Forecasting OMS 335

1. Forecasting OMS 335 Welcome to Forecasting Summer Semester 2002 Introduction

2. 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

3. 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

4. Introduction to Forecasting • 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

5. 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.

6. 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.

7. 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.

8. 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

9. HBR: Selecting the Right Forecasting Technique • 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

10. 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

11. Evaluation of Techniques • 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

12. Introduction to Forecasting • 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

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

14. 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

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

16. 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

17. 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.

18. 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.

19. 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, R2, 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

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