Sales Forecasting free essay sample

Usage can differ between areas of application: for example, in hydrology, the terms forecast and forecasting are sometimes reserved for estimates of values at certain specific future times, while the term prediction is used for more general estimates, such as the number of times floods will occur over a long period. Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible. Although quantitative analysis can be very precise, it is not always appropriate. Some experts in the field of forecasting have advised against the use of mean square error to compare forecasting methods. Forecasting involves the use of information at hand to make statements about the likely course of future events. In technical terms, conditional on what one knows, what can one say about the future? Forecasting techniques include uni-variant, multi-variant, and qualitative analysis. We will write a custom essay sample on Sales Forecasting or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Time series used to forecast future trends include exponential smoothing, ARIMA (Autoregressive Integrated Moving Average) and trend analysis. Multi-variant prediction methods include multi regression model, econometrics, and state space. Delphi marketing research, situational analysis, and historical analogue belong to qualitative methodologies. These forecasting methods forecast trends over different time horizons. There are significant differences in time length being considered when using these forecasting methods. Basically, uni variant methods in short-term forecasting usually generate higher accuracy than those of multi variants (Box et al. , 1994). LITERATURE REVIEW Forecasting techniques can be categorized in two broad categories: quantitative and qualitative. The techniques in the quantitative category include mathematical models such as moving average, straight-line projection, exponential smoothing, regression, trend-line analysis, simulation, life-cycle analysis, decomposition, Box-Jenkins, expert systems, and neural network. The techniques in the qualitative category include subjective or intuitive models such as jury or executive opinion, sales force composite, and customer expectations (Kress, 1985; Mentzer Kahn, 1995). Along with qualitative and quantitative, forecasting models can be categorized as time-series, causal, and judgmental. A time-series model uses past data as the basis for estimating future results. The models that fall into this category include decomposition, moving average, exponential smoothing, and Box-Jenkins. The premise of a causal model is that a particular outcome is directly influenced by some other predictable factor. These techniques include regression models. Judgmental techniques are often called subjective because they rely on intuition, opinions, and probability to derive the forecast. These techniques include expert opinion, Delphi, sales force composite, customer expectations (customer surveys), and simulation (Kress, 1985; Wilson Keating, 1994). Typically, the two forms of forecasting error measures used to judge forecasting performance are mean absolute deviation (MAD) and mean absolute percentage error (MAPE). For both MAD and MAPE, a lower absolute value is preferred to a higher absolute value. MAD is the difference between the actual sales and the forecast sales, absolute values are calculated over a period of time, and the mean is derived from these absolute differences. MAPE is used with large amounts of data, and forecasters may prefer to measure error in percentage (Wilson Keating, 1994). The forecasting process It is important to know when we should use qualitative or quantitative forecasting techniques. Managers apply quantitative forecasting techniques when environment is predictable and if they have data from past period about sales. These techniques are good when we want to predict existing products and technologies. They often used mathematics’ techniques for forecasting. Qualitative forecasting techniques are used in the not predictable environment and when we don’t have enough data. These techniques are usually used when managers forecast launching the new product line or new technologies. QUALITATIVE FORECASTING TECHNIQUES Qualitative forecasting techniques are sometimes referred to as judgmental of subjective techniques because they rely more upon opinion and less upon mathematics in their formulations. The absence of past sales means that you have to be more creative in coming up with prediction in the future. Sales forecast for new products are often based on executive judgments, sales force projection, surveys and user’s expectation. We summarized qualitative forecasting techniques which include: Jury of executive opinion consists of combining top executives’ views concerning future sales. This type of forecasting technique is term a ‘top down’ technique whereby a forecast is produced for the industry. Customer expectations use customer’s expectations of their needs and requirements as the basis for the forecast. The data are typically gathered by a survey of customers or by the sales force Sales force composite combines the individual forecasts of salespeople. This technique involves salesperson making a product-by-product forecast for their particular sales territory. Such a method is a bottom-up approach. Delphi method is a similar to jury of executive opinion technique. The main difference the members do not meet in committee. A project leader 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 second stage which asks questions about the individual company. The process go on to further stages where appropriate. The ultimate objective is to translate opinion into some form of forecast. Bayesian decision theory has been placed under techniques although it is really a mixture of subjective and objectives techniques. This technique is similar to critical path analysis in that it uses a network diagram and probability must be estimated for each event over the network. We already mention that qualitative techniques are often used when managers have little data to incorporate into forecast. New products are a classic example of limited information and qualitative techniques are frequently employed to predict sales revenues for these items. Qualitative techniques are recommended for those situations where managers or sales force are particularly adept at predicting sales revenues. These techniques are often utilized when markets have been disturbed by strikes, wars, natural disasters, recessions or inflation. Under these conditions historical data are useless and judgmental procedures that account for the factors causing market stocks are usually more accurate. Regression Analysis: statically relates sales to one or more explanatory (independent) variables. Explanatory variables may be marketing decisions (price changes, for instance), competitive information, economic data, or any other variable related to sales. †¢ Exponential smoothing makes an exponentially smoothed weighted average of past sales, trends, and seasonality to derive a forecast. †¢ Moving aver age takes an average of a specified number of past observations to make a forecast. As new observations become available, they are used in the forecast and the oldest observations are dropped. Box-Jenkins uses the auto correlative structure of sales data to develop an autoregressive moving average forecast from past sales and forecast errors. †¢ Trend line analysis fits a line to the sales data by minimizing the squared error between the line and actual past sales values. This line is then projected into the future as the forecast. †¢ Decomposition breaks the sales data into seasonal, cyclical, trend and noise components and projects each into the future. †¢ Straight-line projection is a visual extrapolation of the past data, which is projected into the future as the forecast. Life-cycle analysis bases the forecast upon whether the product is judged to be in the introduction, growth, maturity, or decline stage of the life cycle. †¢ Simulation uses the computer to model the forces, which affect sales: customers, marketing plans, competitors, flow of goods, etc. The simulation model is a mathematical replication of the actual corporation. †¢ Expert systems use the knowledge of one or more forecasting experts to develop decision rules to arrive at a forecast. †¢ Neural networks look for patterns in previous history of sales and explanatory data to uncover relationships. These relationships are used to produce the forecast. QUANTITATIVE TECHNIQUES Quantitative techniques are sometimes termed objective or mathematical techniques as they rely more upon mathematics as less upon judgment in their computation. These techniques are now very popular as a result of sophisticated computer packages. There are many quantitative techniques: Regression analysis statistically relates sales to one or more explanatory (independent) variables. Explanatory variables may be marketing decisions (price changes, for instance), competitive information, economic data on any other variable that can be related to sales. Exponential smoothing makes an exponentially smoothed weighted average of past sales, trend and seasonality to derive the forecast Moving average takes an average of a specified number of past observations to make a forecast. As new observations become available, they are used in the forecast and the oldest observations are dropped. Box-Jenkins uses the auto correlative structure of sales data to develop autoregressive moving average forecast from past sales and forecast errors Trend line Analysis fits a line to sales data by minimizing the squared error between the line and actual past sales values. The line is that projected into the future as the forecast. Decomposition breaks the sales data into seasonal, cyclical, trend and noise components and projects each into the forecast Straight-line projection is a visual extrapolation of the past data which is projected into the future as the forecast Life cycle analysis bases the forecast upon whether the product is judged to be in the introduction, growth, maturity or decline stage of its life cycle Simulation uses computer to model the forces which affect sales: customers, marketing plans, competitors, flow-of-goods, etc. The simulation model is mathematical replication of the actual corporation. Experts systems use the knowledge of one or more forecasting experts to develop decision rules to arrive at a forecast Neutral networks look for patterns in previous history of sales and explanatory data to uncover relationships. These relationships are then used to produce the forecast. †¢ Jury of executive opinion consists of combining top executives’ views concerning future sales. †¢ Sales force composite combines the individual forecasts of salespeople. †¢ Customer expectations (customer surveys) use customers expectations as the basis for the forecast. The data are typically gathered by a customer survey by the sales force. †¢ Delphi model is similar to jury of executive opinion in taking advantage of the wisdom of experts. However, it has the additional advantage of anonymity among participants. †¢ Naive model assumes that the next period will be identical to the present. The forecast is based on the most recent observation of data. Three planning horizons for forecasting exist. The short-term forecast usually covers a period of less than three months. The medium-term forecast usually covers a period of three months to two years. And, the long-term forecast usually covers a period of more than two years. Generally, the short-term forecast is used for the daily operation and plans of a company. The long-term forecast is used more for strategic planning (Kress, 1985; DeLurgio Bhame, 1991). Finally, the distinction between the forecasting method and forecasting system is important. A forecasting method is a mathematical or subjective technique that forecasts some future value or event. While many statistical forecasting software packages are implementations of forecasting methods, they are not forecasting systems. A forecasting system is a computer-based system that collects and processes demand data for thousands of items, develops forecasts using forecasting methods, has an interactive managementuser interface, maintains a database of demands, and has report file-writing capabilities. A forecasting system is much more complex than a forecasting method. The method is a part of the system (DeLurgio Bhame, 1991). Time Series Data Time Series Data is usually plotted on a graph to determine the various characteristics or components of the time series data. There are 4 Major Components: Trend, Cyclical, Seasonal, and Irregular Components Components of a Time Series The trend component accounts for the gradual shifting of the time series over a long period of time. Any regular pattern of sequences of values above and below the trend line is attributable to the cyclical component of the series. The seasonal component of the series accounts for regular patterns of variability within certain time periods, such as over a year. The irregular component of the series is caused by short-term, unanticipated and non-recurring factors that affect the values of the time series. One cannot attempt to predict its impact on the time series in advance. Moving Average Method The moving average method consists of computing an average of the most recent n data values for the series and using this average for forecasting the value of the time series for There are usu ¬ally five basic steps in any fore ¬cast ¬ing task. Step 1: Prob ¬lem definition. Often this is most dif ¬fi ¬cult part of fore ¬cast ¬ing. Defin ¬ing the prob ¬lem care ¬fully requires an under ¬stand ¬ing of how the fore ¬casts will be used, who requires the fore ¬casts, and how the fore ¬cast ¬ing func ¬tion fits within the orga ¬ni ¬za ¬tion requir ¬ing the fore ¬casts. A fore ¬caster needs to spend time talk ¬ing to every ¬one who will be involved in col ¬lect ¬ing data, main ¬tain-ing data ¬bases, and using the fore ¬casts for future planning. Step 2: Gath ¬er ¬ing information. There are always at least two kinds of infor ¬ma ¬tion required: (a) sta ¬tis ¬ti ¬cal data, and (b) the accu ¬mu ¬lated exper ¬tise of the peo ¬ple who col ¬lect the data and use the fore ¬casts. Often, a dif ¬fi ¬culty will be obtain ¬ing enough his ¬tor ¬i ¬cal data to be able to fit a good sta ¬tis ¬ti ¬cal model. How ¬ever, occa ¬sion ¬ally, very old data will not be so use ¬ful due to changes in the sys ¬tem being forecast. Step 3: Pre ¬lim ¬i ¬nary (exploratory) analysis. Always start by graph ¬ing the data. Are there con ¬sis ¬tent pat ¬terns? Is there a sig ¬nif ¬i ¬cant trend? Is sea ¬son ¬al ¬ity impor ¬tant? Is there evi ¬dence of the pres ¬ence of busi ¬ness cycles? Are there any out ¬liers in the data that need to be explained by those with expert knowl ¬edge? How strong are the rela ¬tion ¬ships among the vari ¬ables avail ¬able for analy ¬sis? Var ¬i ¬ous tools have been devel ¬oped to help with this analy ¬sis. Step 4: Choos ¬ing and fit ¬ting models. Which model to use depends on the avail ¬abil ¬ity of his ¬tor ¬i ¬cal data, the strength of rela ¬tion-ships between the fore ¬cast vari ¬able and any explana ¬tory vari ¬ables, and the way the fore-casts are to be used. It is com ¬mon to com ¬pare two or three poten ¬tial mod ¬els. Each model is itself an arti ¬fi ¬cial con ¬struct. It is based on a set of assump ¬tions (explicit and implicit) and usu ¬ally involves one or more para ¬me ¬ters which must be â€Å"fit ¬ted† using the known his-tor ¬i ¬cal data. A com ¬mon area of fore ¬cast ¬ing that deserves spe ¬cial atten ¬tion is demand fore-cast ¬ing for supply-chain man ¬age ¬ment. Step 5: Using and eval ¬u ¬at ¬ing a fore ¬cast ¬ing model. Once a model has been selected and its para ¬me ¬ters esti ¬mated, the model is used to make fore ¬casts. The per ¬for ¬mance of the model can only be prop ¬erly eval ¬u ¬ated after the data for the fore ¬cast period have become avail ¬able. A num ¬ber of meth ¬ods have been devel ¬oped to help in assess ¬ing the accu ¬racy of fore ¬casts. There are also orga ¬ni ¬za ¬tional issues in using and act ¬ing on the fore ¬casts. . CONCLUSION The forecasting process refers to a series of procedures used to forecast. It begins when an objective is determined. For example sales objectives can be (estimation of dollar sales, number of sales people to hire, etc. ). Next step is determination of dependent refer to what is being forecasting: sales or the number of sales people to hire next year) and independent variables. After this step we should determine forecast procedure and methods for analyzing data. Data are then gathered and analyzed often assumptions must be made about the forecast. The forecast is made, finalized, and, estimate passes, evaluated One of the keys to success in sales knows where are customers are located and being able to predict how much they will buy. Sales forecasting is so important that more then 50% of companies include this topic in their sales manager training programs. Inaccurate demand predictions can have disastrous effects of profitability. Managers should calculate and record the forecasting errors produced by the qualitative techniques they employ so that will know when these methods are best employed. Qualitative techniques are often used in conjunction with the quantitative techniques. Managers identified several sources to learn about sales forecasting techniques. The majority of them identified colleagues as an important source.