Defn forecasting is the art and science of predicting future events
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Defn : Forecasting is the art and science of predicting future events. 1. Identify the. 2. Collect. 3. Plot data and. purpose of forecast. historical data. identify patterns. 9. Adjust forecast based. 8a. Forecast over. on additional qualitative.

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Defn forecasting is the art and science of predicting future events

Defn: Forecasting is the art and

science of predicting future

events.


Forecasting process

1. Identify the

2. Collect

3. Plot data and

purpose of forecast

historical data

identify patterns

9. Adjust forecast based

8a. Forecast over

on additional qualitative

planning horizon

information and insight

Forecasting Process

5. Develop / compute forecast for

6. Check forecast accuracy

4. Select a forecast model that

period of historical data

with one or more measures

seems appropriate for data

8b. Select new forecast model or

adjust parameters of existing model

7.

Is accuracy

of forecast

acceptable?

10. Monitor results and

measure forecast accuracy


Forecasting process1

1. Identify the

2. Collect

3. Plot data and

purpose of forecast

historical data

identify patterns

9. Adjust forecast based

8a. Forecast over

on additional qualitative

planning horizon

information and insight

Forecasting Process

5. Develop / compute forecast for

6. Check forecast accuracy

4. Select a forecast model that

period of historical data

with one or more measures

seems appropriate for data

8b. Select new forecast model or

adjust parameters of existing model

7.

Is accuracy

of forecast

acceptable?

10. Monitor results and

measure forecast accuracy


Forecasting methods
Forecasting Methods

  • Time Series Models

  • Associative Models

  • Qualitative


Associative models
Associative Models

Assumption: One or more variables can be identified which has a relationship with demand.

Approaches: Simple Linear Regression

Multiple Linear Regression


Time series
“Time Series”

Defn: A time-ordered sequence of

observations that have been taken

at regular intervals.

Examples: past monthly demands,

past annual demands.

Assumption: Future values can be estimated from past values of the series.


Time series approaches
Time Series Approaches

  • Naïve Approach

  • Moving Averages

  • Exponential Smoothing

  • Trend Projection

  • Seasonal Adjustments


Forecasting process2

1. Identify the

2. Collect

3. Plot data and

purpose of forecast

historical data

identify patterns

9. Adjust forecast based

8a. Forecast over

on additional qualitative

planning horizon

information and insight

Forecasting Process

5. Develop / compute forecast for

6. Check forecast accuracy

4. Select a forecast model that

period of historical data

with one or more measures

seems appropriate for data

8b. Select new forecast model or

adjust parameters of existing model

7.

Is accuracy

of forecast

acceptable?

10. Monitor results and

measure forecast accuracy


Step 3 demand behavior
Step 3: Demand Behavior

  • Trend

    • gradual, long-term up or down movement

  • Cycles

    • up & down movement repeating over long time frame

  • Seasonality

    • periodic oscillation in demand which repeats based on calendar schedule (days, weeks, months or quarters)

  • Random movements- follow no pattern

  • Class Exercise:Decompose the TimeSeriesGraphs.xls time series into the behavioral components


Which demand behavior is most prevalent in chart 1
Which demand behavior is most prevalent in Chart 1?

  • Upward trend

  • Downward trend

  • Seasonality

  • Random Variation


Which demand behavior is most prevalent in chart 2
Which demand behavior is most prevalent in Chart 2?

  • Upward trend

  • Downward trend

  • Seasonality

  • Random Variation


Which demand behavior is most prevalent in problem set 1 1
Which demand behavior is most prevalent in Problem Set 1 #1?

  • Upward trend

  • Downward trend

  • Seasonality

  • Random Variation


Step 4 which time series model should you pick
Step 4: Which Time Series Model Should You Pick?

  • Three possible models to choose from when there is no seasonality and not a strong trend pattern:

    • Naïve Approach

    • n Period Moving Average

    • Exponential Smoothing


N period moving average method

Demand in

Previous

n

Periods

MA

n

nperiod Moving Average Method

  • MA is a series of arithmetic means

  • Used if little or no trend

  • Used oftenfor smoothing


Forecasting process3

1. Identify the

2. Collect

3. Plot data and

purpose of forecast

historical data

identify patterns

9. Adjust forecast based

8a. Forecast over

on additional qualitative

planning horizon

information and insight

Forecasting Process

5. Develop / compute forecast for

6. Check forecast accuracy

4. Select a forecast model that

seems appropriate for data

period of historical data

with one or more measures

8b. Select new forecast model or

adjust parameters of existing model

7.

Is accuracy

of forecast

acceptable?

10. Monitor results and

measure forecast accuracy


Step 6 forecast error equations
Step 6: Forecast Error Equations

  • k= # of historical periods forecasted

  • Mean Absolute Deviation (MAD)

  • Mean Absolute Percent Deviation (MAPD)


Exponential smoothing
Exponential Smoothing

  • New Forecast

    • = Last Period Forecast + Correction for error made last period

    • = Last Period Forecast + α (Last Period Demand – Last Period Forecast)

  • Class exercise:

    • Identify the best α for Smooth.xls time series


Guidelines for selecting forecasting model
Guidelines for Selecting Forecasting Model

  • You want to achieve:

    • No pattern or direction in forecast error

      • Error = (Ai - Fi) = (Actual - Forecast)

      • Seen in plots of errors over time

    • Smallest forecast error

      • Mean absolute deviation (MAD)

      • Mean absolute percent deviation (MAPD)

      • Mean square error (MSE)