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ECR Presentation September 4 th 2019

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Page 1: PowerPoint Template Light Version · Specs Customer Basket Analysis: Phase 2 Minimize environmental testing Customer margin visibility Optimise OEE on filling lines Cost analysis

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ECR PresentationSeptember 4th 2019

Page 2: PowerPoint Template Light Version · Specs Customer Basket Analysis: Phase 2 Minimize environmental testing Customer margin visibility Optimise OEE on filling lines Cost analysis

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Supply Chain Complexity Increasing

www.glanbiaireland.com2

Increasing returns processing time,

fraudulent returns

Unpredictability in regulatory requirements

Volatility in prices & supply

Disruptions due to AI, ML, IoT & Blockchain

Next gen analytics of exploding

transactional & master data

NEW BUSINESS MODELS

EXPONENTIAL GROWTH OF

SUPPLY CHAIN COMPLEXITY

PHYSICAL NETWORK COMPLEXITY

REGULATION AND GEOPOLITICAL CHANGES

DEMANDING CUSTOMERS

RAPID PRODUCT PROLIFERATION

GROWTH OF NEW DIGITAL TECHNOLOGIES

EXPANDING DATA

REVERSE LOGISTIC COMPLEXITIES

Personalized products & services,

Omni-channel experience: buy, collect, return

anywhere

Ecommerce, omnichannel, 3PLs/4PLs

Omni-channel & distributed manufacturing

strategies results in fulfilment challengesInaccurate forecasting hampers

the speed to market for NPDs

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Impacts on Operational Performance

www.glanbiaireland.com3

Supplier

Warehouse & Inventory

Distributors Retailers Consumer

Returns

Assembler Plants

Manufacturing Plants

OEMs

OutboundLogistics

Inbound Logistics

OutboundLogistics

Inbound Logistics

▪ Low forecast accuracy (40-60%) ▪ Faulty forecast bias▪ High downstream inventory

Poor Demand Estimation

▪ Shorter product lifecycles ▪ Higher product failure rates▪ Low new product launch effectiveness

Delayed Product Launch

▪ Higher Inventory holding costs▪ Stock Outs & Stagnant fill rate▪ Poor visibility in real-time inventory

Insufficient Inventory Management

▪ High Fulfilment costs▪ Longer lead times▪ Longer Order-to-cash

cycle times

Inefficient Fulfilment Model

▪ Revenue leakage (6%)▪ Fraudulent Returns▪ High Reverse Logistics Cost▪ Poor customer experience

Variable Returns Management

▪ Downtime with line clearance▪ Product deviation impacting available

markets▪ Complexity in QC sign off

Reduced Factory Yield

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Supply Chain & Manufacturing Business Analytics

www.glanbiaireland.com4

Buy Make Store Move Sell

END TO END CONTROL TOWER

MACHINE LEARNING DEMAND

PREDICTION

REAL TIME INVENTORY

OPTIMISATION

DYNAMIC ROUTE

SCHEDULING

AI FOR AUTOMATING SCHEDULING

COMPUTER VISION FOR

QC

INTELLIGENT BATCH

MANAGEMENT

RETURNS & FRAUD

ANALYTICS

CONNECTED WORKER AND NETWORK

DIGITAL TWIN PLANT AND NETWORK DESIGN

Page 5: PowerPoint Template Light Version · Specs Customer Basket Analysis: Phase 2 Minimize environmental testing Customer margin visibility Optimise OEE on filling lines Cost analysis

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RANGE OF BENEFITS

LABOR COST REDUCTION 20 - 40% 500 – 1000 FTEs

DISTRIBUTION COSTS REDUCTION 10 - 20% $ 25M – $ 50M

OBSOLETE INVENTORY REDUCTION

20 - 30% $ 30M – $ 45M

SALES INCREASE 0.5 - 1% $ 50M – $ 100M

30 - 50% $ 5M – $ 10M

LOST SALES REDUCTION

1.5 – 3pp 96.5% – 98%

FORECAST ACURACY IMRPOVEMENT 62% -> 75%8 – 12pp

REDUCTION IN SAFETY STOCKS

Significant Business Benefits

www.glanbiaireland.com5

INTELLIGENT SUPPLY CHAINVALUE

OP

ER

AT

ING

CO

ST

SA

VIN

GS

RE

VE

NU

E

UP

LIF

TM

EN

T

Page 6: PowerPoint Template Light Version · Specs Customer Basket Analysis: Phase 2 Minimize environmental testing Customer margin visibility Optimise OEE on filling lines Cost analysis

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Data Mining, Data Analytics, Business Analytics

www.glanbiaireland.com6

Data Mining is the process of examining large datasets

in order to generate information on possible

outcomes

Data Analytics involves the discovery, interpretation, and communication of patterns identified post Data Mining

Business Analytics develops new insights and understanding of

business performance, based on Data Analytics, yielding data

driven decision making

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The 5 V’s of Big Data in GI SCM

Buy Make Store Move Sell

FLOW OF INFORMATION

FLOW OF GOODS

FLOW OF MONEY

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DESCRIPTIVE

PREDICTIVE

PRESCRIPTIVE

DIAGNOSTIC

VA

LU

E

DEGREE OF DIFFICULTY

Advanced Analytics

WHAT HAPPENED?

WHY DID IT HAPPEN? WHAT WILL HAPPEN?

WHAT WILL I DO?

Information OptimisationHin

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The Analytics Maturity Curve

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Ideation Sessions 1 and 2Brainstorming, Categorisation, Refinement

www.glanbiaireland.com9

Session 1: Brainstorm ideas

(60 ideas)

Session 2: Categorize and refine initiatives

(37 initiatives)

• Insufficient “pipeline of discovery”• Gathered key stakeholders from GI SCM and

Operations to brainstorm in a structured environment

• No ideas out of bounds• Quantity of ideas breeds quality of ideas• It is easier to tame a wild idea than to start a new

one• Individuals write their own ideas

• Developed a database of initiatives, categorized by:• Owner• Value level (risk, cost, revenue, cash flow,

efficiencies) • Analytical maturity• Searchable terms

• Non-analytics ideas removed / consolidated (60->37)• Determined business impact and difficulty of each

initiative to develop a 3X3 matrix of group initiatives• Quick wins• Big ideas• Maintenance / hygiene• Unwise

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Ideation Sessions 3 and 4 Project Selection, PID Creation, Timelines

www.glanbiaireland.com10

Session 3: Selective projects

(9 projects)

Session 4: Develop PIDs and schedule work

(9 PIDs)

• Identified 9 projects balancing the continued delivery of significant benefits with capacity limitations from both GBS and GI

• These 9 projects now move into PID / Project Charter stage, owned by project ideator

• Develop PID documents with each of the project sponsors (using a model document agreed by the project team)

• Record expected benefits for each project and start tracking mechanism

• Schedule program of work for 2019 -> 2020 suggests 28 initiatives (remaining), but governance structures will formalise c. Q3 2019

Project Name Apr-19 May-19 Jun-19 Jul-19 Aug-19 Sep-19 Oct-19 Nov-19 Dec-19 Jan-20 Feb-20

Energy consumption in Evaps

Customer Basket Analysis: Phase 2

Visualise farm level milk quality & temperatures

Optimise product availability for picking

Understand the implications of SLA failure in Agri

Optimise OEE on filling lines

Analysis of CIP interval times

Chilled water systems in Ballyragget

Milk forecasting

Page 11: PowerPoint Template Light Version · Specs Customer Basket Analysis: Phase 2 Minimize environmental testing Customer margin visibility Optimise OEE on filling lines Cost analysis

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Idea Matrix: Impact v Difficulty

Visualise farm level

milk quality &

temperatures

Minimize testing

schedule based on

risk

Trace milk

constituents for UHT

cream

Accuracy and

variability of

measurement

systems - Milk in v

product out yields

Identify drivers of

micro-nutrient

variations in milk

Golden batch -

energy

Formulate feed to

deliver required

constituents in milk

Analysis of cost

benefit of lab testing

in Consumer

production sites

Milk constituent and

quality variabilityMilk forecasting

Golden batch -

quality

Formulate fertiliser

for farms in specific

regions to deliver

required

constituents in milk

Energy consumption

in Evaps

Analysis of CIP

interval times

Pareto of all quality

data generatedGolden batch - CoA

Energy impact of

plants

Protein Phase 2Optimisation of milk

constituent

Àligning Customer

and Manufacturing

Specs

Customer Basket

Analysis: Phase 2

Minimize

environmental

testing

Customer margin

visibility

Optimise OEE on

filling lines

Cost analysis on bulk

feed orders outside

SLA

Predict feed demand

based on grass

growth

Maintenance testing

review

Understand the

implications of SLA

failure in Agri

Waste analysis of

processes and steps

Cost benefit analysis

of distributing third

party stock

Milk Constituents

analysis by delivery

Membranes in

Ballyragget

Chilled water

systems in

Ballyragget

Visualisation of

labour hours

Analysis of yard

throughput

Auto prediction of

loss making SKUs

Visualisation of

health and safety

trends

Visualisation of CIP

Health

Quick Wins Big Ideas

Imp

act

Difficulty

Hig

hM

ed

ium

Low

HighMediumLow

Maintenance / Hygiene Unwise

Proposed Initiatives for discussion

Commitment made

In progress

Prototype developed

Colour Legend

Visualise farm level

milk quality &

temperatures

Minimize testing

schedule based on

risk

Trace milk

constituents for UHT

cream

Accuracy and

variability of

measurement

systems - Milk in v

product out yields

Identify drivers of

micro-nutrient

variations in milk

Golden batch -

energy

Formulate feed to

deliver required

constituents in milk

Analysis of cost

benefit of lab testing

in Consumer

production sites

Milk constituent and

quality variabilityMilk forecasting

Golden batch -

quality

Formulate fertiliser

for farms in specific

regions to deliver

required

constituents in milk

Energy consumption

in Evaps

Analysis of CIP

interval times

Pareto of all quality

data generatedGolden batch - CoA

Energy impact of

plants

Protein Phase 2Optimisation of milk

constituent

Àligning Customer

and Manufacturing

Specs

Customer Basket

Analysis: Phase 2

Minimize

environmental

testing

Customer margin

visibility

Optimise OEE on

filling lines

Cost analysis on bulk

feed orders outside

SLA

Predict feed demand

based on grass

growth

Maintenance testing

review

Understand the

implications of SLA

failure in Agri

Waste analysis of

processes and steps

Cost benefit analysis

of distributing third

party stock

Milk Constituents

analysis by delivery

Membranes in

Ballyragget

Chilled water

systems in

Ballyragget

Visualisation of

labour hours

Analysis of yard

throughput

Auto prediction of

loss making SKUs

Visualisation of

health and safety

trends

Visualisation of CIP

Health

Quick Wins Big Ideas

Imp

act

Difficulty

Hig

hM

ed

ium

Low

HighMediumLow

Maintenance / Hygiene Unwise

Proposed Initiatives for discussion

Commitment made

In progress

Prototype developed

Colour Legend

Visualise farm level

milk quality &

temperatures

Minimize testing

schedule based on

risk

Trace milk

constituents for UHT

cream

Accuracy and

variability of

measurement

systems - Milk in v

product out yields

Identify drivers of

micro-nutrient

variations in milk

Golden batch -

energy

Formulate feed to

deliver required

constituents in milk

Analysis of cost

benefit of lab testing

in Consumer

production sites

Milk constituent and

quality variabilityMilk forecasting

Golden batch -

quality

Formulate fertiliser

for farms in specific

regions to deliver

required

constituents in milk

Energy consumption

in Evaps

Analysis of CIP

interval times

Pareto of all quality

data generatedGolden batch - CoA

Energy impact of

plants

Protein Phase 2Optimisation of milk

constituent

Àligning Customer

and Manufacturing

Specs

Customer Basket

Analysis: Phase 2

Minimize

environmental

testing

Customer margin

visibility

Optimise OEE on

filling lines

Cost analysis on bulk

feed orders outside

SLA

Predict feed demand

based on grass

growth

Maintenance testing

review

Understand the

implications of SLA

failure in Agri

Waste analysis of

processes and steps

Cost benefit analysis

of distributing third

party stock

Milk Constituents

analysis by delivery

Membranes in

Ballyragget

Chilled water

systems in

Ballyragget

Visualisation of

labour hours

Analysis of yard

throughput

Auto prediction of

loss making SKUs

Visualisation of

health and safety

trends

Visualisation of CIP

Health

Quick Wins Big Ideas

Imp

act

Difficulty

Hig

hM

ed

ium

Low

HighMediumLow

Maintenance / Hygiene Unwise

Proposed Initiatives for discussion

Commitment made

In progress

Prototype developed

Colour LegendVisualise farm level

milk quality &

temperatures

Minimize testing

schedule based on

risk

Trace milk

constituents for UHT

cream

Accuracy and

variability of

measurement

systems - Milk in v

product out yields

Identify drivers of

micro-nutrient

variations in milk

Golden batch -

energy

Formulate feed to

deliver required

constituents in milk

Analysis of cost

benefit of lab testing

in Consumer

production sites

Milk constituent and

quality variabilityMilk forecasting

Golden batch -

quality

Formulate fertiliser

for farms in specific

regions to deliver

required

constituents in milk

Energy consumption

in Evaps

Analysis of CIP

interval times

Pareto of all quality

data generatedGolden batch - CoA

Energy impact of

plants

Protein Phase 2Optimisation of milk

constituent

Àligning Customer

and Manufacturing

Specs

Customer Basket

Analysis: Phase 2

Minimize

environmental

testing

Customer margin

visibility

Optimise OEE on

filling lines

Cost analysis on bulk

feed orders outside

SLA

Predict feed demand

based on grass

growth

Maintenance testing

review

Understand the

implications of SLA

failure in Agri

Waste analysis of

processes and steps

Cost benefit analysis

of distributing third

party stock

Milk Constituents

analysis by delivery

Membranes in

Ballyragget

Chilled water

systems in

Ballyragget

Visualisation of

labour hours

Analysis of yard

throughput

Auto prediction of

loss making SKUs

Visualisation of

health and safety

trends

Visualisation of CIP

Health

Quick Wins Big Ideas

Imp

act

Difficulty

Hig

hM

ed

ium

Low

HighMediumLow

Maintenance / Hygiene Unwise

Proposed Initiatives for discussion

Commitment made

In progress

Prototype developed

Colour Legend

Visualise farm level

milk quality &

temperatures

Minimize testing

schedule based on

risk

Trace milk

constituents for UHT

cream

Accuracy and

variability of

measurement

systems - Milk in v

product out yields

Identify drivers of

micro-nutrient

variations in milk

Golden batch -

energy

Formulate feed to

deliver required

constituents in milk

Analysis of cost

benefit of lab testing

in Consumer

production sites

Milk constituent and

quality variabilityMilk forecasting

Golden batch -

quality

Formulate fertiliser

for farms in specific

regions to deliver

required

constituents in milk

Energy consumption

in Evaps

Analysis of CIP

interval times

Pareto of all quality

data generatedGolden batch - CoA

Energy impact of

plants

Protein Phase 2Optimisation of milk

constituent

Àligning Customer

and Manufacturing

Specs

Customer Basket

Analysis: Phase 2

Minimize

environmental

testing

Customer margin

visibility

Optimise OEE on

filling lines

Cost analysis on bulk

feed orders outside

SLA

Predict feed demand

based on grass

growth

Maintenance testing

review

Understand the

implications of SLA

failure in Agri

Waste analysis of

processes and steps

Cost benefit analysis

of distributing third

party stock

Milk Constituents

analysis by delivery

Membranes in

Ballyragget

Chilled water

systems in

Ballyragget

Visualisation of

labour hours

Analysis of yard

throughput

Auto prediction of

loss making SKUs

Visualisation of

health and safety

trends

Visualisation of CIP

Health

Quick Wins Big Ideas

Imp

act

Difficulty

Hig

hM

ed

ium

Low

HighMediumLow

Maintenance / Hygiene Unwise

Proposed Initiatives for discussion

Commitment made

In progress

Prototype developed

Colour Legend

Page 12: PowerPoint Template Light Version · Specs Customer Basket Analysis: Phase 2 Minimize environmental testing Customer margin visibility Optimise OEE on filling lines Cost analysis

When formatting content in any placeholder, insert into placeholder and use these

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Furthering Analytics in GI SCM

Buy Make Store Move Sell

Develop Predictive Pricing Models

Optimise Energy Consumption in Evaporators

Visualise Milk Quality Attributes at Farm

Optimise Protein Harvesting In Raw Milk

Develop Cost Effective Environmental Testing

Attribute Relaxation to Align Customer and Manufacturing Specs

Understand Cost Implications of SLA

Violations

Improve OEE on Filling Lines

Model Alternate (Minimised) CIP Intervals

Page 13: PowerPoint Template Light Version · Specs Customer Basket Analysis: Phase 2 Minimize environmental testing Customer margin visibility Optimise OEE on filling lines Cost analysis

When formatting content in any placeholder, insert into placeholder and use these

two buttons in above “HOME” ribbon to adjust

When creating pie charts right click on chart, go to

format data series and

If you have a long title that goes across two lines or

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VA

LU

E

Hin

dsig

ht

In

sig

ht

Fo

resig

ht

The Analytics Maturity Curve

Descriptive Diagnostic Predictive Prescriptive

Information Optimisation

Attribute Relaxation to Align Customer and Manufacturing Specs

Predictive Pricing

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Predictive PricingCure the Basket vs. Kill the SKU

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Attribute RelaxationAligning Customer and Manufacturing Specifications

PHASE 1: K MEANS CLUSTERING BASED ON ATTRIBUTE ANALYSIS

FUTURE PHASE 2: “IN FLIGHT” CLUSTER OPERATING CONDITION CORRECTION

• Identify strongly correlated variables (latent) with a disproportionate impact on overall attainment of desired specification outcome – for that cluster of similar customers.

• Establish variable output range that effect end result, and set UCL/LCL on MES system via input/output signal reads on the effecting inputs/processes.

• Alarm when we are out of control, correct the event, in flight, in real time.

• All other output variables are important, but not all variables are equal.

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Summary of Business Analytics in GI SCM

www.glanbiaireland.com

BOUNDARIES

UNDERSTANDING

PROCESS

MODEL BUILD

SUCCESS

WHERE WE WERE… … WHERE WE ARE NOW

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SC&M Business Analytics Lessons Learned

www.glanbiaireland.com

Prioritise on an early set of high value use cases to get everyone onboard

Apply Design Thinking to ensure all insights are actionable and change what happens each day in your supply chain

Your data is generally good enough. It also won’t improve unless you use it for insights

Small Proof of Values provide significant insight. Ensure you have clarity and leadership buy-in on how they will scale