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FORECASTING

FORECASTING. Quantitative Methods For Business. Outline. What’s Forecasting Applications of Forecasting Forecasting in Frames x 3 Forecasting on the Move Examples of Movement Approaches to Forecasting Types of Quantitative Forecasting Time Series Defining Moving Averages

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FORECASTING

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  1. FORECASTING Quantitative Methods For Business

  2. Outline • What’s Forecasting • Applications of Forecasting • Forecasting in Frames x 3 • Forecasting on the Move • Examples of Movement • Approaches to Forecasting • Types of Quantitative Forecasting • Time Series • Defining Moving Averages • Calculating Moving Averages • Examples of Moving Averages • Defining Weighted Moving Averages • Formula for Weighted Averages • Define Exponential Smoothing • Examples and Formulas for Exponential Smoothing • Exponential Continued • Solutions • Types of Quantitative Forecasting • Simple Linear Regression • Examples of Linear Regression • Examples Continued • Graph Solution for Examples • Multiple Regression/Casual Regression • Examples of Qualitative Methods • Example Market Research • Accuracy, Error, Objective of Forecasting • Sample Table of Calculations • Mean Absolute Deviation (MAD) • Additional Techniques • Mean Absolute Percentage Deviation (MAPD) • Cumulative Error Calculation • Average Calculation • Monitoring the Forecast • MAD and Standard Deviation Relationship • Rules for Forecasting • Conclusion • References

  3. What is Forecasting ?? THE DEFINITION Forecasting in business is a process used to estimate or predict future patterns using business data.

  4. Applications of Forecasting could be based on • Estimating quarterly sales • Product demand • Customer lifetime value and churn potential • Inventory and supply-chain reorder timing and control • Workforce attrition • Website traffic • Predicting exposure to fraud and risk • Weather • Medical Issues • Investment Policy • Economic Policy

  5. Forecasting in Frames x 3 • Short-Range Forecast – encompasses the immediate future and are concerned with the daily operations of a business firm. • Medium Range Forecast – encompasses anywhere from 1-2 months to a year. • Long-Range Forecasting- encompasses a period longer than 1-2 years.

  6. How Forecast works • Random Variations- unpredictable movements which follow no pattern. • Cycle- undulating movement in demand, up and down movement, repeats itself over a period of time specially of more than a year. • Seasonal Pattern- oscillating movement in demand occurring periodically and is repetitive. • Trend – long-term movement of the item being forecast.

  7. Examples of Movement

  8. Two Distinct Approaches to Forecasting • Quantitative Methods use numerical data and math models to predict future conditions. Future outcomes based on past conditions and patterns. • Qualitative Methods/ Technological Forecasting Methods is used in a vague situation & little data exist. Based on intuition, experience, and educated opinions of knowledgeable persons. Uses actual data to discern market trends. It uses non-numerical data.

  9. Types and of Quantitative Forecasting Methods • Time Series – statistical techniques that use historical data to predict future outcomes. Types of Time Series Moving Average Weighted Moving Average Exponential Smoothing

  10. Types of Time Series Moving Average Weighted Moving Average Exponential Smoothing

  11. Defining Moving Average A moving average uses several values during the recent past to develop a forecast. Moving averages are used to emphasize the direction of a trend and to smooth out price and volume fluctuations, or "noise", that can confuse interpretation. Typically, upward momentum is confirmed when a short-term average (e.g.15-day) crosses above a longer-term average (e.g. 50-day). Downward momentum is confirmed when a short-term average crosses below a long-term average.

  12. Calculating the Moving Average • Moving Average = (most recent n data values)/n • A XYZ refrigerator supplier has experienced the following demand for refrigerator during past five months. Forecast for the month of July using five-period moving average & three-period moving average using simple moving average method.

  13. Another Examples of Moving Averages3Month MA (Oct+Nov+DEC0/3 = 258.336 Month MA (Jul + Aug+…..+DEC)/6 = 249.3312 Month MA (Jan + Feb+……+Dec)/12 =205.33

  14. Weighted Moving Average Definined Weighted Moving Average – any average that has multiplying factors to give different weights to data in different positions.

  15. Formula: Weighted Average = (x1 w1 + x2 w2. .+ xn wn) / (w1 + w2. . + wn) = Σi = 1 to n (xi wi) / Σi = 1 to n wi In Excel = Weighted Average = SUMPRODUCT (A2:A15, B2:B15) / SUM (B2:B15) The manager of a restaurant wants to make decision on inventory and overall cost. He wants to forecast demand for some of the items based on weighted moving average method. For the past three months he experienced a demand for pizzas as follows: Month Demand October 400 November 480 December 550 The demand for the month of January by assuming suitable weights to demand data.

  16. Exponential Smoothing Defined • Average method that weighs the most recent past data more strongly than more distant past data. It allows inclusion trends and other factors. All data in the life of the instrument is part of its calculations. Users are able to adjust the weights to assign importance as necessary. The sum of percentage values = 100

  17. Example and Formula for Exponential Smoothing Data on the average monthly network traffic on a software module during a year: Where: Dt is the actual value Ftis the forecasted value α is the weighing factor 0-1. t = current time period

  18. Exponential Example Continued • we use the same formula to find the value of Ft and substitute it in the above formula we get Ft+1 = Dt + ( 1 - ) (Dt-1 + ( 1 - ) Ft-1 )Now Dt-1 is multiplied by and ( 1 - ). If we keep on substituting the forecasted values, the historical data (actual values) is weighted exponentially. That is why this method is called exponential smoothing. Solution: Solve this problem using weight of 0.6 and an initial forecast of 1000 the results will be as shown below:

  19. Solution : • Using weight of 0.6 and an initial forecast of 1000 the results will be as shown below: • The exponential smoothing value for the 2nd month = (0.6)(1050) + (1 - 0.6)(1000) = 1030, and the other values were calculated in a similar • manner.

  20. Types and of Quantitative Forecasting Methods Continued: • Regression/Casual Methods- develops a relationship (mathematical) between the forecast item and factors which affect its behavior. Based on the assumption of the existence of a constant and linear trend. There are two types: Simple Linear and Multiple Linear.

  21. Simple Linear Regression Simple Linear Regression- expressed in the format Y = a +bX (X is the independent variable and Y is dependent, with b as the slope. the line representing a simple linear regression may also be expressed through a basic equation: Y = a0 + a1 X. Here X is hours spent studying per week, the “independent variable.” Y is the exam scores, the “dependent variable,” since — we believe — those scores depend on time spent studying. Additionally, a0 is the y-intercept (the value of Y when X is zero) and a1 is the slope of the line, characterizing the relationship between the two variables.

  22. Example of Simple Linear Regression Model • The equations for r, a, and b are as follows: • (r is the correlation coefficient) • ('a' is the slope of the line of best fit) • ('b' is the y-intercept)

  23. Examples Continues • The following data was obtained in a Physics experiment and plotted. Determine if a straight line is a good model for this data and if so, determine the line of best fit. • Time(s) Distance (cm) • 1 2 • 3 5 • 5 8 • 7 11 • 9 15 • r=0.9981, which indicates a strong correlation.According to the flowchart above, the next step is to calculate 'a' and 'b', which are calculated to bea= 1.6 s and b=0.2 cm.The equation of the line of best fit is d=1.6t+0.2.

  24. Linear Regression Example Graph Solution

  25. Multiple Regression/Casual Methods • Multiple Regression- is statistical tool used to derive the value of a criterion from several other independent, or predictor, variables. It is the simultaneous combination of multiple factors to assess how and what extent they affect a certain outcome.This technique breaks down when the nature of the factors themselves is of an immeasurable or pure-chance nature.

  26. Examples of Multiple Regression Methods

  27. More Multiple Regression Examples and Correlations • A zonal planner wants to know how the value of houses is affected by factors like the average household income in an area, the house’s square footage, the house’s land acreage and the year it was built. After plotting all these into a system that can perform multiple regression, he then find out the factors that most affect a house’s selling price whether is due to the square footage and average income in the area. Multiple regression may even go more further and show him that the high-priced houses are affected by the same two factors to a much larger extent than lower- and medium-priced houses. • Correlation and regression indicate how and to what extent variables are connected. Correlation coefficients measure the degree of linear association between two or more variables.

  28. Types and Examples of Qualitative/ Technological Forecasting Methods Delphi Method: uses a panel of experts to produce a forecast. Each expert is asked to provide a forecast specific to the need at hand, responses are fed back to panel members. This process repeats itself until each expert nears agreement on the needed scenario or numbers.

  29. Market Research- uses panels, plans, reports, questionnaires, test markets, market analysis, etc.

  30. Example of Market Research

  31. Accuracy, Error and Objective of Forecasting • ACCURACY- forecasting is never completely accurate; there always remains deviation from the actual demand. • OBJECTIVE- forecasting is that its as slight as possible. There are many things to look at, forecast error, the more popular ones are; mean absolute deviation (MAD), mean absolute percent deviation (MAPD), cumulative error, and average error or bias (E). • ERROR- the difference between the actual demand and the outcome of the forecast.

  32. Calculating Demand for Forecast Accuracy • This is the process of determining how accurate a forecast may be in reference to consumer demand for a particular product. Forecast error is calculated using the actual number of sales as the basis. The methods used for calculating errors are Mean Absolute Percent Deviation (MAPD), Average Error, Cumulative Error, and Mean Absolute Deviation (MAD).

  33. Sample Table For Calculations

  34. MAD Error Calculations in Forecasting • MEAN ABSOLUTE DEVIATION (MAD)· MAD is and average of the difference between the forecast and the actual demand· Formula: MAD= (Sum |Dt - Ft|) = .81/12=0.0675 nWhere: t = the period numberDt = the demand in period t Ft = the forecast for period t n = the total number of periods| | = the absolute value

  35. Another Forecasting Techniques Using the same data table Exponential smoothing (α = .30; n = 11) MAD = 0.78

  36. MAPD Error Calculations • MEAN ABSOLUTE PERCENT DEVIATION (MAPD)· Measures the absolute error as a percentage of demand rather than per period.· Resulting in elimination of the problem of interpreting the measure of accuracy relative to the magnitude of the demand and forecast values, as MAD does. Formula: MAPD= (Sum |Dt - Ft|) = .81/21.22=0.0382 Sum(Dt)

  37. Cumulative Error Calculations • CUMULATIVE ERROR· Formula: E= Sum(et) From table = .81· A large positive value indicates the forecast is probably consistently lower than the actual demand, or is biased low.· A large negative value implies the forecast is consistently higher than actual demand or is biased high.· The cumulative error for exponential smoothing forecast is simply the sum of the values in the error column.

  38. Average Error Calculation • AVERAGE ERROR· A measure closely related to cumulative error is the average error or bias. It is computed by averaging the cumulative error over the number of time periods.· Formula: E= Sum (et) = .81/12 = 0.0675 n· A positive value indicates low bias and a negative value indicates a high bias. A value close to zero implies a lack of bias.

  39. Monitoring the Forecast • FORECAST CONTROL· There are many ways to monitor forecast error over time to make sure the forecast is performed correctly.· Can provide inaccurate forecasts for several reasons;· Change in trend· Unanticipated appearance of a cycle· Irregular variation· Promotional campaign· New competition· Political event that distracts consumers· A tracking signal monitors the forecast to see if it is high or low. It is recomputed each period and each movement is compared to control limits.· It is computed as simply the error divided by MAD.

  40. MAD and Standard Deviation Relationship • Forecast errors are normally distributed which results in the following relationship between MAD and the standard deviation of the distribution of error: 1MAD is about 0.8 standard deviations. This enables us to establish statistical control limits for the tracking signal that corresponds to the more familiar normal distribution.· Statistical control charts is another method for monitoring forecast error.· For example, ±3 standard deviation, control limits would reflect 99.7 percent of the forecast errors (assuming they are normally distributed).· Formula: Divide the Sum (Dt - Ft)^2 n-1· This formula without the square root is the mean squared error (MSE).· MSE is the average of the squared forecast errors and is sometimes used as a measure of forecast error.

  41. Rules for Forecasting • CRITERIA FOR SELECTING AFORECASTING METHOD • Objectives: 1. Maximize Accuracy and 2. Minimize Bias • Potential Rules for selecting a time series forecasting method. Select the method that • gives the smallest bias, as measured by cumulative forecast error (CFE); or • gives the smallest mean absolute deviation (MAD); or • gives the smallest tracking signal; or • supports management's beliefs about the underlying pattern of demand • or others. It appears obvious that some measure of both accuracy and bias should be used together. How? • What about the number of periods to be sampled? • if demand is inherently stable, low values of and higher values of N are suggested • if demand is inherently unstable, high values of and lower values of N are suggested

  42. Conclusion This assignment has taught forecasting is not a tool for simply informing us of the weather. Like the slides of the weather used for this assignment, it dictates the changes in business. It allows us to dress properly for the changes in the weather, it also allows us to prepare our businesses for changes in the economy and demand for products. Forecasting guides managers in making informed decisions based on past, present and futures predictions. Many methods to forecast the viability of a company, the previous slides have given examples and formulas that may be applied and the conditions and rules for which they are used. One should always be aware that forecasting while a substantial tool, it may not be 100% accurate.

  43. References • http://www.microstrategy.com/business-forecasting/ • http://businesshelp.lloydstsbbusiness.com/planning/performance/forecas ting/ • http://scm.ncsu.edu/scm-articles/article/basic-rules-of-forecasting- • approaches-to-forecasting-a-tutorial • http://www.jarnoldassociates.com/images/comp-summary.gif • http://www.cartoonstock.com/newscartoons/cartoonists/mba/lowres/mban639l.j pg • http://www.investopedia.com/terms/m/movingaverage.asp#ixzz2BI3696lo • http://mdm4u1.wetpaint.com/page/Linear+Regression+(good+example) • http://cs.gmu.edu/cne/modules/dau/stat/expsmoothg/merits_frm.html

  44. http://cs.gmu.edu/cne/modules/dau/stat/expsmoothg/merits_frm.htmlhttp://cs.gmu.edu/cne/modules/dau/stat/expsmoothg/merits_frm.html • http://www.techopedia.com/definition/27369/multiple-regression • http://www.uoguelph.ca/~dsparlin/forecast.htm • http://faculty.txwes.edu/trdanderson/course1/documents/HeizerChapter4Solutio ns.pdf

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