Financial-Planning (Financial Management) Lecture notes PDF

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Financial PlanningLearning Objectives:At the end of the chapter, the learners are expected to: Explain the basic concepts on financial forecasting. Determine the different users of financial forecasting.. Differentiate the different approaches of forecasting. Use the different techniques in their fo...

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Financial Planning Learning Objectives:

1. 2. 3. 4.

At the end of the chapter, the learners are expected to: Explain the basic concepts on financial forecasting. Determine the different users of financial forecasting.. Differentiate the different approaches of forecasting. Use the different techniques in their forecasting.

Financial Forecasting Everyone doing business dreams to be somebody in the future such as the lead distributor of product X for example. However, we cannot just attain the dream without doing something. One has to exert efforts and should be guided with its VGMO and be forward looking. . One of the greatest challenges facing owners and managers is how to improve profitability and generate growth. A crucial business process for meeting such challenge is financial forecasting. Financial forecasting is an essential part of business planning that uses past financial performance and current conditions or trends to predict future company performance. In short, financial forecasts are tools by which businesses can set and meet goals. It is the starting point of business planning, making it as one of the most important functions to be applied in business. Forecasting is the projection of future sales, revenues, earnings, costs and other possible variables that are helpful in the firm’s operation. It is the basis for budgeting activities and estimating future financing needs. Financial forecasts begin with forecasting sales and their related expenses. Users of Forecast Forecast can be used by individuals within and outside the company for various reasons or purposes. Some of the are as follows; 1. Top Management Forecast is used as a tool for long-range planning. It serves as basis for making targets and implementing long range strategic decisions and making capital budgeting decisions. 2. Production Manager Makes use of forecast to determine the amount of raw materials that will be needed in the production, the budget, schedule of production activities, inventory levels to maintain to avoid disruption in the production process, labor hours, and the schedule of shipments.

3. Purchasing Manager Makes use of the forecast to ascertain the volume of materials that should be purchased for a certain period. 4. Marketing Manager The forecast is used to estimate how much sales should be made for a particular period and to plan promotional and advertising activities for the products. 5. Finance Manager He makes use of the forecast to anticipate the funding requirements of the firm. He must establish the firm’s cash inflows and outflow, and indicate the exact moment when the firm will be needing additional funding. 6. Human resource Manager He utilizes the forecast to supply the human resources needed in achieving the firm’s objectives. 7. Colleges and Universities It utilizes the forecast to identify possible enrollees in a school year. The figures on hand can help determine the revenues to be obtained from the tuition fees, the faculty to be hired, planning of room assignments, and building of facilities. Approaches in Forecasting In general, there are two approaches in forecasting namely (1) qualitative and quantitative. (Shim et. al, 2006) Qualitative Forecasts These types of forecasting methods are based on judgments, opinions, intuition, emotions, or personal experiences and are subjective in nature. They do not rely on any rigorous mathematical computations. In practice, the combination of both qualitative and quantitative methods is usually the most effective. Methods of Qualitative Forecasting 1. Expert opinion The views of the managers or a group with a high level of expertise, often in combination with statistical models, are synthesized to generate a consensual forecast. The forecasting method is simple and easy to implement. The opinion of the experts become the basis of forecasting, thus no statistical tools being employed. 2. Delphi Method

This is similar to the expert opinion, as it is also done by a group of experts. However, under this method, members are asked individually through a questionnaire about their forecast of future events. The participants in this method are the decision-makers, staff assistants, and respondents where the decision-makers usually consist of experts who make the actual forecast. Staff assistants aid decision-makers by preparing, distributing and collecting the questionnaire, and analyzing and summarizing the survey results. The respondents are people from different places who provide inputs to the decision-makers before the forecast is made. 3. Sales Force Polling The sales force is used by companies to arrive at their sales forecast. The sales people having direct contact with the consumers, envision the condition of the future market. Under this approach, every sales person estimates the sale in his region or territory. The forecasts are then reviewed to ensure that the data are realistic. Then they are combined at the district or national levels to arrive at a general forecasts. 4. Consumer Market Survey Firms conduct their own consumer or potential consumer surveys to accumulate information regarding future purchasing plans. Surveys may be conducted through telephones, inquiries, questionnaires and interviews. Surveys can help not only in preparing a forecast but also in improving product design, planning for new products, and determining consumer behavior. In summary, a table is presented.

ll. Quantitative Forecasts

These types of forecasting methods are based on mathematical (quantitative) models, and are objective in nature. They rely heavily on mathematical computations.

Illustrations: 1. Naïve Method Compute for the demand forecast for year 6. Year 1 2 3 4 5 6 (answer 500)

Actual demand 350 380 400 425 500

2. Simple Moving Average Method

Forecast 350 380 400 425 ?

Note No data to use Uses last period’s actual Value as forecast

Simple moving average method: The forecast for next period (period t+1) will be equal to the average of a specified number of the most recent observations, with each observation receiving the same emphasis (weight). In this illustration we assume that a 2-year simple moving average is being used. We will also assume that, in the absence of data at startup, we made a guess for the year 1 forecast (300). Then, after year 1 elapsed, we made a forecast for year 2 using a naïve method (310). Beyond that point we had sufficient data to let our 2-year simple moving average forecasts unfold throughout the years. Year 1 2

Actual demand 310 365

Forecast 300 310

3

395

337.50

Note/solutions Guess forecast at the beginning Forecast for year 2 – Naïve Method was used From this point forward, these forecasts were made on a yearby-year basis using a 2-yr moving average approach (310 + 365 = 675/2)

4 5 6 7

415 450 465

380 405 432.50 457.50

395 + 365 = 760/2 415 +395 = 810/2 450 + 415 = 865/2 465 + 450 = 915/2

3. Weighted Moving Average Method Weighted moving average method: The forecast for next period (period t+1) will be equal to a weighted average of a specified number of the most recent observations. In this illustration we assume that a 3-year weighted moving average is being used. We will also assume that, in the absence of data at startup, we made a guess for the year 1 forecast (300). Then, after year 1 elapsed, we used a naïve method to make a forecast for year 2 (310) and year 3 (365). Beyond that point we had sufficient data to let our 3-year weighted moving average forecasts unfold throughout the years. The weights that were to be used are as follows: Most recent year, .5; year prior to that, .3; year prior to that, .2. Year 1 2

Actual demand 310 365

3

395

Forecast 300 310 365

Note/solutions Guess forecast at the beginning Forecast for year 2 – Naïve Method was used This forecast was made using a naïve

approach. 4

415

369

5 6 7

450 465

399 428.50 450.50

From this point forward, these forecasts were made on a year-by-year basis using a 3-yr weighted. moving average approach . (395 x.5 + 365 x.3 + 310 x.2) (415 x .50 + 395 x .3 + 365 x .2) (450 x .5 + 415 x .3 + 395 x .2) (465 x .5 + 450 x .3 + 415 x .2)

4. Trend Projections Trend projection method: This method is a version of the linear regression technique. It attempts to draw a straight line through the historical data points in a fashion that comes as close to the points as possible. (Technically, the approach attempts to reduce the vertical deviations of the points from the trend line, and does this by minimizing the squared values of the deviations of the points from the line). Ultimately, the statistical formulas compute a slope for the trend line (b) and the point where the line crosses the y-axis (a). This results in the straight line equation Y = a + bX Where X represents the values on the horizontal axis (time), and Y represents the values on the vertical axis (demand). For the demonstration data, computations for b and a reveal the following (NOTE: I will not require you to make the statistical calculations for b and a; these would be given to you. However, you do need to know what to do with these values when given to you.) b = 30 a = 295 Y = 295 + 30X This equation can be used to forecast for any year into the future. For example: Year 7: Forecast = 295 + 30(7) = 505 Year 8: Forecast = 295 + 30(8) = 535 Year 9: Forecast = 295 + 30(9) = 565 Year 10: Forecast = 295 + 30(10) = 595...