A forecast is an estimate of future demand & provides the basis for planning decisions

• The goal is to minimize deviation between actual demand and forecast
• The factors that influence demand must be considered when forecasting.
• Good forecasting provides reduced inventories, costs, & stockouts, & improved production plans & customer service

 

• Qualitative forecasting is based on opinion & intuition.
• Quantitative forecasting uses mathematical models & historical data to make forecasts.
• Time series models are the most frequently used among all the forecasting models.

 

1. Qualitative Forecasting Methods

Generally used when data are limited, unavailable, or not currently relevant.  Forecast depends on skill & experience of forecaster(s) & available information

Four qualitative models used are –

1. 1. Jury of executive opinion
   2. Delphi method
   3. Sales force composite
   4. Consumer survey

 

1. Jury of executive opinion

Group of senior management executives who are knowledgeable about their markets, competitors, and the business environment collectively develop the forecast

 

1. Delphi method

 Group of internal and external experts are surveyed during several rounds in terms of future events and long-term forecasts of demand, to develop a forecast

 

• Sales force composite

Forecast is based on the sales force’s knowledge of the market and estimates of customer needs.

 

1. Consumer survey

Forecasts are developed from the results surveying customers on future purchasing needs, new product ideas and opinions about existing or new products

 

2- Quantitative Methods

1. Time series forecasting –  based on the assumption that the future is an extension of the past. Historical data is used to predict future demand

 

1. Cause & Effect forecasting –  assumes that one or more factors (independent variables) predict future demand

It is generally recommended to use a combination of quantitative & qualitative techniques

 

1. Components of Time Series

Data should be plotted to detect for the following components –

 

• • • Trend variations: increasing or decreasing over many years
    • Cyclical variations: wavelike movements that are longer than a year (e.g., business cycle)
    • Seasonal variations: show peaks & valleys that repeat over a consistent interval such as hours, days, weeks, months, seasons, or years
    • Random variations: due to unexpected or unpredictable events

 

Time Series Forecasting Models

 

1. Weighted Moving Average Forecast – is based on an n-period weighted moving average

 

1. Simple Moving Average Forecast – uses historical data to generate a forecast.  Works well when demand is stable over time.

 

1. Exponential Smoothing Forecast – a type of weighted moving average where only two data points are needed

F_t+1 = F_t+a(A_t – F_t) or F_t+1 = aA_t + (1 – a) F_t

Where  F_t+1         = forecast for Period t + 1

F_t          = forecast for Period t

A_t         = actual demand for Period t

a          = smoothing constant (0 ≤ a  ≤1)

 

1. Cause & Effect Models 

One or several external variables are identified that are related to demand

 

1. a) Simple regression – Only one explanatory variable is used & is similar to the previous trend model. The difference is that the x variable is no longer time but an explanatory variable.

Ŷ  =  b₀ + b₁x

Where    Ŷ        = forecast or dependent variable

  x         = explanatory or independent variable

  b₀         = intercept of the line

  b₁       = slope of the line

 

1. b) Multiple regression several explanatory variables are used to make the forecast

Ŷ  =  b₀ + b₁x₁ + b₂x₂ +  . . . B_kx_k

Where

Ŷ          = forecast or dependent variable

x_k          = kth explanatory or independent                                variable

b₀            = intercept of the line

b_k         = regression coefficient of the                                     independent variable x_k

 

Several measures of forecasting accuracy follow –

• • Mean absolute deviation (MAD)- a MAD of 0 indicates the forecast exactly predicted demand
  • Mean absolute percentage error (MAPE)- provides a perspective of the true magnitude of the forecast error
  • Mean squared error (MSE)- analogous to variance, large forecast errors are heavily penalized

 

 

Mean absolute deviation (MAD)-

• MAD of 0 indicates the forecast exactly predicted demand.

 

Where  e_t             = forecast error for period t

A_t         = actual demand for period t

n          = number of periods of evaluation

 

Mean absolute percentage error (MAPE) – provides a perspective of the true magnitude of the forecast error.

 

Where  e_t             = forecast error for period t

A_t         = actual demand for period t

n          = number of periods of evaluation

Mean squared error (MSE) –  analogous to variance, large forecast errors are heavily penalized

 

Where  e_t             = forecast error for period t

n          = number of periods of evaluation