Crunching Big Data into actionable insights

Transcription

1 Crunching Big Data into actionable insights Eyeon Planning Inspiration Day 2017 Kalle Rasmussens Global Lead Demand Planning

2 The Company 3

3 INDEPENDENT & RESPONSIBLE GLOBAL BREWER THE WORLD S MOST INTERNATIONAL BREWER # 1 IN EUROPE # 2 IN THE WORLD BRANDS PRESENT IN >170 COUNTRIES COMPANY PRESENT IN >70 COUNTRIES SURPRISING AND EXCITING CONSUMERS EVERYWHERE BREWING GREAT BEERS AND CIDERS, BUILDING GREAT BRANDS LONG AND PROUD HISTORY AND HERITAGE 4

4 Truly global presence >165 BREWERIES, MALTERIES, CIDER PLANTS AND OTHER PRODUCTION FACILITIES IN OVER 70 COUNTRIES 73,500+ EMPLOYEES OVER 250 BEER AND CIDER BRANDS CONSOLIDATED BEER VOLUME IN 2016: MHL HEINEKEN OPCO JOINT VENTURES EXPORT LICENSES 5

5 Pressure is increasing on our Planning performance External drivers Internal drivers Consumer Demands Economic Volatility Improving our Planning Capability E2E Optimization Breaking the Silos Big Data Globalisation Cutting Costs Fragmented Planning Landscape Omni Channel Growing Competition Capability building HEINEKEN 2020

6 Big Data Big Deal?

7 Potential impact of Sell-out Data on the Supply Chain Increase Forecast Accuracy Reduce Out of Stocks Increase Customer Service Lower Inventory in the chain Increase On-Shelf Availability 15% 5% 5% 15% 11% Suppliers HEINEKEN Customer DC Customer Point of Sale 7% 6% 12% 15% Lower Warehousing Costs Increase OTIF Reduce Lead Time Maximize Promotions & NPI effects 8 External figures based on average & low indices of CPFR project results & research in FMCG

8 Collaborative Planning, Forecasting and Replenishment

9 CPFR: Getting Grip on Demand using downstream data HEINEKEN S&OP, planning and forecasting Historical Sell-In Forecasts Trends / Seasonality NPI & Promotions & Events Data & Information scope Shared Forecast Customer S&OP, planning and forecasting Store / DC sell-out forecasts Consumer POS patterns Promotions & Events Suppliers HEINEKEN Customer DC Customer CONSUMER Point of Sale 10 Order behaviour, risk avoidance, stock policies, last minute changes A bull-whip effect is created throughout the chain

10 Is it Relevant? CPFR eliminates inefficiencies in the complete chain and is relevant when: Short product life cycles High inventory in the supply chain High innovation rate Product expiry/ freshness is an issue Long production and/or replenishment lead-times 11 ~75% valid for HEINEKEN Seasonal demand variances are significant Demand is difficult to predict Consumer expectations are not always met Promotion pressure is high Low forecast accuracy

11 r=10mm r=4.6mm HEINEKEN CPFR Framework r=3.2mm 20mm 12

12 From Customer Collaboration to CPFR Joint NPI Aligned 6 Planning 7 Performance Management % % % 5 Joint Promotion Planning 4 Shared Forecast 3 Shared (Pre-) orders & Inventory 13 Key enablers Customer segmentation Trusted partnership Internal organisation Aligned KPI measurement Information exchange Data alignment 1 Joint Business Planning 2 Category Development

13 CPFR for HEINEKEN C Joint ways of working; trusted partnerships on all levels P 3-13 weeks Promotion & NPI planning Focus: mid-term Sales Forecast > Monthly & Weekly S&OP F 1-18 months Forecast Management Focus: long-term Volume Forecast > Monthly S&OP R 0-2 weeks Replenishment Optimization Focus: short term Order Forecast > Weekly S&OP 14

14 HEINEKEN S CPFR implementation approach Prepare Gain insight & support Internal Plan Plan for action Pilot Engage Customer Scale up Broaden scope Readiness Assessment Enabler Development Plan Approach & kick-off Widen topics, horizon, integration Customer Segmentation Workshop Customer Account Plans 1 topic 1 KPI 1 success Expand to other customers Understand customer s needs Compelling Benefit Case Monitor & Evaluate performance Evaluation & Benefits tracking 15

15 CPFR Enablers Buy-in & Support Processes & Performance Trusted Partnerships Internal Organisation

16 Key Success Factors TRUST Trust to build relationships, both internal and external Both parties are willing to invest and achieve results WILL CPFR key success factors DATA Quality and reliability of data to base decisions on Prepare and plan to have the right focus and basics in place PLAN 17

17 a Case Study

18 Our Journey with our Key Customer Daily Data Standardized Service as Measured by Customer CPFR OSA Supplier Implants Daily Data Received First Joint SC Plan First Supplier Implant

19 20 Our Journey with our Key Customer

20 Simplify Daily Sell-Out Data Promotional Information Stockholding DC CUSTOMER DATA Stockholding Store EML DB Product Master Data Store Master Data Buyers Forecast Supplier Service Level Orders from Store to DC Orders from DC to Supplier Planogram Information etc. 21

21 Scope Suppliers HEINEKEN Customer DC Customer CONSUMER Point of Sale 22

22 Harmonize Data Harmonization Master Data Management KPI calculation in standard Dashboard 23

23 How data insights are used Pain point Data insight Action Low forecast accuracy resulting in high stocks and/or out of stocks Service Level to customer DC / store On Shelf Availability Forecast accuracy of HEINEKEN vs. customer Common OSA measurement Undelivered orders in E2E supply chain Demonstrate lost sales at store level Customer reason codes Customer promotion forecast Enhance HEINEKEN forecast with customer forecast insights Align reason codes Analyse differences and propose actions Discuss actions with customer Promotion effectiveness and execution Promotion forecast accuracy of customer Timing of stock building Forward buying Promotion effectiveness Cannibalisation Customer forecast on new products Enrich forecast with customer insights Analyse (and adjust) orders Analyse promotion ROI and adjust strategy Alignment on first orders/sell-out sales and timing of marketing support 24 Improve NPI Planning

24 The future of CPFR and Big Data Prescriptive Analytics What should I do? Decision Automation Predictive Analytics What will happen? Human input Descriptive Analytics What happened? Human input Creating actionable insights Predicting patterns Proposing actions Examples: CPFR, Customer Stock level, Forecast Accuracy, Sell-in vs. Sell-out, pre-stocking, OSA, etc. Examples: CPFR, Demand Sensing, Vendor Managed Inventory, Predictive Forecasting, etc. Examples: Automatic replenishment, sell-out forecasting, scenario creation, etc. 25

25 Learnings... BIG DATA IS A BIG DEAL! START SIMPLE AND EXPAND 1 TOPIC, 1 KPI, 1 SUCCESS BE READY INTERNALLY BEFORE COLLABORATING EXTERNALLY ENABLERS AND BUSINESS CASE START FROM REQUIREMENTS AND CURRENT SUPPLY CHAIN PAIN POINTS THEN TOOLING DATA, DATA, DATA AND DATA WILLINGNESS, TRUST AND SUPPORT ON ALL LEVELS, INTERNALLY AND EXTERNALLY 26

26 Contact Information César Martínez Ramírez Global Lead Customer Service Kalle Rasmussens Global Lead Demand Planning