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Demand Forecasting

Predicting future customer demand

Demand Management

Influencing either pattern or consistency of demand

Demand Planning

Both forecasting and managing customer demand to reach operational and financial goals

Demand Forecasting

Environmental markets
Historical demand
Expert judgments

Demand Management

Order scheduling

Long term goals

Strategic Operations Planning and Design

Intermediate Term

Sales and Operations Planning

Short Term

Materials and Resource Requirements Planing

Forecasting Process

Step 1: Define users and processes
Step 2: Identify data sources
Step 3&4 Select and document forecasting tools
Step 5 : Monitor and continuously improve the

Forecast Qualitative

Market Research

Forecast Quantitative

Time Series


Input from those close to products or customers

Executive Judgment

Input from experience

Historical Analogy

Assume past demand is a good predictor of future demand

Delphi Method

Input for panel of experts

Marketing research

Examine patterns of current customers (focus groups, customer, surveys)

Time Series Analysis

Forecasts are based solely on past historical demand
Identifies and measures patterns in data
Assumes that patterns will continue in the future (Black Box approach)

Causal Modeling

Assumes demand is influenced by one or more factors
Identified important factors and measures their effects

Simulation models

Sophisticated techniques that allow for the evaluation of multiple scenarios

Characteristics of Qualitative Forecasts

Subjective, judgmental
Can integrate soft data
Can be used for new products
Better with one off events

Characteristics of Quantitative Forecasts

Objective, statistical
Can handle large data sets
Requires hard data

Advantages of Causal Modeling

Best for long range forecasting
Best for predicting turning points or shifts in data patterns
Can generate a better understanding of the mechanisms influencing sales/demand etc.

Disadvantages of Causal Modeling

Requires more data
Requires special expertise that many managers do not have

Time Series Analysis Characteristics

Multiple identifiable patterns in demand:
Random fluctuations, Trends, Seasonality, Cyclical
Typically multiple patterns coexist and they can be separated and identified. The goal is to

Naive Method

Tomorrow's demand will be the same as today's demand

Simple Moving Average

Simple average places the same weight on all past demand periods.
This method works well when the demand is fairly stable over time.
Decreasing the number of periods in forecast, creates more responsive data.
The forecast lags the demand because of averaging effect
This method doesn't do a good job forecasting when trends are present in a data

Weighted Moving Average

Allows greater emphasis to be placed on more recent data points to reflect changes in the demand.
Information used can be based on the forecaster's experience.
Decreasing # of periods in forecast or increasing the size of the weights creates a more responsive forecast.
The forecast still lags the demand because of averaging effect
This method does not do a good job of forecasting when trends are present in the data.

Simple & Weighted Moving Average

A simple or weighted moving average does not "hold to the past" will be forgotten by a moving average. Thus, outliers and extreme observations will eventually cease to negatively affect a moving average forecast.

Simple Exponential Smoothing

Like other time serious models, it is suitable for data that show no trend patterns. Due to its simplicity, exponential smoothing is a very popular forecasting technique. however the forecast will also lag any trend in the actual data. , Smoothing Methods
a) Simple moving averages
b) Simple exponential smoothing

Evaluating Forecasts

Quality of forecast depends on accuracy and bias. Forecasting errors are absolute or relative.

Mean Forecast Error

Average of forecasting errors from period 1 to period current period. MFE is a measure of bias, but not accuracy. Smaller MFE means relatively unbiased forecasts.
Larger MFE means biased forecasts which indicates overestimation or underestimation

Mean Absolute Deviation

Average of absolute values of forecasting errors. MAD measures the dispersion(variance) of the forecasting errors. positive and negative forecast errors do not cancel each other out.
Smaller MAD means relatively more accurate forecasts
Larger MAD means relatively less accurate forecasts

Mean Squared Error

Average of squared values of forecasting errors. It is dispersion measure of the forecasting errors.
Positive and negative errors do not cancel each other
Smaller MAD means relatively more accurate forecasts, larger MAD means relatively less accurate forecasts.
The difference between MSE and MAD is that MSE penalizes larger forecast errors to a greater degree. And MSE is sensitive to extreme forecast errors.

Mean Absolute Percent Error

Standardizes the forecast errors with respect to the actual demand. Measure the average relative magnitude of the forecasts errors. Expressed as a % of the actual demand.

Forecast Quality

Short term forecast are more accurate than long term.
Aggregate forecast are more accurate than detailed forecasts.
Information from more sources yields a more accurate forecast

Fluctuating Customer Demand

cause operational inefficiencies. Timing and level of activities determine operational costs.
Smoother operations means lower costs and higher profits
Fluctuating operations means higher costs and lower profits.

Fluctuating Operations

Lead to expansion/contraction of capacity (equipment, labor, materials)
Backlog (delayed fulfillment of orders)
Customer dissatisfaction
System Buffering (safety stock, safety lead time, capacity cushions)


Collaborative planning, Forecasting, and Replenishment: Focuses on information sharing among trading partners, and eliminates typical order processing. The goal to ensure effecient replenishment

Market Planing:

changes to products, locations, pricing and promotions

Demand and Resource Planing



order fulfillment


data on key performance metrics

CPFR in process

Step1: Market/item knowledge store planing item planing by individual stores(marketing and promotional input from sales/marketing)
Step2: POS Data(production planners validate item -level forecast)
Step 3:Create Item level forecast and special even calendar. (forecast drives productions)
Step 4: Create purchase order item (product shipped purchase order specification)

Value of CPFR

an exchange of forecasting information
not from complex algorithms to forecasting accuracy
Better information leads to forecasting accuracy
Better forecasting accuracy leads to better decisions, better resource allocation, better utilization,better coordination.

Benefit of CPFR

Strength partner relationship
Provides analysis of sales and order forecasts upstream and downstream
Uses POS data, seasonal activity, promotions, new product intros, and store openings or closing to improve forecast accuracy
Manages the demand chain by exception and proactively eliminates problems before the appear
Allows collaboration on future requirement and plans

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