10B11PD311 Economics

Process of predicting a future event on the basis of past as well as present knowledge and experience Underlying basis of all business decisions –Production –Inventory –Personnel –Facilities To reduce risk and uncertainty To take business decisions For planning Sales will be $200 Million!

10B11PD311 Economics  Setting the objective  Selection and classification of goods  Selection of method  Interpreting the results

10B11PD311 Economics  Purpose of Short- term Forecasting  Scheduling of production  Inventory management  Price strategy  Sales strategy  Financial requirements  Purpose of Long- term Forecasting  Planning a new Project  Financial requirements  Manpower

 Capital Goods (Producer goods)- goods which help in further production of goods  Replacement Demand  New Demand  Information required  Growth possibilities of industry demanding such goods  Life expectancy  The norm of consumption NATURE OF PRODUCT Capital Goods Durable Consumer goods Non durable consumer goods

10B11PD311 Economics  Durable consumer goods - consumer goods which can be used repeatedly  Replacement demand  New demand  Information required  Life expectancy tables  Purchasing power  Number of households/firms  Existence and growth of cooperating facilities

10B11PD311 Economics  Non durable consumer goods - goods which can be used once  Information required  Disposable income of consumer  Price of the product  Price of related products  Demography

10B11PD311 Economics  Time horizon  Stability  Data Availability  Cost  Accuracy  Ease of Application

10B11PD311 Economics Qualitative Forecasts & Sources of Data Consumer Survey method Expert Opinion Method Market Experiments Complete enumeration Sample survey Composite Opinion Method Forecasting Methods Qualitative Methods Quantitative Methods

10B11PD311 Economics  Iterative group process  3 types of people  Decision makers  Staff  Experts  Proposes to reduce ‘group-think’  Demerits  Expensive  Time consuming  Biased opinion Experts Staff Decision Makers (Sales?) (Sales will be 50!) ( What will sales be? survey ) (Sales will be 45, 50, 55)

10B11PD311 Economics Ask customers about purchasing plans What consumers say, and what they actually do are often different Sometimes difficult to answer How many hours will you use the Internet next week?

10B11PD311 Economics  Direct method of assessing information from primary sources  Simple method  Insufficient Information  Lack of time  Biased information  Utility limited to very short period  There may be sampling error, if sample is not properly chosen

10B11PD311 Economics Each salesperson projects their sales Combined at district & national levels Sales rep’s know customers’ wants Tends to be overly optimistic

10B11PD311 Economics Involves small group of high-level managers –Group estimates demand by working together Combines managerial experience with statistical models Relatively quick ‘Group-think’ disadvantage

10B11PD311 Economics  Simple  Based on first-hand knowledge of salesman  Biased opinion  Restricted to short-term forecasting

10B11PD311 Economics Can help in determining the demand function Expensive Time consuming Risky Difficult to satisfy the condition of homogeneity

10B11PD311 Economics Quantitative Forecasting Methods Quantitative Forecasting Linear Regression Associative Models Exponential Smoothing Moving Average Time Series Models Trend Projection

10B11PD311 Economics  Time -Series data - values of a variable arranged chronologically by days, weeks, months, quarters or years  Past Values plotted on y- axis  Time plotted on x- axis  Time Series analysis- attempts to forecast future values of the time series by examining past observations of the data  Assumption - past pattern will continue unchanged in the future

10B11PD311 Economics Time Series Sales Data

10B11PD311 Economics  Secular Trend - long run increase or decrease in data series Secular Trend  Cyclical fluctuations - changes that recur over years Cyclical fluctuations  Seasonal variation - regularly recurring fluctuation Seasonal variation  Irregular or random influences - variations resulting from unique events Irregular or random influences

10B11PD311 Economics  Persistent, overall upward or downward pattern  Due to population, technology etc  Several years duration Mo., Qtr., Yr. Response

10B11PD311 Economics  Repeating up & down movements  Due to interactions of factors influencing economy  Usually 2-10 years duration Mo., Qtr., Yr. Response Cycle

10B11PD311 Economics  Regular pattern of up & down fluctuations  Due to weather, customs etc  Occurs within 1 year Mo., Qtr. Response Summer

10B11PD311 Economics  Erratic, unsystematic, ‘residual’ fluctuations  Due to random variation or unforeseen events  Union strike  Cyclone  Short duration & nonrepeating

Time Values of Dependent Variable Trend Projection – Graphic Curve Fitting Random Influences Cyclical Fluctuation Secular Trend

Time Values of Dependent Variable Actual observati on Trend Projection – Graphic Curve Fitting

Deviation Time Values of Dependent Variable Point on the line Actual observati on Trend Projection – Graphic Curve Fitting

Deviation Time Values of Dependent Variable Point on the line Actual observation Trend Projection – Graphic Curve Fitting

10B11PD311 Economics Assumptions  Relationship is assumed to be linear  Relationship is assumed to hold only within or slightly outside data range  Deviations around least squares line are assumed to be random Trend Projection – Graphic Curve Fitting

10B11PD311 Economics  Projecting the past trend by fitting a straight line to the data  Constant Rate of Change S t = S o + bt  Where:  S t = value of time series to be forecasted for period t  S o = estimated value of time series in the base period  b = absolute amount of growth per period  t = time period for which series is to be forecasted

10B11PD311 Economics Time Series Sales Data

10B11PD311 Economics  S = nS o + b  t  S*t = S o  t + b  t 2 Time Series Sales Data S t = S o + bt

10B11PD311 Economics Where:

10B11PD311 Economics S t = S o + bt  S = nS o + b  t  S*t = S o  t + b  t 2 S t = t Time Series Sales Data

10B11PD311 Economics Sales per Quarter Quarter

10B11PD311 Economics Seasonal Variation Ratio to Trend Method Actual Trend Forecast Ratio = Seasonal Adjustment = Average of Ratios for Each Seasonal Period Adjusted Forecast = Trend Forecast Seasonal Adjustment

10B11PD311 Economics Seasonal Adjustment using Ratio- Trend method

10B11PD311 Economics  Limited to short term predictions  Fluctuation in economic growth are not considered  Assumes that historical relationships will not change

10B11PD311 Economics  Predicting values of a time series on the basis of some average of its past values  Used when time series exhibit irregular or random variation  Moving Averages  Exponential Smoothing

10B11PD311 Economics Moving Average

10B11PD311 Economics Moving Average

10B11PD311 Economics Moving Average

10B11PD311 Economics Moving Average

10B11PD311 Economics RMSE = To decide on the better moving average forecast calculate the root-mean-square error(RMSE) of each forecast and use the moving average which results in the smallest RMSE

10B11PD311 Economics Moving Average

10B11PD311 Economics  Gives equal weightage to all observations in computing the average.

10B11PD311 Economics  Forecast for next period (ie, t + 1) is a weighted average of the actual and forecasted values of the time series in period t

10B11PD311 Economics Exponential Forecasting

10B11PD311 Economics Exponential Forecasting Mean of A

10B11PD311 Economics Exponential Forecasting

10B11PD311 Economics  Gives greater weight to recent data  It is easy to update the forecasts  No need to re-estimate the equations  When time trend is positive, forecasts are likely to be too low  When time time trend is negative, forecasts are likely to be too high

10B11PD311 Economics  A time series that is correlated with another time series is called an indicator  Coincident indicators two series change at the same time  Leading indicators one series consistently occurs prior to changes in another series

10B11PD311 Economics Coincident indicator Business Cycle Time Indicator level Value Economic Indicators Leading indicator A B D C

10B11PD311 Economics  Must be accurate  Provide adequate lead time  Lead time should be constant  Logical explanation why it is a leading indicator  Cost and time necessary for data collection

10B11PD311 Economics  Indices represent a single time series made up of a number of individual leading indicators.  Purpose is to smooth out the random fluctuations in each individual series. Construction of an Index

10B11PD311 Economics  Composite index- weighted average of individual indicators in each group. Good indicators are given more weightage.  Index is interpreted in terms of percentage change from period to period.  Diffusion Index- gives the percentage of the leading indicators that increase from one time period to the next.

10B11PD311 Economics MonthLeading Leading Leading Indicator I Indicator II Indicator II The 1 month represents the base period All series to be given equal weight Construct a composite & diffusion index

10B11PD311 Economics Composite Index: [ 25/400 + (-1)/ /100] / 3 = 4.31 [ 60/ /30 +35/100] / 3 = 20 MonthDiffusion IndexComposite Index MonthLeading Leading Leading Indicator I Indicator II Indicator II

10B11PD311 Economics  Forecast turning points in the business cycles  Prediction record not perfect  Variability in lead time  Difficult to identify accurate indicators  Provides only qualitative forecast of turning point