Supply chain Planing & Optimisation with Automated Replenishment in Promotion Driven Markets -...

OPTIMIZACIJA LANCA SNABDEVANJA

Godišnja Konferencija Srpske Logističke AsocijacijeNovember 15, 2016

Richard D WallisTechnical Director & CSA Global Operations

ToolsGroup

Supply Chain Planning & Optimisation with Automated

Replenishment in Promotion Driven Markets

Company Confidential All Rights Reserved2

Who are ToolsGroup

EUROPEAmsterdamMilanMunichBarcelonaLondonStockholmBelgrade

AMERICASBostonBuenos AiresMexico D.F.OntarioQuitoSao Paulo

ASIA/PacificManilaBombayTokyoPenangSydney

MIDDLE EASTTel Aviv

AFRICACape Town

Our Vision

Accelerating Business Performance

through Market Driven Demand Analytics,

Demand Planning and Supply Chain Optimization

Supply Chain Specialist

ToolsGroup is a Supply Chain specialist with over

25 years business engineering & optimisation

experience in global and regional operations with

almost 400 customer references


Single Bolt-On Model Solution for Integrated Supply Chain Planning

DEMAND

MODELING

&

ANALYTICS

ERP SCP

SO99+ Single Model

Forecast &Variability

StockingLevelsMULTI-

ECHELON

INVENTORY

OPTIMIZATION

RecommendedOrders

DASHBOARDS & PERFORMANCE MANAGEMENT

INTEGRATED BUSINESS PLANNING

DemandCollaboration

Hub

SupplyCollaboration

Hub

REPLENISHMENT

and PRODUCTION

PLANNING

Demand Scenarios


Recommended by Gartner


Retailer 1 Network

Retailer M Network

Retailer N Network

Vendor Network

At each network tier/location:• Orders are fulfilled from stock

Typical Single Stage Planning & Replenishment Model

At each network tier/location:• Projected demand and inventory are compared

• .. replenishment orders are calculated

• .. and passed upstream (as demand)

Demand=Tražnja

Supply=Snabdevanje


• The right hand does not know what the left hand is doing

• The system is (blindly) reactive

• The Bullwhip (Forrester) disease is present

• Inventory at any network tier is typically oversized

• The inefficiency adds excessive cost to the total supply chain

The Single Stage Planning & Replenishment Model

• “Beer Game” anyone ?


Plants /

Suppliers

Central

Warehouse

Regional

Warehouses

How much of this is YOUR network ?

Retailer DC’s

CONSUMERS

Stores

RETAILER NETWORKVENDOR NETWORK


The Fully Automated Supply Chain: Dynamic Multi-Echelon Replenishment

Processing a ‘time-phased’

Stochastic Demand Signal

across the entire network


New Generation Planning & Replenishment Models

Three key processes must be tamed and synchronised in order to

enable a fully automated supply chain:

• Forecast

• Inventory Plan

• Replenishment (supply and deployment)

But how can you automate the forecast in a business with highly promotion

driven demand ?


Mastering the Long Tail

• Ever larger number of SKU/Ls with highly

heterogeneous behaviour:• Fast

• Slow

• Erratic

• …

• The challenge is to understand the

behaviour of each SKU-Location across

multiple dimensions: • Quantity

• Order-line frequency

• Variability

• Forecast errorSelf-adaptive forecasting techniques allow

business to address this growing issue and

provide a reliable base line forecast at the most

detailed market levels


New Generation Promotional Planning Models

Machine Learning (AI) techniques facilitate the automation of promotion planning

in the forecast.

The first step is to establish a set of promo types, characterised by:• characteristics of the promo

• duration

• location

Then the promos can be clustered into behavioural types: uplift characteristics.

This allows:• the modelling of the historical performance

• the application of the model to future events


-3 -2 -1 0 1 2 3 4

1.0

1.5

2.0

2.5

#week

norm

. sal

e

Automating the Forecasting of Promotions & the Cleaning of the History

Profitable promotion planning entails accurately splitting baseline demand from promotional uplift

PAST | Learning FUTURE | Forecasting

PROMO 01 PROMO 02 PROMO 03 PROMO 04 PROMO 05 PROMO 06FUTURE

PROMO 01FUTURE

PROMO 02

Baseline Demand Actual Demand Baseline Forecast Promotional Forecast

Uplift models are applied to the future baseline to generate an accurate and reliable forecast


Demand Phenomena

Historical DemandHistorical Demand

Machine Learning Engine in SO99+

New Products

Web

Media

Promotions

(auxiliary engines)

OTHERSpecific dataset

extraction

Demand Modeling

SPECIALACTIONS

Promo Variables

EXTERNAL VARIABLES

MediaVariables

SentimentData LAUNCH

PROFILESIntroductionVariables

SEASONALITYStatistical Variables


Automating the Forecast enables an automated Replenishment Plan


ToolsGroup serves customers with complex demand

environments, intermittent and irregular demand, promotions,

frequent NPIs and heterogeneous product categories

Core Verticals include

CPG Electronics Food &

Beverage

Fashion Healthcare

& Pharma

Aftermarket

Parts

Industrial &

Durables

Retail Specialty

Chemicals

Wholesale

Distribution

Our 98% customer

retention rate is

among the highest in

the industry

Frequently these customers

have large and complex

distribution networks

including vertically

integrated retailers,

aftermarket parts

manufacturers,

multi-enterprise networks,

and multi-channel supply

chains

Innovator in machine learning

and advanced analytics for

supply chain planning

25 years of demand analytics,

supply chain planning and

optimization experience

15-30%Average decrease

In inventory

& costRATED #1 In

Inventory

Optimization By Nucleus

Research

2X to 5XImprovement In

Planner Productivity

2-15% Average

Improvement in

Service Levels

390 + Customers with

Offices around the

World

Vaulted into the

LEADERS

quadrant

SOR - 2016

By GARTNER


Some Businesses using the SO99+ Technology


SME running SO99+ Technology

Retail SMEs in competitive growing markets

Company Sector Locations Item/Locations * Country

Fybeca PC & Pharma 150 300,000 Ecuador

Sana Sana PC + HC 500 250,000 Ecuador

Kronans Apotek PC & Pharma 304 350,000 Sweden

KIKO Cosmetics 700 200,000 Italy

Costa Express Coffee >5,000 1,000,000 UK

Nikora Food 130 60,000 Georgia

Generika Pharma >650 1,500,000 Philippines

Oki Doki Snack & Beverage >20 4,000 Ecuador

Novo Mundo White Goods 130 150,000 Mexico

Lilly Personal Care 200 2,500,000 Serbia/Bulgaria

Andrew Page Automotive 135 15,000,000 UK

* Approximate figures

Case Studies


Danone Case Study

DEMAND SHAPING

KPIs (PROMO)

2010 2011 2012*

Net UPLIFT %

“TRADITIONAL”

SUPPLY CHAIN KPIs

…BEYOND

NUMBERS…

TOTAL

FORECAST

ERROR-20%

ROBUST FOUNDATION

OF A VALUE DRIVEN

S&OP

CONTINUOUS

OPTIMIZATION OF

INVESTMENT EFFICIENCY

CONSISTENT BUSINESS

STEERING CYCLE

FLEXIBLITY AND

RESPONSIVENESS TO

BUSINESS OBJS SHIFTS

-30%

-30%

LOST SALES

(SELL IN)

FG

OBSOLESCENCE

+36% +55%

2010

+6% +8%

2011 2012*

Net ROI

Recent Update

• Forecast accuracy improved to 92%

• 37 consecutive months have exceeded 98.7%service level goal

• Number of demand planners reduced to 2

• APO used only as data transmission tool


POS Demand Sensing & Replenishment with 15 Minute Latency

Supply chain Planing & Optimisation with Automated Replenishment in Promotion Driven Markets -...

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