Enabling a Demand Driven Supply Chain with Teradata at ECR Moscow Feb 2014

Category Management supported by Detailed data

ENABLING A DEMAND

DRIVEN SUPPLY CHAIN

(DDSC)

ENABLING A DEMAND

DRIVEN SUPPLY CHAIN

(DDSC)

Moscow,

February 12th , 2014

Frank Vullers

Senior Industry Consultant Retail

Teradata EMEA

Category Management supported by Detailed data

AGENDA

Supply Chain Management

SCM Solutions Customer

Cases Supply Chain Management

3 2/18/2014 Enabling a Demand Driven Supply Chain (DDSC)

Effect of Poor shelf availability

Both Retailers and Manufacturers suffer when Item is Out of Stock


From Traditional to Demand Driven SCM

• Focus on Assets & Products

• SCM Costs as % of turnover

• Supply chain scope is Logistics

• Visibility only inventory on-hand

• incremental SC improvements

• Sales & Operations planning is not a business discipline

• Focus on Customer service

• Service levels, Out of Stock

• Scope on Supply networks

• Visibility across the entire chain

• Temporal models & Analytics sense future problems & Opportunities

• Sales & Operations plans are disciplined and effective

Traditional Demand Driven


From Traditional to Demand Driven SCM Traditional Demand Driven

Not every company is at once fully Demand driven


The Demand Chain Inputs

• Store/Sku: Forecasts, Orders, Allocations

• Individual location based historical sales, future events

Results

• Accurate demand forecast

• Replenishment plans

• Allocation plan

Inputs

• PO data, Partners, Orders, ASN‟s

• Expected ship and arrival (ETA‟s)

• Anticipated events

End Results

• Product in the right place at the right time

• Improved sales and turns

• Customer satisfaction

How much of each item do I need to have at each Location and when?

Demand Pulls Product

Is the ordered product moving efficiently through the supply chain to the Locations,

according to plan and latest trends?

Product Flows Efficiently

Consumer DC Source Carrier Store


Strategic Challenges: SCM & Russia

Courtesy to G. Moore‟s “Living on the fault line”

Supply chain Planning

Sourcing Returns

• Customers & large territory

• Forecasting challenging

• local suppliers poor reliability

• importing from abroad • convincing foreign suppliers

to move to Russia

• Staff turnover is high • lack of well-trained

qualified people

• Russia the largest country

• lack of warehouses • fragmented market

• reverse logistics processes & cross the border again.

Production Delivery


Strategies for these Challenges

Courtesy to G. Moore‟s “Living on the fault line”

Supply chain Planning

Production Delivery Returns

supply chain strategy oriented

towards effectiveness, flexibility and

responsiveness

demand forecasts and out-of-stock levels can

be improved through better information sharing

the right transportation mode

And balancing transportation costs, inventory costs and

required responsiveness levels

Sourcing

keep extra safety stocks to overcome improvements in

information sharing

Consumers do want a broad range of choice,

but are willing to switch if products are unavailable

9 2/18/2014 Enabling a Demand Driven Supply Chain (DDSC) Category Management supported by Detailed data

AGENDA



Cases


SCM Solutions

10

SCOR® Framework How Can Teradata Help ?


Quick Start Forecasts for Basic and Short Season Products

Use Actual Results or Forecast Values, link multiple skus

Orders/Allocations calculated on

forecasts/ current-projected on hand

inventory

Point in Time and Time Phased Orders

Calculates Forecast Lifts, Simulation

Modeled Versus Historical Events

Assigns Sku‟s to Models

Long and Short Seasons

Seasonal Profile

Best Fit Models

Store SKU, Scored Weekly

Client Defined Adaptive Response

Deseasonalized ARS, Best Fit

Intelligent Product Introduction

Weekly SKU Ranking

Ranking by location,

merchandise groups SKU

Ranking

A B C D E

A

B

C

D

E

60 %

Promotion Lifts

Time Phased Demand Plan

Allocation

Demand Chain Manager DCM Better Forecasting & Replenishment


Poor BI Performance & Monitoring

Ad-Hoc Tools and Processes

Multiple Sources

Multiple Data Marts

Inconsistent Data Sourcing

Disparate Reports

Frustrated End-users & Decision Makers

Product Management

Regulatory Reporting

Customer

Profitability

Customer Care

Sales

Operations

CRM

Marketing

Finance

The IT Hair Ball Analytics and Reporting

The Excel Army

Real Time Insight


Integrated Data Warehouse+

Dedicated Tools and Aligned

Processes

Multiple Sources

Integrated Data

Warehouse

Consistent Data Sourcing

Integrated reports

Happy End-users & Decision Makers

Product Management

Regulatory Reporting

Customer

Profitability

Customer Care

Sales

Operations

CRM

Marketing

Finance

IT Lasagna Analytics and Reporting

Proper BI Tools

ITEM

LOCATION SALES

Retail Data Model (RDM)

Real Time Insight


Retail / Manufacturing experiences

Scalable Size – Users – Usage

Realtime Reporting – Alerting – Monitoring

Fast integration of datamarts

Retail business Knowledge

Benefits

Real Time Insight

Detailed Cost Analysis


AGENDA



Cases


Customer cases

Case 1: HEMA Demand Chain Manager

Better Forecast & Replenishment

Case 2: Monoprix On Shelf Availability

Case 3: Shipment Tracking

Case 4: Tesco Link

Real time insight

Case 5: Transportation optimization



Become demand driven

Objective

• Replacement of supply chain steering model

• Teradata Demand Chain manager (DCM) application became foundation of HEMA new supply chain model

Analysis & Actions

Case 1: HEMA DCM

• Opened 200 stores (450->664) still delivered from same DC

• Inventory levels exceeded objectives

• Less frequent delivery to stores

• Side effect: Reporting speeded up

Result

Better Forecasting & Replenishment


HEMA Today…

• More than 650 stores in multiple countries (NL, BE, F, D)

> Including 150 + Franchise stores

• Variety store

• Hardgoods, food and fashion items

• Private label

• Award winning formula 0

100

200

300

400

500

600

700

1960 1980 2000 2020



Suppliers DC Continuous (Pull)

DC Promotions (Push)

Stores

Wholesale

Internet

• 500 suppliers

• Europe and Far East

• Leadtime 1 day – 26 weeks

• All private label and HEMA design

HEMA Supply Chain

• One central DC

> Highly automated

• Continuous items

> Call off orders based on forecast

• Seasonal items and promotions

> Promotion uplift calculation; confirmed by all stores

• General

> Modular assortments

> Assortments per store

• Pull (continuous items)

> Min – Max system;

> 1-5 times per week

> Lead time DC: 1-2 days

• Push (including promotions of pull items)

> Allocation of stocks

> Delivered 1 – 2 weeks before



20

What is „Deliver to Demand‟ concept?

• Deliver to Demand enables us to become

demand driven

• Logistical streams will become automatic

• Share forecast with suppliers early in

planningsprocess

„Deliver

to

Demand

‟



21

Why did HEMA need this concept ?

The supply chain steering model was out of date

Based on old principles

No relation between store, DC planning and promotional push

No input of local influences

Not proactive but reactive;

Forecasts created outside systems (Excel) for Push streams. No

link with real Sales.



• Phase 1: Continuous products 2009

• Phase 2.1: Long season: Push-Pull-Push 2010

• Phase 2.2: Short season Push- 2nd Push 2010

• Phase 3.1: Ambient Food (Whine, etc) 2011

• Upgrade to DCM version 4.1 2012

• Phase 3.2 Food Cakes, pastry

• Next Steps

Hema Project Better Forecasting & Replenishment


Fashion/ Softlines

Grocery/ Liquor

Hardlines

Food & Beverage

Drugs

Health & Beauty

Apparel

Shoes

Home Furnishings

Consumer Electronics

Furniture/Appliances

Building Materials

Auto Parts

Office Supplies

Books,Music,Software

Accessories

Demand Chain Manager(DCM) Customers

Handleman Company

SILKROUTE


http://www.radioshack.com/home/index.jsp


Needed to understand the causes of OOS

Objective

• Analysis root causes

• On Shelf Availability(OSA) report for top 2000 SKUs

• to identify both operational and supply chain issues

• predicts on shelf out of stocks in order to prevent them

Analysis & Actions

Case 2: On Shelf Availability (OSA)

• Reduction OOS resulting in 1% increase in sales

• Improved in-store procedures

• Improved stock accuracy

Result

Real Time Insight


Letting Business customers check their shipment status online, and get rid of separate datamart

Objective

• Data loads into Data Warehouse to meet service level agreements, and for route optimization and profitability analytics

• Transaction data now loads within 15 minutes to Teradata with direct access via the web.

Analysis & Actions

Case 3: Shipment Tracking

• 36,000 self-service queries per day

• 35% reduction in calls to Customer Care center

• Eliminated ODS

Result

Real Time Insight


Case 4: Tesco Link

Leverage Suppliers knowledge on categories

Objective

• Lean backoffice • One consistent way of

working

Result

• Give Suppliers entrance to Tesco data

• Sharing detailed information on sales data

• Not only viewing but also Downloading data

Analysis & Actions

Real Time Insight


optimize Freight loads over the different Grocery retail chains in order to have a profitable own Fleet

Objective

• Started with defining best KPI‟s

• Making right choice between internal fleet and Contractors

• Create best route based on costs, and service levels

Analysis & Actions

Case 5: Transportation Optimization

• Own Application gives insight in break even point and when to choose which transport

Result



AGENDA



Cases


QUESTIONS?


QUESTIONS? THANKS YOU FOR

ATTENTION

Frank Vullers

Senior Industry Consultant Retail Teradata EMEA

[email protected]

https://twitter.com/FrankVullers

mailto:[email protected]

Enabling a Demand Driven Supply Chain with Teradata at ECR Moscow Feb 2014

Technology

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