FORECASTING

1. FORECASTING FOREST GROUP Jeff Ferron Mike Hauert Brooke Larson Jamie Weinbauer

2. Objectives • To understand correlational forecasting methods and their relevance to a company’s operations • To apply the correlational information into methods of regression through various computer applications • To see correlation as a continuously improving tool of a company, realize its limitations, and its future

3. What is a Forecast? • A forecast is an estimate or description of a future value or condition

4. Why Forecast? • Forecasting is most often part of a larger process of planning and managing • A forecast is necessary to provide accurate estimates of the future for this larger process • Forecasts are necessary to reduce uncertainties about the future

5. Timescale Timescale Type of Decision Examples short-term Operating Inventory control up to 3-6 mo. Distribution medium-term Tactical Leasing of plant 6 mo-2 yrs. Employment Long-term Strategic R & D Over 2 Yrs Product changes

6. Applications for Forecasting • Inventory Control/Production Planning • forecasting the demand for a product enables us to control he stock of raw materials and finished goods, plan the production schedule, etc. • Investment Policy • Forecasting financial information such as interest rates, exchange rates, share prices, etc.

7. Applications for Forecasting Cont. • Economic Policy • forecasting economic information such as the growth in the economy, unemployment, the inflation rate, etc. is vital both to government & business in planning for the future

8. Forecasting & Strategic Planning • Forecasting answers: • What new products it customers will want • How much of the product • Level of quality and other features

9. Forecasting & SCM • Strategic planning and design tend to focus on SCM • Forecasts of products demand determine how much inventory is needed, how much product to make, and how much material to purchase from suppliers to meet forecasted customer needs • An accurate representation of future demand is necessary for supply chain management planning purposes

10. Forecasting & SCM Cont. • Long-run forecast of technology advances, new products, and changing market are especially critical for the strategic of a company’s supply chain in the future

11. Push To Pull • Making only what the customer demand forecasts • make to forecast • ECR-Efficient Consumer Response • VMI-Vendor Managed Inventory • CRP-Continuous Replenishment Planning • JIT-Just-In-Time

12. Forecasting & TQM • Customers perceive good-quality service to mean having a product when they demand it • Continuous replenishment & JIT inventory compliment TQM • TQM requires a finely tuned, efficient production process, with no defects, minimal inventory, and no waste • Accurate forecasting is required

13. Forecasting Methods • Qualitative-no formal method because data unavailable (long-term) • Multiple equation-number of dependent variables that interact with a series of equations • Time series-a single variable that changes with time and whose future values are related in some way to its past values

14. Forecasting Methods Cont. • Regression-an extension of linear regression where a variable is thought to be linearly related to a number of other independent variables

15. Types of Forecasting • Time Series • Uses historical demand data over a period of time to predict future demand • Only use time as forecasting factor • Moving Average • Weighted Moving Average • Exponential Smoothing

16. Types of Forecasting Cont. • Correlational • Relating demand to other factors that cause demand behavior • Mathematical relationship • Uses Regression

17. Regression • Established a mathematical relationship between two or more variables, so in the future, we can predict what demand will be

18. Linear Regression • Relates one independent variable to another dependant variable • Simplest method • Uses least squares method (Carl Friedric Gauss) • Uses sample data to find estimated regression equation

19. Linear Regression Cont. • Y = A + BX • Dependant variable • Intercept • Slope of the line • Independent variable

20. Linear Regression Cont. • Y = Dependant variable • Represents demand • A = Intercept • B = Slope of the Line • X = Independent variable • Causes demand to behave in a linear behavior

21. Linear Relationships • Positive Linear Relationship • +B • Negative Linear Relationship • -B • No Linear Relationship • B = 0

22. Correlation & Regression • A measure of the strength of a relationship between independent and dependant variables • varies between -1 & +1 • +1 = an increase in independent variable would result in corresponding linear increase in dependant variable

23. Correlation & Regression Cont. • -1 = an increase in independent variable would result in corresponding linear decrease in dependant variable • 0 = No linear relationship r

24. Coefficient of Determination • Percentage of variation in the dependant variable that is a result of a behavior in the independent variable r2

25. Multiple Regression • Study of how a dependant demand variable is related to two or more independent variables • Multiple Regression model • Y = A + B1x1 + B2x2 +…. Bkxk • The only means for forecasting is with a computer

26. Regression & Computers • Forecasting software significantly reduces the time required for preparing forecasts with different models • Excel • Excel OM • Mini-Tab

27. Forecasting with Excel • Useful Excel features • Capability to store, retrieve, slice and dice data • Data can be easily exported to other programs and databases • Capability of drawing all kinds of charts and graphs • Analysis tools dialog • Add-ins command

28. Forecasting Success • Forecasts that are easiest to be accepted are those which fit in with the management goals • It is important to match the forecasting model not only to the data, or situation, but also to the culture of the organization for which forecasts are being prepared

29. 10 Prescriptions for Forecasting success • 1. Understand the factors which create value for the company as well as for the management • 2. Incorporate technology into all forecasting processes and methods • 3. Work carefully to make realistic promises

30. 10 Prescriptions for Forecasting Success Cont. • 4. Understand the nature of the decision to which the forecast is going to be applied • 5. Look at and illustrate the relevant conditions and operative variables • 6. Use a band of projections along with scenario development

31. 10 Prescriptions for Forecasting Success Cont. • 7. To ensure that a monitoring process is established to inform management of decisions which should be revisited as a result of actual conditions observed • 8. Develop specific operational models objectives business strategies and a set of business variables which are critical to their operational goals

32. 10 Prescriptions for Forecasting Success Cont. • 9. Spend time with management to ensure that methods fit management culture and business problems • Understand statistical quality • Reliability of parameters of the projection • Alternative scenarios or assumptions

33. 10 Prescriptions for Forecasting Success Cont. • 10. Acquire journal management skills in perspective • Understand the business, the markets, the competition, and the operations as well as than those who are making the management decisions

34. Forecasting Error • Forecast error is the numerical difference between the actual demand and the forecasted demand for a specific period of time

35. Limitations of Forecasting • A forecast is never completely accurate; forecasts will always deviate from the actual demand • Effectiveness of a forecast must be measured in order to evaluate alternative forecasting methods

36. Limitations of Forecasting • Regression techniques can only determine relationships • They can not determine causation • Fire vs. fireman • Damage Correlation • Does the size of the fire determine the damage or the number of fireman there?

37. Monitoring and Controlling • Compare actual data vs. sample data • Monitor trends (seasonal and quarterly) • Notice irregular variation (outliers)

38. Happy Easter 