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Copyright © 2007, SAS Institute Inc. All rights reserved. Demystifying Forecasting: The Future of Demand-Driven Forecasting for S&OP Charlie Chase Business.

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Presentation on theme: "Copyright © 2007, SAS Institute Inc. All rights reserved. Demystifying Forecasting: The Future of Demand-Driven Forecasting for S&OP Charlie Chase Business."— Presentation transcript:

1 Copyright © 2007, SAS Institute Inc. All rights reserved. Demystifying Forecasting: The Future of Demand-Driven Forecasting for S&OP Charlie Chase Business Enablement Manager Manufacturing & Supply Chain Global Practice Saturday, May 09, 2015

2 Copyright © 2007, SAS Institute Inc. All rights reserved. Planets are now aligned to facilitate Demand- Driven Forecasting… Data Access/Storage Data Processing Forecasting Methods

3 Copyright © 2007, SAS Institute Inc. All rights reserved. Over the past decade…  Data availability and quality have improved substantially along with the ability store it  Demand for -- and understanding of -- Predictive Analytics is accelerating rapidly across all industry verticals  Companies are leveraging predictive analytics to: Uncover patterns in consumer behavior, Measure the effectiveness of marketing investment strategies, and Optimize financial performance  Everyone is trying to move toward a demand pull forecasting strategy

4 Copyright © 2007, SAS Institute Inc. All rights reserved. Key changes due to Supply Chain initiatives  Sales Forecasting is the primary driver of the Integrated Supply Chain Management Process  Current Sales Forecasting Methods and Applications are changing Causal techniques are becoming more widely used −Predictive Modeling & Simulation  Everyone is under pressure to integrate demand- based forecasts with supply-based forecasts To improve forecast accuracy

5 Copyright © 2007, SAS Institute Inc. All rights reserved. Still Challenges Ahead  Organizations struggle to analyze and make practical use of the mass of data collected and stored  Others are trying to synchronize their data across their technology architectures  All are looking for solutions that: Provide actionable insight To make better decisions Improve corporate performance

6 Copyright © 2007, SAS Institute Inc. All rights reserved. Cultural Challenges  Convincing executive management that statistical forecasts Over time tend to out perform judgmental forecasts  Forecasting is a collaborative effort Between statisticians and domain knowledge experts  Excel spreadsheets can no longer support the forecasting process Task is too large (scalability issues)

7 Copyright © 2007, SAS Institute Inc. All rights reserved. Question …  How can forecasters do a better job evaluating information/data through the use of predictive analytics to improve forecast accuracy? While dealing with the cultural challenges of change management.

8 Copyright © 2007, SAS Institute Inc. All rights reserved. There are four key forecasting challenges  Process  Methods  Systems  Performance Metrics

9 Copyright © 2007, SAS Institute Inc. All rights reserved. Process Challenges

10 Copyright © 2007, SAS Institute Inc. All rights reserved. Generally speaking the word forecast…  There is only one forecast, a Sales forecast.  Most companies have a hard time distinguishing the difference between the forecasting process and the planning process… Dr. John (Tom) Mentzer, University of Tennessee at KnoxvilleDr. John (Tom) Mentzer, University of Tennessee at Knoxville Sales Forecast (Unconstrained) Financial Plan Production Plan Marketing Plan

11 Copyright © 2007, SAS Institute Inc. All rights reserved. Creation of an Unconstrained Demand Forecast  Should be based on: Statistical analysis of the historical data Including cause and effect information/data −Establishing a hypothesis using domain knowledge –Not “gut” feeling judgment −Validating or not validating assumptions with data and analytics  Creating not just a baseline for manual adjustments But, a “What If” simulation capability  Initiating “Fact-Based” discussions that drive better supply chain decisions

12 Copyright © 2007, SAS Institute Inc. All rights reserved. Simple Judgmental techniques  Not science is used exclusively when developing demand based forecasts  In fact, “Juries of Executive Opinion” −Still most widely used technique across all industries −This technique is not forecasting, but goal (or target) setting  More proactive “Fact-Based” forecasting is needed Separate the process of forecasting from that of goal setting  The process needs to be more systematic and objective Rather than subjective removing personal bias Using Domain Knowledge/Directional Consistency –Rather than, Judgment/Bias

13 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecasting Art or Science?  Forecasting is neither Art of Science!!! It’s mathematics and domain knowledge  There is always some element of Domain Knowledge, “not” Judgment in every forecast  Use domain knowledge to create hypothesis Use analytics to validate or not validate the hypothesis

14 Copyright © 2007, SAS Institute Inc. All rights reserved. Law of Universal Forecasting…  The more people who touch the forecast, the more inaccurate the forecast, and  The more fact-based (information\data supported) and mathematically derived the forecast, the more accurate the forecast…

15 Copyright © 2007, SAS Institute Inc. All rights reserved. Everyone wants to touch the forecast…  But no one wants to be accountable for the results  “Cultural” Issue  Requires change management driven by a “Champion”

16 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecasting Methods Challenges “Forecasters have fallen short in this area.”

17 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern (s) + Randomness Theory of Forecasting Methods...

18 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern (s) + Randomness This simple equation is really saying that when the average pattern of the underlying data has been identified some deviation will occur between the forecasting method applied and the actual occurrence. That deviation is called “Error”, or unexplainable variance. Theory of Forecasting Methods...

19 Copyright © 2007, SAS Institute Inc. All rights reserved. This simple equation is really saying that when the average pattern of the underlying data has been identified some deviation will occur between the forecasting method applied and the actual occurrence. That deviation is called “Error”, or unexplainable variance. Theory of Forecasting Methods... Forecast = Pattern (s) + Randomness Error/Unexplained

20 Copyright © 2007, SAS Institute Inc. All rights reserved. This simple equation is really saying that when the average pattern of the underlying data has been identified some deviation will occur between the forecasting method applied and the actual occurrence. That deviation is called “Error”, or unexplainable variance. MaximizeMinimize Theory of Forecasting Methods... Forecast = Pattern (s) + Randomness Error/Unexplained

21 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern (s) + Randomness Two Broad mathematical categories 1.Time Series 2.Causal Theory of Forecasting Methods...

22 Copyright © 2007, SAS Institute Inc. All rights reserved. Trend Seasonality Cyclical Forecast = Pattern (s) + Randomness Error/Unexplained Theory of Forecasting Methods...

23 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern (s) + Randomness Trend Seasonality Cyclical Error/Unexplained Theory of Forecasting Methods... Time Series Methods  Exponential Smoothing  Brown’s  Holts  Winter’s  Census X11/12  ARIMA

24 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern + Randomness Theory of Forecasting Methods...

25 Copyright © 2007, SAS Institute Inc. All rights reserved. Event Forecast = Pattern + Randomness Theory of Forecasting Methods...

26 Copyright © 2007, SAS Institute Inc. All rights reserved. Event Three Basic Types: 1.Point Intervention 2.Step Intervention 3.Ramp-up (or down) Intervention Forecast = Pattern + Randomness

27 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern (s) + Randomness Error Trend Seasonality Cyclical Events Sales Promotions Marketing Events

28 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern (s) + Randomness Error Trend Seasonality Cyclical Events Sales Promotions Marketing Events Time Series Methods  ARIMA  W/Interventions

29 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern (s) + Randomness Error Trend Seasonality Cyclical Events Sales Promotions Marketing Events Causal Price Advertising Competitive Activities Etc.

30 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern (s) + Randomness Error Trend Seasonality Cyclical Events Sales Promotions Marketing Events Causal Price Advertising Competitive Activities Etc. Causal Methods  ARIMAX  Dynamic Regression  Unobserved Component Models (UCM)

31 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecast = Pattern (s) + Randomness Error/Unexplained The Ultimate Algorithm (No Greek Symbols Required)

32 Copyright © 2007, SAS Institute Inc. All rights reserved. Selecting the Appropriate Method Based On Portfolio Management Winter’s Dynamic Regression Weighted Combined Models Judgmental ARIMA Tool Box Approach to Forecasting ARIMAX UCM

33 Copyright © 2007, SAS Institute Inc. All rights reserved. Harvest Brands Growth Brands Product Portfolio New Products Niche Products

34 Copyright © 2007, SAS Institute Inc. All rights reserved. Harvest Brands Growth Brands Product Portfolio New Products Niche Products  Line Extensions  Some surrogate history available  Short Life Cycle Products  Only primary research available

35 Copyright © 2007, SAS Institute Inc. All rights reserved. Harvest Brands Growth Brands Product Portfolio New Products Niche Products  Line Extensions  Some surrogate history available  Short Life Cycle Products  Only primary research available  Low Priority Products  Highly Seasonal  Trend  Cyclical  Minor Sales Promotions

36 Copyright © 2007, SAS Institute Inc. All rights reserved. Harvest Brands Growth Brands Product Portfolio New Products Niche Products  Line Extensions  Some surrogate history available  Short Life Cycle Products  Only primary research available  Low Priority Products  Highly Seasonal  Trend  Cyclical  Minor Sales Promotions  High Priority Products  Seasonal Fluctuations  Sales Promotions  National Marketing Events  Advertising Driven  Highly Competitive

37 Copyright © 2007, SAS Institute Inc. All rights reserved. Harvest Brands Growth Brands Product Portfolio New Products Niche Products  Line Extensions  Some surrogate history available  Short Life Cycle Products  Only primary research available  Regional Specialty Products  Irregular Demand  Little Seasonality  Some Trend  Local Events  High Priority Products  Seasonal Fluctuations  Sales Promotions  National Marketing Events  Advertising Driven  Highly Competitive  Low Priority Products  Highly Seasonal  Trend  Cyclical  Minor Sales Promotions

38 Copyright © 2007, SAS Institute Inc. All rights reserved. Product Portfolio New Products “Juries” of Executive Opinion Sales Force Composites Delphi Committees Independent Judgment Judgmental Niche Products Harvest Brands Growth Brands Bass Diffusion Model

39 Copyright © 2007, SAS Institute Inc. All rights reserved. Product Portfolio New Products “Juries” of Executive Opinion Sales Force Composites Delphi Committees Independent Judgment Judgmental ARIMA Box-Jenkins Census X-11 Winters Decomposition Simple Moving Average Holt’s Double Exponential Smoothing Niche Products Harvest Brands Growth Brands Time Series Bass Diffusion Model

40 Copyright © 2007, SAS Institute Inc. All rights reserved. Product Portfolio New Products “Juries” of Executive Opinion Sales Force Composites Delphi Committees Independent Judgment Judgmental ARIMA Box-Jenkins Census X-11 Winters Decomposition Simple Moving Average Holt’s Double Exponential Smoothing ARIMAX ARIMA with Interventions & Regressors Dynamic Regression Simple Regression UCM Procedure Niche Products Harvest Brands Growth Brands Time SeriesCausal Modeling Bass Diffusion Model

41 Copyright © 2007, SAS Institute Inc. All rights reserved. Product Portfolio New Products “Juries” of Executive Opinion Sales Force Composites Delphi Committees Independent Judgment Judgmental ARIMA Box-Jenkins Census X-11 Winters Decomposition Simple Moving Average Holt’s Double Exponential Smoothing ARIMAX ARIMA with Interventions & Regressors Dynamic Regression Simple Regression UCM Procedure Combined Weighted: Judgment Time Series Causal Combined Average: Judgment Time Series Causal Croston’s Intermittent Demand Multiple Methods Niche Products Harvest Brands Growth Brands Time SeriesCausal Modeling Bass Diffusion Model

42 Copyright © 2007, SAS Institute Inc. All rights reserved. Product Portfolio New Products Niche Products JudgmentalMultiple Methods Vanilla Coke Diet Coke W/Lemon Dasani Water Poweraid Cherry Coke Barq’s Root Beer Minutemaid Orange Juice Classic Coke Diet Coke Sprite Mr. PIB TAB Harvest Brands Growth Brands Time SeriesCausal Modeling

43 Copyright © 2007, SAS Institute Inc. All rights reserved. Product Portfolio New Products Niche Products JudgmentalMultiple Methods Vanilla Coke Diet Coke W/Lemon Poweraid Cherry Coke Barq’s Root Beer Minutemaid Orange Juice Classic Coke Diet Coke Sprite Mr. PIB TAB 10% 50% 35% 5% Dasani Water Harvest Brands Growth Brands Time SeriesCausal Modeling

44 Copyright © 2007, SAS Institute Inc. All rights reserved. One methodology fits all philosophy…  Most systems are driven by this philosophy Always, time series methods −Exponential Smoothing −Winter’s most widely used mathematical method –Easy to systematize  We have 99 different models  Actually 99 versions of the same model Exponential Smoothing and/or ARIMA (Box Jenkins) What about Dynamic Regression, ARIMAX, and UCM? −Dynamic versus static?

45 Copyright © 2007, SAS Institute Inc. All rights reserved. Bottom Line…  There is no Best Method The best method depends on the data, the purpose, the organizational environment and the perspective of the forecaster. Your market, products, goals, and constraints should be considered when selecting the forecasting tools best for you...

46 Copyright © 2007, SAS Institute Inc. All rights reserved. Enabling Solution Challenges

47 Copyright © 2007, SAS Institute Inc. All rights reserved. Selection of Software  Software should include: Advanced analytics with optimized model selection Scalability Reduced forecast cycle times Exception-based forecasting The ability to support collaborative/consensus forecasting & planning “You can’t install an off-the-shelf solution without some customization (tailoring)...”

48 Copyright © 2007, SAS Institute Inc. All rights reserved. Assess the current forecasting process  Conduct SVA Strategic Value Assessment −Identify strengths and weaknesses −Design a process flow model that improves upon the weaknesses −Then, build the enabling solution around the process  Rather than bending and twisting an off-the-shelf solution to fit your process  You can’t install an off-the-shelf solution without some customization (tailoring)

49 Copyright © 2007, SAS Institute Inc. All rights reserved. In reality, most companies are saying…  “Automate what I do, but don’t change what I do”.  Result: They go out of business faster and more efficiently.  Justification: “We don’t feel comfortable with system generated forecasts.” “But, we don’t mind using the system to make manual (judgmental) overrides.”

50 Copyright © 2007, SAS Institute Inc. All rights reserved. Challenges Measuring Forecasting Accuracy

51 Copyright © 2007, SAS Institute Inc. All rights reserved. Why Measure Forecast Accuracy?  “What get measured gets fixed…”  Remember, tracking forecast error alone is not the solution. Instead of asking the question, “ what is this months forecast error?” We need to ask, “ why was this months error so high (or low), and has it improved since last month?”

52 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecasting Performance Metrics  90% of the companies we interview don’t monitor and track their forecast accuracy No performance metrics  Those who do use MAPE (most widely used metric), but don’t compare it to KPI’s WAPE is becoming more popular  Balance scorecard is the best way to demonstrate the impact of an improved forecast With drill down capabilities Will get the attention of a C-level manager

53 Copyright © 2007, SAS Institute Inc. All rights reserved. Typical Forecast Accuracy Report, right?

54 Copyright © 2007, SAS Institute Inc. All rights reserved. Typical Forecasting Balanced Scorecard (Must be drillable) Need to focus on the improvement, not the accuracy!!!

55 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecasting Is Like Taking Pictures High Speed Digital Film Sophisticated Professional Camera Nikon, Minolta, Hasselblatt Multiple Lens & Filters Zoom, Wide Angle Highly Skilled ProfessionalPhotographer

56 Copyright © 2007, SAS Institute Inc. All rights reserved. Forecasting Is Like Taking Pictures High Speed Digital Film Easy Access High Quality Data Enterprise Data Warehouse Sophisticated Professional Camera Nikon, Minolta, Hasselblatt Sophisticated Forecasting Solution Solution Network Server RemoteAccessGSMGlobal Forecasting ForecastingRegions/AffiliatesOperationalPresidents Finance Multiple Lens & Filters Zoom, Wide Angle Multiple Forecasting Methodologies Methodologies Tool Box Time-Series Causal CompositeForecastingJudgmentalARIMA Highly Skilled ProfessionalPhotographer ProfessionalForecasters

57 Copyright © 2007, SAS Institute Inc. All rights reserved. In Closing…  Be careful about being too accurate with your forecasts… “The mustard forecast that was too accurate…”

58 Copyright © 2007, SAS Institute Inc. All rights reserved.


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