Synchronizing the Value ChainValue Driven Application

18th April 2019

Page 2

EY – A global leader

EY is a $34.8 billion global leader

and one of the world’s leading

professional services firms, with

over 261,000 people in 728 locations

across 150 countries.

$34.8bGlobal

Revenues

261,000Professionals

globally

150Countries

globally

EY in Greece

EY has been operating in Greece

since 1926. It is the market leading

firm in advisory, assurance, tax and

transaction services, with

approximately 1,180 people, in two

offices in Athens and Thessaloniki.

With our services we

build a better working

world

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Transaction Advisory Services

Helping clients develop/execute

business strategies, blending local

technical knowledge with regional

and global tax insights

Helping clients create social and

economic value by strategically

managing capital and transactions

Assurance

Helping clients build stakeholders’

confidence, manage regulatory

responsibilities and drive long-term

sustainable growth

Advisory

Helping clients solve big, complex

issues and capitalize on

opportunities to grow, optimize

and protect the business

#1In the

market in

Greece

Professionals

in EY Greece

1,180

CESA Region brings together

the practices of 31 countries and

creates a strong network where

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and expertise is leveraged to

provide best of class solutions.

Central & South East Europe and Central Asia

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in CESA

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in EY CESA

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Page 3

EY’s Supply Chain Reinvention framework helps companies to meet the demands of today’s digital world

Supply Chain & Operations

Autonomous supply chain, automation, robotics

Global trade and the financial supply chain

Workforce of the future

OT/IT cyber threat

End-to-end (E2E) supply chain visibility and insights

Supply chain strategyand segmentation

Integrated supply chain operating

model

Supply chain network and trade

flow optimization

Integrated digital planning — supply chain

planning and synchronization

Smart product

and portfolio

management

Supply side optimization & procurement

Smart factoryDigital

logistics &

fulfilment

0

1

2Smart services and after-market

Key enablers

Advanced analytics, AI, cloud, big data, blockchainand machine learning

Supply Chain

Intelligence

Strategic

Architecture

Integrated

Operational

Excellence

Extended supply chain risk intelligence, optimization and sustainability3 Supply Chain

Resilience

Our reinvention framework provides a solution with flexibility across industry value chains

Route-To-Market Optimization

Logistics Engineering

(WH Design & Operations)

Outsourcing & SC partners selection

Ag

ile

Bu

sin

es

s S

up

ply

Ch

ain

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Degree of Taylorism / distribution of value creating processes on multiple entities within partner ecosystem

Leve

l o

f syste

m a

nd

da

ta in

teg

ration

/ u

sag

e o

f clo

ud

-base

d IT

syste

ms

Cloud-based SW platforms

External Systems

Integration

On-Premise IT-Systems

InternalBusiness Functions

Collaboration with Partners

Service & Platform-basedBusinesses

The old world: Value-creation

within entities, on-premise IT

Traditionally, creators have been selling products and

services through linear value chains. Companies were

owning a dedicated part of the value chain, competing

with competitors.

The new world: Ecosystems on

cloud-enabled platforms

Digital ecosystems do not work linear, they are shaping

market networks and enable hybrid forms of cooperation and

competition: Coopetition. Ecosystems create and serve

communities, and harness their creativity and intelligence.

Entities may play multiple roles in an ecosystem.

CustomersSuppliers

Innovation & Product Lifecycle Management

ManufacturerOEM

Finance & Controlling

Sales & Marketing

Service &Spare Parts Management

Supply Chain & Operations

Pro-duction

Ware-housing

Logis-tics

Distri-bution

OrderMgmt

Plan-ning

QualityMgmt

Mainte-nance

Sourcing & Purchasing

Supplier

Customer

Customer

Retail/Consumer

Customer

Con-nector

SC asa Service

For-warder

Eng.Contractor

LogisticsContractor

Supplier

Platform-basedBusiness Model

SC PlanningSupplier Mgmt.

Risk Management

Coopetition

Retail/Consumer

OEM

ContractManufact.

EngineeringCollaboration

PLM in the Cloud

Co-Creation

Com-petitor

“SUPPLY CHAIN OF THE FUTURE”

Supply Chain Of the Future

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Examples of our typical clients issues

Source: EY CPR Balance executive survey

Accelerated and faster planning cycles

Gaps in supply chain talent

Increasing product complexity and customer expectations

The changing role of companies in their ecosystems

New planning technologies, robotics and AI

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► The Planning Challenge:

► Complexity is increasing

► Customers more demanding

► Large planning teams with mixed capability

► Silo operation

► Planning Systems investment hasn’t delivered

► Supply chains remain unsynchronized

► Need to improve planning

► Speed, accuracy and agility

► Efficiency and cost

► Collaboration and cross company optimization

If autonomous cars are possible, why not

autonomous planning?

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Leading companies are using Digital disruption to step change planning and move towards increasingly ‘autonomous planning’

‘Always-On’ Planning - 5 Step Journey

‘Always-

On’

planning

High

intervention

manual

planning

Degree of

automation

Digital disruption –

efficiency step

change

Today Future

Integrated

Planning Towers

1

Increasing the scope of

services performed in

integrated planning

towers or control towers

Digital EBP

Enterprise Business

Planning

2

Integrated EBP to

visualise, monitor and

control the end-to-end

supply chain in real-time

Segment and

Synchronise

Value Chain

Synchronisation

3

Segmentation of portfolio

and synchronization of

the end-to-end value

chain to align supply

response with demand

signal

Responsive

Planning

Demand sensing,

concurrent planning,

real-time scenario

evaluation

4

Addition of ‘best of breed’

applications such as

concurrent planning,

demand signal repository,

trade promo

management, integrated

cash up

‘Always-On’

Planning

Intelligent

automation with

machine learning

and artificial

intelligence

5

Step change

improvement in accuracy

and transaction cost

effectiveness through use

of robotic process

automation (RPA),

machine learning (ML)

and artificial intelligence

(AI)

1. 2. 3. 4. 5.

Structures: Which

activities are performed in

market, above market, in

planning centers? What’s

the optimal model and

level of resource?

Speed: How far can I

compress planning

processes? Does a

daily/weekly cycle give a

competitive advantage?

Simplification: Which

processes can be

eliminated? Can

standardization accelerate

the deployment of cloud

solutions?

Skills: What are the future

capabilities required? Do I

have data scientists and

automation experts in the

business already?

Operating Model

Implications

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Transforming S&OP to IBP to EBPAn integrated, vertical and horizontal planning process

Enterprise Business Planning (EBP) is the next evolutionary step in end-to-end planning. In

contast to Sales and Operations Planning (S&OP) or Integrated Business Planning (IBP)

granular strategic planning prozesses are also taken into account

Benefits of an

Enterprise Business

Planning process

Typically achievements

consistent

outperformance of

peers, increase in

revenues and Gross

Margin, reduction in

inventories, increased

return on invested

capital and capital

efficiency, reduced and

more productive

SG&A* spend

MAKE DEPLOY SELL DELIVER SERVEBUY

INTEGRATED BUSINESS PLANNING (monthly)

DEMAND & SUPPLY SYNCHRONIZATION (week)

ORDER MANAGEMENT (day)

Mo

nth

lyW

eekly

Dail

y

Supplier Factory FW DC/LC ConsumerExecu

tio

n

Enterprise Business Planning

FINANCIAL & TARGET PLANNING (quarter/year)

Qu

art

er

INTEGRATED COMMERCIAL PLANS(3-year rolling with dropdown into Annual Plan)

An

nu

al+

R

M

FG FG S

G

Vertical / horizontal integration

► The integration between planning horizons

is as important as the integration between

silos (sales, marketing, supply chain,

finance)

► A planning framework needs to

concurrently orchestrate the top down,

bottoms up, as well as end to end

connections of a value chain

► Marketing, Sales, Supply Chain and

Finance work together to prioritize and

financial allocations against product,

channel, and customer

► Supply chain must eliminate inefficient

complexity while building flexibility to

address demand-side requirements

* Selling, General and Administrative Expenses

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Segmenting, Synchronizing and Optimizing Creating Flow across the Segmented Value Chain

Our Segmentation methodology focuses on systems and tools but also recognises

behaviour change is key to success Benefits of Segment

and Synchronise

Reduction in

unnecessary demand

forecast changes

Confidence to dip

into safety stocks

using them to buffer

variability

Stable weekly

production carefully

synchronised with

demand

Determine the overarching product-

customer segmentation and commercial

service requirements

Synchronisation

Behaviors and Change

Management

Reconfigure and align the Supply Chain

strategies, planning and fulfilment

parameters by segment

Embed behaviours, routines, processes,

organisation, CoE’s

and KPIs to support and sustain change

2

Segmentation1

3

Behavioural Change Method

Ways of working, routines, KPI’s, CoE / organisational models

Transformation Change management

Value Chain Synchronization Optimiser

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Responsive Planning based and agile decision-making

Responsive Planning enables more agile and reactive replanning wíth more flexibility

throughout the connected value chain Benefits of

Responsive

Planning

Less cyclic, more

reactive and agile

planning

Focus on value-

adding planningFoundation for

automation

No silos but working

along the entire

value chain

Focus on value chain

Focus is no longer functional silos, but the entire connected value chain

Reactive Responsive Re-planning

Less cyclic planning, but more agile and reactive re-planning

More flexibility

Exception-based will give more flexibility, more focus, more efficiency

Prepare for automation

The intention here is to establish already well-working mechanisms to

prepare for automation

Change Management

From change management side, this is about building trust for what is to

come

LEAN approachBased on the segmentation, exceptions, etc. the planners can focus on the more complex or value-adding elements of planning

1

2

3

4

5

6

Evolution of Forecasting & Demand Planning

Statistical

Forecasting

Demand

Planning

Demand

Modelling

Machine

Learning/AI

Naive

Forecasting

• Assumes last periods

demand value will

occur again this

period, with some

adaptations

• Fits a forecast curve

through historical

demand quantities

• Statically predicts

monthly or weekly

demand patterns

• Hierarchy & causal

effects incorporated

in the forecast

• Incorporates

promotional activities

NPI, etc.• Incorporates trend

data, seasonality

& moving averages

• Leverages more

granular & down-

stream data to get a

cleaner demand

signal, reduce

volatility & bullwhip

effect

• Includes techniques

like demand sensing

and shaping to

further improve

accuracy

• Takes advantage of

extended and even

big data to further

improve accuracy

• Relies on powerful

models to consider

demand drivers such

as promotional

activities, NPI, Social

Media, Weather, etc.

Error

Accu

racy

Where are you today?/Where do you want to be?

Climate, Weather and promotion data are the keys of an efficient decision making process

Past (history) Present (real time) Future (forecast)

Determinist Probabilist Trend

Exam

ple

s o

f u

ses

D+1 D+7 D +30

Sales Performance Analysis

Better performance management and

achievement of sales force objectives based on

a detailed history of weather data

Merch optimization & web site management

Arrange shelves and schedule advertising spots depending on the weather-sensitivity of audiences and

products

Shelving in store

Increase sales by promoting an offer in line using the 1-day or 2-day

weather forecast

Production and storage management

Reduce storage and production costs by using

demand forecasts that are based on weather trends over the next

month

Several M€/ an: of gain on the supply chain costs (beverage distribution)

10 to 25: efficiency points earned by adapting advertising depending on today weather

-27%: of waste and losses in the daily production of vegetables (food group)

-30%: store breaks for weather-sensitive products (distribution group)

80%: reliability in demand forecasting at D+7 at the sub-shelves level (DIY)

Illu

str

ati

on

s

Thanks to new data science developments and Big data technologies demand forecasting can go further

Recent algorithms are the beginning of a new era in forecasting paradigm

Main issues raised previously by econometric approach are solved by new Machine Learning, Deep Learning and Feature Engineering technics.

Forecasting at SKU level becomes more precise

Raise of Open data and the recovery of 3rd party data allow a better knowledge of the area of catchmentOpen and 3rd party data give access to more information concerning the catchment area (socio-economic indicators, weather data, …) and competitors. Smart Cities initiatives allow to forecast road and zone traffic, public transportation, …

Thanks to big data technology, real-time forecasting has become a reality in

industrial process Maturity achieved by Hadoop ecosystem

including Spark, Kafka make real time forecasting a reality. Forecasting at SKU

level can be done faster

The right Momentum

Demo Video of the Store Level Sales Forecast

Data alone cannot improve Demand Planning: a Demand Excellence Framework is required

Policy

► Set clear rules of the game,

including management of

exceptions,

► Clear links to performance

management and KPI

frameworks

People & Organization

► Define one operating model with clear

split of responsibilities between different

entities (global, regional, local)

► Assign clear roles and responsibilities

► Create demand planning center of

excellence

► Train & Educate for the right capabilities

e.g. analytics,

Data

► Ensure data is consistent, accurate

and timely available to users, to

ensure right first time decisions

► Clear ownership of data underpin

data quality eg product master data,

sales history and tool parameter set

up

Tools & Methodology

► Managing demand in a

complex environment and use

segmentation - driven by

product portfolio, geographies,

channels and customers,

requires sophisticated tools

and tested methodology

Process

► An aligned and harmonized

set of processes to drive the

activities both at tactical and

operational level

► Process swimlanes align with

organization and people

responsibilities

Performance

management

► Have one set of performance

metrics to measure

performance

► Benchmark both internally and

externally

Forecast accuracy BenchmarksProcess model

Segmentation

Forecasting rules

CoE setup

Process

People &Organization

Demand Excellence

Framework

0-3

Mo

nth

s D

em

and

Pla

nn

ing

3-2

4 M

on

ths

De

man

d P

lan

nin

g

Ho

rizo

n/P

roc

es

s

Pro

du

ct F

amily

/Co

un

try

SKU

/Lo

cati

on

Gra

nu

lari

ty

Define/Update DP

Strategy

Establish

demand

planning

policies

Review Stat/

Consumption

Model

performance

Revise/Create Consumption/

Stat Model

• Master Data• External and

Internal Factors• Segmentation Rules• Promotional

Calendar

History & Current Trends

Load and prepare

data

Measure KPIs

and publish

dashboards

Manage

performance

Send out Sales and Marketing activities

Statistical forecast

Generate

base-line

forecasts• Causal Models • Analytical toolkit• Manual overrides

Incorporate

Building

Blocks

• Advertisements• Innovation• Trade• Ship Adjustments• Pricing• Competition

IBP

Supply Review

Demand

ReviewIR/MBR

SKU level Demand Plan(0-3 months)

Incorporate customer

forecast data

• Weekly Pacing• SKU/Location Mix• Customer

Intelligence

Release DP

to Supply Hub Customer forecast data

Demand Sensing: SKU fcst(0-6 wks)

Agg

rega

tio

n

Disaggregation

Statistical

forecasting

Validate

Demand

Forecast

Data

Extraction

Demand Planning Strategy

(ad hoc / Yr)

Define DP

Strategy

Manage

Performance

• Product Segmentation

• Customer Segmentation

• Statistical baseline & initial model

tuning

• Test for best fit forecasting

algorithms by segment

• Test Forecast Strategy

Recommendations

• Apply Forecast Strategy

Recommendations To Live

Environment

• Data extraction from client

systems

• Data cleaning

• Data aggregation to agreed

forecasting hierarchy levels

• Generation of statistical

forecasts at agreed

hierarchy levels

• Use of tailored forecasting

algorithm by product /

customer segment (per DP

strategy)

• Disaggregation /

reaggregation

• Preparation of demand

review meeting materials

• Prioritisation of key

segments

• Prioritisation of exception

reports (delta of actual to

forecast etc.)

• Support for demand

forecast meeting

• Agreed forecast

adjustments

• Creation of demand planning

reports and documentation

• Example Metrics:

• Forecast accuracy

• Forecast bias

• Statistical forecast accuracy

• User forecast accuracy

• Example Reports

• Demand review meeting

forecast summary

• S&OP Demand side reports

Activities

Best fit forecast algorithm test

Data extraction routines Statistical forecast generation Demand review reporting KPI reporting KPI scorecard Current month Jul-10 Region XYZ

2010 20092010 YTD

Avg

2009 YTD

Avg

Measure Units Apr-10 May-10 Jun-10 3mth Avg 3mth Avg YTD YTD TargetStatus vs

Target

Status vs PY

Qtr

OTIF (CSL) Qty% Weighted

Volume82% 83% 81% 82% 80% 82% 86% 95%

OTIF (CSL) Line fill % lines in full 82% 83% 81% 82% 78% 81% 77% 95%

ComplaintsNumber of

complaints4 5 3 4.0 3.0 4 3.0 4

DSO Number of days 48 45 46 46.3 46.0 47.3 46.0 40

Forecast Error % Weighted Value 90% 100% 95% 95% 95% 92% 95% 90%

Forecast bias % Weighted Value 23% 30% 24% 26% 24% 24% 24% 25%

Fwd cover Number of days 107.0 110.0 108.0 108.3 108.0 107.6 108.0 100.0

DIO Number of days 122.0 120.0 125 122.3 125.0 122.1 125.0 110

Stock FGs (€’m) Value 40.9 41.2 40.8 41.0 40.8 40.9 40.8 45

Stock Raw (€’m) Value 33.1 30.2 31.2 31.5 31.2 32.4 31.2 35

Excess Inventory

(m)Value 0.3 0.3 0.3 0.3 0.5 0.6 0.5 7

Supply OTIF % Weighted

Volume96% 98% 97% 97% 97% 96% 97% 97%

Supply Qty OTIF % Weighted

Volume98% 97% 97% 97% 97% 98% 97% 97%

Production OTIF% Weighted

Volume96% 98% 97% 97% 97% 96% 97% 97%

OEE % 93% 92% 91% 92% 94% 94% 95% 97%

Supplier OTIF (Qty)% Weighted

Volume96% 99% 97% 97% 97% 97% 97% 97%

Supplier OTIF (line)% Weighted

Volume96% 99% 97% 97% 97% 97% 97% 97%

DPO Number of days 83 80 82 81.7 82.0 82.4 82.0 90

Air freight costs Value 0.0 0.2 0 0.1 0.0 0.0 0.1 0.1

Working Capital W. Capital % Sales % 0% 20% 0% 7% 0% 3% 3% 35%

Serious Quality

Failure (SQF)Number of failures 0 0 0 0 0 0 0 4

Right First Time

Quality (RFT)% 100% 0% 0% 33% 400% 143% 250% 90%

Quality

Production

Supply

Are

a Last 3 months

Customer

Demand

Inventory

9 box segmentation

Tools

Demand Planning Core Process

(Weekly process)

Performance Management

(monthly process)

Phase

Demand Management Methodology

Timeliness

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Incorporating Automation & Machine Learning into all planning processes

Routine tasks can increasingly be handled by automated processes which learn over time

and allow planners to focus on the real value-add Benefits of

Automation and

Artificial Intelligence

More efficient,

volume and value-

oriented planning

processes across the

supply chain to drive

reactivity and focus

on value-add

Segmentation

Harnessing Robotic Process Automation (RPA) or Artificial Intelligence (AI) and

Machine Learning to digitize the process

Demand Planning

Supply PlanningProduction Planning

Materials Resource Planning

► Conduct

segmentation

► Identify machine

planning candidates

► Conduct

forecasting

segmentation

► Determine optimal

forecast model and

parameters

► Update demand

forecasts for

Commercial input

► Update master

data planning

parameters

► Review and update

master data (lead

times, safety stock

levels, MOQ, EOQ

etc)

► Manage routine

planning exceptions

► Run Supply Plan

creating versions for

Service, Inventory,

Utilisation

► Manage plan

exceptions

► Create alerts for

remaining

exceptions

► Review and update

master data (lead

times, safety stock

levels, MOQ, EOQ

etc)

► Review supplier

availability and

refine production

plans for realistic

availability

Pla

nn

ing

Flo

wE

Y’s

Co

gn

itiv

e

Au

tom

ati

on

RPA (Robotic Process Automation) AI (Artificial Intelligence or Machine learning)

Applications of RPA and AI technologies in typical planning processes

EY’s Cognitive Automation for Supply chain Planning

Runs APS effectively

without planner’s

intervention

Learns from planner’s behaviour and predicts the right set of input

parameters dynamically

Emulates planner execution of

processes with existing APS system

Dynamic rules driven

Planning

Nature of Process handled

► Intelligent system to predict and mimic activities handled by SC production planner by learning his/her behaviour over a period of time

► Machine learning model based on neural network concepts

► Learns relationship between demand & supply situation on one hand and input combinations selected by planner on the other

► High complex/critical process

► Due to the dynamicity of inputs to be considered

► Significant impact of decision taken on the value chain - production, logistics and customer service

► Not a typical SOP driven process

► Conventional robotics and automation systems cannot handle this

Unique attempt to deploy an Advanced Intelligent system to handle and support one of the complex business functions (Supply chain Planning)

Data captured by Bot while working along

with planner, for Cognitive decision

making/evaluation of goodness of plan

Machine learning model implemented in

R, is invoked from Blue Prism providing

an integrated workflow

Planner-Bot handshake: Provision

for takeover by planner at any point of time and return to

Bot

EY’s Cognitive Automation for Supply chain Planning

Client demo

Proper profitable Allocation Planning is an Integral Part of the overall Supply Chain Planning Processes

Customers

Strategic Planning

Marketing and Sales Planning

Demand Planning

Supply Planning

Business Unit Plans

and Budgets

MANUFACTURESuppliers

Price Plans, Promotion

Schedules, etc.Sales Forecast

Short Term Demand Analytics

Demand SensingDemand Forecast

Sales &

Operations

Planning

Material

Planning

Capacity

Planning

Distribution

Planning

• All materials included (FG, Raw, etc)

• Produced and procured

• All plants, storage locations

• Constraints included

• Planned stock

transfers for all material types

Inventory

Planning

• All stocked materials• Multiple replenishment

methods

Medium Term (weeks/months)

Material Purchase

Orders

Supply Limitations

Packing

SchedulesProduction Capabilities

Replenishment Orders

Distribution Capabilities

Su

pp

ly

Ma

na

gem

en

tD

em

an

d

Ma

na

gem

en

t

Co

lla

bo

rati

on

Co

llab

ora

tion

Materials

ManagementShort Term

(days/weeks)

Productions

Schedules

Production

ManagementShort Term

(days/weeks)

Material

Schedules

Inventory

DeploymentShort Term

(days/weeks)

Deployment

Requirements

DELIVERPROCURE

Strategic and Financial Planning

Advanced

Planning

Optimization

Integrated Business

Panning

Actuals

Escalations

Decisions

Approvals

Allocation

Planning

Constrained forecast

and allocated sales

orders & inventory

Profitable

Allocation

Order

Promising

PROMISE

Cost to

Serve

Are we serving our category, channel and

customers in a profitable way ?

Faced with supply constraints, can we

correctly shape and prioritize demand to

maximize availability and profit ?

How do we allocate inventory in our

distribution network in front of short supply

situation ?

Can we control demand in the short term to

avoid actual demand to go above allocation ?

Are we promising sales orders vs. allocated

amount and not FIFO ?

Yearly

Monthly

Weekly

Daily

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Globally we have built a substantial global Supply Chain and Operations practice and have grown over 25% since 2015

Americas

2019 YTD 812

Planning 110

A recognized thought leader from strategy to implementation

supply chain

Reinvention

Logistics and

Warehouse

Management and

SAP solutions

Disruptive supply

chain trends

Digital supply

chain:

it’s all about that

data

Balancing the

growth-profit

paradox

Integrated margin

management

Creating value in

brand new order

Growth with Omni-

channel

Analytics to boost

performance

Named a Leader for Digital

Operations Consulting 2019 by

IDC

Ranked among the highest for

digital supply chain Planning

and Design Consulting 2019 by

ALM Intelligence

Ranked among the highest for

Digital Logistics Consulting 2016

by ALM Intelligence

Selected for the 2017 World’s

Best Outsourcing Advisors list

managed by IAOP

EY is among the highest in digital supply chain planning and design

EMEIA

2019 YTD 2,092

Planning 260

Global headcount

2019 YTD 3,572

Planning 460Japan

2019 YTD 137

Planning 20

Asia-Pacific

2019 YTD 531

Planning 70

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