Demand forecasting
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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

    EMBA 512 Demand Forecasting

    Boise State University


    A more realistic forecast

    A More Realistic Forecast

    EMBA 512 Demand Forecasting

    Boise State University


    Forecast error

    Forecast Error

    • 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

    • 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

    • 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)

    The MSE estimates the variance of the forecast error.

    EMBA 512 Demand Forecasting

    Boise State University


    Mean absolute deviation mad

    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

    EMBA 512 Demand Forecasting

    Boise State University


    Demand forecasting

    Bias

    • 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

    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(Normally Distributed Demand)

    EMBA 512 Demand Forecasting

    Boise State University


    Issues

    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

    EMBA 512 Demand Forecasting

    Boise State University


    Forecasting success story

    Forecasting Success Story

    Taco Bell

    EMBA 512 Demand Forecasting

    Boise State University


    Taco bell

    Taco Bell

    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

    • 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

    • 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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