Train

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Pilot Station

Inlet Pipeline

LNG Train

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Pilot Station

Inlet Pipeline

LNG Train

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Pilot Station

Inlet Pipeline

LNG Train

Fixed CapacityIncreased when 2

Train comes online

1 initially with a 2nd

phased in

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Pilot Station

Inlet Pipeline

LNG Train

Train comes online

phased in

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Pilot Station

Inlet Pipeline

LNG Train

Train comes online

phased in

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Pilot Station

Inlet Pipeline

LNG Train

Train comes online

phased in

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Pilot Station

Inlet Pipeline

LNG Train

Train comes online

phased in

Stoppages

Storage Tank

Stoppages

Laden Voyage

Offloading

Ballast Voyage

Pilot Station

Kitimat Model Scope

Example Objectives – Questions to be answered by the model

• For planned production what is the optimal level of storage and number of storage tanks?

• How much production will be lost due to full tank(s) or stoppages in the pipeline flows?

• How much production will be lost due to stoppages in the liquefaction plant?

• How long will ships be kept waiting to berth?

• What will be the berth utilization?

• Which shipping schedules can the terminal support?

Kitimat Model Structure

Structuring a model well is not an easy task. A combination of domain experience and simulation proficiency is needed to do this well. Time to develop a model is helped by utilizing modules previously developed – however these rarely match the requirement EXACTLY.

An assessment of the data available and consideration of how a model’s validity is to be established is essential .

Selected example modeling constructs in this model included:

• the inlet pipeline modeled as a single flow with appropriate interruptions / stoppages In WITNESS this is modeled as a PIPE element

• the liquefaction facility also modeled as a single flow per facility (train) with interruptions/stoppages

• Simple tank storage with a loss over time due to ‘boil off’ where the some of the liquid reverts to gas. Liquefaction flows reduced severely when tank nearing storage limit. Modeled with a WITNESS Tank element

• Three phase loading operation – connection, loading, disconnection – flow rates determined by attributes of ship, berth, liquefaction rate where tank empty.

Further selected example modeling constructs in this model included:

• Shipping modeled in varying levels of detail to reflect different stages of planning. Initially the model has been used with a subset of these features – those starred are planned at a later date as plans evolve.

• Set schedule consisting of one off journeys and repeating cycles to selected ports • Ballast requirements included • Ship boil off rates included • Links to a master schedule * • Channel restrictions • Pilot boats * • Some queuing permitted (one ship by berth, other ships queue by pilot station). • Weather interruptions

• Stoppages in the model offering options to reduce rates of flow by different percentages or stop flow altogether at the plant inlet pipe, the liquefaction trains and during ship loading.

Model Animation

Kitimat Model Interface

• The model interface in Excel – essential for ease of use of the model

• The menu too – very useful to aid navigation of data, run control and results

Kitimat Model Interface

• A page of operational constraints entered into the model offering interruptions such as

– Weather delays

– Ship drydocking

– Liquefaction train stoppages

– Inlet pipeline stoppages

Kitimat Model Verification and Validation

• This important phase of the project was carried out jointly by Lanner and Apache

– Vital for full understanding and checking between domain expert and modeling expert

– Provided familiarity with the model and its interface for the end users; also facilitated a deeper understanding of how the model worked

• This phase resulted in some model changes

• Model validated through the experience of Apache in running similar operations.

– Data sets with expected outcomes were run through the model

– Where results differed from expectations the reasons were easy to see from the results tables and charts and proved to be logical extensions to the expectations due to the unique characteristics of this facility

Results

• The interface contained many different charts and tables

Results

• One particularly useful report to show causality is the Gantt Chart

Kitimat Model Evolution

• As Apache used the model, new business questions emerged. Due to its flexibility, the model could be updated in response.

– Apache expressed an interest in monitoring not only tank top events, but also tank bottom events. A reporting feature was added that provides information about the time storage spends less than x% full, with x being a parameter set by the user.

Inputs: Outputs:

Conclusions

The model has been used to test out the plans and options for the new terminal. For example it has established

• The maximum volumes of LNG that can be exported using various storage tank configurations

• The critical factors that limit volume. Key indicators of this – e.g. - berth occupancy figures, ship capacity, cargo destinations

• The type of schedule and frequency of ships where ships begin to have to queue

• The identification of optimal configurations of storage and shipping logistics under various production expansion scenarios

The model has enabled Apache to proceed with the design plans with increased certainty of success, data on the best options to get the most out of the proposed design and an evaluation tool to be used as plans evolve and options change.

New University Initiatives

• Energy Use and Sustainability modelling – Exeter University (and Texas State University)

• Mind Map conversion to Simulation models – Sheffield University (Advanced Manufacturing Research Centre)

An approach for energy

consumption reduction of a

manufacturing process using

simulation modeling.

Presented by Ruiqiang Lu

Energy in Manufacturing

• Why?

• Political Pressures

• Consumer Opinion

• Financial Savings

• How?

• Energy Mapping

• Equipment development

and Technology

• Energy Recovery Solutions

Manufacturing

Process

Energy

Materials Products

Ceramic Tile Manufacture

• Slip House

• Spray Dryer

• Pressing

• Drying & First Fire

• Glazing

• Second Fire

• Sort and Packing

• Visualisation of the problem

• Communication throughout the business levels.

• Analysis of potential problems and energy saving approaches.

• Complex product parameters – Failure rates, etc.

• One example of model use is better scheduling to reduce power

consumption

Mindmap to Witness Conversion Program

Witness User Group Presentation

Produced as part of European Union Framework 7 FRAME project

Presented by Dr Phil Yates and Adrian Hirst

11.10.2011 AMRC Proprietary Information.

The FRAME Network

FreeMind mind-maps FreeMind is a free mind-mapping software used for easy

graphical drawing of mind-maps on PC.

It uses .MM files, which follow XML schema used

by many programs, including many

mindmapping softwares.

Using a template laid down by our EU project work, we had

mindmaps (like the one above, but far more complex) of

production systems

Conversion to The program interface allow the user to

select a mind-map, runs the conversion

process and then gives options to look at,

edit and run the converted model.

The interface consists of 3 parts:

-The opening page, for picking a

-the Parameter editing page, for

controlling the models parameters

-Query simulator, for demonstrating

the query system for out program

There’s further interface for problems

reading the mind-maps, prompting the

user to provide the information needed to

complete the model.

Auto-Created Witness model The graphical model wasn’t a major concern for our project work, but is very simply

generated for further expansion in the future

Optimization and Virtual Reality VIDEOS

Optimization

Virtual Reality

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