1 TUTORIAL NOTES CHAPTER 6 FINANCIAL FORECASTING 6.1 INTRODUCTION Forecasting represents an integral part of any planning process that is undertaken by all firms. Firms must make decisions today that will affect the firm in the future. This is part and parcel of the overall objective of the firm (and that of the finance manager in particular) as discussed earlier which is to maximize shareholders wealth. If the firm does not plan for its future, then the management of the firm would end up firefighting most of the time, i.e. the management waits for problems to crop up, and only then will corrective actions be taken. In this age of hypercompetitive economic environment, this may prove too little too late. To be able plan for the future, forecasts must be made of what the future might look like. However, many authors have argued that it is not always easy to forecast and plan for the future, as there exists many uncertainties surrounding the future and many assumptions of what shall take place then, must be made today. After all, if one could predict the future with certainty, there would not be a need to plan as then, there ought to be only one possible outcome and the finance manager s work would be easily cut out for him or her to meet and ensure that the firm achieves that one and only outcome. If forecasting the future is difficult and fraught with uncertainties, the obvious question arises: Why bother to forecast? The answer is simple. The act of forecasting and hence, planning for the future, is a discipline in itself. It is very much hoped that the management of the firm, when undertaking such an activity, would take pains to consider the internal aspects of the firm, the external environment that impacts the firm and attempt to identify all possible outcomes. In short, it undertakes a SWOT (strengths, weaknesses, opportunities and threats) analysis. From there, appropriate actions can then be developed and considered for the benefit of the firm. Forecasting and planning is thinking in advance about the future, and be able to devise alternative actions that can be implemented should events of the future take unfortunate turns, or represent opportunities for the firm. In short, it represents the firm s ability to respond to events that occur in the future. Finance managers, as part of the firm's management, assist in the financial forecasting process in the firm. 6.2 DEFINITION AND PURPOSE Financial forecasting is defined as the process of estimating a firm s future financial needs and requirements. In the main, every decision to be made by a firm incurs costs. It is hence incumbent on the finance manager to ensure that at the end of the day, the firm has adequate finances (cash flows) to meet the financing requirements of the firm in the future.
2 For instance, if the firm seeks to expand aggressively in the near future, through the opening of more outlets in the Klang Valley, this would involve: Expectations of greater sales Identification of choice locations Planning for renovations to be conducted Hiring and training of staff Purchasing and ordering of raw materials Logistics planning and many more steps Planning for growth hence means that the finance manager must anticipate and prepare for the situation when the firm will require additional financing. Besides planning for situations when the firm requires cash, finance managers must anticipate when excess cash can possibly occur, plus an estimation of its quantum. Finance manager must decide what shall be done with the excess cash situation, and the appropriate investment vehicle to be decided on is also dependent on the time period of excess cash. 6.3 STEPS IN FORECASTING There are several basic steps that managers must undertake when undertake forecasts of the future. The following discussions relate to the scenario when one attempts to use scientific approaches towards forecasting, though that may not always be the case, as will be discussed in paragraph 6.4 below. The steps concerned are: Understanding why changes in the past have occurred: One of the basic principles of statistical forecasting is that the forecaster should use the data on past performance (though this may not always be the case - especially when it is noted that past data does not reflect the future environment and conditions). The current rate and changes in the rate constitute the basis of forecasting. Once they are known, various mathematical techniques can develop projections from them. If an attempt is made to forecast business fluctuations without understanding why past changes have taken place, the forecast will be purely mechanical based solely upon the application of mathematical formulae and subject to series error. Determining which phases of business activity must be measured: After it is known why business fluctuations have occurred, it is necessary to measure certain phase of business activity in order to predict what changes will probably follow the present level of activity. Selecting and compiling data to be used as measuring devices: There may exist an independent relationship between the selection of statistical data and determination of why business fluctuations occur. Statistical data cannot be collected and analysed in an intelligent manner unless there is sufficient understanding of business fluctuations. It is important that reasons for business fluctuations be stated in such a manner that is possible to secure data that are related to the reasons.
3 Analysing the data: Lastly, the data are analysed in the light of understanding of the reason why change occurs. For example, if it is reasoned that a certain combination of forces will result in a given change, the statistical part of the problem is to measure these forces, from the data available, to draw conclusions on the future course of action. The methods of drawing conclusions may be called forecasting techniques. 6.4 TYPES OF FORECASTS Typically, forecasting models fall under three categories: Time series models Casual models Qualitative models These are summarised in Figure 6.1.
4 6.4.1 Time Series Models Models under this category predict the future by using historical data. The typical assumption made when using these models is past represent (or is linked to) the future. In other words, what will happen in the future is dependent on what happened in the past. For example, if we seek to predict next week s sales of a firm s products, we look at what sales were achieved in the previous weeks. Examples of time series models are: Moving average This is used when we assume that market demands stay fairly steady over time and because it smooths out variations. For example, a four-month moving average is found by summing up the demand during the past four months and dividing it by four. With each passing month, the most recent month s data is added to the sum of the previous three months data, and the oldest month s data is dropped. This is argued to be able to smooth out short-term irregularities in the data series. The formula is: Exponential smoothing This is given by the formula: New forecast = last period s forecast + α (last period s actual value last period s forecast) The concept behind this method is that the latest estimate is equal to the previous estimate, but adjusted by a fraction of the error. The smoothing constant is given by α, which 'can be increased to give more weight to recent data or decreased to give more weight to past data. Trend projections Attempt to fit a trend line to a series of historical data points and then, projects the line into the future for medium to long range forecasts.
5 Decomposition This involves breaking down past data into components and then, projecting the data forward. Here, it typically comprises four components: Trend - gradual upward or downward movement of the data over time. Seasonality - pattern of demand fluctuation above or below the trend line that occurs every year. Cycles - patterns in the data that occur every several years, which are usually tied into the business cycle. Random variations - these are blips in the data caused by chance and unusual circumstances. They do not follow any discernible patterns Causal Models These models identify and incorporate variables or factors that might influence the quantity being forecasted, into the forecasting model. For example, the sales of icecream may be dependent on the temperature (hot or cold), the types of flavours available, the price, time of the day (day or night), season (rainy or dry) and so on. Hence, when using causal models, factors to be included in the model would be temperature, season, types of flavour, price, time, time of the day, season and so on. The basic models under this category are: Regression analysis - for single factor models. The basic model is: y = a + bx Multiple regression analysis - for multi-factor models. This is given by the equation: y = a + b1x1 + b2x2 + b3x3 +.
6 6.4.3 Qualitative Models The earlier two types of models, i.e. time-series and causal models, make use of quantitative data. Qualitative models, on the other hand, incorporate factors that are either judgemental or subjective into the forecasting model. In this case, subjective matters such as opinions, individuals past experience and judgements may be considered. Qualitative models are used when quantitative data may not be available or when subjective factors play important roles in the final outcome. Examples of qualitative models are: Delphi method - this allows experts to make forecasts. It involves three different types of participants: decision makers, staff personnel and respondents. Decision makers consist of experts who would be making the actual forecasts. The staff personnel prepare, distribute, collect and summarise a series of questionnaires and survey results. The respondents are the people whose judgements and opinions are valued and sought - their inputs are used by the decision makers to make decisions.. Jury of executive opinion - this method makes use of opinions of a small group of high level managers. Then, using statistical models, a forecast figure is obtained. Sales force composite - each salesperson is asked to provide estimates of what sales he or she is likely to achieve or what sales may be generated in a particular location or region under that particular sales person s responsibility. Adjustments may be made by higher level management to make the forecasts more realistic or acceptable. These forecasts are then compiled and grouped together, at branch, district, regional and finally national level. If it is for a multinational corporation, it might also be accumulated up to geographic regions and international levels.. Consumer market survey - inputs from customers or potential customers are solicited regarding their future purchasing plans. It is typically used when a firm seeks to launch a new product, i.e. market research as to a product s desirability and the potential purchase pattern of individuals. 6.5 SALES FORECASTS AND SALES BUDGET The following sections discuss the definition of and the reasons for preparing sales forecasts and budgets. Additionally, the different methods that may be used to undertake sales forecasting (for example, using moving averages, exponential smoothing and linear regression) will be covered Definition of Sales Forecasts The sales forecasts represent the firm s estimate of the quantity of the firm s products that the firm expects to sell in the future. The forecasting methods to arrive at the suitable figures in terms of quantity of the firm s products to be sold have been discussed in brief in section 6.3 above.
7 6.5.2 Lengths of Time for Sales Forecasting Although any forecast has a percentage of uncertainty, the farther into the future the firm projects, the greater will be the uncertainties. As a rule, there are three lengths of time for sales forecasting: Short-range forecasts are for fewer than three months. They are used to make continual decisions about planning, scheduling, inventory and staffing in production, procurement and logistics activities. Intermediate forecasts have a span of three months to two years. For most firms, it is usual to prepare up to one year s forecasts. They are used for budgetary planning, cost control, marketing new products, sales force compensation plans, facility planning, capacity planning and process selection and distribution planning. Long-range forecasts cover more than two years. They are used to decide whether to enter new markets, develop new products or services, expand or create new facilities, or arrange long-term procurement contracts. Once the sales forecast has been made, the sales budget is prepared, which shows the quantity of each product that the firm plans to sell and the intended selling price. Hence, it provides predictions of the total revenue (usually by month) from which cash receipts from customers may be estimated. It also provides the basic data for preparing budgets for production costs, and also for selling, distribution and administrative expenses. Hence, it is commonly argued that the sales budget represents the foundation of all other budgets, since all expenditure is ultimately dependent on the volume of sales. 6.6 COMPONENTS IN PRO FORMA FINANCIAL STATEMENTS Pro forma financial statements show the effects and impact of the firms decisions on its future financial statements. Firms make use of pro forma financial statements throughout the planning process to assess the effects of alternative decisions on various line items in the financial statements. This then allows financial managers to conduct what-if tests. For example, what would the effect on profit before tax for the coming year, if sales were to increase by 5%? What would be the effect on profit before tax if interest expenses were to increase by 5% as a result of using more debt to finance the firm s expansion plan? Besides assisting the management of the firm in the decision making process, pro forma financial statements also help the firm to make contingent plans to meet unexpected situations. Components of pro forma financial statements are Pro forma income statement projected revenues, expenses, net income, dividends to be paid and amounts retained for the year Pro forma statement of financial position projected assets, liabilities and equity Pro forma cash flow statement projected cash flows for operations, investing and financing activities
8 Besides the pro forma financial statements, firms also prepare many budgets that show in greater detail, the resources and responsibilities of each unit and division. The different types of budgets include the following: process Cash budget cash inflows, outflows and cash balances Sales budget planned sales in units and sales price amount Production budget scheduled production (quantities and costs) Stock budget planned levels of stocks to maintain Purchasing budget planned purchases of raw materials that the firm uses in the production Labour budget estimates of the labour hours required to meet the planned production schedule 6.7 Conflicting Roles of Budgets This section serves to merely highlight and caution readers that budgets prepared are usually used to satisfy several purposes. There is hence a probability that the budgets may conflict with each other. For example, the planning role of budgets may conflict with the motivational role of budgets. Demanding budgets that may not be achieved may be more appropriate to motivate maximum performance. However, such budgets are unsuitable for planning purposes. In the case of planning purposes (which has been the emphasis and concentration of this chapter), budgets should be based on easier targets that are expected to be met. Once again, the cautionary explanation serves to highlight that in businesses budgets may also be prepared at high standards or expectations. How this impacts on the motivational level and the actual performance of the firm and its employees becomes a separate issue. There may also be a conflict between planning and performance evaluation roles. For planning purposes, budgets are usually set in advance of the budget period, based on a set of assumptions and anticipated set of circumstance or environment. Such assumptions and anticipations may be based on the forecast figures that were derived based on the forecasting techniques that were earlier discussed. Performance evaluation should be based on a comparison between actual performance and adjusted budgets (i.e. budgets that have been adjusted to reflect changed circumstances and situations). The reality is that in practice, firms tend to compare actual performance figures with the original unadjusted figures. However, readers should note that if the circumstance and situations have changed so as to render the previously held assumptions and expectations out-dated or no longer applicable, then there will naturally be a conflict between the planning and evaluation activities.
9 6.8 PER CENT OF SALES FORECASTING METHOD Section 6.7 had covered the more detailed approach towards forecasting financial statements. However, it should be noted that in reality, this represents a complex and time consuming approach. Hence, managers tend to use a shorter approach - the percent of sales forecasting method. This method is somewhat crude but would represent a simpler method to estimate the funds required to finance growth. A firm would be successful in achieving sales growth, provided that it is willing to make additional investments in stock, trade debtors, and possibly fixed assets as well. Some short-term financing may come from the additional sales generated, it may entail additional purchases, hence the increase in creditors. Other forms of financing may also come from retained earnings. Whatever remaining necessary financing will have to be obtained from other sources, for example short-term borrowings. The per cent of sales forecasting formula is given as follows: Where: AFN = additional financing needed A/S = typical ratio of quantity of assets to sales achieved (This indicates the increase in assets required per Ringgit of increased sales) L/S = typical ratio of liabilities to sales achieved (This indicates the increase in liabilities per Ringgit of increased sales) S = current year sales g = forecast growth in sales P = net profit margin on sales D = cash dividends to be paid
10 6.9 STEPS THAT MAY BE TAKEN WHEN THERE IS A FORECAST SHORTFALL OF FUNDS Example 6.5 in section 6.8 illustrated a case of predicted or forecast shortfall in funds in the next year. Developing further from that example, the finance manager of Lucky sdn Bhd has several options in hand that the finance manager may consider: Obtaining new financing - the finance manager may approach the bank and apply for a bigger increase in bank overdraft facility than the current existing RM that the bank has approved to provide Reducing assets balance - this may be achieved through better collections from debtors and reduction in stock levels. This approach was discussed in Chapter 5 Working Capital Management. The finance manager would have to evaluate the impact of shortening the credit period offered to its customers. Additionally, better stock management systems should be considered. Increasing liabilities balance - in this case, Lucky Sdn Bhd might consider delaying its payment to its creditors. However, the efficacy of the approach has to be weighed against the adverse consequence that the firm might suffer, in the form of loss of goodwill for instance. Reducing the sales growth rate - perhaps the main contributing factor for the additional financing needed is none other than the forecast sales growth. The easiest decision that the firm may take is just to decide that it seeks to achieve less sales than originally planned for and this will automatically reduce the pressure for more financing. Reducing the amount of dividends to be paid - by reducing the amount of dividends that the firm may pay in the next year, the cash flow saved may be ploughed back into the firm and used to meet its operating requirements. The management of Lucky Sdn Bhd must weigh the advantages and disadvantages of each action before deciding on the final action to be taken. For example, if the firm currently has excess production capacity for its factory, then in the interest of maximizing the wealth of its shareholders, it should increase its production quantity and hence, the firm would then have more units of products to sell. Of course, it is expected that the management would have performed market analysis to identify if the market can absorb the increase in supply of finished goods in the said market LIMITATIONS OF THE PER CENT OF SALES FORECASTING METHOD The percent of sales method is merely a quick estimation approach of a firm s financing requirements. Readers should be aware of its limitations. Its main limitation is that it provides reasonable estimation only when asset requirements and financing sources can be assumed to be a constant percentage of sales. Whilst the forecast of future stocks was not discussed earlier using this approach, readers should be able to foresee that the use of percent of sales' method to forecast future stock level would be given by the formula:
11 The assumption surrounding such a method is a 'straight line or direct proportion relationship between stock levels and sales. This however ignores stock management principles that are discussed in Chapter 5: Working Capital Management, whereby firms might have to hold 'safety stock levels to provide for possible situations of stockouts should deliveries from suppliers become delayed. Also, firms may also seek to use just-in-time stock management whereby firms liaise closely with the suppliers. Suppliers then time their deliveries of stock (raw materials) to a firm just in time for use by a firm in the manufacturing process. This then reduces the quantity of stocks (raw materials) that the firm may have to hold. Instead, sufficient stocks will have to be held by the suppliers. This in turn reduces the quantity and hence, the cost of stocks in the firm. The limitation of per cent of sales forecasting method is further amplified when applied to estimation of required fixed assets. Once again, the formula to estimate desired level of fixed assets would be similarly given by: An example of fixed assets would be property, plant and equipment. In this case, finance managers will never be able to purchase portions of a property, plant and equipment. These are whole, discrete numbers. For machinery, it will be a case of having to consider its production capacity. By purchasing one additional unit of machinery, the firm may perhaps be able to increase its production by 60%. However, what happens if the forecast increase in sales is 20%. Can the firm decide to purchase 1/3 (i.e. 20% / 60%) of a machinery? This situation is known as a case of the existence of lumpy assets.
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