ForecastingOMS 335 Welcome to Forecasting Summer Semester 2002 Introduction

Assignment One: Web Research Research the Internet for Forecasting Information. Select one of the topics listed on the Syllabus Use a search engine, such as Yahoo and search for that topic (see what organizations, data sets, and other information are available) Prepare a 1 page summary - typed Discuss results in class Due: Wednesday

Introduction to Forecasting Begin with a macro level of forecasting and link to a micro level by concentrating on forecasting techniques at the industry and firm level Two areas: Forecasting with Regression Analysis and Time Series Analysis

Forecasting is important in the business decision-making process in which a current choice or decision has future implications: Routine decisions very near in the future small gains or losses assume future is like the past Introduction to Forecasting

Business is the main user of Forecasting Methods, but other areas such as State and Federal governments and non-profit organizations (university, hospital, services) use forecasting. Marketing is the most obvious function in business to use forecasting. A valid sales strategy depends on demand expectations. Introduction to Forecasting

As business majors, you operate and make decisions within the framework of a complex, interrelated, social, economic, and competitive structure. The success of a firm depends on its ability to compete with firms producing similar products or services from the same market. Firms must secure information concerning potential market sales to plan effectively. Introduction to Forecasting

Sales forecasts become the primary information input depicting the state of the environment. The better and more complete the data, the better the decision will be. Introduction to Forecasting

Forecasting alleviates uncertainty: Long and short term forecasts Forecasts relating to industry trends Market research relating to consumer surveys Advertising & Sales promotions Market penetration Sales forecasts Introduction to Forecasting

Period of Time Immediate: less than one month Short term: 1-3 months Medium term: 3 months - 2 years Long term: More than 2 years Level of Detail Aggregate Planning Weekly or other summary # of Items Single items are more complex HBR: Selecting the Right Forecasting Technique

Pattern of Data - methods vary by ability to id patterns Seasonal, Trend, or Random fluctuations Type of Model Time may be the most important element (Time Series) Statistical or robust model (Regression) Cost Factors Development, storage of data, opportunity cost, time Accuracy  10% vs.  2% Ease of Application Sometimes only those that are easily understood are used - more complex models are more accurate. Start with straight forward models HBR: Selecting the Right Forecasting Technique

Underlying Pattern of Data All methods assume some pattern exists (even if random) that can be used as the basis for preparing a forecast Horizontal Seasonal Cyclical Trend Evaluation of Techniques

Horizontal Pattern No trend, stationary Equally likely chance that the next value will be above the mean or below it Stable sales, # of defects in production process Introduction to Forecasting

Seasonal Fluctuations occur in certain months/quarter during the year. Examples: weather, holidays Introduction to Forecasting

Cyclical Similar to seasonal, but the length of cycle is longer than one year: Housing starts, GNP Difficult to predict because it does not repeat itself at constant intervals

Introduction to Forecasting Trend General increase or decrease in value over time Examples: sales, stock

Accuracy of Techniques & Measurement Error There will always be some deviation between actual and forecasted values. Our objective is to minimize the deviations with sound analysis Errors are squared to eliminate signs and emphasize the extreme errors

Types of Models Time Series Identify historical patterns and forecast into the future. If we know that sales are 20% above average each January, the forecast for next January should be upward 20%. This is an inappropriate method for weekly sales fluctuations that are the result of price and advertising changes.

Types of Models Causal Assumes that the value of a certain variable is a function of several other variables. Time Series could be considered causal since actual values are assumed to be a function of the time period. Usually, variables other than time are used. For example, sales as a function of price and advertising. This is a more complex method than time series.

Types of Models Statistical Models Statistical analysis can be used to identify patterns in the variables and in making statements about the reliability of the forecast. Confidence Intervals, R 2, Test of Significance Non-Statistical Models These models do not follow rules of statistical analysis and probability theory. Usually, they are easy to understand and apply. They are limited because they lack guidelines. Qualitative models

Introduction to Forecasting Next Time: Review of Statistics Formulas Hypothesis Testing Regression Analysis