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- 1. Management Science Sales Forecasting By Magdy AbdelsattarLinkedIn.com/pub/Magdy-abdelsattar-omarmagdysattar@gmail.com+2012700009704/8/2013 Eng. Magdy Abdelsattar 1
- 2. Profile ?4/8/2013 Eng. Magdy Abdelsattar 2
- 3. Objectives ?• What is sales forecasting?• Why doing sales forecasting?• How Sales Forecasting Works• What methods are used?• How to use these methods?Just like a ships captain, its up to salesforecasting professionals to keep businesses oncourse.4/8/2013 Eng. Magdy Abdelsattar 3
- 4. Objective
- 5. First day: Agenda1. introduction to sales forecasting oWhat? oWhy? oHow?2. Qualitative methods oDelphi oExpert Judgment oScenario Writing oIntuitive approach oGroup Work3. Quantitative methodsI. Time Series Methods oTrend Component oCyclical Component oSeasonal Component oIrregular Component oExcel work4/8/2013 Eng. Magdy Abdelsattar 5
- 6. AgendaSecond day:Quantitative methodsI. Smoothing Methods oMoving Average oWeighted Moving Average oExponential Smoothing oExcel workII. Casual Section oRegression analysis casual method oRegression analysis with time series oExcel workIII. Trend and Seasonal oMultiplicative Model oSeasonal Indexes oDeseasonalized the Time Series oUsing DTS to Identify trends oSeasonal Adjustment oExcel Work4/8/2013 Eng. Magdy Abdelsattar 6
- 7. Introduction to Sales Forecasting4/8/2013 Eng. Magdy Abdelsattar 7
- 8. What is sales forecasting?4/8/2013 Eng. Magdy Abdelsattar 8
- 9. Sales forecasting.A forecast is simply a prediction of what will happen in the future. Managersmust learn to accept the fact that, regardless of the technique used, they willnot be able to develop perfect forecasts.Sales forecasting is a difficult area of management. Most of us believe we aregood at forecasting. However, forecasts made usually turn out to be wrong!Marketers argue about whether sales forecasting is a science or an art. Theshort answer is that it is a bit of both.Most companies can forecast total demand for all products, as a group, witherrors of less than 5%. However, forecasting demand for individual productsmay results in significantly higher errors.With sales forecasting, companies can plan for future inventory on a monthly basis.4/8/2013 Eng. Magdy Abdelsattar 9
- 10. Key terms in sales forecasting Market demand: for a product or service is the estimated total sales volume in a market (or industry) for a specific time period in a defined marketing environment, under a defined marketing program or expenditure. Market demand is a function associated with varying levels of industry marketing expenditure. Market forecast (market size): is the expected market (industry) demand at one level of industry marketing expenditure. Market potential: is the maximum market (industry) demand, resulting from a very high level of industry marketing expenditure, where further increases in expenditure would have little effect on increase in demand. Company demand: is the company’s estimated share of market demand for a product or service at alternative levels of the company marketing efforts (or expenditures) in a specific time period.4/8/2013 Eng. Magdy Abdelsattar 10
- 11. Key terms in sales forecasting Sales potential: is the maximum estimated company sales of a product or service, based on maximum share (or percentage) of market potential expected by the company. Sales forecast: is the estimated company sales of a product or service, based on a chosen (or proposed) marketing expenditure plan, for a specific time period, in a assumed marketing environment Sales budget: is the estimate of expected sales volume in units or revenues from the company’s products and services, and the selling expenses. It is set slightly lower than the company sales forecast, to avoid excessive risks4/8/2013 Eng. Magdy Abdelsattar 11
- 12. Type of forecastingThere are two major types of forecasting:Macro forecasting:is concerned with forecasting markets in total. This is about determining theexisting level of Market Demand and considering what will happen tomarket demand in the future.Micro forecasting:is concerned with detailed unit sales forecasts. This is about determining aproduct’s market share in a particular industry and considering what willhappen to that market share in the future.4/8/2013 Eng. Magdy Abdelsattar 12
- 13. types of forecasting InformationSales forecasts can be based on three types of information:What customers say about their intentions to continue buying products inthe industryWhat customers are actually doing in the marketWhat customers have done in the past in the market4/8/2013 Eng. Magdy Abdelsattar 13
- 14. Sales forecasts also rely on obtaining information on existing market demand:As a starting point for estimating market demand, a company needs to know the actualindustry sales taking place in the market. This involves identifying its competitors andestimating their sales.An industry trade association will often collect and publish (sometime only to members)total industry sales, although rarely listing individual company sales separately. By usingthis information, each company can evaluate its performance against the whole market.4/8/2013 Eng. Magdy Abdelsattar 14
- 15. Factors affecting Forecasting External Factors o Relative state of the economy o Direct and indirect competition o Styles or fashions o Consumer earnings o Population changes o Weather4/8/2013 Eng. Magdy Abdelsattar 15
- 16. Factors affecting Forecasting Internal Factors o Labour problems o Inventory shortages o Working capital shortage o Price changes o Change in distribution method o Production capability shortage o New product lines4/8/2013 Eng. Magdy Abdelsattar 16
- 17. forecasting problems.The selection of which type of forecasting to use depends on several factors:The degree of accuracy required – if the decisions that are to be made on the basis of the sales forecast have highrisks attached to them, then it stands to reason that the forecast should beprepared as accurately as possible although this involves more cost.The availability of data and information –in some markets there is a wealth of available sales information (e.g. clothingretail, food retailing, holidays); in others it is hard to find reliable, up-to-dateinformation.4/8/2013 Eng. Magdy Abdelsattar 17
- 18. forecasting problems.The time horizon that the sales forecast is intended to cover.For example, are we forecasting next weeks’ sales, or are we trying to forecastwhat will happen to the overall size of the market in the next five years?The position of the products in its life cycle.For example, for products at the “introductory” stage of the product lifecycle, less sales data and information may be available than for products at the“maturity” stage when time series can be a useful forecasting method.4/8/2013 Eng. Magdy Abdelsattar 18
- 19. How to Improve Forecasting Accuracy? Sales forecasting is an important & difficult task Following guidelines may help in improving its accuracy oUse multiple (2/3) forecasting methods. oSelect suitable forecasting methods, based on application, cost, and available time. oUse few independent variables / factors, based on discussions with salespeople & customers. oEstablish a range of sales forecasts – minimum, intermediate, and maximum. oUse computer software forecasting packages.4/8/2013 Eng. Magdy Abdelsattar 19
- 20. Forecasting Approaches• Two basic approaches: • Top-down or Break-down approach • Bottom-up or Build-up approach• Some companies use both approaches to increase their confidence in the forecast4/8/2013 Eng. Magdy Abdelsattar 20
- 21. Steps followed in Top-down / Break- down Approacho Forecast relevant external environmental factorso Estimate industry sales or market potentialo Calculate company sales potential = market potential x company shareo Decide company sales forecast (lower than company sales potential because sales potential is maximum estimated sales, without any constraints)4/8/2013 Eng. Magdy Abdelsattar 21
- 22. Steps followed in Bottom-up / Build-up Approacho Salespersons estimate sales expected from their customers.o Area/Branch managers combine sales forecasts received from salespersons.o Regional/Zonal managers combine sales forecasts received from area/branch managers.o Sales/marketing head combines sales forecasts. received from regional/zonal managers into company. sales forecast, which is presented to CEO for discussion and approval. 4/8/2013 Eng. Magdy Abdelsattar 22
- 23. Why we need sales forecasting? 1. Businesses are forced to look well ahead in order to plan their investments, launch new products, decide when to close or withdraw products and so on. 2. The sales forecasting process is a critical one for most businesses. 3. Key decisions that are derived from a sales forecast include: oEmployment levels required oPromotional mix oInvestment in production capacity oPlant Capacity & Projected Utilization oAvailability of Raw Materials oWorking Capital Requirements oCapital Expenditure oReturn on Investment Sales forecasting helps retailers decide how many styles of a product to stock.4/8/2013 Eng. Magdy Abdelsattar 23
- 24. Why doing sales forecasting? A sales forecast is a projection of the coming years sales revenue based on information collected from the individual members of the sales team and sales management.Distribution Process:Sales forecasts identify not only the volume of sales, but where those sales are projectedto come from.By using sales forecasting to do demand planning, the company can determine wherenew distribution outlets are needed and decide on the best way to expand its productnetwork.Manufacturing:The level of manufacturing for any company is determined by the forecast of productdemand. In order to properly plan the acquisition of materials, schedule manufacturingand determine the adequate personnel to meet that schedule.Revised sales forecasts during the course of the year are also helpful in keepingmanufacturing up to date on needs and trends.4/8/2013 Eng. Magdy Abdelsattar 24
- 25. Why doing sales forecasting?Logistics:An increase or decrease in sales forecasting is going to affect the logistics portion ofdemand planning.Sales forecasting is used to determine whether or not new logistics agreements need tobe negotiated with carriers and if the company needs to revise shipping policies.Sales Force Expansion:A growing company is going to experience a rise in demand that needs to be addressedwith an increased sales force.Some potential sales force changes include creating new sales territories, splittingexisting territories into more sales regions and adding new sales representatives toattend to those regions, and hiring more sales professionals to take care of anexpanding client demand.4/8/2013 Eng. Magdy Abdelsattar 25
- 26. 4/8/2013 Eng. Magdy Abdelsattar 26
- 27. How Sales Forecasting Works Collect and analysis data Calculate sales forecast Determine forecasting methodsIt is all about determining future market demand, through an analysis of the current marketand past sales data 4/8/2013 Eng. Magdy Abdelsattar 27
- 28. What are the steps for that? “The forecast” Step 7 Validate and implement results Step 6 Make the forecast Step 5 Obtain, clean and analyze data Step 4 Select a forecasting technique Step 2 Select the items to be forecasted Step 1 Determine purpose of forecast4/8/2013 Eng. Magdy Abdelsattar 28
- 29. Preparing a Sales Forecast• Very few products or services lend themselves to easy forecasting .• In most markets, total demand and company demand are not stable – which makes good sales forecasting a critical success factor.Prepare a Prepare an industry Prepare a companymacroeconomic sales forecast sales forecastforecast • what will happen to • what will happen to • based on what overall economic overall sales in an management expect activity in the industry based on to happen to the relevant economies the issues that company’s market in which a product is influence the share to be sold. macroeconomic forecast;4/8/2013 Eng. Magdy Abdelsattar 29
- 30. How should we pick our forecasting model? 1. Data availability 2. Time horizon for the forecast 3. Required accuracy 4. Required Resources4/8/2013 Eng. Magdy Abdelsattar 30
- 31. Some Important Questions• What is the purpose of the forecast?• Which systems will use the forecast?• How important is the past in estimating the future? Answers will help determine time horizons, techniques, and level of detail for the forecast.4/8/2013 Eng. Magdy Abdelsattar 31
- 32. Forecasting MethodsForecasting is the process in business of determining what the business market that you are engaged in looks like demographicallyIt can also involve attempting to predict the movements of the existing market going forward so market strategies and business plans can be developed to anticipate and meet the changing demands4/8/2013 Eng. Magdy Abdelsattar 32
- 33. Forecasting Methods Forecasting Methods Quantitative Qualitative Casual Trends & Scenario Expert Time Series Smoothing Delphi(explanatory) Seasonal Writing judgment 4/8/2013 Eng. Magdy Abdelsattar 33
- 34. •Qualitative Forecasting Methods:Qualitative forecasting methods attempt to use actual data to determine a qualitative oractual market trend toward a certain position or function in the market. These methodsinvolve looking at non-numerical data. Qualitative forecasting methods are not aseffective as quantitative methods,•Quantitative Forecasting Methods:In general, quantitative methods use numbers -- sales numbersExplanatory MethodsExplanatory forecasting methods use data to attempt to explain trends and to forecastfuture market direction based on existing data. Explanatory methods involve looking atmarket activity to explain how and why trends occurred, not just to predict what willoccur.Time-series MethodsTime-series methods are used only with historical data to predict future performance. 4/8/2013 Eng. Magdy Abdelsattar 34
- 35. Qualitative Methods4/8/2013 Eng. Magdy Abdelsattar 35
- 36. Delphi Method•originally developed by a research group at the RandCooperation, attempts to develop forecasts through “group consensus”.•The members of a panel of experts-all of whom are physicallyseparated from and unknown to each other-are asked to respond to aseries of questionnaires.•The response of the first questionnaire are tabulated and used toprepare a second questionnaire that contains information provided.•This process continues until the coordinator feels that some degree ofconsensus has been reached.•The objective is to produce a relatively narrow spread of opinionswithin which the majority of experts concur.4/8/2013 Eng. Magdy Abdelsattar 36
- 37. Expert Judgment•Often are based on the judgment of a single expert orrepresent the consensus of a group of experts.•In doing so, the experts individually considerinformation that they believe will influence the market, then they combine their conclusions into a forecast.•No formal model is used, and no tow experts are likelyto consider the same information in the same way.4/8/2013 Eng. Magdy Abdelsattar 37
- 38. Scenario Writing•Scenario writing consists of developing aconceptual scenario of the future based on awell-defined set of assumptions.•The job of the decision maker is to decidehow likely each scenario is and then to makedecision accordingly.4/8/2013 Eng. Magdy Abdelsattar 38
- 39. Intuitive Approaches •Subjective, or intuitive qualitative approaches, are based on the ability of the human mind to process information that, in most cases, is difficult to quantify. •These techniques are often used in group work, wherein a committee or panel seeks to develop new idea or solve complex problem through a series of “brainstorming sessions”.4/8/2013 Eng. Magdy Abdelsattar 39
- 40. Group Activity In teams try to implement qualitative analysis to demonstrate the concepts4/8/2013 Eng. Magdy Abdelsattar 40
- 41. Quantitative Methods4/8/2013 Eng. Magdy Abdelsattar 41
- 42. Quantitative Methods Decomposition Process: includes breaking down the company’s previous periods’ sales data into components like trend, cycle, seasonal, and erratic events. These components are recombined to produce sales forecast Advantages: Conceptually sound, fair to good accuracy, low cost, less time Disadvantages: complex statistical method, historical data needed, used for short-term forecasting only4/8/2013 Eng. Magdy Abdelsattar 42
- 43. Time-Series Using time series analysis to prepare an effective sales forecast requires management to: •Smooth out the erratic factors (e.g. by using a moving average) •Adjust for seasonal variation •Identify and estimate the effect of specific marketing responsesTime series analysis are accurate for short termand medium term forecasts and more so whendemand is stable or follows the past behavior. 4/8/2013 Eng. Magdy Abdelsattar 43
- 44. Time-Series•Sales History: Sales history is an important tool in forecasting. Its the basis for inventory, staffing and business resource planning. Knowing previous years sales allows the establishment of a baseline, or starting point, for setting goals. Sales history, analyzed with knowledge of the market, customers, industry and products, is the main indicator of future sales opportunities. 4/8/2013 Eng. Magdy Abdelsattar 44
- 45. Time-Series Open-model time-series techniques involve analyzing sales history data for patterns to use in sales forecasting. These are patterns in level, trends, Cyclical and seasonality, combined with "noise." Level: is the sales history without trends. Trends Component: are increases or decreases in sales that continue year after year. Cyclical Component: Any frequent sequence of sales above and below the trend line lasting more than one year. Sales are often effected by swings in general economic activity as consumers have more or less disposable income available4/8/2013 Eng. Magdy Abdelsattar 45
- 46. Time-Series Seasonality Component: is a pattern of sales of particular items at particular times of the year, Noise (Irregular Component): involves random effects in sales that dont have a repeatable pattern in previous sales. Analyzing sales history trends and reasons for changes in sales enables sales personnel to produce more accurate forecasts.4/8/2013 Eng. Magdy Abdelsattar 46
- 47. Time-Series Patterns4/8/2013 Eng. Magdy Abdelsattar 47
- 48. Naïve Forecasts The forecast for any period equals the previous period’s actual value. oSimple to use oVirtually no cost oQuick and easy to prepare oData analysis is nonexistent oEasily understandable oCannot provide high accuracy Stable time series data F(t) = A(t-1) Seasonal variations F(t) = A(t-n) Data with trends F(t) = A(t-1) + (A(t-1) – A(t-2))4/8/2013 Eng. Magdy Abdelsattar 48
- 49. Naïve ForecastsNaïve / Ratio method Assumes: what happened in the immediate past will happen in immediate future Simple formula used: Actual sales of this year Sales forecast for next year Actual sales of this year Actual sales of last year Advantages: simple to calculate, low cost, less time, accuracy good for short-term forecasting Disadvantages: less accurate if past sales fluctuate 4/8/2013 Eng. Magdy Abdelsattar 49
- 50. Using Excel 1. Using Time Series4/8/2013 Eng. Magdy Abdelsattar 50
- 51. Second day Good Morning4/8/2013 Eng. Magdy Abdelsattar 51
- 52. Objective
- 53. Day one Recap oDefinitions oWHY DO WE FORECAST ? oScope of Forecasting oAdvantages & Disadvantages oForecasting Time Horizon oSources of Data oTypes of Forecasting4/8/2013 Eng. Magdy Abdelsattar 53
- 54. DefinitionsoIt is estimating the future demand for products & services & the resourcesnecessary to produce these outputs oroForecasts is the essence of management . Its techniques are used in everytypes of organization may be it government or private, production or service &social or religious Forecasts are critical inputs to business plans, & budgets .Finance – predict cash flows & capital requirements.Human Resource – To anticipate hiring & training needs.Operations – forecasts to plan output levels, purchase, outputschedules, inventory , capacity planning 4/8/2013 Eng. Magdy Abdelsattar 54
- 55. Why ?1) Short term fluctuations in Demand2) Better materials management – Organizations can benefit from better materials management, & ensure materials are available in time.3) Manpower Decisions – Hiring or layoff4) Basis for Planning & scheduling- planning & scheduling can be done effectively5) Strategic Decisions – Useful for Long range strategic decision making. This includes planning for product line decisions, new products etc. 4/8/2013 Eng. Magdy Abdelsattar 55
- 56. Advantages & Disadvantages Advantages: oHelps in Effective planning oHelps in better co-ordination oAchieves co-operation in Enterprises oEffective Control Disadvantages: oBased on assumptions oBased on past data oNot Full Proof oInadequate data4/8/2013 Eng. Magdy Abdelsattar 56
- 57. Sources of DataSales Force Estimate:One of the most valuable sources of data & quality of data that is available is the sales force that operates inthe field. Since sales force spans the entire geographic range of operation they have access to data pertaining toconsumption, changing patterns , market growthPoints of Sales ( POS ) Data Systems:sort of information technology . In supermarket if you buy Surf excel , at check counter when sales personswipes pack through POS system, the data is captured & transmitted to the relevant database for the companyto analyzeForecasts from supply Chain Partners:Obtaining POS data from distributors & suppliersTrade / Industry Association Journals:These journals provide research data on the sector in which the organization is operating ( Automobile sector ) 4/8/2013 Eng. Magdy Abdelsattar 57
- 58. Sources of DataB2B Portals / Market Places :Another source of data in the era of www is the existence of industry portals & B2Bmarket places. For agricultural www.industryindiaagronert.com , for small &medium sector enterprises www.sme.inEconomic Surveys & Indicators :Studied conducted by research organizations on macroeconomic trends are goodindicators of emerging trends in the consumption patterns of several classes ofgoods & services .e.g. Centre for monitoring Indian Economy (CMIE), ConsensusEconomicsSubjective Knowledge :Long-term Forecasts enable strategic decision making. Senior Managers, subjectexperts are vital source of data. 4/8/2013 Eng. Magdy Abdelsattar 58
- 59. Smoothing methods4/8/2013 Eng. Magdy Abdelsattar 59
- 60. Moving AverageMoving average• The moving average model uses the last t periods in order to predict demand in period t+1.• There can be two types of moving average models: simple moving average and weighted moving average• The moving average model assumption is that the most accurate prediction of future demand is a simple (linear) combination of past demand. 4/8/2013 Eng. Magdy Abdelsattar 60
- 61. Moving AverageMoving averagesProcedure:is to calculate the average company sales for previous yearsMoving averages name is due to dropping sales in the oldest period and replacing itby sales in the newest periodAdvantages:simple and easy to calculate, low cost, less time, good accuracy for short term andstable conditionsDisadvantages:can not predict downturn / upturn, not used for unstable market conditions andlong-term forecasts 4/8/2013 Eng. Magdy Abdelsattar 61
- 62. simple moving average In the simple moving average models the forecast value is At + At-1 + … + At-n Ft+1 = n t : is the current period. Ft+1 : is the forecast for next period n :is the forecasting horizon (how far back we look), A :is the actual sales figure from each period.4/8/2013 Eng. Magdy Abdelsattar 62
- 63. Example: Coca-Cola sells (among other stuff) bottled water Month Bottles Jan 1,325 What will the sales be Feb 1,353 for July? Mar 1,305 Apr 1,275 May 1,210 Jun 1,195 Jul ?4/8/2013 Eng. Magdy Abdelsattar 63
- 64. What if we use a 3-month simple moving average? AJun + AMay + AAprFJul = = 1,227 3 What if we use a 5-month simple moving average? AJun + AMay + AApr + AMar + AFebFJul = = 1,268 5 1400 1350 1300 5-month MA forecast 1250 1200 3-month 1150 MA forecast 1100 1050 1000 0 1 2 3 4 5 6 7 8 4/8/2013 Eng. Magdy Abdelsattar 64
- 65. Stability versus responsiveness in moving averages What do we observe? 950 900 850 800 Demand 750 700 3-Week 650 6-Week 600 550 500 1 2 3 4 5 6 7 8 9 10 11 12 5-month average smoothes data more; 3-month average more responsive4/8/2013 Eng. Magdy Abdelsattar 65
- 66. weighted moving average We may want to give more importance to some of the data… Ft+1 = wt At + wt-1 At-1 + … + wt-n At-n wt + wt-1 + … + wt-n = 1 t :is the current period. Ft+1 :is the forecast for next period n :is the forecasting horizon (how far back we look), A :is the actual sales figure from each period. w :is the importance (weight) we give to each periodWhy do we need the WMA models? Because of the ability to give more importance to what happened recently, without losing the impact of the past. 4/8/2013 Eng. Magdy Abdelsattar 66
- 67. Example: Coca-Cola sells (among other stuff) bottled water Month Bottles Jan 1,325 What will the sales be Feb 1,353 for July? Mar 1,305 Apr 1,275 May 1,210 Jun 1,195 Jul ?4/8/2013 Eng. Magdy Abdelsattar 67
- 68. 6-month simple moving average… AJun + AMay + AApr + AMar + AFeb + AJan FJul = = 1,277 6 In other words, because we used equal weights, a slight downward trend that actually exists is not observed…4/8/2013 Eng. Magdy Abdelsattar 68
- 69. What if we use a weighted moving average?Make the weights for the last three months more than the firstthree months… 6-month WMA WMA WMA SMA 40% / 60% 30% / 70% 20% / 80% July 1,277 1,267 1,257 1,247 ForecastThe higher the importance we give to recent data, the more wepick up the declining trend in our forecast.4/8/2013 Eng. Magdy Abdelsattar 69
- 70. How do we choose weights? 1. Depending on the importance that we feel past data has 2. Depending on known seasonality (weights of past data can also be zero). WMA is better than SMA because of the ability to vary the weights!4/8/2013 Eng. Magdy Abdelsattar 70
- 71. Exponential Smoothing (ES) Main idea: The prediction of the future depends mostly on the most recent observation, and on the error for the latest forecast. Smoothing Denotes the constant alpha importance of the (α) past error4/8/2013 Eng. Magdy Abdelsattar 71
- 72. Exponential Smoothing:Exponential smoothing is a sales forecasting technique that compares a previousforecast to actual results to get an error figure to use in current and future forecastsTrend: A trend is the upward or downward movement of the numbers in the baseline over time. Trends indicate some action is necessary, such as ensuring enough inventory is ordered and enough shipping staff is on hand for high sales months, or additional sales and marketing efforts are needed for lower sales months. Trends are important forecasting tools for planning and preparation.Excel:Excel is an accounting spreadsheet program that enables users to organize saleshistory data for forecasting.Excel has many features important to sales forecasting, such as pivot tables, averagingtools and graphing4/8/2013 Eng. Magdy Abdelsattar 72
- 73. Why use exponential smoothing? 1. Uses less storage space for data 2. Extremely accurate 3. Easy to understand 4. Little calculation complexity 5. There are simple accuracy testsExponential smoothing: the method Assume that we are currently in period t. We calculated the forecast for the last period (Ft-1) and we know the actual demand last period (At-1) … Ft Ft1 ( At1 Ft1 ) The smoothing constant α expresses how much our forecast will react to observed differences… If α is low: there is little reaction to differences. If α is high: there is a lot of reaction to differences. 4/8/2013 Eng. Magdy Abdelsattar 73
- 74. Where:Ft = forecast of the time series for the period tFt-1 = forecast of the time series for the period t-1At-1 = actual value of the time series for the period t-1 Ft Ft1 ( At1 Ft1 )α = the smoothing constant (0 ≤ α ≤ 1)Or:F = forecast of the time series for the period tFt = forecast of the time series for the period t-1At = actual value of the time series for the period t-1 F Ft ( At Ft )α = the smoothing constant (0 ≤ α ≤ 1) the forecast error in previous period 4/8/2013 Eng. Magdy Abdelsattar 74
- 75. Forecast AccuracyAn important consideration in selecting a forecasting method is the accuracy ofthe forecast.The mean square error (MSE) is an often-used measure of the accuracy of aforecasting method. Week Time Series value Forecast F-Error Square-FE (t) (Yt) (Ft) (Yt-Ft) (Yt-Ft)^ 1 17 2 21 17 4 16 3 19 17.8 1.2 1.44 4 23 18.04 4.96 24.6 5 18 19.03 -1.03 1.06 6 16 18.83 -2.83 8.01 7 20 18.26 1.74 3.03 8 18 18.61 0.61 0.37MSE= 54.51/8= 6.81……by using α = 0.2MSE=54.96/8= 6.87…….by using α = 0.3By trial-and-error we calculate (MSE), The less MSE the most probably forecast we have4/8/2013 Eng. Magdy Abdelsattar 75
- 76. Forecast Accuracy• Mean Absolute Deviation (MAD): measures the total error in a forecast without regard to sign, Simply the average of the absolute values of all forecast errors. actual forecast M AD n• Cumulative Forecast Error (CFE): Measures any bias in the forecast. CFE actual forecast• Mean Square Error (MSE): Penalizes larger errors. actual - forecast 2 MS E n• Tracking Signal: Measures if your model is working. CFE TS MAD• Spreadsheet packages are an effective aid is choosing a good value of α for exponential smoothing and selecting weights for the weighted moving averages method. 4/8/2013 Eng. Magdy Abdelsattar 76
- 77. Forecasting Software1. Spreadsheets oMicrosoft Excel, Quattro Pro, Lotus 1-2-3 oLimited statistical analysis of forecast data2. Statistical packages oSPSS, SAS, NCSS, Minitab oForecasting plus statistical and graphics3. Specialty forecasting packages oForecast Master, Forecast Pro, Autobox, SCA4/8/2013 Eng. Magdy Abdelsattar 77
- 78. Which Forecasting Method Should You Use ?oGather the historical data of what you want to forecastoDivide data into initiation set and evaluation setoUse the first set to develop the modelsoUse the second set to evaluateoCompare the MADs and MFEs of each model4/8/2013 Eng. Magdy Abdelsattar 78
- 79. Using Excel 1. Using Excel Function 2. Using charting forecasting 3. Using Control Chart 4. Using forecast accuracy4/8/2013 Eng. Magdy Abdelsattar 79
- 80. Casual Section4/8/2013 Eng. Magdy Abdelsattar 80
- 81. Casual SectionRegression analysis It is a statistical forecasting method Process: consists of identifying causal relationship between company sales (dependent variable, y) and independent variable (x), which influences sales If one independent variable is used, it is called linear (or simple) regression, using formula; y=a + b x, where ‘a’ is the intercept and ‘b’ is the slope of the trend line In practice, company sales are influenced by several independent variables, like price, population, promotional expenditure. The method used is multiple regression analysis Advantages: Objective, good accuracy, predicts upturn / downturn, short to medium time, low to medium cost Disadvantages: technically complex, large historical data needed, software packages essential 4/8/2013 Eng. Magdy Abdelsattar 81
- 82. Regression analysis (terminology)A statistical technique that can be sued to develop a mathematical equation showing how variables are related.• Dependant or response variable: the variable that is being predicted.• Independent or predictor variables: the variable or variables being used to predict the value of the dependant variable.• Simple liner regression: analysis involving one independent variable and one dependant variable for which the relationship between the variables is approximated by a straight lin.• Multiple regression analysis: analysis involving two or more independent variables.4/8/2013 Eng. Magdy Abdelsattar 82
- 83. Liner Regression• Identify dependent (y) and independent (x) variables• Slope of the line b XY n X Y X nX 2 2• The y intercept a Y bX• Develop your equation for the trend line Y=(a) + (b) X4/8/2013 Eng. Magdy Abdelsattar 83
- 84. Liner Regression Rest. Y X YX X^• The slop (b) = 60 1 58 2 116 4 2 105 6 630 36• The interception (a) = 5 3 88 8 704 64 4 118 8 944 64• The relation (Y=b+aX) = 5 117 12 1404 144 6 137 16 2192 256 Y = 60 + 5 (X) 7 157 20 3140 400 8 169 20 3330 400Each time we need to estimate 9 149 22 3278 484 the quarterly sales (Y) knowing 10 202 26 5252 676 the location population we use Total 1300 140 21040 2528 the above equation.4/8/2013 Eng. Magdy Abdelsattar 84
- 85. Correlation Coefficient• Correlation coefficient (r) measures the direction and strength of the linear relationship between two variables. The closer the r value is to 1.0 the better the regression line fits the data points. n XY X Y r X X Y Y 2 2 2 2 n * n• Coefficient of determination ( r 2 ) measures the amount of variation in the dependent variable about its mean that is explained by the regression line. Values of (r 2 ) close to 1.0 are desirable.4/8/2013 Eng. Magdy Abdelsattar 85
- 86. Using Excel4/8/2013 Eng. Magdy Abdelsattar 86
- 87. Trend and Seasonal4/8/2013 Eng. Magdy Abdelsattar 87
- 88. Using trend projection in forecasting• The type of time series for which the trend projection method is applicable shows a consistent increase or decrease over time. Y=(a) + (b) XY: trend value for sales(a) : the intercept of the trend line a Y bX(b) : the slope of the trend line b XY n X Y X nX 2 24/8/2013 Eng. Magdy Abdelsattar 88
- 89. Using trend and seasonal components in forecasting• How to forecast the values of a time series that has both trend and seasonal component.o First step is to compute seasonal indexes SI.o De-seasonalized the time series by using the SIo Using regression analysis on the DTS to estimate the trend.4/8/2013 Eng. Magdy Abdelsattar 89
- 90. Calculating the seasonal indexes (SI) year Q1 Q2 Q3 Q4 YA 2003 72 64 63 75 68.5 2004 75 66 64 89 73.5 • First calculate the yearly average. (YA) 2005 76 68 67 95 76.5 • Calculate the yearly proportions Yearly proportions • calculate the overall seasonal 2003 1.051 0.934 0.920 1.095 index for all quarters 2004 1.020 0.898 0.871 1.211 • The seasonal indexes will always 2005 0.993 0.888 0.876 1.242 be the ad up to the number of time period. SI 1.021 0.907 0.889 1.183 44/8/2013 Eng. Magdy Abdelsattar 90
- 91. De-seasonalized the time series year Q1 Q2 Q3 Q42003 71 71 71 63 • De-seasonalized is dividing the actual value2004 73 73 72 75 by the SI.2005 74 75 75 804/8/2013 Eng. Magdy Abdelsattar 91
- 92. time series Deseasonalized100 95 90 85 80 75 70 65 60 55 50 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q124/8/2013 Eng. Magdy Abdelsattar 92
- 93. Estimate the trend Q value Using regression analysis on the DTS X Y YX X^ • The slop (b) = 0.7797 1 71 71 1 2 71 142 4 b XY n X Y X nX 2 2 3 71 213 9 4 63 252 16 • The interception (a) = 67.681 5 73 365 25 a Y bX 6 73 438 36 7 72 504 49 • The relation (Y=b+aX) = 8 75 600 64 9 74 666 81 Y = 67.681 + 0.7797 (X) 10 75 750 100 11 75 825 121 12 80 960 144 The value of the time quarter 13 78 873 5786 650 Total (Q1_Y2006) = 77.81 6.50 72.75 482.17 54.17 Average4/8/2013 Eng. Magdy Abdelsattar 93
- 94. Seasonal adjustments• The final step in developing the forecast when both trend and seasonal components are present is to use the (SI) of the first Quarter to adjust the trend projected,The value of the time quarter 13 (Q1_Y2006) = 77.81 * 1.021 = 79.454/8/2013 Eng. Magdy Abdelsattar 94
- 95. Day tow Recap oScope of Forecasting oForecasting Time Horizon oTypes of Forecasting4/8/2013 Eng. Magdy Abdelsattar 95
- 96. Scope of forecasting Forecasting can be at international level depending upon the area of operation of particular institution or It can also be confined to a given product or service supplied by a small firm . It can be determined in three dimension : TIME PRODUCT GEOGRAPHY4/8/2013 Eng. Magdy Abdelsattar 96
- 97. Time Horizon Short term Forecasts – ( 1-3 Months ) - These forecasts are tactical decisions. How much inventory should be planned for next month , how much raw materials to be scheduled for next month Mid Term Forecasts ( 12-18 months ) - These are annual plans . How much product should we plan next year? How much capacities needs to be increased next year ? Long Term Forecasts ( 5 – 10 Years ) - These are purely strategic decisions. What new products to be planned , What new Technology . E.g maruti planning for mid segment car ( compete with nano)4/8/2013 Eng. Magdy Abdelsattar 97
- 98. Types of Forecasting 1) Qualitative –: These rely on experts opinion in making a prediction for the future . These are useful for intermediate to Long range forecasting: o Consumers Survey Methods o Sales Force Opinion Method o Delphi Technique o Scenario Writing4/8/2013 Eng. Magdy Abdelsattar 98
- 99. Types of Forecasting1) Quantitative –o Time Series - Simple average method, Moving averageo Exponential smoothingo Linear Regressiono Trend & Seasonal 4/8/2013 Eng. Magdy Abdelsattar 99
- 100. Thank you4/8/2013 Eng. Magdy Abdelsattar 100

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