Choosing your first AI project: How to get a quick ROI

│ 28 November 2017

Current state of the industryOverwhelmed with digitalisation

Smart factory

How to close the gap?

And how to fund all these innovations?

Capital investments

Process redesign

Lengthy deployment

ROI in 5-10 years

And how to fund all these innovations?

Capital investments

Process redesign

Lengthy deployment

ROI in 5-10 years

… Results are not guaranteed!

Agenda

1. What industrial AI is

2. How to map specific processes fit for AI

3. How to prioritise AI projects

4. Bonus: what is wrong with predictive maintenance

+ Q&A

What is AI and machine learning

Complex algorithms that:

〉accomplish tasks by themselves instead of being explicitly programmed

〉learn inductively from past historical data (or data generated for training)

〉can predict events or prescribe actions

What is AI and machine learning for industrial sector

Newbusiness

models

New products orservices

Process automation and optimisation

The ”invisible” AI

Low capital investment

Quick wins, ROI < 1 year

How it differs from systems already in use?

Processes relying on traditional knowledge-based models

Results of chemical analyses

Equipment telemetry

Process parameters

Traditional models of physical processes

embedded in process control

systems

Expert judgement

L(z)

0 z

Processes relying on traditional knowledge-based models

Results of chemical analyses

Equipment telemetry

Process parameters

L(z)

0 z

How AI creates value

Learns from how the process actually ran on specific plant

Accounts for many weak dependencies in past data

Operates on top of knowledge-based models

“Personalises” production decisions in every iteration

New level of precision for the cases where it really matters

Complementary to existing process control

Direct effect with no capital investment

Steps to choose your AI project

Step 1: Start with business needsEstablish the foundation

Start with business needs

“Let’s do deep learning / chatbots”-> Technology for its own sake

“Let’s buy sensors / clean up databases”-> Data generates costs, not value-> Postponing outcomes for no reason

–

Start with business needs

Review the usual pain points

Quality control and assurance

Productivity and yield

Energy consumption

Raw material use

+

Start with business needs

High-volume, low-margin product-> Decrease raw material use

Recurring fluctuations in quality-> Decrease losses

Specific shop is a bottleneck-> Improve throughput

+

Step 2: Identify where AI is applicableScan the use case horizon

Uncertainty and complexity

Process has fluctuations

Many factors involved

Some parameters not known with precision

Non-linear dependencies

Often experienced operator makes the decisions

You need advanced process control

? ??

??

Uncertainty and complexity: gas fractionation

Sources of complexity:

— Continuous process

— Chemical composition of gas feed varies

— Adjusting parameters (e.g. temperature) takes time

— Changing them too fast may lead to disruptions

Optimisation goals:

— Improve throughput / energy efficiency

Uncertainty and complexity: examples

Oil and gas well drilling(increase drilling speed,

avoid deviations)

Animal food production(optimise moisture content,

decrease variability)

Blast furnace process in steelmaking (decrease

energy use)

Uncertainty and complexity

Perfect “lab” environment

Low number of measured / controlled factors

Rule-based systems work well enough

–

Established, repetitive process

Historical data is available

Historical data remains relevant to rely for future

Even small improvements are significant due to process frequency

Optimisation makes long-term business sense

The process is likely already efficient enough

Established processes: steelmaking

— Equipment lifespan is typically decades

— Core processes are essentially same

Possible applications

— Decrease raw material (ferroalloy, fluxes) use in oxygen converter

— Decrease energy use in electric arc furnace

— Decrease defect occurrence during rolling

Established process: examples

Catalytic cracking Beer fermentation Bottle counting

Established process

One-time, irregular, or too diverse processes with insufficient historical data

〉Design a new plant

〉Invent new product recipe

〉Forecast market trends

–

Measurable KPI

AI has no ”common sense”, it needs a metric to optimise

Should be as close to business as possible

Should be measured in a straightforward way

Measurable KPI: optimisation of ferroalloy use

— Goal: Decrease the use of raw material without affecting steel quality

— Metric: Average costs of ferroalloys in smeltingsperformed following AI recommendations

— Restriction: Chemical composition of steel should fit in required ranges (specification)

5%average decrease

>$4.3myearly effect

Measurable KPI: optimisation of ferroalloy use in steelmaking

$$$$$$Optimisation potential

$$$Cost savings achieved

KPI: optimisation of raw material use

Zinc use in steel coating

Cyanide use in ore leaching

Cocoa butter use in chocolate conching

Summing up: prerequisites for AI

Established,repetitive process

to be able to rely on data

Summing up: prerequisites for AI

Established,repetitive process

Uncertainty and complexity

for AI to create value to be able to rely on data

? ??

??

Summing up: prerequisites for AI

Established,repetitive process

Uncertainty and complexity

Well-defined, measurable outcomes

for AI to create value to be able to rely on data to measure success

? ??

??

Step 3: Prioritise the ideasReaching the low-hanging fruits

3 criteria

Data availability Expected effect Time to value

Data availability

Not necessary “big”

Starting from 10000+ process iterations, 1-2 years of logs (could be less for frequent processes)

Raw data

Errors and gaps are not that crucial

+

Data availability

“We measure it, but do not store”“We store only the last month, and then delete”-> Algorithm will have nothing to learn from

Significant process changes in history (e.g. full revamping of the line)-> Makes past data obsolete

–

Expected effect

Alternatives

Direct results are significant for business

Limited pilot that can still confirm the case

Conscious choice to do R&D to learn from

+

Expected effect

20-40% of total costs spent on cyanide in ore processing

Even 1% decrease in ferroalloy use is business significant

30% of total costs on testing costs and yield losses in semiconductors

Expected effect

Direct results are too small

〉Total quality losses on the line are only $50K per year

Absence of specific quantifiable metric

〉“We want to discover route causes of defects”

Inability to influence the outcome

〉Quality predicted at point when nothing can be done

–

Time to value

Scope of the project involves only one production stage and team

Data is readily available or is extracted easily

Experimentation easily possible

+

Use case definition

Data gathering

Model training

Model testing

Production use

Time to value: slab quality prediction

— Goal: Identify slabs that are likely to lead to defects during rolling

— Metric: Portion of slabs with high defect mass revealed

— Data: Historical data on 17000 slabs

— Pilot scope: Quality forecast tested historically.

— Outcome: Total pilot length less than 3 months. Ability to establish business case given known processing costs.

Time to value: quality prediction

Metals Plastics Glass and optical fiber

What is wrong with predictive maintenance

+

Definitely huge potential economic effect

–

Either too few examples of failures or minimal economic effect

Lots of noise and anomalies

Difficult to measure direct economic effect in short term

Predictive maintenance: approaches for pilots

Change the focus

Predict defects that occur due to equipment wear-out

Specify the object

Choose specific equipment element with frequent failures

Shift the task

Predict demand for spare parts

Predicting anomalies

Multi-stage process, combination of AI and human expertise

Summing up

Why you should use artificial intelligence for process optimisation

No capital investments

No disruption of existing process

3-6 months to implement

Immediate ROI

Capital investments

Process redesign

Lengthy deployment

ROI in 5-10 years

Why it is important to start now?

When best practices are established, it is already too late

AI is too different from traditional software products, and many things are to be learned from experience

Organisation and management will be affected, and it’s important to learn from experience to be able to prepare

Q&A

Elena SamuylovaMarketing and business development director

Emeli DralChief data scientist