Continuously Improving Forecast Accuracy: A Manufacturers Approach

Transcription

1 Collaborative Promotion Optimization & Cont inuous I mprovement Summit TPM-TPO- Collaborat ive Marketing Continuously Improving Forecast Accuracy: A Manufacturers Approach

2 Mr. Davis will lead our session on how Dannon engages Predictive Analytics I have been in the CPG industry for nearly 20 years, helping companies leverage technology to delivery on business strategies by designing, implementing, and maintaining best in class solutions. One of my areas of emphasis is analytic planning and evaluation. Industry Experience The Dannon Company ConAgra Foods, Inc. Consulting Experience Booz and Company The Coca Cola Company The Clorox Company Kraft Foods Inc.

3 Danone is a French Multinational Food Products Company Danone is one of the fastest-growing food companies in the world. Its mission is to bring health through food to as many people as possible The group, whose products are sold on five continents, has more than 180 production plants and around 100,000 employees In 2013, Danone generated sales of 21 billion, of which more than half were in emerging markets The group holds top positions in healthy food through four businesses: Fresh Dairy Products, Early Life Nutrition, Bottled Water, and Medical Nutrition

4 Dannon US is the largest of the four Business Units in North America With over 1,300 employees and four dedicated production facilities DANNON is committed to bringing health to the greatest number of people across America through our product s benefits. We exceed customer expectations by transforming the dairy category to attract and build daily consumption. Dannon is the share leader in one of the fastest growing and dynamic categories within CPG

5 Today s session focuses on key considerations for predictive analytics Build Upon a Strong Foundation One Size does not Fit All Analytics not Rocket Science Measure what you Plan Strategies will Evolve

6 Agenda Build Upon a Strong Foundation One Size does not Fit All Analytics not Rocket Science Measure what you Plan Strategies will Evolve

7 Defining our goals by scope/channel starts creating action Scope Strategic Goals

8 Once goals are identified we can define the level of capabilities Increasing Level of Sophistication Automated Optimization Constraint Based Planning Facilitated Optimization Event Generators Profilers for Base and Incremental Drivers What-If Optimization Library of Events Event and Plan Scenarios Post-Event Analytics Minimal Optimization Predictive Engine ROI Calculations Manual Alignment of Plans to Actuals Event Level and Full Plan Reporting Because we defined our goals by Channel the capability can vary by Channel Historical Insights

9 With the goals and capabilities we can determine the data required Pricing Base price Promoted price Demographics Pop. by age, HH income, ethnicity TV & Cinema GRPs, TRPs, Media Spend, Impressions One Time Events Packaging change, new product, sponsorships Volume Syndicated, POS, Shipment Economic Indicators GDP, CPI, Household Consumption Weather/ seasonality Temp, precipitation, natural disaster Coupons Circulation, face value, redemptions In Store Promos Syndicated metrics Number of stores Key Considerations Timing At what frequency is the data a available At what frequency is the data needed for analytics At what frequency is the data needed for reporting Format Is the format compatible with downstream systems Ownership Who is the internal and external owner of the data

10 Goals, Capabilities, and Data are the foundation to the analytics process Assemble Data Import Data into Statistical Tool Analytics Design Model Form Produce Coefficient Tables Planning & Execution Enter Drivers Predict Results Causals / Drivers Coefficients Causals / Drivers Base Price 1.8m Base Price $2.65 EDLP 5.3m TPR 3.2m EDLP $0.15 X TPR $0.25 = FSIs 0.4 FSI 2m TV GRPs 10k TV GRPs 40 Coefficients Yoplait TPR -3.2m Yoplait TPR $0.25 Input data is assembled and cleaned Collected from multiple sources Cleansed data loaded into both tools Statistics are done independently Model form requires an iterative process to discover best predictor of history Determine which drivers are necessary for predictable models Coefficient tables show the impact of each causal on the predicted output Validated with internal model validation User then enters the desired causals to predict Entering historical values will test model accuracy Multiplying each causal by its coefficient creates a predicted Volume

11 Agenda Build Upon a Strong Foundation One Size does not Fit All Analytics not Rocket Science Measure what you Plan Strategies will Evolve

12 What gets predicted and planned should change by channel Channel Merch Assortment Price Coupons TV Competition Club Mass Retail Drug C-Store AFH

13 Similar to Channels, Brands can also have unique drivers for analytics

14 Agenda Build Upon a Strong Foundation One Size does not Fit All Analytics not Rocket Science Measure what you Plan Strategies will Evolve

15 The analytics communication needs to fit our audience Rocket Science Terms MAPE, R2, Durbin Watson Drivers Intercepts Coefficients Neural Network Models Analytic Planning Terms How Well can the Models Predict History What are the KPI s we are going to plan What data is being used to predict the business

16 Today s session focuses on a framework to improve predictive analytics Build Upon a Strong Foundation One Size does not Fit All Analytics not Rocket Science Measure what you Plan Strategies will Evolve

17 Ensure that reporting is in place to measure what is being planned Driver 1 (Plan v Actual) Driver 2 (Plan v Actual) Driver 3 (Plan v Actual) The ability to maintain highly accurate execution levels provides two immediate impacts: 1. Maintain accuracy with predicted volume 2. Early visibility to levels other than agreement with Retailer

18 With clear objectives and a strong foundation predictability is achieved In this 16 week snapshot the plans had a variance of 1.74% to actual consumption. Just as important is the fact all brands predicted with the same level of accuracy for both base and incremental volume.

19 Case 1 Pricing Thresholds Measuring results will also show business opportunities Premium Price Points Mainstream Price Points Value Price Points Understanding the thresholds to attract consumers from another price segment helps with overall predictability.

20 Measuring results will also show business opportunities Case 2 Lift by Brand by Merchandising Type LIft by Merchandising Type Feature Display TPR Brand A Brand B Brand C Brand D Understanding the lift, by retailer, for each merchandising type is key to understand the cost per incremental unit.

21 Agenda Build Upon a Strong Foundation One Size does not Fit All Analytics not Rocket Science Measure what you Plan Strategies will Evolve

22 Header text max. 2 lines How will plan need to be adjusted? Based upon results, strategies should evolve Re-planning Process Step 1 - Assess Changes to Plan Minor Changes Strategy changes Strategic Brand / Customer Shifts Minor Plan Adjustments Strategize changes Step 2 - Make changes to Plan Adjust Plan in M-Factor Approval of changes Get approval of desired changes Update Planned Driver in MF Analyze Model Output Identify Gaps Submit Plan Contemplate Additional Changes Communicate Key Metric Changes Volume projection and targets Financial targets - CANN, Revenue Strategy changes (if any) Step 3- Finalize & Communicate Plan Full Reconciled Plan Released Teams update plans with changes since start of Re-Planning

23 Questions Questions