Segmentation in Demand Planning for Enhanced Forecast Accuracy

Transcription

1 Segmentation in Demand Planning for Enhanced Forecast Accuracy Jim Davis Jeff Metersky CHAINalytics

2 Jim Davis, CPIM Director, Demand Planning & Customer Service at Colgate-Palmolive (Global Customer Service & Logistics) Jim currently serves as Director of Demand Planning and Customer Service at Colgate-Palmolive. Jim has more than 34 years of supply chain experience at Colgate including manufacturing, planning, customer service and logistics. Prior to joining Colgate s global supply chain team, he lead the US Customer Service and Logistics organization. His current responsibilities include global process ownership for demand planning and customer supply chain collaboration. Jim holds a Bachelor of Science in Industrial Engineering from Lehigh University and an MBA in Operations Management from Fairleigh Dickinson University.

3 Jeff Metersky Vice President, S&OP Practice Chainalytics Jeff is a co-founder of Chainalytics and Vice President of the Sales & Operations Planning Practice. His global consulting experience which spans more than 100 clients across a variety of industries is focused on supply chain design and analysis, inventory strategy and optimization, demand planning, and cost-to-serve analytics. Jeff has authored multiple articles and is frequently cited by leading industry publications and analysts. In 2006, Jeff was recognized as a Pro to Know by Supply & Demand Chain Executive. Jeff holds a Bachelor of Science in Industrial Engineering from The University of Illinois and a Master of Business Administration in Materials and Logistics Management from Michigan State University.

4 Applying Segmentation to Evaluating Forecast Accuracy

5 Forecasts Drive the Majority of Demand & Supply Planning Decisions in Most Manufacturers Product Flow Production Plans & Financial Planning Marketing and Promotion Planning Orders to Suppliers, & forecasts of orders Production Staff Planning Production Schedules Plant ship to Retailer DC s Deployments To Field Distribution Centers DC Staff Planning Outbound Trans Planning Forecasts Of Product Sales Customer orders Financial Planning typically uses aggregated forecasts Demand Planning relies on a mix of aggregate and SKU-specific forecasts Supply Chain management mostly uses quite detailed forecasts: o SKU / Shipping location / day or week VMI service of Retailer DC s & DSD service to stores Inventory & POS Data

6 Improving Item-Location Forecast Accuracy Drives Operational Efficiency Company Channels Categories Networks Time Buckets Sales Regions Brands Echelon Levels Yearly Customers Product Groups Locations Quarterly Ship-To s Items Monthly Weekly

7 Today s Demand Planning Environment Supply Chain Pain Points (1) Demand and Supply Variability Top Pain of Supply Chain Leaders and its increasing SKU level Forecast Accuracy (2) 74% 1 Month Lag 75% 72% 2 Months Lag 72% Consumer Products 68% 4 Months Lag 72% 74% 1 Month Lag 76% 77% 2 Months Lag 72% Food and Beverage 74% 73% 4 Months Lag 79% 1 Month Lag 74% 68% 69% 2 Months Lag Industrial Products 67% 4 Months Lag 74% Demand Planning performance improvements are mixed mostly down/flat (1) Supply Chain Insights 5/13 Survey of 92 Companies (2) IBF Research Summer 2011

8 Traditional Benchmarking has Not Provided a Path for Improvement 100% Conventional 90% Surveys Questionnaire-based Participants self-report 80% forecast accuracy as they measure it Forecasting process checklist Attempt to define best practice Limited root-cause and comparative analysis 70% 83% Forecast Accuracy (FCA) Lag 0 Lag 1 Lag 2 Lag 3 81% 79% 77% 60% 63% 50% Can I really improve this much? If so, where and how can I improve? 54% 40% 43% 40% 30%

9 There Must be a Better Way: Sales & Operations Variability Consortium (SOVC) How do we prioritize improvement opportunities? What is our underlying demand uncertainty? How well do we forecast, relative to that inherent uncertainty? Is our forecast accuracy and bias reasonable compared to competitors and peers using common metrics? Does the way we compute error distort comparisons? Are we better at forecasting some types of products than others? Industry: Non-Durable Consumer Product Goods, Food & Beverage Geography: US Customer Demand Members: 40+ Participants Item-Locations: 300,000+ What is causing our challenges in forecasting? Home Care 12% What are the underlying drivers of error, such as product portfolio, customer order patterns, economic cycles, seasonality, new product launches, etc.? Personal Care 30% Pet Care 7% Food & Beverage 51%

10 What is Segmentation? Process of dividing a large unit into various small units which have more or less similar or related characteristics How can this help me improve demand planning?

11 % of Units Shipped in Pattern Demand Pattern Mix Influences Forecast Accuracy Stable Trending Seasonal/Uplift Intermittent Launch/End Other FCA Bias 81% 18.7% 61% 54% My planning world is more dynamic! 5.6% 2.9% Member 1 Member 2 Member 3

12 Harder to Forecast Weekly Forecasters Easier to Forecast Forecastability of Demand Patterns I have the most difficult patterns to forecast. Stable Range Trend Up Round Uplift Trend Down Sharp Uplift Phase In Phase Out Harder to Forecast Intermittent Non-Consecutive Intermittent Consecutive Monthly Forecasters Easiest to Forecast

13 Ship Qty Ship Qty Item Loc Item Loc Variability Ship Qty Variability Ship Qty Item Loc Item Loc Ship Qty Ship Qty Item Loc Item Loc Demand Variability and Velocity Influence Forecast Accuracy My demand is way more variable. High 13.2% 0.1% 0.1% 0.4% 0.0% 0.1% High 26.5% 23.9% 30.0% 4.7% 13.2% 54.7% Medium 15.6% 4.0% 2.5% 2.0% 2.6% 5.1% Medium 1.4% 3.1% 11.5% 0.3% 1.8% 24.6% Low 16.0% 18.2% 30.3% 2.6% 12.4% 74.8% Low 3.0% 0.0% 0.5% 0.0% 0.0% 0.7% Low Medium High Velocity Low Medium High Velocity FCA 81% Member % High Variability 89.8 % Low Variability 74.8% High Velocity, Low Variability 0.1% High Velocity, High Variability Member 3 FCA 54% 72.6% High Variability 0.7% Low Variability 0.7% High Velocity, Low Variability 54.7% High Velocity, High Variability

14 Harder to Forecast Weekly Forecasters Easier to Forecast Forecastability of Demand Variability & Velocity I have the most difficult variability to forecast High-Velocity, Low-Variability Medium-Velocity, Low-Variability Low-Velocity, Low-Variability Low-Velocity, Medium-Variability Medium-Velocity, Medium-Variability High-Velocity, Medium-Variability Low-Velocity, High-Variability Medium-Velocity, High-Variability High-Velocity, High-Variability Harder to Forecast Monthly Forecasters Easiest to Forecast

15 Location Network Segmentation Framework for Benchmarking Product Portfolio Characteristics Drive Performance Nearly 1,600 Benchmarks of Forecast Accuracy & Bias Lag Weekly Monthly

16 Overall Item-Location FCA Benchmarking Based on Forecastability Index What should my business accuracy be? 90% Monthly Forecasters Lag 1 Expected Under Over 80% 70% I m over performing! 60% 50% 40% 30% S&OVC Forecastability Index

17 Improvement Opportunities & Setting Realistic Targets Finally, Differentiated Accuracy Targets Based on Reality

18 Colgate-Palmolive s Demand Planning Segmentation Strategy

19 Who is Colgate-Palmolive? Started in 1806 in the U.S. $17+ Billion in global sales 35,000 People worldwide Operations in over 80 countries Selling products in 225 countries Four core categories: Oral Care Personal Care Home Care Pet Nutrition

20 Oral Care Personal Care Pet Nutrition Home Care

21 Business Challenges Rapidly evolving retail environment in developed and developing markets Greater demands for customized products and new product innovations Increasing competitive pressures in global and local markets Volatile demand with less lead time Managing information flow across multiple networks Limited planning resources challenged to manage demand

22 Effective demand planning across Colgate s cross functional team is critical to overcoming these challenges.

23 Demand planning segmentation is the tool that has allowed us to drive effectiveness.

24 What is Segmentation in Colgate Demand Planning? A process that splits a portfolio up into SKU segments that have similar demand characteristics SKUs with the same demand characteristics can be forecasted using similar approaches

25 Segmentation Objectives Manage portfolio complexity Identify variability or volatility in demand Prioritize and focus planning activities and resources Leverage statistical models Increase Demand Plan Accuracy and Effectiveness

26 Not Every SKU Behaves the Same Way Channel of Distribution and Retail Environment Item Usage (Impulse vs. Everyday) Seasonality Volatility of Demand Volume or Velocity

27 SKU Segmentation Our approach to segmenting SKUs is based on criteria that considers Volume & Volatility

28 Segmentation Matrix Volatility (CV%) vs. Volume HIGH LOW VOLUME HIGH VOLATILITY HIGH VOLUME HIGH VOLATILITY High Priority SKUs Up to 50% of SKUs < 10% of Volume < 20% of SKUs Up to 40% of Volume Volatility Threshold CV < 40% LOW VOLUME LOW VOLATILITY HIGH VOLUME LOW VOLATILITY LOW LOW ~10% of SKUs 10% of Volume Volume 10 to 20% of SKUs Up to 40% of Volume HIGH

29 Segmentation Matrix Volatility (CV%) vs. Volume HIGH LOW VOLUME HIGH VOLATILITY HIGH VOLUME HIGH VOLATILITY Hard to Predict Low Impact Items LOW VOLUME LOW VOLATILITY Hard to Predict High Impact Items HIGH VOLUME LOW VOLATILITY Volatility Threshold CV < 40% LOW Easy to Stat Forecast Low Impact Items LOW Volume Easy to Plan/Forecast High Impact Items HIGH

30 Focus Efforts & Collaboration on High Volume & Volatility SKUs HIGH LOW VOLUME HIGH VOLATILITY Use Inventory Strategies to Manage Volatility Focus Collaborative Demand Planning Efforts & Resources Customer Inputs CPFR LOW VOLUME LOW VOLATILITY HIGH VOLUME LOW VOLATILITY LOW LOW Use Statistical Models & Manage by Exception Volume Model Baseline Volume and Collaborate on Uplifts HIGH

31 Segmentation Works! U.S. business decreased Demand Planning Error (weighted MAPE) by over 5% Global subsidiaries that have applied Segmented Demand Planning: DPA Inventory Coverage

32 Keys to Success Segment your category SKU portfolio to: Improve accuracy Increase DP efficiencies Need cross functional understanding of benefits to drive ownership and focus Start by prioritizing high volume SKUs with higher volatility and lower DPA Focus organization on quick wins!

33 Demand Planning Error Demand Planning Error vs. Volatility Focus on Higher Volume & Volatility SKUs with High Error 50% 45% 40% Opportunity to Improve DPA 35% 30% 25% 20% 15% 10% 5% Error aligned with Volatility in Demand 0% 0% 5% 10% 15% 20% 25% 30% 35% Volatility

34 Keys to Success Leverage statistical forecasting Stable demand Baselines for higher volatility SKUs Evaluate lower volume and higher volatility SKUs Rationalize where possible Cover with safety stock Move to make-to-order

35 Next Steps Expand global roll-out and application Leverage Chainalytics SOVC analysis to expand segmentation Increase stat modeling to cover some predictable demand volatility Fully implement integrated DP segmentation tools in SAP APO

36 Questions & Answers

37 Jim Davis Jeff Metersky

38 Survey