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Demand Forecasting. Objectives. Understand the role of forecasting Understand the issues Understand basic tools and techniques . Forecasting. Developing predictions or estimates of future values Demand volume Price levels Lead times Resource availability . The Role of Forecasting.

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Demand forecasting
Demand Forecasting

EMBA 512 Demand Forecasting

Boise State University


Objectives
Objectives

  • Understand the role of forecasting

  • Understand the issues

  • Understand basic tools and techniques

EMBA 512 Demand Forecasting

Boise State University


Forecasting
Forecasting

  • Developing predictions or estimates of future values

    • Demand volume

    • Price levels

    • Lead times

    • Resource availability

    • ...

EMBA 512 Demand Forecasting

Boise State University


The role of forecasting
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

EMBA 512 Demand Forecasting

Boise State University


Demand forecasting1
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

EMBA 512 Demand Forecasting

Boise State University


Demand forecasting2
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.

EMBA 512 Demand Forecasting

Boise State University


Characteristics of forecasts
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.

EMBA 512 Demand Forecasting

Boise State University


Independent vs dependent demand
Independent vs. Dependent Demand

  • Independent

    • Exogenously controlled

    • Subject to random or unpredictable changes

    • What we forecast

  • Dependent or Derived

    • Calculated or derived from other sources

    • Do not forecast

EMBA 512 Demand Forecasting

Boise State University


Forecasting methods
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

  • EMBA 512 Demand Forecasting

    Boise State University


    Basic approach to demand forecasting
    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)

    EMBA 512 Demand Forecasting

    Boise State University


    Time series methods
    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.

    EMBA 512 Demand Forecasting

    Boise State University


    Time series models
    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

    EMBA 512 Demand Forecasting

    Boise State University


    Time series methods1
    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.

    EMBA 512 Demand Forecasting

    Boise State University


    Idea behind time series models
    Idea Behind Time Series Models

    Distinguish between random fluctuations and true changes in underlying demand patterns.

    EMBA 512 Demand Forecasting

    Boise State University


    Time series components of demand
    Time Series Components of Demand

    Demand

    Random component

    Time

    EMBA 512 Demand Forecasting

    Boise State University


    Monthly chart of the djia s changes from month to month along with a 3 period simple moving average
    Monthly chart of the DJIA's changes from month to month along with a 3 period simple moving average.

    EMBA 512 Demand Forecasting

    Boise State University


    Time series methods2
    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.

    EMBA 512 Demand Forecasting

    Boise State University


    Moving averages
    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

    EMBA 512 Demand Forecasting

    Boise State University


    Moving average models

    PeriodDemand

    112

    215

    311

    4 9

    510

    6 8

    714

    812

    Moving Average Models

    3-period moving average

    forecast for Period 8:

    =(14 + 8 + 10) / 3

    =10.67

    EMBA 512 Demand Forecasting

    Boise State University


    Weighted moving averages
    Weighted Moving Averages

    Forecast for Period 8

    =[(0.5 14) + (0.3 8) + (0.2 10)] / (0.5 + 0.3 + 0.2)

    =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.

    EMBA 512 Demand Forecasting

    Boise State University


    Table of forecasts and demand values
    Table of Forecasts and Demand Values . . .

    EMBA 512 Demand Forecasting

    Boise State University


    And resulting graph
    . . . and Resulting Graph

    Note how the forecasts smooth out variations

    EMBA 512 Demand Forecasting

    Boise State University


    Simple exponential smoothing
    Simple Exponential Smoothing

    • Sophisticated weighted averaging model

    • Needs only three numbers:

      Ft = Forecast for the current period tDt = Actual demand for the current period t

      a = Weight between 0 and 1

    EMBA 512 Demand Forecasting

    Boise State University


    Exponential smoothing
    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

    EMBA 512 Demand Forecasting

    Boise State University


    Simple exponential smoothing1
    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?

    EMBA 512 Demand Forecasting

    Boise State University


    Exponential smoothing forecast with a 0 3
    Exponential Smoothing Forecast with a = 0.3

    F2 = 0.3×12 + 0.7×11

    = 3.6 + 7.7

    = 11.3

    F3 = 0.3×15 + 0.7×11.3

    = 12.41

    EMBA 512 Demand Forecasting

    Boise State University


    Resulting graph
    Resulting Graph

    EMBA 512 Demand Forecasting

    Boise State University


    Time series with
    Time Series with

    Demand

    random and trend components

    Time

    EMBA 512 Demand Forecasting

    Boise State University


    Linear trend
    Linear Trend

    EMBA 512 Demand Forecasting

    Boise State University


    Exponential trend
    Exponential Trend

    EMBA 512 Demand Forecasting

    Boise State University


    Trends
    Trends

    What do you think will happen to a moving average or exponential smoothing model when there is a trend in the data?

    EMBA 512 Demand Forecasting

    Boise State University


    Simple exponential smoothing always lags a trend
    Simple Exponential Smoothing Always Lags A Trend

    Because the model

    is based on

    historical demand,

    it always lags

    the obvious

    upward trend

    EMBA 512 Demand Forecasting

    Boise State University


    Simple linear regression
    SimpleLinear 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

    EMBA 512 Demand Forecasting

    Boise State University


    Definitions
    Definitions

    Y = a + b(X)

    Y = predicted variable (i.e., demand)X = predictor variable

    “X” is the time period for linear trend models.

    EMBA 512 Demand Forecasting

    Boise State University


    Example regression used to estimate a linear trend line
    Example:Regression Used to Estimate A Linear Trend Line

    EMBA 512 Demand Forecasting

    Boise State University


    Resulting regression model forecast 10 98 period
    Resulting Regression Model:Forecast = 10 + 98×Period

    EMBA 512 Demand Forecasting

    Boise State University


    Time series with1
    Time series with

    Demand

    random, trend and seasonal components

    June

    June

    June

    June

    EMBA 512 Demand Forecasting

    Boise State University


    Trend seasonality
    Trend & Seasonality

    EMBA 512 Demand Forecasting

    Boise State University


    Seasonality
    Seasonality

    EMBA 512 Demand Forecasting

    Boise State University


    Modeling trend seasonal components
    Modeling Trend & Seasonal Components

    Quarter PeriodDemand

    Winter 071 80

    Spring2 240

    Summer3 300

    Fall4 440

    Winter 085 400

    Spring6 720

    Summer7 700

    Fall8 880

    EMBA 512 Demand Forecasting

    Boise State University


    What do you notice
    What Do You Notice?

    EMBA 512 Demand Forecasting

    Boise State University


    Regression picks up trend but not the seasonality effect
    Regression picks up trend, butnot the seasonality effect

    EMBA 512 Demand Forecasting

    Boise State University


    Calculating seasonal index winter quarter
    Calculating Seasonal Index: Winter Quarter

    (Actual / Forecast) for Winter Quarters:Winter ‘07:(80 / 90) = 0.89Winter ‘08:(400 / 524.3) = 0.76

    Average of these two = 0.83

    Interpret!

    EMBA 512 Demand Forecasting

    Boise State University


    Seasonally adjusted forecast model
    Seasonally Adjusted Forecast Model

    For Winter Quarter [ –18.57 + 108.57×Period ] × 0.83

    Or more generally:[ –18.57 + 108.57 × Period ] ×Seasonal Index

    EMBA 512 Demand Forecasting

    Boise State University


    Seasonally adjusted forecasts
    Seasonally Adjusted Forecasts

    EMBA 512 Demand Forecasting

    Boise State University


    Would you expect the forecast model to perform this well with future data
    Would You Expect the Forecast Model to Perform This Well With Future Data?

    EMBA 512 Demand Forecasting

    Boise State University


    The perfect imaginary forecast
    The Perfect (Imaginary) Forecast With Future Data?

    EMBA 512 Demand Forecasting

    Boise State University


    A more realistic forecast
    A More Realistic Forecast With Future Data?

    EMBA 512 Demand Forecasting

    Boise State University


    Forecast error
    Forecast Error With Future Data?

    • Building a Forecast

      • 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

    EMBA 512 Demand Forecasting

    Boise State University


    Forecast error1
    Forecast Error With Future Data?

    • 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

    EMBA 512 Demand Forecasting

    Boise State University


    Measures of forecast accuracy
    Measures of Forecast Accuracy With Future Data?

    • Forecast Errort (Et)= Demandt-Forecastt

    • Mean Squared Error (MSE)

    • Mean Absolute Deviation (MAD)

    • Bias

    • Tracking Signal

    • Relative Forecast Errors

    EMBA 512 Demand Forecasting

    Boise State University


    Mean squared error mse
    Mean Squared Error (MSE) With Future Data?

    The MSE estimates the variance of the forecast error.

    EMBA 512 Demand Forecasting

    Boise State University


    Mean absolute deviation mad
    Mean Absolute Deviation (MAD) With Future Data?

    The MAD can be used to estimate the standard deviation of the random component, assuming the random component is normally distributed:

    σ = 1.25MAD

    EMBA 512 Demand Forecasting

    Boise State University


    Demand forecasting
    Bias With Future Data?

    • To determine whether a forecasting method consistently over-or- underestimates demand, calculate the sum of the forecast errors:

    EMBA 512 Demand Forecasting

    Boise State University


    Tracking signal
    Tracking Signal With Future Data?

    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.

    EMBA 512 Demand Forecasting

    Boise State University


    Forecast accuracy demand variability normally distributed demand
    Forecast Accuracy & Demand Variability With Future Data?(Normally Distributed Demand)

    EMBA 512 Demand Forecasting

    Boise State University


    Issues
    Issues With Future Data?

    • 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

    EMBA 512 Demand Forecasting

    Boise State University


    Forecasting success story
    Forecasting Success Story With Future Data?

    Taco Bell

    EMBA 512 Demand Forecasting

    Boise State University


    Taco bell
    Taco Bell With Future Data?

    Feed the dog

    • 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

    EMBA 512 Demand Forecasting

    Boise State University


    Value meals
    Value Meals With Future Data?

    • 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

    EMBA 512 Demand Forecasting

    Boise State University


    Forecasting system
    Forecasting System With Future Data?

    • 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.

    EMBA 512 Demand Forecasting

    Boise State University


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