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Demand Forecasting Fall, 2016 EMBA 512 Demand Forecasting
Boise State University
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Objectives Understand the role of forecasting Understand the issues
Understand basic tools and techniques Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Forecasting Developing predictions or estimates of future values
Demand volume Price levels Lead times Resource availability ... Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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The Role of Forecasting
Necessary Input to all Planning Decisions Operations: Inventory, Production Planning & Scheduling Finance: Plant Investment & Budgeting Marketing: Sales-Force Allocation, Pricing Promotions Human Resources: Workforce Planning Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Demand Forecasting For manufactured items and conventional goods, forecasts are used to determine Replenishment levels and safety stocks Set production plans Determine procurement schedules Capacity planning, financial planning, & workforce planning Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Demand Forecasting For services, demand forecasts are used for
Capacity planning, workforce scheduling, procurement & budgeting. Because services cannot be stored, demand forecasting for services is often concerned with forecasting the peak demand, rather than the average demand and its range. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Characteristics of Forecasts
Forecast are always wrong. A good forecast is more than a single value. Forecast accuracy decreases with the forecast horizon. Aggregate forecasts are more accurate than disaggregated forecasts. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Independent vs. Dependent Demand
Exogenously controlled Subject to random or unpredictable changes What we forecast Dependent or Derived Calculated or derived from other sources Do not forecast Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Forecasting Methods Qualitative or Judgmental
Ask people who ought to know Historical Projection or Extrapolation Time Series Models Moving Averages Exponential Smoothing Regression based methods Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Basic Approach to Demand Forecasting
Identify the Objective of the Forecast Integrate Forecasting with Planning Identify the Factors that Influence the Demand Forecast Identify the Appropriate Forecasting Model Monitor the Forecast (Measure Errors) Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Time Series Methods Appropriate when future demand is expected to follow past demand patterns. Future demand is assumed to be influenced by the current demand, as well as historical growth and seasonal patterns. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Time Series Models With time series models observed demand can be broken down into two components: systematic and random. Observed Demand = Systematic Component + Random Component Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Time Series Methods The systematic component is the expected demand value. It is comprised of the underlying average demand, the trend in demand, and the seasonal fluctuations (seasonality) in demand. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Idea Behind Time Series Models
Distinguish between random fluctuations and true changes in underlying demand patterns. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Time Series Components of Demand
Random component Time Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Monthly chart of the DJIA's changes from month to month along with a 3 period simple moving average.
Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Time Series Methods The random component cannot be predicted. However, its size and variability can be estimated to provide a measure of forecast error. The objective of forecasting is to filter the random component and model (estimate) the systematic component. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Moving Averages Simple, widely used Reduce random noise One Extreme
Prediction next period = Demand this period Another Extreme Prediction next period = Long run average Intermediate View Prediction next period = Average of last n periods Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Moving Average Models Period Demand 1 12 2 15 3 11 4 9 5 10 6 8 7 14
1 12 2 15 3 11 4 9 5 10 6 8 7 14 8 12 3-period moving average forecast for Period 8: = ( ) / 3 = 10.67 Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Weighted Moving Averages
Forecast for Period 8 = [(0.5 14) + (0.3 8) + (0.2 10)] / ( ) = 11.4 What are the advantages? What do the weights add up to? Could we use different weights? Compare with a simple 3-period moving average. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Table of Forecasts and Demand Values . . .
Period Actual Demand Two-Period Moving Average Forecast Three-Period Weighted Moving Average Forecast Weights = 0.5, 0.3, 0.2 1 12 2 15 3 11 13.5 4 9 13 12.4 5 10 10.8 6 8 9.5 9.9 7 14 8.8 11.4 11.8 Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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. . . and Resulting Graph Note how the forecasts smooth out variations
Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Simple Exponential Smoothing
Sophisticated weighted averaging model Needs only three numbers: Ft = Forecast for the current period t Dt = Actual demand for the current period t a = Weight between 0 and 1 Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Exponential Smoothing
Moving Averages Equal weight to older observations Exponential Smoothing More weight to more recent observations Forecast for next period is a weighted average of Observation for this period Forecast for this period Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Simple Exponential Smoothing
Formula Ft+1 = Ft + a (Dt – Ft) = a × Dt + (1 – a) × Ft Where did the current forecast come from? What happens as a gets closer to 0 or 1? Where does the very first forecast come from? Very first forecast is often set equal to the actual demand to start the process. An alternate approach is to set the first forecast to the moving average of the previous two or three months. Alpha should be large if the demand data is relatively stable, small if the demand data varies quite a bit. Otherwise it takes a long time for the forecast to converge on relatively smooth demand (overdamped correction) and the forecast overshoots the variations for fluctuating demand (underdamped correction) Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Exponential Smoothing Forecast with a = 0.3
Period Actual Demand Exponential Smoothing Forecast 1 12 11.00 (given) 2 15 11.30 3 11 12.41 4 9 11.99 5 10 11.09 6 8 10.76 7 14 9.93 11.15 11.41 F2 = 0.3× ×11 = = 11.3 F3 = 0.3× ×11.3 = Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Resulting Graph Fall, 2016 EMBA 512 Demand Forecasting
Boise State University
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Time Series with random and trend components Demand Time Fall, 2016
EMBA 512 Demand Forecasting Boise State University
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Linear Trend Fall, 2016 EMBA 512 Demand Forecasting
Boise State University
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Exponential Trend Fall, 2016 EMBA 512 Demand Forecasting
Boise State University
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Trends What do you think will happen to a moving average or exponential smoothing model when there is a trend in the data? Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Simple Exponential Smoothing Always Lags A Trend
Period Actual Demand Exponential Smoothing Forecast 1 11 11.00 2 12 3 13 11.30 4 14 11.81 5 15 12.47 6 16 13.23 7 17 14.06 8 18 14.94 9 15.86 Because the model is based on historical demand, it always lags the obvious upward trend Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Simple Linear Regression
Time Series Find best fit of proposed model to past data Project that fit forward Assumes a linear relationship: y = a + b(x) y x Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Definitions Y = a + b(X) Y = predicted variable (i.e., demand) X = predictor variable “X” is the time period for linear trend models. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Example: Regression Used to Estimate A Linear Trend Line
Period (X) Demand (Y) 1 110 2 190 3 320 4 410 5 490 Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Resulting Regression Model: Forecast = 10 + 98×Period
Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Time series with random, trend and seasonal components Demand June
Class discussion: what could account for this? Lawnmower sales? Camping trailer sales? Vacation package sales? June June June June Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Trend & Seasonality Fall, 2016 EMBA 512 Demand Forecasting
Boise State University
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Seasonality Fall, 2016 EMBA 512 Demand Forecasting
Boise State University
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Modeling Trend & Seasonal Components
Quarter Period Demand Winter Spring Summer Fall Winter Spring Summer Fall Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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What Do You Notice? Forecasted Demand = –18.57 + 108.57 x Period
Actual Demand Regression Forecast Forecast Error Winter 07 1 80 90 -10 Spring 2 240 198.6 41.4 Summer 3 300 307.1 -7.1 Fall 4 440 415.7 24.3 Winter 08 5 400 524.3 -124.3 6 720 632.9 87.2 7 700 741.4 -41.4 8 880 850 30 Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Regression picks up trend, but not the seasonality effect
Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Calculating Seasonal Index: Winter Quarter
(Actual / Forecast) for Winter Quarters: Winter ‘07: (80 / 90) = Winter ‘08: (400 / 524.3) = 0.76 Average of these two = 0.83 Interpret! The normal trend line prediction needs to be adjusted downward for Winter quarters. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Seasonally Adjusted Forecast Model
For Winter Quarter [ – ×Period ] × 0.83 Or more generally: [ – × Period ] × Seasonal Index Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Seasonally Adjusted Forecasts
Forecasted Demand = – x Period Period Actual Demand Regression Forecast Demand/Forecast Seasonal Index Seasonally Adjusted Forecast Forecast Error Winter 07 1 80 90 0.89 0.83 74.33 5.67 Spring 2 240 198.6 1.21 1.17 232.97 7.03 Summer 3 300 307.1 0.98 0.96 294.98 5.02 Fall 4 440 415.7 1.06 1.05 435.19 4.81 Winter 08 5 400 524.3 0.76 433.02 -33.02 6 720 632.9 1.14 742.42 -22.42 7 700 741.4 0.94 712.13 -12.13 8 880 850 1.04 889.84 -9.84 Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Would You Expect the Forecast Model to Perform This Well With Future Data?
Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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The Perfect (Imaginary) Forecast
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A More Realistic Forecast
Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Forecast Error Building a Forecast Forecast Error
Fit to historical data Project future data Forecast Error How well does model fit historical data Do we need to tune or refine the model Can we offer confidence intervals about our predictions Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Forecast Error The forecast error measures the difference between the actual demand and the forecast of demand. The forecast is based on the systematic component and the random component is estimated based on the forecast error. Forecast Error = Actual – Forecast Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Measures of Forecast Accuracy
Forecast Errort (Et)= Demandt-Forecastt Mean Squared Error (MSE) Mean Absolute Deviation (MAD) Bias Tracking Signal Relative Forecast Errors Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Mean Squared Error (MSE)
The MSE estimates the variance of the forecast error. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Mean Absolute Deviation (MAD)
The MAD can be used to estimate the standard deviation of the random component, assuming the random component is normally distributed: σ = 1.25MAD Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Bias To determine whether a forecasting method consistently over-or- underestimates demand, calculate the sum of the forecast errors: Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Tracking Signal The tracking signal (TS) is the ratio of the bias to the MAD. Tracking signals outside the range + 6 indicates that the forecast is biased and either under predicting (negative) or over predicting (positive) demand. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Forecast Accuracy & Demand Variability (Normally Distributed Demand)
Coefficient of Variation Probability Demand is Within 25% of the Forecast 0.10 98.76% 0.25 68.27% 0.50 38.29% 0.75 26.11% 1.00 19.74% 1.50 13.24% 2.00 9.95% 3.00 6.64% Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Issues Forecasting is a necessary evil, try to reduce the need for it.
Complexity costs money, does it provide better forecasts? Aggregation provides accuracy, but precludes local information Forecast the right thing Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Forecasting Success Story
Taco Bell Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Taco Bell Labor is 30% of revenue Make to order environment
Feed the dog Taco Bell Labor is 30% of revenue Make to order environment Significant “seasonality” 52% of days sales during lunch 25% of days sales during busiest hour Balance staff with demand Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Value Meals Drove demand Forecasting system in each store
forecasts arrivals within 15 minute intervals Simulation system “predicts” congestion and lost sales Optimization system Finds the minimum cost allocation of workers Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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Forecasting System Customer arrivals by 15-minute interval of day (e.g., 11:15-11:30 am Friday) Fed by in-store computer system 6-week moving average Estimated savings: Over $40 Million in 3 years. Fall, 2016 EMBA 512 Demand Forecasting Boise State University
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