Forecasting Techniques

Forecasting Techniques Interventions required to meet business objectives Anand Subramaniam

“ An ardent supporter of the hometown team should go to a game prepared to take offense, no matter what happens.”
- Robert Benchley

Highlights
Forecasting Methods
Quantitative Methods – Examples
Forecast Accuracy / Error Reduction
Integrate – Sales Forecast / Production
CPFR - Collaborative Planning, Forecasting and Replenishment

Forecasting Methods

Planning Levels

Forecast Horizon
Trend Exploration
Graphical Methods
Exponential Smoothing
Purchasing
Detailed Job Scheduling
1 day ~ I year Short
Time Series
Regression
Staffing Plans
Aggregate Production Plan
1 season ~ 2 years Intermediate
Economic
Demographic
Market Information
Technology
Facility Planning
Capacity Planning
Product Planning
> 5 years Long Methods Applications Horizon Range

Major Areas of Forecasting Economic Forecasting Predicts what the general business conditions will be in the future (Eg. Inflation rates, Gross National Product, Tax, Level of employment) Technology Forecasting Predicts the probability and / or possible future developments in technology (Eg. Competitive advantage or firm’s competitors incorporate into their products and processes) Demand Forecasting Predicts the quantity and timing of demand for a firm’s products

Forecasting Methods Subjective Approach (Qualitative in nature and usually based on the opinions of people) Objective Approach (Quantitative / Mathematical formulations - statistical forecasting)

Qualitative Methods
Executive Committee Consensus
Develop long ~ medium forecast by asking a group of knowledgeable Executives their opinions with regard to future values of the items being forecasted
Presence of a powerful member in the group may prevent reaching consensus
Delphi Method
Involves a group of Experts who eventually develop a consensus
They usually make long range forecasts for future technologies or future sales of a new product
Sales Force Composite
Sales people are a good source of information with regard to customers’ future intentions to buy a product
Customer Surveys
By using a customer survey, a Firm can base its demand forecast on the customers’ purchasing plans

Quantitative Methods Time Series Models (Only independent variable is the time used to analyse 1) Trends, or 2) Seasonal, or 3) Cyclical Factors that influence the demand data) Casual Models (Employ some factors other than Time, when predicting forecast values)

Time Series Models
Trends
Gradual upward or downward movement of data over time
Trends reflect changes in population levels, technology, and living standards
Long term movement
Seasonality
Variation that repeats itself at fixed intervals
It can be as long as a Year, or as short as a few hours
Can correspond to the Seasons of the Year, Holidays, or other special periods
Short-term regular and repetitive variations in data
Cyclical
Has a duration of at least one year. The duration varies from cycle to cycle
Long(er) term, requires many years of data to determine its repetitiveness or unusual circumstances (Eg. ups and downs of general business economy, war)
Random
Variations in demand that cannot be explained by Trends, Seasonality, or Cyclicality
Caused by chance

Time Series Models
Smoothing Models
Moving Average (Simple & Weighted)
Single Exponential Smoothing
Double Exponential Smoothing
Decomposition Models
Additive Models
Multiplicative Models

Quantitative Methods - Examples

Simple Moving Average F 4 =(650+678+720)/3 =682.67 F 7 =(650+678+720 +785+859+920)/6 =768.67

Simple Moving Average

Exponential Smoothing
Premise — determine how much weight to put on recent information versus older information
0 < a < 1
High a such as .7 puts weight on recent demand
Low a such as .2 puts weight on many previous periods
F t+1 =  D t + (1-  )F t (  is the smoothing parameter)

Exponential Smoothing F 1 =820+(0.5)(820-820)=820 F 3 =820+(0.5)(775-820)=797.75

Effect of  on Forecast

Simple Linear Regression Model

Simple Linear Regression Model (Contd)

Simple Linear Regression Model (Contd) Y t = 143.5 + 6.3x 135 140 145 150 155 160 165 170 175 180 1 2 3 4 5 Period Sales Sales Forecast

Simple Linear Regression Model (Contd) Actual observation (y value) Least squares method minimises the sum of the squared errors (deviations) Time period Values of Dependent Variable Deviation 1 (error) Deviation 5 Deviation 7 Deviation 2 Deviation 6 Deviation 4 Deviation 3 Trend line, y = a + bx ^

Forecast Accuracy / Error Reduction

Forecast Accuracy
Forecast bias
persistent tendency for forecasts to be greater or less than the actual values of a time series
Forecast error
difference between the actual value and the value that was predicted for a given period

Forecast Accuracy (Contd.)
where
e t = forecast error for Period t
A t = actual demand for Period t
F t = forecast for Period t

Forecast Error Measures
Bias
indicates on an average basis, whether the forecast is too high (negative bias indicates over forecast) or too low (positive bias indicates under forecast)
Mean Absolute Deviation (MAD)
indicates on an average basis, how many units the forecast is off from the actual data
Mean Absolute Percent Error (MAPE)
indicates on an average basis, how many percent the forecast is off from the actual data
Mean Squared Error (MSE)
a forecast error measure that penalises large errors proportionally more than small errors

Forecast Error Measures
Bias =
MAD =
MSE =
MAPE =
Standard Deviation (σ) =

Mean absolute deviation (MAD)
the average absolute forecast error
where
| e t |= absolute value of the forecast error for Period t
n = number of periods of evaluation

Mean Absolute Percentage Deviation (MAPE)
the average absolute percent error
where
et = forecast error for Period t
n = number of periods of evaluation
A t = actual demand for Period t

Running Sum of Forecast Errors (RSFE)
provides a measure of forecast bias
where
e t = forecast error for Period t

Tracking Signal
The ratio of cumulative forecast error to the corresponding value of MAD
Used to monitor a forecast

Mean Absolute Deviation Month Sales Forecast Abs Error 1 220 n/a 2 250 255 5 3 210 205 5 4 300 320 20 5 325 315 10 40 Note that by itself, the MAD only lets us know the mean error in a set of forecasts.

Forecast Error Measures Period Sales (A) Forecast E |E| E 2 |E|/A 1 1600 1650 -50 50 2500 0.0313 2 2200 2010 190 190 36100 0.0864 3 2000 2200 -200 200 40000 0.1000 4 1600 1580 20 20 400 0.0125 5 2500 2480 20 20 400 0.0080 6 3500 3520 -20 20 400 0.0057 7 3300 3310 -10 10 100 0.0030 8 3200 3200 0 0 0 0.0000 9 3900 3850 50 50 2500 0.0128 10 4700 4720 -20 20 400 0.0043 10 -20 580 82800 0.2639 Bias = -2 low/High MAD = 58 MSE = 8280 MAPE= 2.64%

Integrate – Sales Forecast / Production

Forecasting Process Services Collect Data Select Model Plot Data Develop Forecast Check Accuracy Forecast Adjust Forecast Monitor Forecast Sales and Operations Planning Master Scheduling Customer Scheduling Materials Planning Workforce Scheduling Order Scheduling Manufacturing Forecasting

Integrate - Sales Forecast & Production

CPFR - Collaborative Planning, Forecasting and Replenishment

CPFR - Overview
Developed by Wal-Mart and Warner-Lambert in 1995
Recognised as a breakthrough business model for planning, forecasting, and replenishment which goes beyond the traditional practice
Uses Internet-based technologies to collaborate from planning to execution
Creates a direct link between the consumer and the supply chain
Improves the quality of the demand signal for the entire supply chain through a constant exchange of information from one end to the other
Focuses on information sharing among supply chain trading partners for purposes of planning, forecasting, and inventory replenishment

CPFR Model

CPFR - Process
The plan and the forecast are entered by suppliers and buyers into an Internet accessible system
Within established parameters, any of the participating partners is empowered to change the forecast

“ You may have to fight a battle more than once to win it.”
- Margaret Thatcher

Good Luck
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Forecasting Techniques

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