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FORECASTING. “All forecasts are wrong. The best we can hope for is to reduce the amount of error .” Unknown. If Forecasts are usually wrong, why bother?. To reduce the uncertainty in our decision making. Forecasts aid us in planning. They allow us to plan for contingencies.

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“All forecasts are wrong. The best we can hope for is to reduce the amount of error.” Unknown

if forecasts are usually wrong why bother
If Forecasts are usually wrong, why bother?
  • To reduce the uncertainty in our decision making.
  • Forecasts aid us in planning.
  • They allow us to plan for contingencies.
    • EG: Forecast is a 50% chance of a long recession recovery?
      • Do some planning for that possibility.
forecasting at unilever
Forecasting at Unilever
  • Unilever is an Anglo-Dutch multinational corporation that owns many of the world's consumer product brands in foods, beverages, cleaning agents and personal care products.
  • Theirdemand forecasting blends historicalshipment data with promotional data and current order data.
  • Statistical forecasts are adjusted based on planned product promotions.
  • Forecasts are frequently reviewed and adjusted with the most recent point-of-sale data.
  • This has enabled Unilever to reduce its inventory, and improved its scheduling and customer service.
operations forecasting
  • OPERATIONS MANAGERS are primarily concerned with forecasting demand…

…for capacity planning.

(Effective capacity, capacity cushion, etc.)

…for production scheduling.

(Lot sizes, aggregate planning, etc.)

…to plan inventory needs.

(Order quantities, safety stock, etc.)

…to better match supply and demand.

operations forecasting6
Operations Forecasting

Most operations forecasting is time-series. (Historic data is correlated with time.)

  • Independent variableis time.
    • Time is independent of the data being forecast.
  • Dependent variableis demand.
    • EG: Using the past years of monthly demand data for your product or service to make a forecast for next month’s demand.
basic time series patterns
Basic Time-Series Patterns

There are five basic patterns of most time series.

  • Horizontal: Over time the data fluctuates around a constant mean.
  • Trend: The systematic increase (or decrease) in the data over time.
  • Seasonal: A repeatable pattern of increases and decreases in demand that relates to a specific period, such as the time of day, week, month, or season.
  • Cyclical: Less predictable, gradual increases and decreases over longer periods of time (years or decades), such as the business cycle. No consistent time frame.
  • Random: A variation in demand that has no pattern. The data cannot be used for forecasting.
patterns of demand
Patterns of Demand





rules for time series forecasting

If the data has a pattern, you can select an appropriate forecasting model.


Sales history by month.

If the data has no pattern, you cannot do forecasting!


Sales history by month.

Rules for Time-Series Forecasting
  • RULE 1:Plot your data to see if it has a pattern.
rules for time series forecasting10
Rules for Time-Series Forecasting
  • RULE 2:The number of periods of data depends on how much confidence you want in the results and the technique being used.
    • The number of periods you need varies with the forecasting technique. Some methods require less data; some require lots of data.
  • RULE 3:Time-Series Forecasting is SHORT-TERM forecasting.
    • Generally, you don’t forecast beyond the first unknown period unless your historic data has a clear pattern. (minimal variation/noise)
      • Time-Series forecasting beyond the first unknown period greatly increases forecast error and unreliability.
factors influencing demand
Factors Influencing Demand
  • EXTERNAL FACTORSover which management has little or no control:
      • Economic conditions
      • Government regulation
      • What the competition does
      • Consumer behavior
    • The Leading, Coincident and Lagging Indicators provide forecasters with data on the external factors.
        • Market Research also does this.
  • INTERNAL FACTORS that management can control:
      • Price
      • Promotion
      • Product
      • Quality
      • Reputation

“Few companies err by more than five percent when forecasting total demand for all of their products. However, errors in forecasts for individual items may be much higher.”What does this tell you?

  • Confidence should be low for single-product or single-service forecasts.
  • There are compensating errors when making aggregate forecasts that improve the overall forecast.
  • Aggregation is the act of clustering data for similar services or products.
    • “Aggregate” demand forecast are more accurate than single-product demand forecasts.
judgment methods qual itative
  • Judgment methods rely on the opinionsof experts, or on the judgment and experience of people in the best position to know.
  • Much of market research is qualitative.
    • Surveys of customer preferences and intentions to buy
  • Judgment methods are best for medium or long-term forecasting.
    • Some qualitative methods can take considerable time to obtain, and they can be expensive.
judgment methods
  • Sales force estimates: Forecasts are made by a company’s sales-force members who have first-hand interaction with customers.
  • Executive opinion (Executive intuition): The opinions, experience, and technical knowledge of experienced managers are summarized to arrive at a single forecast.
  • Market research: A systematic approach to determining consumer interest in a service or product through data-gathering surveys. (Quantitative methods are often applied to this data.)
  • Delphi method: A process of gaining consensus from a group of experts, usually external to the organization, while maintaining individual anonymity. (Survey-feedback-survey method)
    • Experts are drawn from across the industry, government, public and private organizations.
using judgment forecasting
Using Judgment Forecasting
  • Judgment forecasting is clearly needed when numerical data arenot available for quantitative forecasting approaches.
    • It should also be used, even when quantitative data is available, as an additional forecasting tool.
    • Good judgment quite often is better than all the statistics in the world.
  • Guidelines for the use of any type of forecasting:
    • Adjust forecasts when you have access to important contextual knowledge.
      • Make adjustments to compensate for specific events, such as advertising campaigns, the actions of competitors, changes in the economic situation, and international developments.
time series methods
Time Series Methods
  • TIME-SERIES is a commonly used statistical approach that relies on historical datafor short-term forecasting.
    • Types of Time Series forecasting models: (Listed in order of increasing complexity)
      • TREND PROJECTIONS (Good for long-term)

* You will need to know how to do these.

causal methods
  • Causal methods are used when historical data are availableand a relationship between the variable and other external or internal factors can be identified.
    • Causal methods use historical data on independent variables, such as promotional campaigns, economic conditions, and competitors’ actions, to predict demand (dependent variable)
    • May be short, medium, or long term, depending on the model.
  • LINEAR & NON-LINEAR REGRESSION(Short & Medium term)
  • ECONOMETRICS (Good for long term)
  • INPUT-OUTPUT MODELS (Good for long-term)
  • LEADING INDICATORS (Only for long-term)
    • These are indicators that precede economic change. (unemployment, inventory changes, building permits, money supply, etc.)
linear regression
Linear Regression
  • Linear Regression is a causal method in which one variable (dependent variable) is related to one or more independent variables using a linear equation.
    • Dependent variable: The variable to be forecasted.
      • In demand forecasting, demand would be the dependent variable
      • Data plot must be linear in order to use Linear Regression
    • Independent variablesare assumed to have a correlation with the dependent variable being forecast.
      • The Independent Variable is some variable to which demand appears to be related. It can be time or some other variable.
      • If the Independent variable is time, then linear regression becomes a Time-Series method of forecasting.
    • NO “Cause and Effect” should be assumed, even though it is called a Causal Method!
which method to use for short term demand forecasting

Short Term: (Up to three months)

  • Purpose:
    • Production scheduling
    • Inventory planning
  • Method:
    • Time Series Forecastingis the most commonly used forecasting technique.
    • It is inexpensive and easy to do.
  • Some judgment models can be used.
    • Sales-force estimates, executive opinion
    • Again, good judgment is ALWAYS important.
which method to use for medium term demand forecasting

Medium Term (3 months–2 years)

  • Purpose:
    • capacity planning
  • Causal Models are the best to use.
    • Regression is common.
  • Qualitative (Judgment) models are also helpful.
    • Executive opinion, Market Research, Sales force estimates
which method for long term demand forecasting
  • Long Term (Beyond 2 years)
  • Causal & Qualitative (judgment) Models are typically used. Purposes include…
    • Location decisions
    • Capacity decisions
    • Layout and Process decisions
  • Most forecasting for strategic decisions is long-term forecasting.
demand forecasting summary

Time Horizon


Short Term

(0–3 months)

Medium Term

(3 months–2 yrs)

Long Term

(over 2 years)

Forecast Focus

• Individual products or services

• Total sales

• Groups of products or services

• Total sales



• Inventory Mgt.

• Final assembly scheduling

• Workforce scheduling

• Master Prod. scheduling

• Staff planning

• Production planning

• Aggregate Prod. scheduling

• Purchasing

• Distribution

• Facility location

• Capacity planning

• Process management


• Time series• Causal

• Judgment

• Causal

• Judgment

• Causal

• Judgment

Demand Forecasting Summary

time series methods23
  • NAÏVE FORECASTING: A time-series method whereby the forecast for the next period is the known demand for the current period.
    • It takes the most recent known period value and projects it to the first forecast period.
  • SIMPLE MOVING AVERAGES is a time-series method that averages demand over a specified period “n” of time.
    • It computes the average for the last “n” periods and uses that as the forecast for the next period.
    • By averaging, it removes the effects of random fluctuations, and it is most useful when demand has no pronounced trend or seasonal influences.
simple moving averages
Simple Moving Averages

The moving average method involves the use of as many periods of past demand as desired or deemed appropriate. The stability of the demand series generally determines how many periods.

This is a 4-period moving average.

22 becomes the forecast for week #5 23.25becomes the forecast for week #6.

comparison of 3 and 6 week moving average forecasts

3-week moving average forecast

6-week moving average forecast

Patient Arrivals

Actual Data Historic Data


Comparison of 3- and 6-Week Moving Average Forecasts

A longer averaging period soothes the fluctuations.

double moving averages averaging the averages
Double Moving Averages(Averaging the averages)

n= 4 periods for the average

Averages are rounded to the nearest whole numbers.

weighted moving averages
  • Weighted moving average method: A time-series method in which each historical data point can have its own weight. (The sum of the weights equals 1.0)
  • It allows the forecaster to give more weight to the more recent data or the more relevant data.
    • Important in trend or cyclical data


other time series methods
Other Time Series Methods
  • EXPONENTIAL SMOOTHING is a complex form of weighted moving averages. It is good for trends and cyclical data.
    • It is the most frequently used formal forecasting method because of its simplicity and the small amount of data needed to support it.
      • Double and Triple Exponential Smoothing are used for highly fluctuating data.
      • This is probably the most complex but often the most accurate of the time-series methods for all data patterns.
      • If you really want to know: It is named after the statisticians George Box and Gwilym Jenkins. It applies autoregressive integrated moving average (ARIMA) models to find the best fit of a time series to past values of this time series, in order to make forecasts.
seasonal patterns
Seasonal Patterns
  • An easy way to account for seasonal effects is to use one of the techniques already described, but to limit the data used to those time periods in the same season.
    • EG: May, June, July, August for the past five years.
  • If the weighted moving-average method is used, higher weights are placed on the more recent periods belonging to the same season.
  • Multiplicative seasonal method is a method whereby seasonal factors are multiplied by an estimate of average demand to arrive at a seasonal forecast. (You don’t have to remember this.)
finding the right technique
Finding The Right Technique
  • Select a variety of forecasting techniques
  • Use historic data with each technique to forecast demand for the most recent known demand period.
  • See which forecasting technique gives you the most accurate forecast.
  • Use that technique to forecast the unknown period of demand.
  • Repeat this selection process each time you need to forecast demand. Use the latest demand data.

This process is called FOCUS FORECASTING.

(You do have to remember this.)

forecasting as a process

Consensus meetings & collaboration

Update historic data

Prepare Initial Forecasts

Forecasting As a Process

The forecast process itself, typically done on a monthly basis, consists of structured steps. They often are facilitated by someone who might be called a demand manager, forecast analyst, or demand/supply planner.

It is not simply a matter of running a computer model!

Review by Operating Committee5

Finalize and Communicate6


some principles for the forecasting process

© 2007 Pearson Education

Some Principles For The Forecasting Process
  • Forecasting is being done in virtually every company. The challenge is to do it better than the competition.
    • Better forecasts result in better customer service and lower costs, as well as better relationships with suppliers and customers.
  • The forecast must make sense based on the big picture, economic outlook, market share, and so on. Context is critical.
  • The best way to improve forecast accuracy is to focus on reducing forecast error.
  • Whenever possible, forecast aggregate levels. Forecast in detail only where necessary.
  • Use Judgment! Far more can be gained by people collaborating, communicating well, and using judgment, than by using the most advanced forecasting technique or model.