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2nd mod papc

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The Jury of Executive Opinion Method relies on the collective judgment of a group of senior managers to develop qualitative forecasts if limited historical data is available. It leverages the knowledge and expertise across different functional areas of managers who have insights into future product developments.

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2nd module Preliminary Screening • Compatibility with the promoter • Consistency with the governmental priorities • Availability of inputs • Adequacy of market • Reasonableness of cost • Acceptability of the risk level
Project Rating Index • Identify factors relevant for project rating • Assign weights to these factors • Rate the project proposal on various factors, using a suitable rate scaling(5-7) • For each factor multiply the factor rating to get factor score • Add all the factor scores to get the overall project rating index
Construction of a Rating Index Factor Factor Rating Factor score weight VG 5 G4 A3 P2 VP 0 Input availability 0.25 y 0.75 Technical know-how 0.10 y 0.40 Reasonableness of cost 0.05 y 0.20 Adequacy of market 0.15 y 0.75 Complementary relationship with 0.05 y 0.20 other products Stability 0.10 y 0.40 Dependence on firm’s strength 0.20 y 1.00 Consistency with government 0.10 y 0.30 policies Total Rating Index 4.00
Sources of positive NPV • Economies of scale • Product differentiation • effective Ad and superior market • Exceptional service • Innovative product features • High quality and dependability • Cost advantage • Accumulated experience and comparative edge on learning curve • Monopolistic access to low cost material • A favorable location • More effective cost control and cost reduction
• Market reach • ex.Avon market network • HUL distribution network • Technological edge • IBM & Intel • Government policy • Restrictive licensing • Import restriction • High tariff walls • Environmental controls • Special tax releifs
Porter 5 force model
Threat of substitute products • Threat of substitute products means how easily your customers can switch to your competitors product. Threat of substitute is high when: • There are many substitute products available • Customer can easily find the product or service that you’re offering at the same or less price • Quality of the competitors’ product is better • Substitute product is by a company earning high profits so can reduce prices to the lowest level.
Threat of new entrants • A new entry of a competitor into your market also weakens your power. Threat of new entry depends upon entry and exit barriers. Threat of new entry is high when: • Capital requirements to start the business are less • Few economies of scale are in place • Customers can easily switch (low switching cost) • Your key technology is not hard to acquire or isn’t protected well • Your product is not differentiated
Industry Rivalry • Industry rivalry mean the intensity of competition among the existing competitors in the market. Intensity of rivalry depends on the number of competitors and their capabilities. Industry rivalry is high when: • There are number of small or equal competitors and less when there’s a clear market leader. • Customers have low switching costs • Industry is growing • Exit barriers are high and rivals stay and compete • Fixed cost are high resulting huge production and reduction in prices
Bargaining power of suppliers • Bargaining Power of supplier means how strong is the position of a seller. How much your supplier have control over increasing the Price of supplies. Suppliers are more powerful when • Suppliers are concentrated and well organized • a few substitutes available to supplies • Their product is most effective or unique • Switching cost, from one suppliers to another, is high • You are not an important customer to Supplier
Bargaining power of Buyers • Bargaining Power of Buyers means, How much control the buyers have to drive down your products price, Can they work together in ordering large volumes. Buyers have more bargaining power when: • Few buyers chasing too many goods • Buyer purchases in bulk quantities • Product is not differentiated • Buyer’s cost of switching to a competitors’ product is low • Shopping cost is low • Buyers are price sensitive • Credible Threat of integration
Qualities of successful Entrepreneur • Willingness to make sacrifices • Leadership • Decisiveness • Confidence in the project • Market orientation ex.Edwin Land Polaroid • Strong ego
MARKET AND DEMAND ANALYSIS 13
Collection of Demand Secondary Forecasting Information Situational Characterization of Analysis and the Market Specifications of Objectives Conduct of Market Planning Market Survey 14
SITUATIONAL ANALYSIS AND SPECIFICATIONS OF OBJECTIVES 15
COLLECTION OF SECONDARY INFORMATION • General Sources of Secondary Information • Industry Specific Sources of Secondary Information • Evaluation of Secondary Information 16
SECONDARY SOURCES OF DATA 1. Indian Economic Survey 2. Indian Basic Facts 3. Reports of Export Working Groups on Various Industries 4. Census of Manufacturing Industries 5. Indian Statistical Yearbook 6. Monthly Statistical Bulletin 7. Annual Report of RBI 8. Annual Reports and Accounts of the Companies Listed on the Stock Exchange 9. Annual Reports of the Various Associations of Manufacturers 17
CONDUCT OF MARKET SURVEY • Census Survey • Sample Survey • Steps in a Sample Survey – Define the Target Population – Select the Sampling Scheme and Sample Size – Develop the Questionnaire – Recruit and Train the Field Investigators – Obtain Information as Per the Questionnaire from the Sample of Respondents – Scrutinizes the Information Gathered – Analyze and interpret the Information 18
CONDUCT OF MARKET SURVEY • Some Problems – Heterogeneity of the Country – Multiplicity of the Languages – Design of Questionnaire 19
CHARACTERISATION OF THE MARKET • Effective Demand in the Past and Present Production + Imports – Exports – Change in stock level • Breakdown of Demand – Nature of Product – Consumer Groups – Geographical Division 20
CHARACTERISATION OF THE MARKET • Price • Methods of Distribution and Sales Promotion • Consumers • Supply and Competition • Government Policy 21
Forecasting • Predicting the future • Qualitative forecast methods – subjective • Quantitative forecast methods – based on mathematical formulas 22 12-22
Types of Forecasting Methods • Depend on – time frame – demand behavior – causes of behavior 23 12-23
Time Frame • Indicates how far into the future is forecast – Short- to mid-range forecast • typically encompasses the immediate future • daily up to two years – Long-range forecast • usually encompasses a period of time longer than two years 24 12-24
Demand Behavior • Trend – a gradual, long-term up or down movement of demand • Random variations – movements in demand that do not follow a pattern • Cycle – an up-and-down repetitive movement in demand • Seasonal pattern – an up-and-down repetitive movement in demand occurring periodically 25 12-25
Causes of Behavior • Analytical • Cause effect relationship basis • Quantitative • Explicit 26
DEMAND FORECASTING • Qualitative Methods – These methods rely essentially on the judgment of experts to translate qualitative information into quantitative estimates – Used to generate forecasts if historical data are not available (e.g., introduction of new product) – The important qualitative methods are: • Jury of Executive Method • Delphi Method 27
JURY OF EXECUTIVE OPINION METHOD • Rationale – Upper-level management has best information on latest product developments and future product launches • Approach – Small group of upper-level managers collectively develop forecasts – Opinion of Group • Main advantages – Combine knowledge and expertise from various functional areas – People who have best information on future developments generate the forecasts 28
JURY OF EXECUTIVE OPINION METHOD • Main drawbacks – Expensive – No individual responsibility for forecast quality – Risk that few people dominate the group – Subjective – Reliability is questionable • Typical applications – Short-term and medium-term demand forecasting 29
DELPHI METHOD • Rationale – Eliciting the opinions of a group of experts with the help of mail survey – Anonymous written responses encourage honesty and avoid that a group of experts are dominated by only a few members 30
DELPHI METHOD • Approach Coordinator Each expert Coordinator Sends Initial writes response performs Questionnaire (anonymous) analysis Coordinator No Coordinator sends updated Consensus Yes summarizes questionnaire reached? forecast 31
DELPHI METHOD • Main advantages – Generate consensus – Can forecast long-term trend without availability of historical data • Main drawbacks – Slow process – Experts are not accountable for their responses – Little evidence that reliable long-term forecasts can be generated with Delphi or other methods 32
DELPHI METHOD • Typical application – Long-term forecasting – Technology forecasting 33
TIME SERIES PROJECTION METHODS • These methods generate forecasts on the basis of an analysis of the historical time series. • Assume that what has occurred in the past will continue to occur in the future • Relate the forecast to only one factor - time The important time series projection methods are: – Trend Projection Method – Exponential Smoothing Method – Moving Average Method 34
Linear Trend Line xy - nxy y = a + bx b = x2 - nx2 a = y-bx where a = intercept of the where relationship n = number of periods b = slope of the line x = time period x x = = mean of the x values y = forecast for n demand for period x y y = n = mean of the y values 35 12-35
Least Squares Example x(PERIOD) y(DEMAND) xy x2 1 73 73 1 2 40 80 4 3 41 123 9 4 37 148 16 5 45 225 25 6 50 300 36 7 43 301 49 8 47 376 64 9 56 504 81 10 52 520 100 11 55 605 121 12 54 648 144 78 557 3867 650 36 12-36
Least Squares Example (cont.) 78 x = = 6.5 12 557 y = = 46.42 12 xy - nxy 3867 - (12)(6.5)(46.42) b = 2 - nx2 = =1.72 x 650 - 12(6.5)2 a = y - bx = 46.42 - (1.72)(6.5) = 35.2 37 12-37
Linear trend line y = 35.2 + 1.72x Forecast for period 13 y = 35.2 + 1.72(13) = 57.56 units 70 – 60 – Actual 50 – Demand 40 – Linear trend line 30 – 20 – 10 – | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 0– Period 38 12-38
Trend Projection Method Advantages • It uses all observations • The straight line is derived by statistical procedure • A measure of goodness fit is available Disadvantages • More complicated • The results are valid only when certain conditions are satisfied 39
Exponential Smoothing  Averaging method  Weights most recent data more strongly  Reacts more to recent changes  Widely used, accurate method 40 12-40
Exponential Smoothing (cont.) Ft +1 = Dt + (1 - )Ft where: Ft +1 = forecast for next period Dt = actual demand for present period Ft = previously determined forecast for present period = weighting factor, smoothing constant 41 12-41
Effect of Smoothing Constant 0.0 1.0 If = 0.20, then Ft +1 = 0.20 Dt + 0.80 Ft If = 0, then Ft +1 = 0 Dt + 1 Ft = Ft Forecast does not reflect recent data If = 1, then Ft +1 = 1 Dt + 0 Ft = Dt Forecast based only on most recent data 42 12-42
Exponential Smoothing (α=0.30) PERIOD MONTH DEMAND F2 = D1 + (1 - )F1 1 Jan 37 = (0.30)(37) + (0.70)(37) 2 Feb 40 = 37 3 Mar 41 4 Apr 37 F3 = D2 + (1 - )F2 5 May 45 = (0.30)(40) + (0.70)(37) 6 Jun 50 = 37.9 7 Jul 43 8 Aug 47 F13 = D12 + (1 - )F12 9 Sep 56 10 Oct 52 = (0.30)(54) + (0.70)(50.84) 11 Nov 55 = 51.79 12 Dec 54 43 12-43
Exponential Smoothing (cont.) FORECAST, Ft + 1 PERIOD MONTH DEMAND ( = 0.3) ( = 0.5) 1 Jan 37 – – 2 Feb 40 37.00 37.00 3 Mar 41 37.90 38.50 4 Apr 37 38.83 39.75 5 May 45 38.28 38.37 6 Jun 50 40.29 41.68 7 Jul 43 43.20 45.84 8 Aug 47 43.14 44.42 9 Sep 56 44.30 45.71 10 Oct 52 47.81 50.85 11 Nov 55 49.06 51.42 12 Dec 54 50.84 53.21 13 Jan – 51.79 53.61 44 12-44
Exponential Smoothing (cont.) 70 – 60 – Actual = 0.50 50 – 40 – Orders = 0.30 30 – 20 – 10 – 0– | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Month 45 12-45
Moving Average • Naive forecast – demand in current period is used as next period’s forecast • Simple moving average – uses average demand for a fixed sequence of periods – stable demand with no pronounced behavioral patterns • Weighted moving average – weights are assigned to most recent data 46 12-46
Moving Average: Naïve Approach ORDERS MONTH PER MONTH FORECAST Jan 120 - Feb 90 120 Mar 100 90 Apr 75 100 May 110 75 June 50 110 July 75 50 Aug 130 75 Sept 110 130 Oct 90 110 Nov - 90 47 12-47
Simple Moving Average n Di i=1 MAn = n where n = number of periods in the moving average Di = demand in period i 48 12-48
3-month Simple Moving Average 3 ORDERS MOVING Di MONTH PER MONTH AVERAGE i=1 MA3 = Jan 120 – 3 Feb 90 – Mar 100 – 90 + 110 + 130 Apr 75 103.3 = 3 May 110 88.3 June 50 95.0 July 75 78.3 = 110 orders Aug 130 78.3 for Nov Sept 110 85.0 Oct 90 105.0 Nov - 110.0 49 12-49
5-month Simple Moving Average ORDERS MOVING MONTH PER MONTH AVERAGE 5 Di Jan 120 – i=1 Feb 90 – MA5 = Mar 100 – 5 Apr 75 – 90 + 110 + 130+75+50 May 110 – = June 50 99.0 5 July 75 85.0 Aug 130 82.0 = 91 orders Sept 110 88.0 for Nov Oct 90 95.0 Nov - 91.0 50 12-50
Smoothing Effects 150 – 125 – 5-month 100 – Orders 75 – 50 – 3-month Actual 25 – 0– | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Month 51 12-51
Weighted Moving Average n  Adjusts moving WMAn = i = 1 Wi Di average method to where more closely Wi = the weight for period i, reflect data between 0 and 100 percent fluctuations Wi = 1.00 52 12-52
Weighted Moving Average Example MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 3 November Forecast WMA3 = Wi Di i=1 = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders 53 12-53
CAUSAL METHODS • Causal methods seek to develop forecasts on the basis of cause-effects relationships specified in an explicit, quantitative manner. – Chain Ratio Method – Consumption Level Method – End Use Method – Leading Indicator Method – Econometric Method 54
CHAIN RATIO METHOD • Market Potential for heated coats in the U.S.: – Population (U) = 280,000,000 – Proportion of U that are age over 16 (A) = 75% – Proportion of A that are men (M) = 50% – Proportion of M that have incomes over $65k (I) = 50% – Proportion of I that live in cold states (C) = 50% – Proportion of C that ski regularly (S) = 10% – Proportion of S that are fashion conscious (F) = 30% – Proportion of F that are early adopters (E) = 10% – Average number of ski coats purchased per year (Y) = .5 coats – Average price per coat (P) = $ 200 55
CHAIN RATIO METHOD • Market Potential for heated coats in the U.S.: Market Sales Potential = UxAxMxIxCxSxFxExY = 280 Million x 0.75 x 0.50 x 0.50 x 0.50 x 0.10 x 0.30 x 0.10 x200 = $7.88 Million 56
CONSUMPTION LEVEL METHOD • This method is used for those products that are directly consumed. This method measures the consumption level on the basis of elasticity coefficients. The important ones are 57
CONSUMPTION LEVEL METHOD • Income Elasticity: This reflects the responsiveness of demand to variations in income. It is calculated as: • E1 = [Q2 - Q1/ I2- I1] * [I1+I2/ Q2 +Q1] • Where E1 = Income elasticity of demand Q1 = quantity demanded in the base year Q2 = quantity demanded in the following year I1 = income level in the base year I2 = income level in the following year 58
CONSUMPTION LEVEL METHOD • Price Elasticity: This reflects the responsiveness of demand to variations in price. It is calculated as: • EP = [Q2 - Q1/ P2- P1] * [P1+P2/ Q2 +Q1] • Where EP = Price elasticity of demand Q1 = quantity demanded in the base year Q2 = quantity demanded in the following year P1 = price level in the base year P2 = price level in the following year 59
END USE METHOD • Suitable for estimating demand for intermediate products • Also called as consumption coefficient method Steps 1. Identify the possible uses of the products 2. Define the consumption coefficient of the product for various uses 3. Project the output levels for the consuming industries 4. Derive the demand for the project 60
END USE METHOD • This method forecasts the demand based on the consumption coefficient of the various uses of the product. Projected Demand for Indchem Consumption Projected Output Projected Demand for Coefficient in Year X Indchem in Year X Alpha 2.0 10,000 20,000 Beta 1.2 15,000 18,000 Kappa 0.8 20,000 16,000 Gamma 0.5 30,000 15,000 Total 69,000 61
LEADING INDICATOR METHOD • This method uses the changes in the leading indicators to predict the changes in the lagging indicators. • Two basic steps: 1. Identify the appropriate leading indicator(s) 2. Establish the relationship between the leading indicator(s) and the variable to forecast. 62
ECONOMETRIC METHOD • An advanced forecasting tool, it is a mathematical expression of economic relationships derived from economic theory. • Economic variables incorporated in the model 1. Single Equation Model Dt = a0 + a1 Pt + a2 Nt • Where Dt = demand for a certain product in year t. Pt = price of the product in year t. Nt = income in year t. 63
ECONOMETRIC METHOD 2. Simultaneous equation method GNPt = Gt + It + Ct It = a0 + a1 GNPt Ct = b0 + b1 GNPt • Where GNPt = gross national product for year t. Gt = Governmental purchase for year t. It = Gross investment for year t. Ct= Consumption for year t. 64
ECONOMETRIC METHOD Advantages • The process sharpens the understanding of complex cause – effect relationships • This method provides basis for testing assumptions Disadvantages • It is expensive and data demanding • To forecast the behaviour of dependant variable, one needs the projected values of independent variables 65
UNCERTANITIES IN DEMAND FORECASTING • Data about past and present markets. – Lack of standardization:- product, price, quantity, cost, income…. – Few observations – Influence of abnormal factors:- war, natural calamity • Methods of forecasting – Inability to handle unquantifiable factors – Unrealistic assumptions – Excessive data requirement 66
UNCERTANITIES IN DEMAND FORECASTING • Environmental changes – Technological changes – Shift in government policy – Developments on the international scene – Discovery of new source of raw material – Vagaries of monsoon 67
COPING WITH UNCERTAINTIES • Conduct analysis with data based on uniform and standard definitions. • Ignore the abnormal or out-of-ordinary observations. • Critically evaluate the assumptions • Adjust the projections. • Monitor the environment. • Consider likely alternative scenarios. • Conduct sensitivity analysis 68
Market planning • Current marketing situation - Market, Competition, Distribution, PEST. • Opportunity and issue analysis - SWOT • Objectives- Break even, % market share… • Marketing strategy- target segment, positioning, 4 Ps • Action program- Quarter 1, Q2, Q3…. 69
Technical Analysis • Manufacturing process/technology – Choice of technology – Plant capacity – Principals inputs – Investment outlay and production cost – Use by other units – Product mix – Latest developments – Ease of absorption • Appropriate technology
Material Input and Utilities • Raw material • Processed industrial materials and components • Auxiliary material and factory supply • Utilities Product Mix
Plant capacity • Technological requirement • Input constraints • Investment costs • Market condition • Resources of the firm • Governmental policy
Location and site • Proximity to raw material and markets • Availability of infrastructure • Labour situation • Governmental policies • Other factors • Climate conditions • General living condition proximity to ancillary units • Ease in coping with environmental pollution • Site selection
• Machinery and equipments – Constraints in selecting machineries and equipment – Procurement of plant and machine • Structure and civil work • Site preparation and development • Building and development • Building and structure • Other works
Project chart and layout

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2nd mod papc

  • 1. 2nd module Preliminary Screening • Compatibility with the promoter • Consistency with the governmental priorities • Availability of inputs • Adequacy of market • Reasonableness of cost • Acceptability of the risk level
  • 2. Project Rating Index • Identify factors relevant for project rating • Assign weights to these factors • Rate the project proposal on various factors, using a suitable rate scaling(5-7) • For each factor multiply the factor rating to get factor score • Add all the factor scores to get the overall project rating index
  • 3. Construction of a Rating Index Factor Factor Rating Factor score weight VG 5 G4 A3 P2 VP 0 Input availability 0.25 y 0.75 Technical know-how 0.10 y 0.40 Reasonableness of cost 0.05 y 0.20 Adequacy of market 0.15 y 0.75 Complementary relationship with 0.05 y 0.20 other products Stability 0.10 y 0.40 Dependence on firm’s strength 0.20 y 1.00 Consistency with government 0.10 y 0.30 policies Total Rating Index 4.00
  • 4. Sources of positive NPV • Economies of scale • Product differentiation • effective Ad and superior market • Exceptional service • Innovative product features • High quality and dependability • Cost advantage • Accumulated experience and comparative edge on learning curve • Monopolistic access to low cost material • A favorable location • More effective cost control and cost reduction
  • 5. • Market reach • ex.Avon market network • HUL distribution network • Technological edge • IBM & Intel • Government policy • Restrictive licensing • Import restriction • High tariff walls • Environmental controls • Special tax releifs
  • 7. Threat of substitute products • Threat of substitute products means how easily your customers can switch to your competitors product. Threat of substitute is high when: • There are many substitute products available • Customer can easily find the product or service that you’re offering at the same or less price • Quality of the competitors’ product is better • Substitute product is by a company earning high profits so can reduce prices to the lowest level.
  • 8. Threat of new entrants • A new entry of a competitor into your market also weakens your power. Threat of new entry depends upon entry and exit barriers. Threat of new entry is high when: • Capital requirements to start the business are less • Few economies of scale are in place • Customers can easily switch (low switching cost) • Your key technology is not hard to acquire or isn’t protected well • Your product is not differentiated
  • 9. Industry Rivalry • Industry rivalry mean the intensity of competition among the existing competitors in the market. Intensity of rivalry depends on the number of competitors and their capabilities. Industry rivalry is high when: • There are number of small or equal competitors and less when there’s a clear market leader. • Customers have low switching costs • Industry is growing • Exit barriers are high and rivals stay and compete • Fixed cost are high resulting huge production and reduction in prices
  • 10. Bargaining power of suppliers • Bargaining Power of supplier means how strong is the position of a seller. How much your supplier have control over increasing the Price of supplies. Suppliers are more powerful when • Suppliers are concentrated and well organized • a few substitutes available to supplies • Their product is most effective or unique • Switching cost, from one suppliers to another, is high • You are not an important customer to Supplier
  • 11. Bargaining power of Buyers • Bargaining Power of Buyers means, How much control the buyers have to drive down your products price, Can they work together in ordering large volumes. Buyers have more bargaining power when: • Few buyers chasing too many goods • Buyer purchases in bulk quantities • Product is not differentiated • Buyer’s cost of switching to a competitors’ product is low • Shopping cost is low • Buyers are price sensitive • Credible Threat of integration
  • 12. Qualities of successful Entrepreneur • Willingness to make sacrifices • Leadership • Decisiveness • Confidence in the project • Market orientation ex.Edwin Land Polaroid • Strong ego
  • 13. MARKET AND DEMAND ANALYSIS 13
  • 14. Collection of Demand Secondary Forecasting Information Situational Characterization of Analysis and the Market Specifications of Objectives Conduct of Market Planning Market Survey 14
  • 16. COLLECTION OF SECONDARY INFORMATION • General Sources of Secondary Information • Industry Specific Sources of Secondary Information • Evaluation of Secondary Information 16
  • 17. SECONDARY SOURCES OF DATA 1. Indian Economic Survey 2. Indian Basic Facts 3. Reports of Export Working Groups on Various Industries 4. Census of Manufacturing Industries 5. Indian Statistical Yearbook 6. Monthly Statistical Bulletin 7. Annual Report of RBI 8. Annual Reports and Accounts of the Companies Listed on the Stock Exchange 9. Annual Reports of the Various Associations of Manufacturers 17
  • 18. CONDUCT OF MARKET SURVEY • Census Survey • Sample Survey • Steps in a Sample Survey – Define the Target Population – Select the Sampling Scheme and Sample Size – Develop the Questionnaire – Recruit and Train the Field Investigators – Obtain Information as Per the Questionnaire from the Sample of Respondents – Scrutinizes the Information Gathered – Analyze and interpret the Information 18
  • 19. CONDUCT OF MARKET SURVEY • Some Problems – Heterogeneity of the Country – Multiplicity of the Languages – Design of Questionnaire 19
  • 20. CHARACTERISATION OF THE MARKET • Effective Demand in the Past and Present Production + Imports – Exports – Change in stock level • Breakdown of Demand – Nature of Product – Consumer Groups – Geographical Division 20
  • 21. CHARACTERISATION OF THE MARKET • Price • Methods of Distribution and Sales Promotion • Consumers • Supply and Competition • Government Policy 21
  • 22. Forecasting • Predicting the future • Qualitative forecast methods – subjective • Quantitative forecast methods – based on mathematical formulas 22 12-22
  • 23. Types of Forecasting Methods • Depend on – time frame – demand behavior – causes of behavior 23 12-23
  • 24. Time Frame • Indicates how far into the future is forecast – Short- to mid-range forecast • typically encompasses the immediate future • daily up to two years – Long-range forecast • usually encompasses a period of time longer than two years 24 12-24
  • 25. Demand Behavior • Trend – a gradual, long-term up or down movement of demand • Random variations – movements in demand that do not follow a pattern • Cycle – an up-and-down repetitive movement in demand • Seasonal pattern – an up-and-down repetitive movement in demand occurring periodically 25 12-25
  • 26. Causes of Behavior • Analytical • Cause effect relationship basis • Quantitative • Explicit 26
  • 27. DEMAND FORECASTING • Qualitative Methods – These methods rely essentially on the judgment of experts to translate qualitative information into quantitative estimates – Used to generate forecasts if historical data are not available (e.g., introduction of new product) – The important qualitative methods are: • Jury of Executive Method • Delphi Method 27
  • 28. JURY OF EXECUTIVE OPINION METHOD • Rationale – Upper-level management has best information on latest product developments and future product launches • Approach – Small group of upper-level managers collectively develop forecasts – Opinion of Group • Main advantages – Combine knowledge and expertise from various functional areas – People who have best information on future developments generate the forecasts 28
  • 29. JURY OF EXECUTIVE OPINION METHOD • Main drawbacks – Expensive – No individual responsibility for forecast quality – Risk that few people dominate the group – Subjective – Reliability is questionable • Typical applications – Short-term and medium-term demand forecasting 29
  • 30. DELPHI METHOD • Rationale – Eliciting the opinions of a group of experts with the help of mail survey – Anonymous written responses encourage honesty and avoid that a group of experts are dominated by only a few members 30
  • 31. DELPHI METHOD • Approach Coordinator Each expert Coordinator Sends Initial writes response performs Questionnaire (anonymous) analysis Coordinator No Coordinator sends updated Consensus Yes summarizes questionnaire reached? forecast 31
  • 32. DELPHI METHOD • Main advantages – Generate consensus – Can forecast long-term trend without availability of historical data • Main drawbacks – Slow process – Experts are not accountable for their responses – Little evidence that reliable long-term forecasts can be generated with Delphi or other methods 32
  • 33. DELPHI METHOD • Typical application – Long-term forecasting – Technology forecasting 33
  • 34. TIME SERIES PROJECTION METHODS • These methods generate forecasts on the basis of an analysis of the historical time series. • Assume that what has occurred in the past will continue to occur in the future • Relate the forecast to only one factor - time The important time series projection methods are: – Trend Projection Method – Exponential Smoothing Method – Moving Average Method 34
  • 35. Linear Trend Line xy - nxy y = a + bx b = x2 - nx2 a = y-bx where a = intercept of the where relationship n = number of periods b = slope of the line x = time period x x = = mean of the x values y = forecast for n demand for period x y y = n = mean of the y values 35 12-35
  • 36. Least Squares Example x(PERIOD) y(DEMAND) xy x2 1 73 73 1 2 40 80 4 3 41 123 9 4 37 148 16 5 45 225 25 6 50 300 36 7 43 301 49 8 47 376 64 9 56 504 81 10 52 520 100 11 55 605 121 12 54 648 144 78 557 3867 650 36 12-36
  • 37. Least Squares Example (cont.) 78 x = = 6.5 12 557 y = = 46.42 12 xy - nxy 3867 - (12)(6.5)(46.42) b = 2 - nx2 = =1.72 x 650 - 12(6.5)2 a = y - bx = 46.42 - (1.72)(6.5) = 35.2 37 12-37
  • 38. Linear trend line y = 35.2 + 1.72x Forecast for period 13 y = 35.2 + 1.72(13) = 57.56 units 70 – 60 – Actual 50 – Demand 40 – Linear trend line 30 – 20 – 10 – | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 0– Period 38 12-38
  • 39. Trend Projection Method Advantages • It uses all observations • The straight line is derived by statistical procedure • A measure of goodness fit is available Disadvantages • More complicated • The results are valid only when certain conditions are satisfied 39
  • 40. Exponential Smoothing  Averaging method  Weights most recent data more strongly  Reacts more to recent changes  Widely used, accurate method 40 12-40
  • 41. Exponential Smoothing (cont.) Ft +1 = Dt + (1 - )Ft where: Ft +1 = forecast for next period Dt = actual demand for present period Ft = previously determined forecast for present period = weighting factor, smoothing constant 41 12-41
  • 42. Effect of Smoothing Constant 0.0 1.0 If = 0.20, then Ft +1 = 0.20 Dt + 0.80 Ft If = 0, then Ft +1 = 0 Dt + 1 Ft = Ft Forecast does not reflect recent data If = 1, then Ft +1 = 1 Dt + 0 Ft = Dt Forecast based only on most recent data 42 12-42
  • 43. Exponential Smoothing (α=0.30) PERIOD MONTH DEMAND F2 = D1 + (1 - )F1 1 Jan 37 = (0.30)(37) + (0.70)(37) 2 Feb 40 = 37 3 Mar 41 4 Apr 37 F3 = D2 + (1 - )F2 5 May 45 = (0.30)(40) + (0.70)(37) 6 Jun 50 = 37.9 7 Jul 43 8 Aug 47 F13 = D12 + (1 - )F12 9 Sep 56 10 Oct 52 = (0.30)(54) + (0.70)(50.84) 11 Nov 55 = 51.79 12 Dec 54 43 12-43
  • 44. Exponential Smoothing (cont.) FORECAST, Ft + 1 PERIOD MONTH DEMAND ( = 0.3) ( = 0.5) 1 Jan 37 – – 2 Feb 40 37.00 37.00 3 Mar 41 37.90 38.50 4 Apr 37 38.83 39.75 5 May 45 38.28 38.37 6 Jun 50 40.29 41.68 7 Jul 43 43.20 45.84 8 Aug 47 43.14 44.42 9 Sep 56 44.30 45.71 10 Oct 52 47.81 50.85 11 Nov 55 49.06 51.42 12 Dec 54 50.84 53.21 13 Jan – 51.79 53.61 44 12-44
  • 45. Exponential Smoothing (cont.) 70 – 60 – Actual = 0.50 50 – 40 – Orders = 0.30 30 – 20 – 10 – 0– | | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 13 Month 45 12-45
  • 46. Moving Average • Naive forecast – demand in current period is used as next period’s forecast • Simple moving average – uses average demand for a fixed sequence of periods – stable demand with no pronounced behavioral patterns • Weighted moving average – weights are assigned to most recent data 46 12-46
  • 47. Moving Average: Naïve Approach ORDERS MONTH PER MONTH FORECAST Jan 120 - Feb 90 120 Mar 100 90 Apr 75 100 May 110 75 June 50 110 July 75 50 Aug 130 75 Sept 110 130 Oct 90 110 Nov - 90 47 12-47
  • 48. Simple Moving Average n Di i=1 MAn = n where n = number of periods in the moving average Di = demand in period i 48 12-48
  • 49. 3-month Simple Moving Average 3 ORDERS MOVING Di MONTH PER MONTH AVERAGE i=1 MA3 = Jan 120 – 3 Feb 90 – Mar 100 – 90 + 110 + 130 Apr 75 103.3 = 3 May 110 88.3 June 50 95.0 July 75 78.3 = 110 orders Aug 130 78.3 for Nov Sept 110 85.0 Oct 90 105.0 Nov - 110.0 49 12-49
  • 50. 5-month Simple Moving Average ORDERS MOVING MONTH PER MONTH AVERAGE 5 Di Jan 120 – i=1 Feb 90 – MA5 = Mar 100 – 5 Apr 75 – 90 + 110 + 130+75+50 May 110 – = June 50 99.0 5 July 75 85.0 Aug 130 82.0 = 91 orders Sept 110 88.0 for Nov Oct 90 95.0 Nov - 91.0 50 12-50
  • 51. Smoothing Effects 150 – 125 – 5-month 100 – Orders 75 – 50 – 3-month Actual 25 – 0– | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Month 51 12-51
  • 52. Weighted Moving Average n  Adjusts moving WMAn = i = 1 Wi Di average method to where more closely Wi = the weight for period i, reflect data between 0 and 100 percent fluctuations Wi = 1.00 52 12-52
  • 53. Weighted Moving Average Example MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 3 November Forecast WMA3 = Wi Di i=1 = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders 53 12-53
  • 54. CAUSAL METHODS • Causal methods seek to develop forecasts on the basis of cause-effects relationships specified in an explicit, quantitative manner. – Chain Ratio Method – Consumption Level Method – End Use Method – Leading Indicator Method – Econometric Method 54
  • 55. CHAIN RATIO METHOD • Market Potential for heated coats in the U.S.: – Population (U) = 280,000,000 – Proportion of U that are age over 16 (A) = 75% – Proportion of A that are men (M) = 50% – Proportion of M that have incomes over $65k (I) = 50% – Proportion of I that live in cold states (C) = 50% – Proportion of C that ski regularly (S) = 10% – Proportion of S that are fashion conscious (F) = 30% – Proportion of F that are early adopters (E) = 10% – Average number of ski coats purchased per year (Y) = .5 coats – Average price per coat (P) = $ 200 55
  • 56. CHAIN RATIO METHOD • Market Potential for heated coats in the U.S.: Market Sales Potential = UxAxMxIxCxSxFxExY = 280 Million x 0.75 x 0.50 x 0.50 x 0.50 x 0.10 x 0.30 x 0.10 x200 = $7.88 Million 56
  • 57. CONSUMPTION LEVEL METHOD • This method is used for those products that are directly consumed. This method measures the consumption level on the basis of elasticity coefficients. The important ones are 57
  • 58. CONSUMPTION LEVEL METHOD • Income Elasticity: This reflects the responsiveness of demand to variations in income. It is calculated as: • E1 = [Q2 - Q1/ I2- I1] * [I1+I2/ Q2 +Q1] • Where E1 = Income elasticity of demand Q1 = quantity demanded in the base year Q2 = quantity demanded in the following year I1 = income level in the base year I2 = income level in the following year 58
  • 59. CONSUMPTION LEVEL METHOD • Price Elasticity: This reflects the responsiveness of demand to variations in price. It is calculated as: • EP = [Q2 - Q1/ P2- P1] * [P1+P2/ Q2 +Q1] • Where EP = Price elasticity of demand Q1 = quantity demanded in the base year Q2 = quantity demanded in the following year P1 = price level in the base year P2 = price level in the following year 59
  • 60. END USE METHOD • Suitable for estimating demand for intermediate products • Also called as consumption coefficient method Steps 1. Identify the possible uses of the products 2. Define the consumption coefficient of the product for various uses 3. Project the output levels for the consuming industries 4. Derive the demand for the project 60
  • 61. END USE METHOD • This method forecasts the demand based on the consumption coefficient of the various uses of the product. Projected Demand for Indchem Consumption Projected Output Projected Demand for Coefficient in Year X Indchem in Year X Alpha 2.0 10,000 20,000 Beta 1.2 15,000 18,000 Kappa 0.8 20,000 16,000 Gamma 0.5 30,000 15,000 Total 69,000 61
  • 62. LEADING INDICATOR METHOD • This method uses the changes in the leading indicators to predict the changes in the lagging indicators. • Two basic steps: 1. Identify the appropriate leading indicator(s) 2. Establish the relationship between the leading indicator(s) and the variable to forecast. 62
  • 63. ECONOMETRIC METHOD • An advanced forecasting tool, it is a mathematical expression of economic relationships derived from economic theory. • Economic variables incorporated in the model 1. Single Equation Model Dt = a0 + a1 Pt + a2 Nt • Where Dt = demand for a certain product in year t. Pt = price of the product in year t. Nt = income in year t. 63
  • 64. ECONOMETRIC METHOD 2. Simultaneous equation method GNPt = Gt + It + Ct It = a0 + a1 GNPt Ct = b0 + b1 GNPt • Where GNPt = gross national product for year t. Gt = Governmental purchase for year t. It = Gross investment for year t. Ct= Consumption for year t. 64
  • 65. ECONOMETRIC METHOD Advantages • The process sharpens the understanding of complex cause – effect relationships • This method provides basis for testing assumptions Disadvantages • It is expensive and data demanding • To forecast the behaviour of dependant variable, one needs the projected values of independent variables 65
  • 66. UNCERTANITIES IN DEMAND FORECASTING • Data about past and present markets. – Lack of standardization:- product, price, quantity, cost, income…. – Few observations – Influence of abnormal factors:- war, natural calamity • Methods of forecasting – Inability to handle unquantifiable factors – Unrealistic assumptions – Excessive data requirement 66
  • 67. UNCERTANITIES IN DEMAND FORECASTING • Environmental changes – Technological changes – Shift in government policy – Developments on the international scene – Discovery of new source of raw material – Vagaries of monsoon 67
  • 68. COPING WITH UNCERTAINTIES • Conduct analysis with data based on uniform and standard definitions. • Ignore the abnormal or out-of-ordinary observations. • Critically evaluate the assumptions • Adjust the projections. • Monitor the environment. • Consider likely alternative scenarios. • Conduct sensitivity analysis 68
  • 69. Market planning • Current marketing situation - Market, Competition, Distribution, PEST. • Opportunity and issue analysis - SWOT • Objectives- Break even, % market share… • Marketing strategy- target segment, positioning, 4 Ps • Action program- Quarter 1, Q2, Q3…. 69
  • 70. Technical Analysis • Manufacturing process/technology – Choice of technology – Plant capacity – Principals inputs – Investment outlay and production cost – Use by other units – Product mix – Latest developments – Ease of absorption • Appropriate technology
  • 71. Material Input and Utilities • Raw material • Processed industrial materials and components • Auxiliary material and factory supply • Utilities Product Mix
  • 72. Plant capacity • Technological requirement • Input constraints • Investment costs • Market condition • Resources of the firm • Governmental policy
  • 73. Location and site • Proximity to raw material and markets • Availability of infrastructure • Labour situation • Governmental policies • Other factors • Climate conditions • General living condition proximity to ancillary units • Ease in coping with environmental pollution • Site selection
  • 74. • Machinery and equipments – Constraints in selecting machineries and equipment – Procurement of plant and machine • Structure and civil work • Site preparation and development • Building and development • Building and structure • Other works