Syllabus Autumn 2015 OPMGT 443: Inventory and Supply Chain Management M W 10:30-12:20 DEM 004

Transcription

1 Syllabus Autumn 2015 OPMGT 443: Inventory and Supply Chain Management M W 10:30-12:20 DEM 004 Prof. Apurva Jain apurva@uw.edu PACCAR 532, Ph: ; Office hours: M W 9:30-10:25 Course Objectives & Structure: Supply-chain management is an umbrella term, popularly used to describe a variety of ideas, tools and techniques devised to generate more value out of the network of suppliers, manufacturers, logistics providers, and retailers that together provide a product or service to the customer. We take an integrated view of all parties and all functions in a supply chain and introduce ideas and tools that can be used to optimize the integrated supply chain. We also focus on transactions and relationships between different firms or functions in a supply chain. We consider these transactions in the framework of a buyer-supplier model and analyze various aspects of these relationships. We close with discussions of issues of current interest in supply chain management. We will organize the course into four modules. Each module is devoted to a part or the function of the supply chain. First module is devoted to the function of forecasting. Second module addresses the planning function in the make part of the supply chain. Third module section is devoted to the deliver part of the supply chain and focuses on inventory and distribution functions. In the fourth module, we discuss some issue related to the source part of the supply chain. At the end of the course, students will be able to apply tools of forecasting and optimization to manufacturing planning, and tools of multi-echelon inventory theory to the distribution function. Through case studies and games, students will gather the experience of actual applications of these tools and concepts. Students will have the information about software applications available to apply these models in practical settings. Finally, students will have opportunity to think about emerging topics like RFID, environmental issues and social responsibility in supply chains. Teaching Materials Either of the following two textbooks is very strongly recommended: Production and Operations Analysis by Steven Nahmias, 6 th edition. Supply Chain Management: Strategy, Planning, and Operation, by Sunil Chopra and Peter Meindl, 5th edition It is also required that you purchase an access code to an online game. Details will be provided later in class. Other reading materials for the course will be distributed in class and posted on Canvas. Grading: 4 Individual quizzes 5% each, total 20% 4 Group Assignments 7.5% each, total 30% Mid-Term & Final 20% each, total 40% Participation/ Sharing 10%

2 Mid-Term exam will cover the material in forecasting and making modules. Final exam will cover the material in delivering and sourcing modules. There are four homework assignments. Each student will work on these assignments individually but submit it as a group (ideal group size is four people). To encourage individual work on assignments, I run individual quizzes related to each homework in class. These quizzes will be graded for effort and can negatively affect an individual s homework grade. Cases are part of the assignments as well. For case questions, I will ask the group to discuss their work with the rest of the class. Be it a case discussion or the development of an analytical model, the real learning comes from actively thinking about it and sharing your thoughts with others in the class. The best way to learn the material and to earn the participation marks is to read the suggested material before the class and to be ready with your comments/observations. You can also gain participation grade by finding related material in business press and discussing it in class. Class Schedule Session Session Title Due Dates Module 1: Introduction & Forecasting 09/30 W 1. Introduction: Mapping, Planning 10/05 M 2. Holt s Model 10/07 W 3. Winter s Model Hw1 Quiz 10/12 M 4. Application: Spreadsheet Homework 1 Module 2: Manufacturing Planning: Making 10/14 W 5. Planning hierarchy, Chase & Level 10/19 M 6. LP Models 10/21 W 7. Application: McPherson Hw2 Quiz 10/26 M 8. Business Process: Planning Homework 2 10/28 W 9. New Products Forecasting 11/02 M 10. Mid-Term Exam Module 3: Distribution & Fulfillment: Delivering 11/04 W 11. Total Logistics Cost 11/09 M 12. Transportation and Warehousing 11/11 W Veterans Day: No class. 11/16 M 13. Application: HP Hw3 Quiz 11/18 W 14. Information Flow 11/23 M 15. Collaborative process: Barilla Homework 3 Module 4: Sourcing 11/25 W 16. Newsboy Model: Quick Response 11/30 M 17. Sourcing Contracts 12/02 W 18. Application: Sport Obermeyer Hw4 Quiz 12/07 M 19. Emerging Topics Homework 4 12/09 W TBD Final Exam: Monday, December 14, 2015, pm, DEM 004

3 Inventory & Supply Chain Management Prof. Apurva Jain Module 1: Session 1-4 Introduction & Forecasting Apple Supply Chain 1

4 Apple Supply Chain Apple Supply Chain Inventory Turns: Annual cost of goods sold divided by Average inventory Higher Inventory turns Lower Inventory Costs General Mfg.: 8; Samsung: 17; Dell: 36; Apple: Ref: Atlantic,

5 Map Inventory Flow in a Supply Chain Source Raw Material Inventory Sub-Assy. Mfg. Capacity Make Work-in- Process Inventory Final Assy. Mfg. Capacity Deliver Finished Goods Inventory Map Locations: Identify all inventory locations and transportation links between them. Identify stages: Source(RM), Make(WIP), Deliver(FG) Map Decisions: List planning decisions at each stage that can be used to improve inventory flow. Map Characteristics: Identify important characteristics of the flow that will influence decision-making and flow performance. store inventory store inventory Relationship between Metrics Cost Inventory carrying cost Other Inventory-related costs Quality Product or Process quality Quality degradation in inventory Flexibility Variety Feature-combinations available on order Availability Fraction of demand from inventory Customer s wait time 3

6 Course Objective: Improve Inventory Flow Performance Course Structure: 1. Forecasting 2. Making 3. Delivering 4. Sourcing Workload at a Glance Session Session Title Due Dates Module 1: Introduction & Forecasting 09/30 W 1. Introduction: Mapping, Planning 10/05 M 2. Holt s Model 10/07 W 3. Winter s Model Hw1 Quiz 10/12 M 4. Application: Spreadsheet Homework 1 Module 2: Manufacturing Planning: Making 10/14 W 5. Planning hierarchy, Chase & Level 10/19 M 6. LP Models 10/21 W 7. Application: McPherson Hw2 Quiz 10/26 M 8. Business Process: Planning Homework 2 10/28 W 9. New Products Forecasting 11/02 M 10. Mid-Term Exam Module 3: Distribution & Fulfillment: Delivering 11/04 W 11. Total Logistics Cost 11/09 M 12. Transportation and Warehousing 11/11 W Veterans Day: No class. 11/16 M 13. Application: HP Hw3 Quiz 11/18 W 14. Information Flow 11/23 M 15. Collaborative process: Barilla Homework 3 Module 4: Sourcing 11/25 W 16. Newsboy Model: Quick Response 11/30 M 17. Sourcing Contracts 12/02 W 18. Application: Sport Obermeyer Hw4 Quiz 12/07 M 19. Emerging Topics Homework 4 12/09 W TBD Final Exam: Monday, December 14, 2015, pm, DEM 004 4

7 Grading Scheme 4 Individual quizzes 4 Group Assignments Mid Term & Final Participation/ Sharing 10% 5% each, total 20% 7.5% each, total 30% 20% each, total 40% Teaching Material: Text book, Group size and formation: Link for game access Quantitative Orientation; Application Orientation Success Tips: Be in class, know & meet deadlines, explore and share. Canvas website Feedback/Response Module 1 Learning Objectives Compute Moving Average & Exponential Smoothing Forecasts Compute Errors, Compare Forecasts Include Trend Include Seasonality Initialize the process Plan step-by-step Forecasting process Spreadsheet applications Make Adjustments Generate Improvement Ideas 5

8 Forecasting Slides Organization 1. Time-Series Forecasting: Naïve, Moving Average (MA) 2. Exponential Smoothing (ES), Comparison between MA & ES 3. Computation of Errors: MAD, MSE 4. Time Series Components, Demand Model 5. Trend: Holt s Method, Examples 6. Seasonality: Winter s Method, Examples 7. Initialization, Spreadsheet 8. Implementation: Step-by-step & other issues 9. Make any causal & qualitative adjustments, Improvements, Other resources Time Series Methods Historical sales data is available and it is best explained by considering the pattern of its own past values over time. Time Series Methods Moving Averages Exponential Smoothing Holt s Method Winter s Method 6

9 A Simple Demand Model Assume a demand model with only two components: demand = level + random Time Series: Naïve Forecasting Period Data

10 Time Series: Moving Average Method Period Demand period moving average 2 40 forecast for period 6, made 3 43 at the end of period = ( ) / 3 = Time Series: Moving Average Method Period Demand 1 42 If actual sales in period 6 = period moving average 3 43 forecast for period 7, made 4 40 at the end of period = ( ) / 3=

11 Forecasting Slides Organization 1. Time-Series Forecasting: Naïve, Moving Average (MA) 2. Exponential Smoothing (ES), Comparison between MA & ES 3. Computation of Errors: MAD, MSE 4. Time Series Components, Demand Model 5. Trend: Holt s Method, Examples 6. Seasonality: Winter s Method, Examples 7. Initialization, Spreadsheet 8. Implementation: Step-by-step & other issues 9. Make any causal & qualitative adjustments, Improvements, Other resources Exponential Smoothing: Period: Actual: D 1 D 2 D 3 D 4 D 5 D 6 D 7 Forecast: F 0+1 F 1+1 F 2+1 F 3+1 F 4+1 F 5+1 F 6+1 *(1-α) + * α Exponential Smoothing with smoothing parameter α: Forecast for next period = α * actual for this period + (1- α ) * forecast for this period 0< α<1 9

12 Time Series: Exponential Smoothing Exponential Smoothing forecast using parameter α (alpha; 0< α<1) = α * actual observed data + (1- α) * forecast Period Demand Suppose an initial forecast for period 5 was given as 40. Actual observed data in period 5 is 41. Exponential smoothing forecast with parameter α=0.2 for period 6, made at the end of period 5 = 0.2*41 + (1-0.2)*40 = 40.2 Time Series: Exponential Smoothing Exponential Smoothing forecast using parameter α (alpha; 0< α<1) = α * actual observed data + (1- α) * forecast Period Demand From previous slide, forecast for period 6 =40.2. Actual observed data in period 6 is 39. Exponential smoothing forecast with parameter α=0.2 for period 7, made at the end of period 6 = 0.2*39 + (1-0.2)*40.2 =

13 MA & ES: Summary Moving Average (MA(n)) method with parameter n: At the end of a period, take an average of n latest data points to make a forecast for the next period. Exponential smoothing (ES(α)) forecast with parameter α: At the end of a period, take a weighted average of that period s actual and the forecast made for that period with α representing the weight on actual and (1-α) the weight on forecast. The result will give the forecast for the next period. Moving Average & Exponential Smoothing Period Data Demand Model: Level + random We recognize the existence of random component but focus only on estimating the systematic (that is, non-random) component. 4-period moving average forecast MA(4) for period 12, made at the end of period 11= Given that forecast for period 11 was 35, what is the exponential smoothing forecast with alpha=0.2, ES(0.2) for period 12, made at the end of period 11= What is ES(0.2) forecast for period 15, made at the End of period 11? 11

14 Moving Average Parameter Impact of averaging parameter: Which line is MA(3) and which one is MA(5)? Actual MA( ) MA( ) Exponential Smoothing Parameter 12

15 Exponential Smoothing Idea--The most recent observations might have the highest predictive value along with the most recent forecast. Let us balance them: F t D t 1 ( 1 ) Ft 1 The role of smoothing parameter alpha is to determine the balance: Weight on new data: alpha Weight on forecast (based on previous data): (1-alpha) 0<alpha<1 Exponential Smoothing: Weight on History F 11,12 = D 11 + (1- F 10,11 = D 11 + (1- ) { D 10 + (1- ) F 9,10 } 0< < = D 11 + (1-X) D 10 + (1- ) 2 F 9,10 =. = D 11 + (1- ) D 10 + (1- ) 2 D 9 + (1- ) 3 D

16 Moving Average vs. Exponential Smoothing Moving Average (MA) is more intuitive and is easier to implement. Exponential Smoothing (ES) is commonly considered to be more sophisticated and is also widely used. MA requires data for multiple past periods. ES requires data for only one past period. MA gives equal weight to last n-periods and no weight to data before that. ES gives decreasing weight to all historical data. Forecasting Slides Organization 1. Time-Series Forecasting: Naïve, Moving Average (MA) 2. Exponential Smoothing (ES), Comparison between MA & ES 3. Computation of Errors: MAD, MSE 4. Time Series Components, Demand Model 5. Trend: Holt s Method, Examples 6. Seasonality: Winter s Method, Examples 7. Initialization, Spreadsheet 8. Implementation: Step-by-step & other issues 9. Make any causal & qualitative adjustments, Improvements, Other resources 14

17 Measuring Forecast Performance Evaluation of Forecasts: Error Et or deviation = Forecast Observed Value Absolute Error = Et = without the +/- sign MAD = Mean Absolute Deviation MSE = Mean Squared Error MAPE = Mean Absolute Percentage Error Smaller MAD MSE MAPE Better Forecasting method Time Series: Exponential Smoothing Month Forecast Demand Error Error Error 2 April 105 May 100 June 80 July 110 August 115 September 105 October 110 November 125 December 120 Use α=0.2 15

18 Exponential Smoothing Practice Period Actual Forecast witherror with Forecast witherror with 1 42 Alpha=0.1 Alpha=0.1 Alpha=0.4 Alpha= Exponential Smoothing Practice Solution Period Actual Forecast wit Error with Forecast wit Error with 1 42 Alpha=0.1 Alpha=0.1 Alpha=0.4 Alpha=

19 Forecasting Slides Organization 1. Time-Series Forecasting: Naïve, Moving Average (MA) 2. Exponential Smoothing (ES), Comparison between MA & ES 3. Computation of Errors: MAD, MSE 4. Time Series Components, Demand Model 5. Trend: Holt s Method, Examples 6. Seasonality: Winter s Method, Examples 7. Initialization, Spreadsheet 8. Implementation: Step-by-step & other issues 9. Make any causal & qualitative adjustments, Improvements, Other resources Time Series Components A time-series has four components: Level, Trend, Seasonality & Random. A demand model defines various components of a time-series and proposes a way to put them together. 17

20 Time Series: Demand Model Identify & define demand components and propose an equation to put them together Time Series: Demand Model Identify demand components 18

21 Forecasting Slides Organization 1. Time-Series Forecasting: Naïve, Moving Average (MA) 2. Exponential Smoothing (ES), Comparison between MA & ES 3. Computation of Errors: MAD, MSE 4. Time Series Components, Demand Model 5. Trend: Holt s Method, Examples 6. Seasonality: Winter s Method, Examples 7. Initialization, Spreadsheet 8. Implementation: Step-by-step & other issues 9. Make any causal & qualitative adjustments, Improvements, Other resources ES with Trend: Holt s Method Demand Model t periods ahead = (Level S + Trend G * t) + Error t periods ahead α: For updating level 0<α<1 β: For updating trend 0<β<1 Given a set of initial values for level and trend estimates, update these estimates: S t = α Dt + (1- α) (S t-1 + G t-1 ) New Level Estimate = α *Observed Data + (1- α) (old level estimate + old trend estimate) G t = β (S t S t-1 ) + (1- β) G t-1 New Trend Estimate = β (New Level Old Level) + (1- β) Old Trend 19

22 Holt s Method: Example Initialization: Level 200 Trend 10 α=0.1 β=0.1 New Observation in Period 1 = 200 At the end of Period 1: New Level = 0.1 * (1-0.1) ( ) = 209 New Trend = 0.1 * ( ) + (1 0.1) * 10 = 9.9 New Observation in Period 2 = 250 At end of Period 2: New Level = New Trend = Forecast For Period 3, F 2,3 = Forecast for period 5, F 2,5 = Holt s Method: Example Example: Question 30, (page 86) Initialization: Level (S 0 ) = Trend (G 0) = α= 0.15 β = 0.1 New Observation in July = 2150 End of July: New Level = New Trend = Forecast for August at end of July = 20

23 Forecasting Slides Organization 1. Time-Series Forecasting: Naïve, Moving Average (MA) 2. Exponential Smoothing (ES), Comparison between MA & ES 3. Computation of Errors: MAD, MSE 4. Time Series Components, Demand Model 5. Trend: Holt s Method, Examples 6. Seasonality: Winter s Method, Examples 7. Initialization, Spreadsheet 8. Implementation: Step-by-step & other issues 9. Make any causal & qualitative adjustments, Improvements, Other resources Seasonality Periodicity? Seasonal Factors? 21

24 ES with Seasonality: Winter s Method Demand Model for t Periods Ahead= {(Level + t * Trend) * Seasonal Factor for t Period Ahead} + Error Expo Smoothing Updating parameters: α, β, γ New Level = α* (New Observation/ Corresponding Seasonal Factor) + (1 α) (Old Level + Old Trend) New Trend = β* (New Level Old Level) + (1-β) * Old Trend New Seasonal Factor for corresponding season = γ * (New Observation / New Level) + (1- γ) * Old Seasonal Factor for Corresponding Season Winter s Method: Example Four Seasons Starting Level = Starting Trend = Starting Seasonal Factors Season 1 = 0.59 α = 0.2 Season 2 = 1.11 β = 0.1 Season 3 = 1.38 γ = 0.1 Season 4 = 0.92 New Observation = 16 in Season 1 New Level = 0.2 * ( 16 /0.59) * ( ) = New Trend = 0.1 * ( ) * = New Seasonal Factor for Season 1 = 0.1 * (16/24.57) * 0.59 = At end of Season 1, forecast for season 2 = At end of Season 1 forecast for next year season 1 = New Observation = 33 in Season 2 New Level = New Trend = New Seasonal Factor for Season 2 = At end of Season 2, forecast for season 3 = 22

25 Winter s Method: Example Four Seasons Starting Level = Starting Trend = 524 Starting Seasonal Factors Season 1 = 0.47 α = 0.05 Season 2 = 0.68 β = 0.1 Season 3 = 1.17 γ = 0.1 Season 4 = 1.67 New Observation = 8000 in Season 1 New Level = New Trend = New Seasonal Factor for Season 1 = At end of Season 1, forecast for season 2 = At end of Season 1, forecast for season 3 = At end of Season 1, forecast for season 4 = At end of Season 1, forecast for next year season 1 = Forecasting Slides Organization 1. Time-Series Forecasting: Naïve, Moving Average (MA) 2. Exponential Smoothing (ES), Comparison between MA & ES 3. Computation of Errors: MAD, MSE 4. Time Series Components, Demand Model 5. Trend: Holt s Method, Examples 6. Seasonality: Winter s Method, Examples 7. Initialization, Spreadsheet 8. Implementation: Step-by-step & other issues 9. Make any causal & qualitative adjustments, Improvements, Other resources 23

26 Initialization To initialize: Deseasonalize the time-series, run regression on deseasonalized data to get level and trend estimates, and finally, use the ratios between actual and regression line to estimate seasonal factors. Here is a step-by-step process. Step 1: Plot the data and notice the periodicity. In this case the periodicity is 4. Suppose time series has 8 data points. Compute the deseasoned actual as following: Deseasonalized demand for period 3 = [D1 + 2*(D2+D3+D4) + D5] / 8 Deseasonalized demand for period 4 = [D2 + 2*(D3+D4+D5) + D6] / 8. Deseasonalized demand for period 6 = [D4 + 2*(D5+D6+D7) + D8] / 8 Step 2: Run a regression between X-range (periods): 3,4,5,6 and Y-range (deseasonalized demand) as determined in step 2. This will provide estimates of intercept and slope giving initial level and trend. Step 3: Compute intercept + period number*slope for each period; this is the regression line giving the the deseasonalized estimate for each period. Step 4: For each period compute a seasonal factor as actual D for that period divided by deseasonalized regression estimate (step 3) for that period. Step 5: Average seasonal factors for periods 1 and 5 to get a seasonal factor for season 1; average seasonal factors for periods 2 and 6 to get a seasonal factor for season 2 and so on. These are initial seasonal factors. Initialization: Example Deseasonalized regression line Seasonal Factors Average seasonal factors 24

27 Spreadsheet: Initialization Inirtial level estimate= Inirtial trend estimate= 524 Deseason Initial estimate Average Observed Deseason Regressio Seasonal Seasonal Period Season Data Data Equation Factors Factors Make a Forecast, Update Estimates Alpha= Beta= Gamma= Old Error= New Seasonal Observed Forecast Seasonal Old Level Old Trend Factor Forecast Period Season Data Demand New Level New TrendFactor

28 Spreadsheet: Compute Errors Error= Forecast Demand Absolute Squared Error Error MAD MSE Forecasting Slides Organization 1. Time-Series Forecasting: Naïve, Moving Average (MA) 2. Exponential Smoothing (ES), Comparison between MA & ES 3. Computation of Errors: MAD, MSE 4. Time Series Components, Demand Model 5. Trend: Holt s Method, Examples 6. Seasonality: Winter s Method, Examples 7. Initialization, Spreadsheet 8. Implementation: Step-by-step & other issues 9. Make any causal & qualitative adjustments, Improvements, Other resources 26

29 Forecasting Implementation: Step-by-Step 1. Plot the time-series; propose a demand model. 2. Initialize the estimates: level, trend, seasonal factors. 3. Make a forecast for next period. 4. Observe new data and update estimates. 5. Compute errors. 6. Make any causal & qualitative adjustments. Use historical data to train the model: assume a smoothing constant alpha make forecasts for periods for which demand is already known evaluate the performance: MAD or MSE look for an alpha (usually ) that improves performance Forecasting Methods: Three situations 1. Historical sales data is available and the forecast is made by considering its pattern over time: Time Series Methods. 2. Historical sales data is available and forecast is made by considering its relationship with set of outside variables : Causal Methods. 3. Historical sales data is not available and forecast is made by surveying opinions: Qualitative Methods. 27

30 Qualitative or Judgmental Methods Historical sales data is not available. - new products, short life-cycle products Market research Experts opinions Methods: Sales force composites Customer Surveys Jury of executive opinion Delphi Method Causal Methods Historical sales data is available and it is best explained by considering the influence of outside variables. Causal Methods: Regression 28

31 Forecasting Methods: Three methods 1. Start with time series methods: Extrapolate the pattern to make a forecast 2. Brainstorm any outside-the-data influences: Adjust the forecast to account for other factors 3. Run it through the organizational forecasting process: gut-check based on internal/external opinions Implementation Issues & Examples: What should be the period-length? What should be the unit of forecasting? How far in future should we forecast? 29

32 Implementation Issues & Examples: What does the time-series actually represent? Clean-up the data. Implementation Issues & Examples: Which error measure should be used? What else can error numbers tell us? 30

33 Implementation Issues & Examples: It is useful to anticipate the trend. How many meals to board? What factors should we consider? FLT From To Sales ID Date NB Actual Sales Meals Boarded 851 SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jun SEA HNL Jul SEA HNL Jul SEA HNL Jul SEA HNL Jul SEA HNL Jul SEA HNL Jul

34 Forecasting Slides Organization 1. Time-Series Forecasting: Naïve, Moving Average (MA) 2. Exponential Smoothing (ES), Comparison between MA & ES 3. Computation of Errors: MAD, MSE 4. Time Series Components, Demand Model 5. Trend: Holt s Method, Examples 6. Seasonality: Winter s Method, Examples 7. Initialization, Spreadsheet 8. Implementation: Step-by-step & other issues 9. Make any causal & qualitative adjustments, Improvements, Other resources Adjusting Time-Series Forecasts More than half of manufacturers surveyed adjusted the statistical forecast. Fewer retailers did it. The main reason they offered for adjustment was promotions. Other factors mentioned: weather, holidays, price changes. Fildes & Goodwin Adjustment Approach: Use only non-promotion periods to update component estimates. Use causal methods (regression) to predict a relationship between price discount and size of demand spike in promotion periods. 32

35 Using Judgment Combination of information and subjective beliefs of individuals. Use Judgment to either adjust the time-series or causal forecasts or, if no historical data is available, make a forecast. Whose judgment should we rely on? How should we combine judgments? Acquiring Opinions Who else can we ask? How many should we ask? 33

36 Heuristics & Biases in Judgmental Forecasting 1. Availability Bias: Forecaster relies on easily available and memorable information 2. Representativeness Heuristic: Forecaster matches a situation to a similar earlier event without taking into account its frequency of occurrence. 3. Anchoring and adjustment Heuristic: Forecaster uses an initial value and then modifies it up or down. 4. Overconfidence In their beliefs as to the accuracy of their forecasts. Beware of Groupthink!! Combining Opinions Key Steps in a Delphi analysis: 1. Determine the questions & objectives 2. Choose the panelists 3. Circulate an info. pack 4. Request responses to a questionnaire by The end of round Summarize responses statistically, & summarize the rationale behind the responses 6. Update information and complete round 2 Of the survey. Repeat, if necessary. 34

37 Making Good Use of Errors Only nine of the 32 retailers in our study said they analyzed the accuracy of their forecasts. And yet, tracking forecast errors, and understanding when and why they occur, is fundamental to improving accuracy. Rocket Science Retailing. Fisher Use error to adjust values for smoothing parameters. 2. Persistence non-zero error indicates bias. 3. It can provide a sense of what to adjust for. 4. It can provide an estimate of demand variability. Prediction Markets The markets were designed to forecast product launch dates, new office openings, and many other things of strategic importance to Google. So far, more than a thousand Googlers have bid on 146 events in 43 different subject areas (no payment is required to play). We designed the market so that the price of an event should, in theory, reflect a consensus probability that the event will occur. To determine accuracy of the market, we looked at the connection between prices of events and the frequency with which they actually occurred. If prices are correct, events priced at 10 cents should occur about 10 percent of the time. 35

38 Comparing Judgmental & Quantitative Forecasting Methods Quantitative + Judgment stage four: synthesis the practice of marrying quantitative insights with old-fashioned subjective experience. Nate Silver himself has written thoughtfully about examples of this in his book, The Signal and the Noise. He cites baseball, which in the post-moneyball era adopted a fusion approach that leans on both statistics and scouting. Silver credits it with delivering the Boston Red Sox s first World Series title in 86 years. Or consider weather forecasting: The National Weather Service employs meteorologists who, understanding the dynamics of weather systems, can improve forecasts by as much as 25 percent compared with computers alone. A similar synthesis holds in economic forecasting: Adding human judgment to statistical methods makes results roughly 15 percent more accurate. And it s even true in chess: While the best computers can now easily beat the best humans, they can in turn be beaten by humans aided by computers. Wired. Com: Why Quants Don t Know Everything Jan USDOT traffic forecast 36

39 Other Ideas for improving Forecast 1. Collect early information and use it to build a timeseries forecast 2. Acquire collaborative information from your customers. 3. Reduce lead-times 4. Reduce variety or aggregate units Other Ideas for improving Forecast Seek forecasts from multiple sources and combine them. 37

40 Forecasting Software Ref. Sanders Interfaces 2003 Software: Model Sophistication Software Selection: Availability of different demand models and methods Ability to search for best smoothing parameters Ability to build a causal analysis on top of time-series model Ability to integrate well with Existing ERP system Ability to incorporate judgment adjustments 38

41 Useful References: International Journal of Forecasting Journal of Forecasting Textbook: Ord & Fildes: Principles of Business Forecasting 39