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Come non impazzire nel gestire la pianificazione della Domanda

Milano, Caffe Panzera, 23 Marzo 2010

Paolo Prandini

Master Principal Sales Consultant, Supply Chain

Da sempre l’uomo cerca di prevedere il futuro

Fondi di Caffè

Maghi

Tarocchi

Strumenti diprevisione

Dadi

Da sempre l’uomo cerca di prevedere il futuro

Strumenti diprevisione

Perche’ la previsione della domanda è importante?

Costituisce la base dei piani di approvigionamento

Costituisce la base per i piani di produzione

Costituisce la base per i piani di Budget

Aiuta a ridurre le scorte

Aiuta ad aumentare la soddisfazione del cliente

Aiuta a massimizzare il ROI promozionale

E‟ alla base delle strategie dei Business „Demand

Driven‟

Vogliamo sicurezza nel futuro

il Demand Management è un processo incompreso

Il motore statistico spaventa

Si pensa servano statistici

esperti in camice bianco

Si pensa sia complicato ed

oneroso da gestire

La figura del Demand Planner

puro è rara da trovare in

azienda e fuori

Ci si affida spesso a quanto

presente a livello di ERP ma

poi non è abbastanza...

Ha le sue strane parole chiave...

MAPE (Mean Absolute

Percentage Error)

MAD (Mean Absolute

Deviation)

SD (Standard Deviation)

Accuracy

Bias

Absolute Error

Baseline

Uplift


“This demand is primarily influenced by factors

outside the company‟s decisions.These external

factors induce random variation in the demand for

such items, thus demand will be projections of

historical patterns. These forecasts estimate the

average usage rate and a pattern of random

variation

Domanda Indipendente,

Prof. Jacobas, Univ.Ilinois

Tipi di Demand

Domanda Dipendente

Pianificazione Prodotti Configurabili

Tipi di Demand

Pianificazione delle Opzioni


“Baseline Forecasting is a methodology

that uses system inputs and the forecast

engine to develop a statistical plan that may

be further adjusted as needed to provide a

common starting point (or „baseline‟ ) for

internal and external collaboration in order

to reduce forecast error”

Baseline Forecasting,

definizione

L’errore sul Forecast produce vari effetti

Forecast Error

Over Forecast Under Forecast

Excess Inventory

Inventory Holding Cost

Trans-shipment Cost

Obsolescence

Reduced Margin

Order Expediting Cost

Higher Product Cost

Lost Revenue

Lost Companion Product Sales

Lower Customer Satisfaction

3.2 Inventory.rm

Esempio

Costs and Lost Sales Example from Forecast Error

Forecast too high:

Monthly SKU Volume 1,000,000 units

Percent Forecast Error 10% Yields: 100,000 units more than required

Average SKU Cost $0.75

Excess Inventory $ per Month $75,000

Annual Excess Inventory $ $900,000

Forecast too Low

Monthly SKU Volume 1,000,000 units

Percent Forecast Error 10% Yields: 100,000 units of lost sales

Average Margin per SKU $0.50

Lost Profit per Month $50,000

Annual Profit Loss $600,000

© 2006 Oracle Corporation – Proprietary and Confidential

E il Forecast dei nuovi prodotti?

• Il forecast dei nuovi prodotti presenta nuove sfide:

– Storia della domanda assente

– Puo‟ assorbire caratteristiche da prodotti simili

– Prezzi e Condizioni di mercato differenti

– La domanda cambia lungo il ciclo di vita

Chaining

New Product C = 30% Product A + 75% Product B

Shape Modeling

•Apply shapes, scaled for volume and time

•Re-scale base on initial demand data

Attribute-Based

Forecasting

Model new item based on past behavior of other items with similar attributes


ColoreCaratteristiche

Tecnico/Commerciali

Formato Prezzo

Forecast basato su Attributi

Item


ColoreCaratteristiche

Tecnico/Commerciali

Formato Prezzo

Forecast basato su Attributi

Item

Si utilizza la Famiglia di attributi di

prodotti simili piuttosto che altre

SKU come input

Il Motore di Forecasting

determina su quali attributi

basarsi in base al prodotto scelto

Questa metodologia analizza il

comportamento del consumatore

piuttosto che il comportamento

del prodotto

Il Forecast viene poi allocato alle

SKU in base a Business Rules

Metodologia

Utile per introduzione massiva di

nuovi prodotti aventi

caratteristiche simili a quelli

esistenti

Parte integrante del processo di

Product LifeCycle Management

(PLM)

Utilizzato in settori quali Fashion,

Hi-Tech, CPG.

Valore di Business


Chi beneficia del Demand Management?

• Food & Beverage

– CG

– FMCG

• Telecom

• Utilities

• Media

• Automotive

• High-Tech

• Banking

Il Tool ideale quindi deve essere...

Facile da usare

Basato su conoscenze di Business

Che non necessita conoscenze statistiche

Di facile Implementazione e Manutenzione

Con una maggior sensibilita‟...

Con miglior accuratezza...

Che posso gestire in azienda come le altre

applicazioni...


Oracle Demantra è una soluzione 'Best in Class' per il

Demand Management, il Sales & Operation

Planning ed il Promotion Planning Management.

Aiuta i clienti ad aumentare l'accuratezza del

Forecast, migliorare i forecast statistici, la

collaborazione interna ed esterna alla ricerca di un

valore condiviso,bilanciare Supply e Demand e

analizzare l'efficacia delle promozioni e dei Budgets.

Oracle Demantra

C:/Documents and Settings/pprandin/My Documents/Presentations & Materials/WIP/FY10/OPN Days/3.1 SalesForecast.rm

CONFIDENTIAL: All capabilities and dates are for planning purposes only and may not be used in any contract

Suppliers

Customers

Finished

Product Mfgr

Distribution

Channels

Raw

Materials

Distributors

GrowersManufacturers

Business

ConsumerWeb Direct

Retailers

Brokers

Demantra and Supply Chain

Demand Mgmt

Trade Promotion

S&OP

La storia da sola non basta per fare previsioni

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(Futuro)

Tutto dipende dal cuore...

C:/Documents and Settings/pprandin/My Documents/Presentations & Materials/WIP/FY10/OPN Days/3.1 SalesForecast.rm

Demantra quindi...

Puo‟ usare tutte le informazioni che avete circa le

vendite:

Ordinato / Spedito

Calendari Marketing

Eventi Promo

Eventi Media

Syndacated Data (Ac Nielsen, Information Resource)

Dati Demografici

Attributi Prodotto / Store

E restituirvi un Forecast sempre aggiornato e

accurato

In alcuni casi addirittura In tempo quasi reale!*

* Dipende dalla disponibilita’ dei dati e dal tempo di elaborazione


Live on Demantra DM, RTS&OP and PTP

Company

• $750 million in revenues

• Leading producer of juices and jams

Planning problem solved

• Promotion planning synchronized with demand planning

• Consistent planning of $100M trade budget and tactics

Unique aspects of implementation

• Sales reps drive forecasting process from trade promotion planning process

• What-if scenario planning enables sales reps to test promotion before selecting it

• Integration with Oracle EBS (MPS and DRP)

• Accurate and timely customer and brand P&Ls and Trade Accrual

Welch’s

• Increased forecast accuracy at SKU level by more than 10 points

• $5 million reduction in supply chain costs

• Over $1 million reduction in trade spending

• Enables trade promotion planning to be integrated with RT S&OP

• Improved HQ and sales planning productivity


Live on Demantra DM, AF&DM

C&S Wholesale Grocers

Company

• At $20B/yr, 2nd largest grocery wholesaler in the US

• Managing forecast of 90,000 SKUs at 25,000 locations

Planning problem solved

• Aligning promotion driven demand spikes across multiple customers and

manufacturer suppliers to maximize service levels while minimizing inventory

held and “leftovers”

Unique aspects of implementation

• Integrated with multiple legacy order mgmt systems

• Live in 7 months

• Platform flexibility supported complex promotional modeling

requirements

• Product Scalability supported very large dataset requirement


Wendy’s International

Dublin, Ohio, USA

www.wendys.com

6,746 locations

$2.4 Billion annual revenue

Quick-Service Restaurant

Strategy:

• Drive the procurement, preparation and labor requirements by

generating accurate demand forecasts

• Improve profitability and store level execution by forecasting

demand every half-hour

• Sense demand and improve forecasts by utilizing attributes

and characteristics

• Evaluate the effectiveness and cannibalization of Promotions

Solution and Results:

• Oracle-Demantra Demand Management provides scalability

and flexibility to support Wendy‟s one billion calculations per

hour

• Achieved 95% accuracy at store level

• Achieved $3.5 million in savings by optimizing labor

supply

• Expected 20% reduction in overall operating costs

Wendy’s

DEMAND MANAGEMENT


Live on Demantra DM, AF&DM

20th Century Fox

Company• Leading producer and distributor of movies

Planning problem solved• Maximize movie sales across thousands of retail stores from Walmart, Kmart,

Toys-R-Us,…

• Better manage the introduction of new titles with no sales history.

• Service key retail customers via Vendor Managed Inventory model.

• Reduce supply chain costs

Unique aspects of implementation• Demantra provides Fox with daily replenishment plans down to the

item/store/shelf level via accurate forecasting, web-based collaboration and VMI technology.

• Some new products are now planned via attribute based forecasting.

• Reduced Planning cycle times (daily planning 10,000 stores)

• Reduced shipping cost

• Revenue improvement of 8%+

Case Study - Fairfax

Anatomy of a Happy Customer

Anatomy of a Win

Fairfax is Australia‟s and New Zealand‟s largest

publishing group(Sydney Morning Herald, The Sun-Herald, The Age,…)

ChallengeImprove demand forecasting (at the kiosk-level)

Improve supply allocation

Reduce lost revenue from sell-outs

Reduce returned copies

Fairfax Overview

Oracle Solution

Demantra ValueScalable forecasting at the most granular level

Effective management of a perishable product (short shelf

life)

More frequent calculation of outlet supply quantities

SolutionDemand Management

Advanced Forecasting and Demand Modeling

Real-time Sales & Operations Planning

$900K license and $300K for implementation

Key Performance

Indicators

showing current

status of

important

information

Focus planners

attention

Direct access to

online reporting and

personalized

worksheets

Quick Data Access

Automated workflow and

Exception Management of

business processes reduces

information handling

Handling service levels

before it is a problem

The Planning Dashboard

Advanced Logic to „clean‟ demand prior to forecasting

Sell Outs

Estimated Returns

Estimated and Projected Subscriptions

Hidden Demand

True Demand Logic

Availability

calculated based

on Service level &

Demand

variability

Agent Band re-

calculated each

week,

Demographics

loaded to allow

focus on key areas

Safety Stock Calculation and Review

Workflow Driven – Agent Refresh Process

Return on Investment

Budgeted savings exceeded for all 7 days for a 6 month period

Budgeted Actual

Weekday -8% -11%

Saturday -1% -5%

Sunday -2% -7%

Change in returned copies: July-December, 2005

• Reduction in returns of Sydney Morning Herald by 15% and 5-10% for other publications

• Increase in availability levels

• Expected savings of $300K/month and have exceeding this


Come introdurre tutte le variabili significative del piano

Supply Chain:Build stock for planned maintenance shutdown

Result: Everyone

unhappy (including consumer) , both Manufacturer and Retailer lose sales

CEO: Very

unhappy. Receives call from irate customer

advising he will sue for lost sales

Customer: Very

unhappy. Not getting stock. Consumers not

happy either

Supply Chain:Alert…Stock levels

dropping fast due spike in sales

Marketing:Confirms there is

sufficient stock. No one tells Supply Chain

about the promotion

Marketing: Sales

check with Marketing if there is sufficient stock,…Two days

before promotion starts

Sales: Decide to

run a major promo. No review of stock levels

Case Study – National Brands Ltd

Diagnosis: Poor Internal Collaboration, Poor Forecasting, Poor Promotion Management

Business Impact: Costs Increased, Profit Decreased, Customer Service Decreased.

La Collaborazione produce sempre i migliori risultati

Finance

Sales

Marketing

Logistics

Demand

ERP

New Products

Supply

Revenue and Cost Contol

Ordini e Previsioni

Strategie di Crescita

Think Tank

Demand Planner

Make, Buy, Plan

Ship

La Collaborazione produce sempre i migliori risultati

Finance

Sales

Marketing

Logistics

Demand

ERP

New Products

Supply

Revenue and Cost Contol

Ordini e Previsioni

Strategie di Crescita

Think Tank

Demand Planner

Make, Buy, Plan

Ship

Sales & Operations Planning


S&OP obiettivi•Allineare diversi

obiettivi

•Portare le strategie

della società su

piani fattibili

•Tradurre e rendere

consistenti diversi

obiettivi

•Evidenziare conflitti

•Trovare il piano

ottimale

considerando i

vincoli

•Convergere su un

solo numero

Finance

Sales

& Marketing Production

Supply Chain

Obiettivi vendite:

• Max ricavi

• Max market share

• Alta disponibilità del prodotto

• Metrica: Sales plan ($$$)

Obiettivi Supply

Chain :

• Fattibilità

• Alta stabilità

• Metrica: The

Demand Plan

Obiettivi

Produzione:

• Ottimizzazio

ne dello

stabilimento

produttivo

• Stabilità

• Metrica:

piano di

produzione

Obiettivi Finance:

• Fare il budget

• Controllo e

predittività degli

eventi

• Metrica:

Budget

S&OP – Allineamento dei processi e

dell’organizzazione

Sales & Operations Planning

Sales Budget Quantity (Manual + Statistical)

Value calculated by List price

Forecast Assumptions, Service Level, Inventory

Stocks, Demand Variability MAPE

Simulate your best Supply Plan based on Demand

and Make/Buy Constraints

Bring Financial Constraints to the table (Revenues or

Costs)

Approve the Final Demand, Supply & Finance Plan.

Execute and Monitor


Come Funziona?

I Baseline Forecasts vengono sviluppati sulla base della demand storica dal motore statistico-analitico

Raggiungimento del Consensus Plan tramitecollaborazione e Workflow

Consensus Plan

Il Baseline Forecast viene distribuito alle persone responsabili della pianificazione.

Il Consensus Plan vienecontinuamentemonitorato e modificato diconseguenza

Vengono generati Alerts ogni volta che il piano modificato si discostadall’originale

Gli alert agganciano ilWorksheet necessario allarisoluzione del problemaper velocizzare il processo

Integrazione Manuale delle informazioni

Simulazione per gli utentipiu avanzati – What-if Analisi – Tuning del Forecast

Demand Plan consolidato nel piano finale


Presentation given at GMA Conference in

March, 2007 by Mike Vincitorio, Sr. Director

Supply Chain, Applica

Case Study - Applica

“This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”

Applica• $600 million + provider of consumer durables

• Principle businesses include small household appliances and

professional hair care products

• Trade names: Black & Decker Home, Littermaid and Gold-N-Hot

• Distribute across the Americas to all retailing channels

• Recently acquired by Harbinger Capital Group – Private Equity

Demantra• Live with Demantra 6.2 in August, 2004

• Demantra is a Tier 1 system supporting Demand Planning

• Forecasted accounts ~ 65

• Active forecasted items ~ 2000

Applica and Demantra


-8.0%

-3.0%

2.0%

7.0%

12.0%

17.0%

22.0%

Sep-05 Oct-05 Nov-05 Dec-05 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06

Month

Bia

s

6 Month Rolling Bias Upper Limit Target

Lower Target Linear (6 Month Rolling Bias)

Targeted Zone

Reduction in Forecast Bias has yielded ~$9 million (13%) reduction in average

inventory.

Applica Historical Forecast Bias

Forecast Accuracy measured by Weighted MAPE


50%

55%

60%

65%

70%

75%

80%

85%

Jun-

05

Jul-0

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Aug

-05

Sep

-05

Oct-0

5

Nov

-05

Jan-

06

Feb-0

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Mar

-06

Apr

-06

May

-06

Jun-

06

Jul-0

6

Aug

-06

Sep

-06

Oct-0

6

Nov

-06

Month

Ac

cu

rac

y

FC Acc 6 Month MA

FC Accuracy has moved from about 58% in early 2005 to near 75% in Nov. 2006

Applica Historical Forecast Accuracy

Lead time of more than 100 days, weekly enterprise planning,

forecast accuracy improved to 80% levels


Processes and Systems:

• Weekly Corporate Real-Time S&OP

• Organization-wide commitment to ONE Forecast

• Demantra is the support tool and sole source for plan data

• No second guessing by Finance or Planning

• Demand Planning is integrated into weekly RT S&OP

The Results:

• Improved inventory turns from <2 in 2004 to 5 in 2006

• Total inventory reduction of ~ 33%

• Fill Rate change from 80% to 93%

• Includes virtually all 2nd tier accounts with Fill Rate > 88%

“This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 52

Presentation given at Oracle

OpenWorld San Fransisco Oct 2006

Case Study – Johnson & Johnson


J&J and LifeScan

• Johnson & Johnson

• World's most comprehensive manufacturer of health care products

and provider of health care services for the consumer,

pharmaceutical, and medical devices and diagnostics markets

• More than 200 operating companies under its management

• LifeScan Inc. – an operating company of J&J

• Leading maker of blood glucose monitoring systems for home and

hospital use

• Dedicated to improving the quality of life for people with diabetes

with OneTouch® Brand Products

OneTouch® Ultra®OneTouch® UltraSmart ®

OneTouch® Ultra®2

OneTouch® UltraMini™


Forecasting (Demand Planning) Is

Collaborative

Multiple People = Multiple Opinions

Forecast

Finance

Customers

Manufacturing

Marketing

Supply Planning

Market Research

Clinical

R & D

Sales


Effective Demand Planning Combines Qualitative and

Quantitative Analysis to Provide Meaningful Outputs

Modeling Tools

Promotions health care

reform

Formulary

status

Physician

perceptions

Managed

care

Marketing

message

Supply

constraints

Competition

Channel

dynamics

Price

Customer

behavior

Trend

analysis

Seasonality

Statistical analysis

Model results

Historical Sales

Quantified effects

Judgment + Experience


Forecasting comes with its own brand of

politics

• No single person or input is able to capture the entire picture

• Each input has its own purpose and bias

• In the end there is no accountability

• Accuracy cannot be easily measured

• What number should supply and operations plan to?

• What number should management report to HQ?

Finance Supply PlanningMarketingSales

Are we making our numbers? Drivers: Bottom line

Am I getting compensated?Drivers: Quota

Is my brand healthy? Am I getting enough product?Drivers: Brand image, Sales

Are we meeting customer demand?Drivers: Order Fulfillment metrics, Inventory costs, Backorder


The Forecast Consensus Process

Forecasting

Sales (field intelligence -> short term forecast)

Marketing (market intelligence -> long term forecast)

Stat forecast (customer order history -> short and long term forecast)

Finance (commitments to corporate -> business plan)

Category/Competitive Insight(competitive intelligence; share goals; market data - > forecast 3 to 4 years out)

Other tools

Regional and franchise consensus demand plan

Forecast Error (MAPE)

Forecast Changes

Inputs Outputs


The demand planning mantra

– “What’s not in the system, does not exist”

• The „Final Consensus Forecast‟ series is the final

answer

• Numbers entered in the system get locked after end

of cycle

• SKU level information is rolled over to Supply and

Operations group for planning purposes

• Numbers in the system are used for all S & OP,

Sales, Marketing and Finance related discussions

• Forecast reports are generated off of numbers in the

system

58


S&OP Process Overview – S&OP

activities, inputs and outputs

Senior Mgmt.

review and

approval in

Executive

S&OP

Meetings*

Franchise

Consensus

Forecast

(from DP)

Preliminary Global

Supply/Demand

Balancing

Recommendations

Global Forecast

Directions (to DP)

Supplier forecast

(via SP to suppliers)

Review and

discuss future

outlook and

scenarios with

key supply &

demand

stakeholders in

S&OP Meetings

Identify projected

supply/ demand

imbalances

Execute

Financial

Review

Supply Information:

Capacity, Lead

Times, etc.

Supply/Demand balancing and scenario planning with a medium to long term focus

Execute Supply

Review

Execute

Portfolio

Review


The Flip Side of a Single Number

Consensus Process

• Occasional need for offline communication:

• Upsides/Downsides to forecast (Market intelligence)

• What-if scenarios and contingency planning

• Major changes since last forecast lock

• Sometimes there is really no single number:

• Competitive product launches - Large uncertainty in outcome

• Internal new product launches - Large range in forecast

• The politics of single numbers:

• Internal new product launches – Different groups have different opinions on launch dates

• Numbers in the system may not meet needs of all the groups involved

• For example, production may use MAPEs/experience/inventory policies on top of the number in the system for planning


The Key To Success With a Consensus

Forecasting Process

• A well developed demand forecast process combined with a strong S & OP process

• Process compliance

• Process is more important than the number itself

• Unbiased group that acts as a liaison between all involved groups

• Assumption based forecasting

• A forecast is as good as its assumptions

• Visibility to assumptions drives belief in the forecast

• One voice

• Effective communication

• Understanding that range is NOT a bad thing, but having multiple numbers floating around IS


Advantages of a Single Number

Consensus Forecasting Process

• Everyone speaks the same language – ONE VISION

• Range and uncertainty is still correctly captured and communicated in forecast assumptions and upsides/downsides discussions with planners

• Marketing strategies are focused – can be measured

• Company resources are optimized

• Long Term Planning becomes easier

• Drives accountability/responsibility

• Easily measure performance – MAPE/Forecast change

• In the end – everyone knows the health of the company (Visibility Visibility Visibility)


Eliminare

Fogli excel

Rolling forecasts

Collaborare per creare un

numero univoco

Usare statistiche, allert,

ridurre i fogli di lavoro

Creare fogli di lavoro ad hoc

per ogni figura

Introdurre il forecast di nuovi

prodotto

Collaborare con I clienti

Usare statistiche avanzate con

fattori causali

Allet complessi con fogli di

lavoro customizzati

Forecast basato su attributi e

cartatteristiche prodotto

Calcolare il lift delle promozioni e

l’analisi degli impatti promozionali

sulla domanda

Calcolo del forecast in base a

simulazioni di eventi

Da minor complessità a best in class

Rolling forecasts


numero univoco




per ogni figura

Introdurre il forecast di nuovi

prodotto

Collaborare con I clienti

Usare statistiche avanzate con

fattori causali

Allet complessi con fogli di

lavoro customizzati

Rolling forecasts


numero univoco




per ogni figura

Si parte

da un punto

qualsiasi

Oracle DemantraEvolvere gradualmente verso la soluzione best in class

CONFIDENTIAL: All capabilities and dates are for planning purposes only and may not be used in any contract

Questions

Caffe Panzera For Distribution

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