Perth Masterdeck 2026

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09:00 - 10:00 – Registration and Networking

10:00 - 10:10 – Welcome & Kick Off

10:10 - 10:40 – Keynote Speaker - Dr. Lisa Graham, CEO of Seeq

10:40 - 11:10 – Data Driven Condition Monitoring for Hydropower Assets - AGL

11:10 - 11:40 – Leveraging tissue paper production data using Seeq - Kimberly-Clark

11:40 - 12:00 – Coffee and Networking Break

12:00 - 12:30 – Real-Time Predictive Models for Cracker Run Length – TPC

12:30 - 13:00 – Scaling Condition-Based Monitoring through Data Democratization with Seeq - Meridian Energy

13:00 - 14:00 – Lunch and Networking Break

14:00 - 15:30 – Industrial AI Hands-On Workshop: Business Impact at Scale, from the Shop Floor to the Top Floor

15:30 - 15:50 – Product Overview and Roadmap - James Higgie, Staff GenAi Engineer at Seeq

15:50 - 16:00 – Closing Session

16:00 - 17:00 – Happy hour and networking

AGENDA

NAMA Conneqt June 1-3, 2026 Orlando, Florida

EMEAPAC Conneqt Autumn 2026 Amsterdam, Netherlands

Points to Note During the Day

• Safety

• WiFi o Network Name: SEEQ o Password: FRASERS100326!

• Breaks & Lunch o Will take place in this room

• Happy Hour o Will take place on the terrace

Ask Your Questions using Slido

Scan the QR Code to join our active Slido Session where you can ask questions throughout presentations and the presenter will answer them at the end of their session.

Questions can be submitted anonymously or with your name attached.

From Analytics to Intelligence

Dr. Lisa Graham, P.E. CEO

Welcome

◎

2.7x

Users 2024 → 2025

$17M Economic Impact*

43 Customers

Growth in APAC

MerciGracias

Grazie

谢谢

감사합니다 धन्यवाद

Спасибо

شكراً

תודה

Cảm ơn

Teşekkür ederim Tack

Ευχαριστώ

Obrigado

Danke

ありがとう

ขอบคณุ

Dank je

Dziękuję

Thank You

The Demands of Today’s Manufacturing Environment

Make More

Make them Faster

Make them with Impeccable Quality

Make them at Lower Cost

Make it with Less Impact

Make them at the Right Time

Make the Right Mix of Products

Cycle Times

Quality Deviations

Time to Identify Anomalies

Time to Issue Resolution

Maintenance Cost

Unplanned Downtime

Meet Delivery Commitments

Energy Efficiency

Equipment Life

Yield & Capacity

Forecast Accuracy

Emissions Reduced

The SME is the Key

Keep process expertise at the center of all workflows and

extend their domain knowledge throughout the enterprise

Process Engineer CIO

Operational LeadersCOOESG

Reliability Team QualityOperator

Data Scientist

New Opportunities Exist with Industrial AI

Gut Feel Faster, Better Data-Driven Decisions

Siloed Expertise Global Teamwork / Collaboration

Possibility Reality

Local Decisions Enterprise Impacts

Transient, Siloed Knowledge Documented Experiential Knowledge

Data Value, Outcomes

Reactive Real Time & Predictive

The Best Intelligence Isn’t Artificial. It’s Human Intelligence, Amplified.

Go Faster. Do More. Go Farther.

Our Mission is to Provide Value to

every manufacturer

every plant

every manufacturer

every role

Downtime

↓ 25%

Mineral Recovery

↑ 25%

Capacity

↑ 25%

Emissions

↓ 15%

Raw Material Use

↓ 6%

Engineering Savings

1.5 – 3 hrs/day/engineer

MTBF

↑ 800%

Taste Complaints

↓ 75%

Time to Resolution

↓ 97%

Capacity

↑ 10%

Off-Spec Product

↓ 50%

OEE

↑ 2.7%

O&G Customer

> $1B value recorded

The Impact of unlocking SME Expertise at Scale

https://share.vidyard.com/watch/Wpk6QpyQUDaDEzg9F3JtXt

The Impact of unlocking SME Expertise at Scale

From Analytics to Intelligence

• Time Series Data

• SME Context

+ Corporate Artifacts

+ OEM Documentation + Transactional Data (Operational +)

+ Historical Decisions / Actions

+ SME / Institutional Knowledge

The Richest Operational Data

Faster, Better Decision Making Improved Outcomes

Continuous Improvement Competitive Advantage=

Time to Value

& Strategic

& Enterprise

Daily

Local

Shop Floor & Top Floor

& Transformative Value

Unified, (A)I Powered Decision Intelligence Platform

MerciGracias

Grazie

谢谢

감사합니다 धन्यवाद

Спасибо

شكراً

תודה

Cảm ơn

Teşekkür ederim Tack

Ευχαριστώ

Obrigado

Danke

ありがとう

ขอบคณุ

Dank je

Dziękuję

Thank You

Any Questions?

Please raise your hand and a team member will bring you a microphone.

Scan the QR code to submit your questions using Slido.

Data-Driven Condition Monitoring for Hydro Assets

Dr. Georgia Hunter

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Who am I?

G Hunter| March 2026

Dr. Georgia Hunter Part of a team of engineers that has specialised in analytics/data science

Approach data problems with an engineer/science-backed mindset

Background

What I do at AGL Advanced Analytics Engineer

PhD in Materials Engineering

BMS/HVAC Analytics

Central team that works across all of the AGL energy assets

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A Saberi | March 2025 30

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G Hunter| March 2026 31

Hydro – How It Works

River

Dam/Reservoir

Generator

Turbine

Penstock

Main Inlet Valve

Circuit Breaker

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G Hunter| March 2026 32

Condition Monitoring of Hydro Stations

What are the key components that can fail?

What data do we have for those components?

How can we use the data to monitor the condition of

these components?

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G Hunter| March 2026 33

Analysis 1: Monitoring of MIV

What are we monitoring?

Previous Monitoring Method

Main Inlet Valve (MIV)

Manual monitoring with a stopwatch

What Data Do We Have

Limit Switches for MIV Open and MIV Closed

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G Hunter| March 2026 34

Monitoring of MIV MIV Closed Limit Switch

MIV Open Limit Switch Raw Data 1

Create Capsules when MIV Opening or Closing

Calculate Time for MIV to Open or Close

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G Hunter| March 2026 35

Monitoring of MIV

Define upper and lower limits for expected timing4

Contextualise data with Unit ‘Mode’

Identify when timing is outside of limits AND unit is in Auto Mode

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G Hunter| March 2026 36

Monitoring of MIV

Steadily increasing opening and closing time

Large step change in timing – general large variation in timing

Examples of condition degradation identified by the analysis

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G Hunter| March 2026 37

Analysis 2: Monitoring of Sump Pump

What are we monitoring?

Previous Monitoring Method

Leakage in station (i.e., valve leakage)

None

What Data Do We Have

Sump Pumps On/Off switch

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G Hunter| March 2026 38

Monitoring of Sump Pumps

Raw Data 1

Convert to Numerical Data2

Calculate proportion of time pump is running on a rolling hourly basis (by taking hourly average of numerical data every 5 minutes)

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G Hunter| March 2026 39

Monitoring of Sump Pumps

Define Upper Limit for expected time for sump pump to run

Create x-y plot to observe changes in pump runtime over time relative to

power

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G Hunter| March 2026 40

Monitoring of Sump Pumps

Shaft seal replacement

Example of a leakage issue at one of the stations

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G Hunter| March 2026 41

Analysis 3: Monitoring for Tunnel Collapse

Consequences of Failure

Previous Monitoring Method

Disrupts Power Generation

Requires long and expensive outages to repair

Geotechnical inspections and remote operating

vehicles

Challenges:

• Requires station outage

• Expensive

• Only retrospective (cannot identify a rock collapse as it happens)

Monitoring for Tunnel Collapse

𝐾𝑛 = 2𝑔 𝐻𝑠𝑡𝑎𝑡𝑖𝑐 − 𝐻𝑛𝑒𝑡

𝑣2

Static Head

(dam level)

Net Head

Velocity (or flow)

Measured

Parameters

Gravity constant

Application of the Model:

• Steady State Only

• Units running at the same time

G Hunter| March 2026

Monitoring for Tunnel Collapse

G Hunter| March 2026

Raw Data 1

Identify steady state using 10mins rolling stdev of head and flow

Identify when both units running within 1MW

Calculate loss coefficient when in steady state and when both units are running within 1MW

Monitoring for Tunnel Collapse – Model Validation

Rockfall identified in geotechnical inspections in 2023

Blocked ~40% of the tunnel’s cross-sectional area

Last geotechnical inspection was 2013

No knowledge of WHEN the rockfall occurred

Can we retrospectively identify this rock collapse with our new data-modelling method?

G Hunter| March 2026

Monitoring for Tunnel Collapse – Model Validation Physical Identification of Rockfall

Proposed Time

of Rockfall Δ𝐾 ≈ 1.2

→ 𝐴𝑟𝑒𝑎 𝐵𝑙𝑜𝑐𝑘𝑎𝑔𝑒 ≈ 36%

Actual Observed Blockage ~ 40%

G Hunter| March 2026

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G Hunter| March 2026 46

Making the Analyses Accessible

3rd Party Platform

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G Hunter| March 2026 47

How are these analyses used?

Notifications set up on all analyses to alert on deviation from

set limit

Dashboard review integrated into 6-weekly Condition

Monitoring rounds

Organiser Topic used to create dashboards -> Dashboards

embedded in Asset Intellect for easy accessibility

Engineers investigate any notifications or observed changes

and raise a work order with rectifying actions.

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G Hunter| March 2026 48

Summary

Analyses are already showing high value by identifying issues that would have

otherwise gone unnoticed

Analyses have been embedded into engineering workflows

A series of Condition Monitoring analyses were set up using already available data

Analyses were set up using in-built SEEQ tools

Final analysis outputs include dashboards and automatic notifications

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G Hunter| March 2026 49

Questions?

McKay Power Station penstock

Rocky Valley Dam, Falls Creek Eildon Spillway

Any Questions?

Please raise your hand and a team member will bring you a microphone.

Scan the QR code to submit your questions using Slido.

Kimberly-Clark Overview Seeq Summit

1 9 1 8 1 9 2 0 1 9 2 4 1 9 8 9

153+ Years of Consumer-Centric Innovation

We Are Inventors

We Compete in Large, Global Categories With Powerhouse Brands

Source: Euromonitor 2023 Market Sizes & Internal Estimates

$56B $36B $13B BABY & CHILD CARE FEMININE CARE ADULT CARE

$101B $37B FAMILY CARE PROFESSIONAL

Millicent Mill

Kimberly-Clark’s Millicent Mill is in South Australia and

is the home of our iconic Kleenex and Viva brands, as well as our Professional products.

About Us

Kathleen de Regla

• Process Engineer

• With K-C since June 2023

• Dog Person

• Prefers Summer

Nina Yu

• R&D (former Process Engineer)

• With K-C since July 2022

• Cat Person

• Prefers Winter

Leveraging Tissue Paper Production Data Using Seeq

Kimberly-Clark IFP ANZ Pty. Limited Data shown is illustrative and has been simplified for presentation purposes

Use case #1: Monitoring of Paper Towel Converting Process

A rolled converting asset transforms a large parent roll from the tissue machine into consumer-ready rolls we see in the market.

Use case #1: Monitoring of Paper Towel Converting Process

Challenge:

• Reduced overall equipment efficiency due to diameter variations on specific products

Solution:

• Create signals to show upper and lower limits to visualize variability

Solution: • Use ‘conditions’ and ‘capsule view’ to compare machine parameters for each product

run.

Use case #1: Monitoring of Paper Towel Converting Process

Result:

• Easily compared good and bad runs

• Helped support troubleshooting using real-time data → improved diameter control → increased machine speed and reduced waste

• Improved product OEE by 10%

Use case #1: Monitoring of Paper Towel Converting Process

Pulpers are used to process virgin, recycled, or broke bales into useable pulp for tissue manufacturing.

Use case #2: Monitoring Pulper Efficiency

Challenge:

• Decreasing efficiency can cause longer pulping time → increase batch process time → higher energy consumption

• No readily available tags to measure efficiency

• Processing time / pulping time can vary on type of product

Use case #2: Monitoring Pulper Efficiency

Relying on operator feedback

for issue

No proper monitoring

Reactive vs Proactive

Solution:

• Transform ‘raw’ signals into ‘conditions’ to isolate desired event

Use case #2: Monitoring Pulper Efficiency

Solution:

• Create signals based on condition aggregations to establish KPI

Use case #2: Monitoring Pulper Efficiency

Result:

• 10-years worth of historical data loaded in a few minutes vs 1-2 hours in excel.

• Used to identify periods of decline which triggers preventive maintenance

• Transformed the approach from reactive to predictive and avoid machine breakdowns.

Use case #2: Monitoring Pulper Efficiency

Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements

Tissue Machine Background:

• Large loops of fabric

=> form and dewater pulp

=> tissue paper!

• Fabric material and structure

=> conditioning requirements

Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements

Challenge:

• Fabric conditioning strategy => manual settings

• Fabric life => highly variable

• Significant replacement cost (USD XX,000) => how to prolong life?

Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements

Solution:

• Create a condition where a capsule = life of one fabric

( 𝑑𝑦

𝑑𝑥 > 0) OR (y > 20000 revs) OR (no data)

Negligible dummy value to visually

differentiate capsules

Refresher from high school!

If a derivative is positive, its function is increasing

No change in revolutions

= Machine is shut

Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements

Solution:

• Use ‘Capsule View’ => overlay other signals for comparison

Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements

• Results:

• Expedited comparison of settings vs. using Excel • E.g., statistical analysis: obtain with just few clicks

• Easily visualize and overlay signals atop each other, vs. creating and merging multiple charts

• Export from database • Organise and collate data • Practice data hygiene • Type in formulae, etc.

Use case can be set up within 10 minutes 1-2 hours to do equivalent study on Excel

Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements

Results:

• More optimal, consistent conditioning strategy => prolong life from

1 week (worst case) to >30 days

• Reduce spend (each fabric is USD XX,000)

Any Questions?

Please raise your hand and a team member will bring you a microphone.

Scan the QR code to submit your questions using Slido.

Natchanon Naruesatyan / Pattama Poorahong Focused Improvement Engineer

Real-Time Predictive Models

for Cracker Run Length

Thai Plastic and Chemicals Public Company Limited (TPC)

TPC Group’s Company Profile

VIETNAM

PVC Resin

PVC compoundTHAILAND

PVC resin & VCM

Research center

INDONESIA PVC Resin

Pipes & Fittings

PVC Resin

VCM

Manufacturing Sites:

PVC Compound Established: since 1966

TPC Supply Chain Diagram

Olefin Plant

Ethylene

EDC

PVC Plant

Marine

Packaging type

Tank car Flexible Bag

PVCVCM

VCM Plant

(Vinyl Chloride Monomer) (Poly Vinyl Chloride)

Sea Bulk

Customers

PP Woven Bag

PVC Finish Goods VCM = Vinyl Chloride Monomer EDC = Ethylene Di-Chloride HCl = Hydrogen Chloride PVC = Poly Vinyl Chloride

VCM Process Flow Diagram

VCM = Vinyl Chloride Monomer EDC = Ethylene Di-Chloride HCl = Hydrogen Chloride PVC = Poly Vinyl Chloride

EDC Cracking

EDC

HCI

VCM

Fuel Gas

Temperature

Productivity

Coke Formation

Run length

Cracking Operating Trade-offs:

IMPORTED EDC

Challenge

Cracker run length dictates our shutdown schedule

The challenge is moving from guesswork to precision

The ability to predict the exact shutdown date is crucial. It allows us to:

• Enable effective maintenance planning

• Maximize productivity and eliminate profit loss from early shutdowns

Without real-time data, we are trapped:

• By Ineffective SD Planning:

• By a High-Stakes Trade-Off:

Previous Attempts

●Manual Process: Run length projections are manually calculated by

engineers using PI data exported to Excel

● Labor-Intensive: The process is time-consuming and requires

significant manual intervention for each update

● High Volatility: Forecasts fluctuate frequently with operational

changes, requiring constant recalculations to remain

accurate and reliable

Export PI

data to

Excel

Cleansing

data Regression Forecast Report

Seeq Solution

Export PI

data to

Excel

Cleansing

data Regression Forecast Report

Real-time auto update / Online Visual / Notification

Results

Automated,

Real-Time Forecasting

Replaces slow, manual Excel calculations with

a dynamic forecast that updates every 1

hour while achieving a predictive

accuracy of over 80%

Enhanced Online Visibility

& Collaboration

Provides a shared, interactive dashboard

for all teams (Operations, Maintenance) to align on one consistent forecast for faster

decision-making.

Proactive Notification

& Alerting System

Automatically notifies teams before a

shutdown is required, shifting maintenance

from a reactive emergency to a planned,

proactive event

Next Challenge

This success led us to ask a next bigger question:

“What if we could do more than just Predict the shutdown?

and What if we could actually Control it?”

Perform correlation analysis

Modeling & Forecasting

Create A Prescriptive Model

Y= f(X1,X2,X3,…,Xn)

Prediction Prescription

Two fundamental questions:

1. What are the Key operational factors that directly accelerate the need for a shutdown?

2. How can we adjust these factors in real-time to Safely extend our run length?

Any Questions?

Please raise your hand and a team member will bring you a microphone.

Scan the QR code to submit your questions using Slido.

Presented by William Herewini

Meridian Energy at Seeq Summit 2026

Scaling Condition-Based Monitoring through Data Democratization with Seeq

MARCH 10TH 2026

Presentation Agenda

MAR CH 1 0TH 2 02 6 | 91

Who are we?

Our business challenge

Our data pipeline

Use cases

Results and benefits

West Wind, WellingtonHarapaki Wind Farm, Hawke’s Bay

Who are we?

MAR CH 1 0TH 2 02 6 | 92

Aotearoa/New Zealand's largest energy generator with over 3000 MW of installed capacity equating to approx. 30% of the country’s electricity.

100% renewable generation – Wind, Water, and Sun

• 7 hydro stations

• 6 wind farms

• Grid-scale Battery Energy Storage Systems (BESS)

We retail electricity to more than 400,000 customers (or about 15% of household and business) across Aotearoa through our Meridian and Powershop brands.

Who are we?

MAR CH 1 0TH 2 02 6 | 93

The power to make a difference through data.

Improve processes from a routine-based maintenance approach to a data informed condition-based maintenance (CBM) approach.

Why?

• Make it easier for our on-site teams to do their jobs effectively.

• Improve asset health.

• Do our bit to keep the lights on in Kiwi homes and Aotearoa/New Zealand powered through the cold winter months.

Aviemore Hydro Station, Waitaki ValleyGodley River delta, Lake Tekapo Godley River Delta, Lake Tekapo

Our Business Challenge

MAR CH 1 0TH 2 02 6 | 94

Problem

• Technology gap in exploratory data analysis

• Insufficient data-driven information to inform important business decisions

• Complex data pipeline for CBM model development

Goal

• Meridian Energy would like to close the gap between our data team and our site crew, leveraging SME knowledge to accelerate the growth of our digital capability.

• Move from routine-based maintenance to CBM, reducing operations and maintenance cost.

Aviemore Hydro Station, Waitaki Valley

Our Data Pipeline

MAR CH 1 0TH 2 02 6 | 95

AVEVA PI System collects data points from our assets and stores them within our PI Data Archive

AVEVA PI Asset Framework and PI AF SDK builds analytical models to format and contextualise data

AVEVA PI Vision constructs platform to collate and present relevant information from various data sources

White Hill Wind Farm, SouthlandManapōuri Hydro Station, Southland

Databricks builds models for forecasting and predicting events

Our Data Pipeline

MAR CH 1 0TH 2 02 6 | 96

White Hill Wind Farm, SouthlandManapōuri Hydro Station, Southland

Seeq offers a solution that caters to different levels of expertise, centralising the data, analytics, and visualisation in one place

Seeq Organizer works well alongside existing tools to deliver updates, and report on significant changes

Use Cases

MAR CH 1 0TH 2 02 6 | 97

SF6 Monitoring and Alerting

Brake Service Forecasting

Turbine Bearing Oil Level Monitoring

Ōhau C Hydro Station, Mackenzie Basin

Use Cases

MAR CH 1 0TH 2 02 6 | 98

SF6 Monitoring and Alerting

Brake Service Forecasting

Turbine Bearing Oil Level Monitoring

Ōhau C Hydro Station, Mackenzie Basin

SF6 Monitoring and Alerting

MAR CH 1 0TH 2 02 6 | 99

Problem

• Though SF6 is an extremely efficient insulator for our circuit breakers, it is a potent greenhouse gas that must be monitored closely, as a leak would pose environmental risk.

• Site crew perform biweekly monitoring of the pressure and temperature in the SF6 containers, reporting the readings to the engineering team to verify.

Manapōuri Hydro Station, SouthlandManapōuri Hydro Station, SouthlandWhite Hill Wind Farm, Southland

MAR CH 1 0TH 2 02 6 | 10

Approach

• Install IIoT devices on one of our circuit breakers to capture pressure and temperature.

• Utilise Seeq to replicate the work that is currently undertaken by our site crew.

• Monitor SF6 pressure in Seeq, alerting on low pressure state, and high deviation between phases.

• Ingest alerts via Databricks, forwarding the data to PowerBI and PowerApps for user interaction.

Manapōuri Hydro Station, SouthlandManapōuri Hydro Station, Southland

SF6 Monitoring and Alerting

White Hill Wind Farm, Southland

MAR CH 1 0TH 2 02 6 | 10

Fig: SF6 model in Seeq, alerting during maintenance period.

SF6 Model in Seeq

TESTING PERIOD

MAR CH 1 0TH 2 02 6 | 10

Alerting Channel POC

Use Cases

MAR CH 1 0TH 2 02 6 | 10

SF6 Monitoring and Alerting

Brake Service Forecasting

Turbine Bearing Oil Level Monitoring

Ōhau C Hydro Station, Mackenzie Basin

Brake Service Forecasting

MAR CH 1 0TH 2 02 6 | 10

Problem

• Brake servicing relies on routine-based maintenance.

• There is difficulty in converting to maintenance decisions based off model output due to the disconnect between the model and SMEs.

• We risk over or under-servicing our assets when we rely on routine-based maintenance.

Benmore Hydro Station, Waitaki Valley

MAR CH 1 0TH 2 02 6 | 10

Approach

• Find an interim approach to data-driven maintenance by triggering maintenance based on brake operations.

• Use Seeq to monitor the operations of our brakes, forecasting when we expect maintenance to be due.

Benmore Hydro Station, Waitaki Valley

Brake Service Forecasting

MAR CH 1 0TH 2 02 6 | 10

Fig: Brake operation forecasting model in Seeq.

Brake Service Forecasting Model in Seeq

TEST THRESHOLD

Use Cases

MAR CH 1 0TH 2 02 6 | 10

SF6 Monitoring and Alerting

Brake Service Forecasting

Turbine Bearing Oil Level Monitoring

Ōhau C Hydro Station, Mackenzie Basin

Turbine Bearing Oil Level Monitoring

MAR CH 1 0TH 2 02 6 | 10

Problem

• Site crew must perform manual checks to ensure there are no oil leaks, and that the oil level is within expected bounds.

• There can be difficulties with visual inspection while the unit is online, due to confined spaces and large moving parts.

Benmore Hydro Station, Waitaki Valley

MAR CH 1 0TH 2 02 6 | 10

Approach

• Use Seeq and existing sensor data to calculate the daily rate of change.

• Alert when this metric is outside of normal operational ranges, based on historic data.

• Note: We are only interested in slow leaks, as anything sudden will be addressed by generation controllers.

Benmore Hydro Station, Waitaki Valley

Turbine Bearing Oil Level Monitoring

MAR CH 1 0TH 2 02 6 | 11

Fig: POC turbine oil level model in Seeq.

Oil Leak Model in Seeq

Results and Benefits

MAR CH 1 0TH 2 02 6 | 11

Closing the Gap

• Seeq’s low-code/no-code environment is appealing to those who don’t have the time to learn complex tools, while providing functionality that other tools cannot provide.

Trust in Models

• The Seeq UI provides accessible views of the data and analytics that are involved when developing models.

A Large Step towards CBM

• By centralizing the data, analytics, and visualisation, Seeq makes it easy for users to pick up and create CBM models.

• This aids us in moving current processes from a routine approach to a data-driven approach.West Wind, Mākara

Meridian Energy has efficiently adopted Seeq, enabling data democratisation, and accelerating the growth in our digital environment

MAR CH 1 0TH 2 02 6 | 11

Challenge

• Technology gap in exploratory data analysis

• Insufficient data-driven information to inform important business decisions

• Complex data pipeline for CBM model development

Solution

• Rolling out and utilising Seeq to cater to different levels of expertise.

• Develop clear model solutions, mimicking existing processes such that SMEs can build trust in the models.

Results

• Low-code/No-code analytics environment with full functionality.

• Clear UI and views into how models operate.

• Development of models to replace routine tasks with CBM.

Ōhau A Hydro Station, Mackenzie Basin

Looking Ahead

MAR CH 1 0TH 2 02 6 | 11

• Continue to on-board users around Meridian, increasing data capability and fostering a Seeq user community.

• Incorporate Seeq into our daily workflow.

• Replace routine maintenance on one hydro unit with CBM models.

• Using Seeq’s Data Lab, develop more complex models to gain insights, and add-on develop tools for users to use and explore.

Waitaki Power Station, Lake Waitaki

Questions?

MAR CH 1 0TH 2 02 6 | 11

Te Āpiti wind farm, Manawatū

William Herewini

Meridian Energy

@: will.herewini@meridianenergy.co.nz

Engineering Data Analyst

Any Questions?

Please raise your hand and a team member will bring you a microphone.

Scan the QR code to submit your questions using Slido.

1. Sign into preview.seeq.dev • Username: Your Email Address • Password: Your Email Address all lower case*

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2. Navigate to your folder: My Folder > AI Assistant Training > AI Workshop - Perth Summit 2026

WiFi SSID: SEEQ Password: FRASERS100326!

The Best Intelligence Isn’t Artificial. It’s Human Intelligence, Amplified.

Go Faster. Do More. Go Farther.

AI Workshop Using AI to Accelerate Value from Shop Floor to Top Floor

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2. Navigate to your folder: My Folder > AI Assistant Training > AI Workshop - Perth Summit 2026

WiFi SSID: SEEQ Password: FRASERS100326!

From Analytics to Intelligence

• Time Series Data • SME Autonomy • Local Insights & Optimizations • AI - Accelerates Decisions & Expands Skill Sets

Adds: • Enterprise Insights & Impact • Enterprise Governance • SME Expertise Capture

Adds: • Decision Intelligence

• SME Expertise → Shared Intelligence at Scale • Connected Ecosystem

• Agentic Workflows & Automation

Seeq Workbench Demo

AI Workshop – from Shop Floor to Top Floor

Enable a new engineer to expedite analytics

Set up an early notification on low production at Unit 22

Document analytics value and provide visibility to others

Scale across global sites in a low code environment

Identify low production issue at site S2L3

Enable experience engineer at S2L3 to narrow down the root cause

Identify opportunity for continuous improvement

Actions Agent

Agent Q

Document Agent

Please…

• Ask Questions

• Feel free to interrupt

• Get a Seeq person for help

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Seeq's Product Strategy

Seeq Analytics Expanded Tooling

Empower SMEs to do more: Statistics, Multivariate, Batch

Expanded Possibilities

Empower SMEs to solve for more: Time-Series + Tabular Data, work

order integration

Expanded Audience

Empower more users to be SMEs: Zero to Hero in minutes

Now Next Future

Seeq's Product Strategy

Seeq Enterprise

Seeq Analytics

Workflow at scale, Monitoring at scale

10x ROI through rapid amplification of analysis outcomes

and built-in workflows

Governance at scale, Models at scale

20x ROI through sustained, widespread deployment of

continuous improvement drivers

Expanded Tooling

Empower SMEs to do more: Statistics, Multivariate, Batch

Curiosity at scale, Action at scale

30x ROI through broad adoption of powerful what-if modeling

and tailored optimizations

Expanded Possibilities

Empower SMEs to solve for more: Time-Series + Tabular Data, work

order integration

Expanded Audience

Empower more users to be SMEs: Zero to Hero in minutes

Now Next Future

Seeq's Product Strategy

Seeq Intelligence

Seeq Enterprise

Seeq Analytics

Workflow at scale, Monitoring at scale

10x ROI through rapid amplification of analysis outcomes

and built-in workflows

Governance at scale, Models at scale

20x ROI through sustained, widespread deployment of

continuous improvement drivers

AI Workers AI Chief of Staff

Expanded Tooling

Empower SMEs to do more: Statistics, Multivariate, Batch

Curiosity at scale, Action at scale

30x ROI through broad adoption of powerful what-if modeling

and tailored optimizations

AI Profit Agents

Expanded Possibilities

Empower SMEs to solve for more: Time-Series + Tabular Data, work

order integration

Expanded Audience

Empower more users to be SMEs: Zero to Hero in minutes

Now Next Future

TASK-ORIENTED USER-ORIENTED MISSION-ORIENTED

Integration with Microsoft Copilot and Teams

Get Complex

Any Questions?

Please raise your hand and a team member will bring you a microphone.

Scan the QR code to submit your questions using Slido.

Key Takeaways

• Choose Outcomes that Matter oIdentify 1 high value operational challenge

• Scale Today oMoving from a single application to enterprise wide can be done

today

• Build your Internal Champion Network oEmpower the team to drive change

• Start your Outcome Initiative with Seeq oDefine for measurable value for 90-180 days

Intro Slides Slide 1 Slide 2 Slide 3: NAMA Conneqt June 1-3, 2026 Orlando, Florida Slide 4: EMEAPAC Conneqt Autumn 2026 Amsterdam, Netherlands Slide 5 Slide 6 Slide 7 Slide 8

Lisa Slide 9 Slide 10: From Analytics to Intelligence Slide 11 Slide 12: Growth in APAC Slide 13 Slide 14: The Demands of Today’s Manufacturing Environment Slide 15 Slide 16 Slide 17: New Opportunities Exist with Industrial AI Slide 18 Slide 19 Slide 20 Slide 21 Slide 22 Slide 23: From Analytics to Intelligence Slide 24 Slide 25

AGL Slide 26 Slide 27: Data‑Driven Condition Monitoring for Hydro Assets Slide 28: Who am I? Slide 29 Slide 30 Slide 31: Hydro – How It Works Slide 32: Condition Monitoring of Hydro Stations Slide 33: Analysis 1: Monitoring of MIV Slide 34: Monitoring of MIV Slide 35: Monitoring of MIV Slide 36: Monitoring of MIV Slide 37: Analysis 2: Monitoring of Sump Pump Slide 38: Monitoring of Sump Pumps Slide 39: Monitoring of Sump Pumps Slide 40: Monitoring of Sump Pumps Slide 41: Analysis 3: Monitoring for Tunnel Collapse Slide 42: Monitoring for Tunnel Collapse Slide 43: Monitoring for Tunnel Collapse Slide 44: Monitoring for Tunnel Collapse – Model Validation Slide 45: Monitoring for Tunnel Collapse – Model Validation Slide 46: Making the Analyses Accessible Slide 47: How are these analyses used? Slide 48: Summary Slide 49: Questions? Slide 50

Kimberly Clark Slide 51 Slide 52: Kimberly-Clark Overview Slide 53: We Are Inventors Slide 54: We Compete in Large, Global Categories With Powerhouse Brands Slide 55 Slide 56: About Us Slide 57: Leveraging Tissue Paper Production Data Using Seeq Slide 58: Use case #1: Monitoring of Paper Towel Converting Process Slide 59: Use case #1: Monitoring of Paper Towel Converting Process Slide 60 Slide 61 Slide 62 Slide 63 Slide 64 Slide 65 Slide 66 Slide 67: Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements Slide 68: Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements Slide 69: Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements Slide 70: Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements Slide 71: Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements Slide 72: Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements Slide 73: Use case #3: Comparing Tissue Machine Run Settings between Felt Fabric Replacements Slide 74

Break Slide 75 Slide 76

TPC Slide 77 Slide 78 Slide 79 Slide 80 Slide 81: TPC Supply Chain Diagram Slide 82 Slide 83 Slide 84 Slide 85 Slide 86 Slide 87 Slide 88

Meridian Slide 89 Slide 90 Slide 91: Presentation Agenda Slide 92: Who are we? Slide 93: Who are we? Slide 94: Our Business Challenge Slide 95: Our Data Pipeline Slide 96: Our Data Pipeline Slide 97: Use Cases Slide 98: Use Cases Slide 99: SF6 Monitoring and Alerting Slide 100: SF6 Monitoring and Alerting Slide 101 Slide 102 Slide 103: Use Cases Slide 104: Brake Service Forecasting Slide 105: Brake Service Forecasting Slide 106 Slide 107: Use Cases Slide 108: Turbine Bearing Oil Level Monitoring Slide 109: Turbine Bearing Oil Level Monitoring Slide 110 Slide 111: Results and Benefits Slide 112: Meridian Energy has efficiently adopted Seeq, enabling data democratisation, and accelerating the growth in our digital environment Slide 113: Looking Ahead Slide 114: William Herewini Slide 115

Lunch Filler Slide 116 Slide 117

Sharlinda (own laptop) Slide 118 Slide 119 Slide 120: Seeq Architecture Slide 121: Seeq Data Connectors and Integrations Slide 122: From Analytics to Intelligence Slide 123: Seeq Workbench Demo Slide 124: AI Workshop – from Shop Floor to Top Floor Slide 125: AI Workshop – from Shop Floor to Top Floor Slide 126 Slide 127

James Higgie Slide 128 Slide 129 Slide 130: Seeq's Product Strategy Slide 131: Seeq's Product Strategy Slide 132: Seeq's Product Strategy Slide 133 Slide 134: Integration with Microsoft Copilot and Teams Slide 135 Slide 136: Get Complex Slide 137

Closing Session Slide 138: Key Takeaways

End Slide Slide 139

Perth Masterdeck 2026

Item Type: pdf