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Sales Forecasting

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Sales Forecasting

  1. 1. SALES FORECASTING<br />Aslı KARABULUT<br />264012017-2D<br />
  2. 2. AGENDA<br />Sales Forecasting<br />Levels of Forecasting<br />Sales Forecasting Procedure<br />Importance of Sales Forecasting<br />Sales Forecasting Process<br />Forecasting Errors<br />Sales Forecasting Techniques<br />Budgeting<br />Types of Budgeting<br />Budget Determination<br />The Sales Budget<br />Budget Allocation<br />References<br />
  3. 3. SALES FORECASTING<br />Forecasting is the art of estimating future demand by anticipating what buyers are likely to do under a given set of future conditions.<br />A sales forecast is a projection into the future of expected demand given a stated set of environmental conditions.<br />
  4. 4. SALES FORECASTING<br />Sales Manager<br />accountants<br />
  7. 7. SALES FORECASTING PROCEDURE<br />Companies commonly use a three-stage procedure to arrive at a sales forecast.<br />Environmental<br />Forecast<br />Industry<br />Forecast<br />Sales Forecast<br />
  9. 9. SALES FORECASTING PROCESS<br />Steps of Sales Forecasting Process<br />
  10. 10. FORECASTING ERRORS<br />Possible reasons for errors in forecasting<br />Flaws in data used in forecasting process,<br />Insufficient data,<br />Unpredictable economic and socio-political environment,<br />Non-realistic and accurate assumptions,<br />Technical and technological changes,<br />Shifts in economic structure,<br />Administrative errors.<br />
  12. 12. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Consumer / User Survey Method<br />Panels of Executive Opinion<br />Salesforce Composite<br />Delphi Method<br />Bayesian Decision Theory<br />Product Testing and Test Marketing<br />
  13. 13. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Consumer / User Survey Model (Market Research Method)<br />Involves asking customers about their likely purchases for the forecast period.<br />Best possible use: A small numbers of users who are prepared to state their intentions with a reasonable degree of accuracy.<br />Limitations: Organisational Buying.<br />Problems: to ascertain what proportion of likely purchases will accrue to your company. Customers and salespeople tend to be optimistic when making predictions for the future.<br />
  14. 14. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Consumer / User Survey Model (Market Research Method)<br />Several research organizations conduct periodic surveys of <br />consumer buying intentions. The table above represents a purchase <br />probability scale.<br />
  15. 15. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Panels of Executive Opinion (Jury Method)<br />Specialists or experts are consulted who have knowledge of the industry being examined.<br />Best possible use:Developing a general, rather than specific product-by-product forecast.<br />Problems: Difficulty in allocating the forecast among individual products and sales territories because the statistics have not been collected from basic market data. Such allocation will probably be arbitrary.<br />
  16. 16. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Salesforce Composite<br />Each salesperson makes a product-by-product forecast for their particular sales territory. Thus individual forecasts are built up to produce a company forecast.<br />Problems: When the forecast is used for remuneration there might be a tendency for salespeople to produce a pessimistic forecast. When remuneration is not linked to the sales forecast there is a temptation to produce an optimistic forecast.<br />
  17. 17. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Salesforce Composite<br />Sales Quota<br />(Q)<br />Sales Effort & Skill (X)<br />Expected Sales<br />(SE)<br />Motivation<br />(M)<br />SE = f2(X)<br />X = fi(Q,M)<br />
  18. 18. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Delphi Method<br />A project administers a questionnaire to each member of the team which asks questions, usually of a behavioural nature. The questioning then proceeds to a more detailed or pointed second stage which asks questions about the individual company.<br />Objective: To translate opinion into some form of forecast.<br />Best possible use: Providing general data about industry trends and as a technological forecasting tool. Providing information about new products or processes that the company intends to develop for ultimate manufacture and sale.<br />
  19. 19. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Delphi Method<br />Forecasters<br />Forecast<br />Coordinator<br />11<br />11<br />12<br />Forecast<br />Coordinator<br />Forecast<br />Summary<br />(I=)<br />13<br />12<br />...<br />Forecast<br />Summary<br />(I=)<br />13<br />...<br />...<br />...<br />Im<br />Im<br />
  20. 20. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Bayesian Decision Theory<br />To use the Bayesian approach, the decision maker must be able to assign a probability of each specified event or state of nature. The sum of these probabilities must add to one. These probabilities represent the strength of the decision maker’s feelings regarding the likelihood of the occurence of the various elements of the overall problem.<br />
  21. 21. SALES FORECASTING TECHNIQUES<br />Qualitative Techniques<br />Product Testing and Test Marketing<br />Involves placing the pre-production model(s) with a sample of potential users beforehand and noting their reactions to the product over a period of time asking them to fill in a diary noting product deficiencies, how it worked, general reactions etc. <br />Best possible use: New or modified products for which no previous sales figures exist and where it is difficult to estimate likely demand.<br />
  22. 22. SALES FORECASTING TECHNIQUES<br />Quantative Techniques<br />Time Series Analysis<br />Causal Techniques <br />
  23. 23. SALES FORECASTING TECHNIQUES<br />Quantitative Techniques<br />Time Series Analysis<br />They are relatively simple to apply, but the danger is that too much emphasis might be placed upon past events to predict the future. Time Series Analysis include:<br />Moving Averages<br />Exponential Smoothing<br />Time Series<br />Z (or Zee) Charts<br />
  24. 24. SALES FORECASTING TECHNIQUES<br />Quantitative Techniques<br />Time Series Analysis<br />Moving Averages: This method averages out and smooths data in a time series. The longer the time series, the greater will be the smoothing. The principle is that one substracts the earliest sales figure and adds the latest sales figure.<br />
  25. 25. SALES FORECASTING TECHNIQUES<br />Quantitative Techniques<br />Time Series Analysis<br />Exponential Smoothing: It apportions varying weightings to different parts of the data from which the forecast is to be calculated. In this technique the forecaster apportions appropriate degrees of “typicality” to different parts of the time series.<br />
  26. 26. SALES FORECASTING TECHNIQUES<br />Quantitative Techniques<br />Time Series Analysis<br />Time Series: It is useful when seasonality occurs in a data pattern. It is of particular use for fashion products and for products that respond to seasonal changes throughout the year.<br />
  27. 27. SALES FORECASTING TECHNIQUES<br />Quantitative Techniques<br />Time Series Analysis<br />Z (or Zee) Charts: In addition to providing the moving annual total, it also shows the monthly sales and cumulative sales; an illustration of the technique shows why it is termed Z chart. Each Z chart represents one year’s data and is best applied using monthly sales data. As a vehicle for forecasting it provides a useful medium where sales for one year can be compared with previous years using three criteria.<br />
  28. 28. SALES FORECASTING TECHNIQUES<br />Quantitative Techniques<br />Causal Techniques<br />It is assumed that there is a relationship between the measurable independent variable and the forecasted dependent variable. The forecast is produced by putting the value of the independent variable into the calculation. Causal techniques include:<br />Leading Indicators<br />Simulation<br />Diffusion Models<br />
  29. 29. SALES FORECASTING TECHNIQUES<br />Quantitative Techniques<br />Causal Techniques<br />Leading Indicators: This method seeks to define and establish a linear regression relationship between some measurable phenomenon and whatever is to be forecasted.<br />
  30. 30. SALES FORECASTING TECHNIQUES<br />Quantitative Techniques<br />Causal Techniques<br />Simulation: Simulation uses a process of iteration, or trial and error, to arrive at the forecasting relationship.<br />
  31. 31. SALES FORECASTING TECHNIQUES<br />Quantitative Techniques<br />Causal Techniques<br />Diffusion Models: Diffusion theory assumes that the new product has four basic units:<br />The innovation: 1) Continuous 2) Dynamically continuous 3) Discontinuous<br />The communication of the innovation among individuals: 1) Formal communication 2) Informal Communication<br />The social system<br />Time<br />Once the innovation has been launched, a measure of the rate <br />of adoption is needed in order to produce a useful forecast. A <br />forecast can be made using only a small amount of data <br />covering the early launch period.<br />
  32. 32. BUDGETING<br />An organization needs to budget to ensure that expenditure does not exceed planned income.<br />The sales forecast is the starting point for business planning activities. Taking the medium-term sales forecast as the starting point the budgets are allocated to departments.<br />Budgets are a means of control.<br />
  33. 33. TYPES OF BUDGETING<br />
  34. 34. BUDGET DETERMINATION<br />Departmental manager determines how the overall departmental budget will be utilised in achieving the planned-for sales.<br />The overall sales forecast is the basis for company plans, and the sales department budget is the basis for marketing plans in achieving those forecasted sales. <br />The sales department budget is consequently a reflection of marketing’s forthcoming expenditure in achieving those forecasted sales.<br />
  36. 36. SALES BUDGET<br />The Sales Budget;<br />Is the total revenue expected from all products that are sold.<br />Comes directly after the sales forecast.<br />Is the starting point of the company budgeting procedure because all other company activities are dependent upon sales and total revenue anticipated from the various products that the company sells.<br />
  37. 37. SALES BUDGET<br />Sales Forecast<br />Sales Budget<br />Sales Department Budget<br />Production Budget<br />Administrative Budget<br />Cash Budget<br />Profit Budget<br />Revenues<br />Expenditures<br />Expenditures<br />Revenues<br />
  38. 38. BUDGET ALLOCATION<br />The sales budget is statement of projected sales by individual salespeople. <br />The amount that must be sold in order to achieve the forecasted sales is the sales quota.<br />
  39. 39. BUDGET ALLOCATION<br />The most common practice of budget allocation is simply to increase / decrease last year’s individual budgets or quotas by an appropriate percentage, depending on the change in the overall sales budget.<br />By assessing sales potential for territories and allowing for workload, the overall sales budget can be allocated in as fair a manner as possible between salespeople.<br />
  40. 40. REFERENCES<br />BOOKS<br />Jobber, D. and Lancaster, G., Selling and Sales Management, Chapter 16, 8th Edition, Prentice Hall, Essex, 2009.<br />Kotler, P. And Armstrong, G., Principles of Marketing, Appendix 2, 11th Edition, Pearson Education, New Jersey, 2006.<br />THESIS<br />Arslan, S., “Satış Gelirlerinin Bütçelenmesinde Uygulanacak Teknikler ve Sürdürülebilir Uygulama Modeli”, T.C. Marmara Üniversitesi – Muhasebe Finansman Bilim Dalı Yüksek Lisans Tezi, İstanbul, 2007.<br />Ekmekçi, A.S., “Endüstriyel Pazarlarda Satış Tahmin Yöntemlerinin Kullanılabilirliği ve Hazır Beton Sektöründe Bir Uygulama”, T.C. Marmara Üniversitesi – Üretim Yönetimi ve Pazarlama Bilim Dalı Yüksek Lisans Tezi, İstanbul, 2006.<br />ARTICLES<br />Bardhan, A.K. and Chanda, U., “A Model for First and Substitution Adoption of Successive Generations of a Product”, International Journal of Modelling and Simulation, Vol.28 Issue 4: p487-494.<br />Das, P. and Chaudhury, S., “Prediction of retail sales of footwear using feedforward and recurrent neural networks”,Neural Comput & Applic, Vol.16 : p491-502.<br />Kumar, M. And Patel, N.R., “Using Clustering to Improve Sales Forecasts in Retail Merchandising”, Ann Oper Res, Vol.174: p.33-46.<br />Mentzer, J.T., “A Telling Fortune: Supply Chain Demand Management is where forecasting meets lean methods”, Industrial Engineer, Vol.38 Issue 4: p42-47.<br />Siriram,R. and Snaddon, D.R., “Forecasting New Product Sales”, South African Journal of Industrial Engineering, Vol.21 Issue 1: p123-135.<br />Zhou, S. and, “The Evolution of Family Level Sales Forecast into Product Level Forecasts: Modelling and Estimation”, Johnson Graduate School of Management, 2006.<br />