Caffe Panzera For Distribution
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Caffe Panzera For Distribution Caffe Panzera For Distribution Presentation Transcript

  • <Insert Picture Here> 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 Strumenti di previsione Dadi Maghi Tarocchi Fondi di Caffè
  • Da sempre l’uomo cerca di prevedere il futuro Strumenti di previsione View slide
  • 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‟ View slide
  • 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
  • <Insert Picture Here> Domanda Indipendente, Prof. Jacobas, Univ.Ilinois “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
  • Tipi di Demand Pianificazione Prodotti Configurabili Domanda Dipendente
  • Tipi di Demand Pianificazione delle Opzioni
  • <Insert Picture Here> Baseline Forecasting, definizione “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”
  • L’errore sul Forecast produce vari effetti Forecast Error Over Forecast Under Forecast Excess Inventory Order Expediting Cost Inventory Holding Cost Higher Product Cost Trans-shipment Cost Lost Revenue Obsolescence Lost Companion Product Sales Reduced Margin Lower Customer Satisfaction
  • 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
  • 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 Attribute-Based Chaining Shape Modeling Forecasting • Apply shapes, scaled for volume Model new item New Product C = based on past 30% Product A + and time • Re-scale base on behavior of other 75% Product B items with similar initial demand data attributes © 2006 Oracle Corporation – Proprietary and Confidential
  • Forecast basato su Attributi Caratteristiche Colore Tecnico/Commerciali Item Formato Prezzo © 2006 Oracle Corporation – Proprietary and Confidential
  • Forecast basato su Attributi Metodologia Valore di Business Si utilizza la Famiglia di attributi di prodotti simili piuttosto che altre Utile per introduzione massiva di SKU come input nuovi prodotti aventi caratteristiche simili a quelli Caratteristicheesistenti Colore Tecnico/Commerciali Il Motore di Forecasting determina su quali attributi basarsi in base al prodotto scelto Parte integrante del processo di Product LifeCycle Management Questa metodologia analizza il Item (PLM) comportamento del consumatore piuttosto che il comportamento del prodotto Formato Prezzo Il Forecast viene poi allocato alle Utilizzato in settori quali Fashion, SKU in base a Business Rules Hi-Tech, CPG. © 2006 Oracle Corporation – Proprietary and Confidential
  • Chi beneficia del Demand Management? • Food & Beverage – CG – FMCG • Telecom • Utilities • Media • Automotive • High-Tech • Banking © 2006 Oracle Corporation – Proprietary and Confidential
  • 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 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. © 2006 Oracle Corporation – Proprietary and Confidential
  • Demantra and Supply Chain Suppliers Finished Growers Raw Manufacturers Product Mfgr Materials Brokers Distributors Trade Promotion Retailers S&OP Demand Mgmt Business Distribution Channels Web Direct Consumer Customers CONFIDENTIAL: All capabilities and dates are for planning purposes only and may not be used in any contract
  • 0 1 2 3 4 5 6 7 8 Gen 09 Feb 09 Marzo 09 Apr 09 Mag 09 Giu 09 Lug 09 Ago 09 Sett 09 Ott 09 Sales Forecast Nov 09 Dic 09 Gen 10 La storia da sola non basta per fare previsioni Sales
  • E’ necessario integrare con eventi Business Evento Promo Evento Promo Sales Forecast (Futuro) 8 7 6 5 4 3 Sales 2 1 0 Mag 09 Feb 09 Lug 09 Dic 09 Marzo 09 Sett 09 Gen 09 Apr 09 Giu 09 Ott 09 Nov 09 Gen 10 Ago 09
  • E’ necessario integrare con eventi Business Evento Promo Evento Promo Sales Forecast (Futuro) 8 7 6 5 4 3 Sales 2 1 0 Mag 09 Feb 09 Lug 09 Dic 09 Marzo 09 Sett 09 Gen 09 Apr 09 Giu 09 Ott 09 Nov 09 Gen 10 Ago 09
  • Tutto dipende dal cuore...
  • Tutto dipende dal cuore...
  • 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
  • Welch’s 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 • 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 © 2006 Oracle Corporation – Proprietary and Confidential • Improved HQ and sales planning productivity
  • C&S Wholesale Grocers Live on Demantra DM, AF&DM 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 © 2006 Oracle Corporation – Proprietary and Confidential
  • Wendy’s 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 Wendy’s International and characteristics Dublin, Ohio, USA • Evaluate the effectiveness and cannibalization of Promotions www.wendys.com 6,746 locations Solution and Results: $2.4 Billion annual revenue • Oracle-Demantra Demand Management provides scalability Quick-Service Restaurant 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 DEMAND MANAGEMENT supply • Expected 20% reduction in overall operating costs © 2006 Oracle Corporation – Proprietary and Confidential
  • 20th Century Fox Live on Demantra DM, AF&DM 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%+ © 2006 Oracle Corporation – Proprietary and Confidential
  • Case Study - Fairfax Anatomy of a Win Anatomy of a Happy Customer
  • Fairfax Overview Fairfax is Australia‟s and New Zealand‟s largest publishing group (Sydney Morning Herald, The Sun-Herald, The Age,…) Challenge Improve demand forecasting (at the kiosk-level) Improve supply allocation Reduce lost revenue from sell-outs Reduce returned copies
  • Oracle Solution Demantra Value Scalable forecasting at the most granular level Effective management of a perishable product (short shelf life) More frequent calculation of outlet supply quantities Solution Demand Management Advanced Forecasting and Demand Modeling Real-time Sales & Operations Planning $900K license and $300K for implementation
  • The Planning Dashboard Direct access to online reporting and Key Performance personalized Indicators worksheets showing current Quick Data Access status of important information Focus planners attention Automated workflow and Exception Management of business processes reduces information handling Handling service levels before it is a problem
  • True Demand Logic Advanced Logic to „clean‟ demand prior to forecasting Sell Outs Estimated Returns Estimated and Projected Subscriptions Hidden Demand
  • Safety Stock Calculation and Review Availability calculated based on Service level & Demand variability Agent Band re- calculated each week, Demographics loaded to allow focus on key areas
  • Workflow Driven – Agent Refresh Process
  • Return on Investment Budgeted savings exceeded for all 7 days for a 6 month period Change in returned copies: July-December, 2005 Budgeted Actual Weekday -8% -11% Saturday -1% -5% Sunday -2% -7% • 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
  • <Insert Picture Here> Come introdurre tutte le variabili significative del piano
  • Case Study – National Brands Ltd Supply Chain: Marketing: Sales Marketing: Build stock for planned Sales: Decide to check with Marketing if Confirms there is maintenance shutdown run a major promo. No there is sufficient sufficient stock. No one review of stock levels stock,…Two days tells Supply Chain before promotion starts about the promotion Supply Chain: Customer: Very CEO: Very Result: Everyone unhappy. Receives call unhappy (including Alert…Stock levels unhappy. Not getting from irate customer consumer) , both dropping fast due spike stock. Consumers not advising he will sue for Manufacturer and in sales happy either lost sales Retailer lose sales 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 Logistics Ship Finance Make, Buy, Plan Revenue and Cost Contol Supply ERP Ordini e Previsioni Demand Planner Sales Demand Strategie di Crescita Marketing New Products Think Tank
  • Sales & Operations Planning La Collaborazione produce sempre i migliori risultati Logistics Ship Finance Make, Buy, Plan Revenue and Cost Contol Supply ERP Ordini e Previsioni Demand Planner Sales Demand Strategie di Crescita Marketing New Products Think Tank
  • S&OP – Allineamento dei processi e dell’organizzazione Obiettivi Finance: S&OP obiettivi • Fare il budget • Allineare diversi Obiettivi vendite: • Controllo e Finance obiettivi • Max ricavi predittività degli • Max market share eventi • Portare le strategie • Alta disponibilità • Metrica: della società su del prodotto Budget piani fattibili • Metrica: Sales plan ($$$) Sales • Tradurre e rendere & Marketing Production consistenti diversi obiettivi Obiettivi • Evidenziare conflitti Produzione: • Trovare il piano • Ottimizzazio ottimale ne dello stabilimento considerando i Obiettivi Supply Supply Chain produttivo vincoli Chain : • Stabilità • Convergere su un • Fattibilità • Metrica: • Alta stabilità solo numero piano di • Metrica: The produzione Demand Plan © 2006 Oracle Corporation – Proprietary and Confidential
  • 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 Integrazione Manuale Raggiungimento del vengono sviluppati sulla delle informazioni Consensus Plan tramite base della demand storica collaborazione e Workflow dal motore statistico- analitico Simulazione per gli utenti piu avanzati – What-if Analisi – Tuning del Il Consensus Plan viene Il Baseline Forecast viene Forecast continuamente distribuito alle persone Demand Plan consolidato monitorato e modificato di responsabili della nel piano finale conseguenza pianificazione. Vengono generati Alerts ogni volta che il piano modificato si discosta Consensus Plan dall’originale Gli alert agganciano il Worksheet necessario alla risoluzione del problema per velocizzare il processo © 2006 Oracle Corporation – Proprietary and Confidential
  • Case Study - Applica Presentation given at GMA Conference in March, 2007 by Mike Vincitorio, Sr. Director Supply Chain, Applica © 2006 Oracle Corporation – Proprietary and Confidential
  • Applica and Demantra 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 “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”
  • Applica Historical Forecast Bias 22.0% 17.0% 12.0% Bias 7.0% 2.0% Targeted Zone -3.0% -8.0% 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 6 Month Rolling Bias Upper Limit Target Lower Target Linear (6 Month Rolling Bias) Forecast Accuracy measured by Weighted MAPE Reduction in Forecast Bias has yielded ~$9 million (13%) reduction in average inventory. “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”
  • Applica Historical Forecast Accuracy 85% 80% 75% Accuracy 70% 65% 60% 55% 50% FC Acc 6 Month MA 6 05 5 06 06 6 5 5 6 5 5 6 6 6 6 6 6 -0 l-0 l-0 -0 -0 -0 -0 -0 0 r-0 -0 -0 -0 -0 n- n- n- b- ay g p g p ov ov ct ct ar Ju Ju Ap Ju Ja Ju Au Se Fe Au Se O O M M N N Month Lead time of more from about 58% in earlyweekly enterprise planning, FC Accuracy has moved than 100 days, 2005 to near 75% in Nov. 2006 forecast accuracy improved to 80% levels “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”
  • 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.”
  • Case Study – Johnson & Johnson Presentation given at Oracle OpenWorld San Fransisco Oct 2006 “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 52
  • 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® Ultra®2 OneTouch® UltraSmart ® OneTouch® UltraMini™ “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 53
  • Forecasting (Demand Planning) Is Collaborative Finance Sales Market Research Marketing Forecast Manufacturing Supply Planning Clinical R&D Customers Multiple People = Multiple Opinions “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 54
  • Effective Demand Planning Combines Qualitative and Quantitative Analysis to Provide Meaningful Outputs Quantified effects Statistical analysis Modeling Tools Model results Historical Sales Physician Marketing perceptions Trend message analysis Channel dynamics Formulary Customer status Competition behavior Supply Price Seasonality constraints Managed care Promotions health care reform Judgment + Experience “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 55
  • 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 Finance Sales Marketing Supply Planning Are we making our Am I getting Is my brand healthy? Are we meeting numbers? compensated? Am I getting enough customer demand? Drivers: Bottom line Drivers: Quota product? Drivers: Order Drivers: Brand image, Fulfillment metrics, Sales Inventory costs, Backorder • 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? “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 56
  • The Forecast Consensus Process Inputs Outputs  Sales (field intelligence -> short term forecast)  Regional and franchise  Marketing (market intelligence -> consensus demand plan long term forecast)  Forecast Error (MAPE)  Stat forecast (customer order history -> short and long term forecast) Forecasting  Forecast Changes  Finance (commitments to corporate - > business plan)  Category/Competitive Insight (competitive intelligence; share goals; market data - > forecast 3 to 4 years out)  Other tools “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 57
  • 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 “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 58
  • S&OP Process Overview – S&OP activities, inputs and outputs Supply/Demand balancing and scenario planning with a medium to long term focus Global Forecast Identify projected Directions (to DP) supply/ demand Franchise imbalances Consensus Forecast Review and (from DP) discuss future Senior Mgmt. outlook and review and scenarios with Preliminary Global Supply/Demand approval in Execute key supply & Balancing Executive Portfolio demand Recommendations S&OP Review stakeholders in Meetings* S&OP Meetings Supply Information: Supplier forecast Capacity, Lead Execute Supply (via SP to suppliers) Times, etc. Review Execute Financial Review “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 59
  • 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 “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 60
  • 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 “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 61
  • 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) “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.” 62
  • Oracle Demantra Evolvere gradualmente verso la soluzione best in class Forecast basato su attributi e Si parte cartatteristiche prodotto da un punto Calcolare il lift delle promozioni e qualsiasi l’analisi degli impatti promozionali sulla domanda Calcolo del forecast in base a simulazioni di eventi Introdurre il forecast di nuovi Introdurre il forecast di nuovi prodotto prodotto Collaborare con I clienti Collaborare con I clienti Usare statistiche avanzate con Usare statistiche avanzate con fattori causali fattori causali Allet complessi con fogli di Allet complessi con fogli di lavoro customizzati lavoro customizzati Rolling forecasts Rolling forecasts Rolling forecasts Eliminare Fogli excel Collaborare per creare un Collaborare per creare un Collaborare per creare un numero univoco numero univoco numero univoco Usare statistiche, allert, Usare statistiche, allert, Usare statistiche, allert, ridurre i fogli di lavoro ridurre i fogli di lavoro ridurre i fogli di lavoro Creare fogli di lavoro ad hoc Creare fogli di lavoro ad hoc Creare fogli di lavoro ad hoc per ogni figura per ogni figura per ogni figura Da minor complessità a best in class “This presentation is for informational purposes only and may not be incorporated into a contract or agreement.”
  • Questions CONFIDENTIAL: All capabilities and dates are for planning purposes only and may not be used in any contract