BUAD 802 Group assignment What do you understand by business forecasting

BUAD 802 Group assignment
What do you understand by business forecasting? Choose any organization of your choice and discuss a classic case of forecasting in that organization
Introduction
Business is the organized effort of individuals to produce and sell, for a profit, the goods and services that satisfy society’s needs.’ A business can also be seen as an organization which seeks to make a profit through individuals working toward common goals. Forecasting is about predicting the future as accurately as possible, given all the information available including historical data and knowledge of any future events that might impact the forecasts. Forecasting is an estimate of an event which will happen in future (Sonia, Asifur; Rayhan and Mosharraf, 2016). The event may be demand of a product, rainfall at a particular place, population of a country, or growth of a technology. The forecast value is not a deterministic quantity. Since, it is only an estimate based on the past data related to a particular event, proper care must be given in estimating it. Forecasting provides a basis for coordination of plans for activities in various part of a company. All the functional managers in any organization will base their decisions on the forecast value. So, it is vital information for the organization.
Business forecasting, therefore, is the art and science of predicting business events. Bello (2007) defined business forecasting as the process of gathering data on any business parameter, evaluating it and making a prediction on such parameters on the basis of the analysed data. Forecasting is a common statistical task in business, where it helps inform decisions about scheduling of production, transportation and personnel, and provides a guide to long-term strategic planning. However, business forecasting is often done poorly and is frequently confused with planning and goals.
Forecasting should be an integral part of the decision-making activities of management, as it can play an important role in many areas of a company (Reid & Sanders, 2011).. Modern organizations require short-medium- and long-term forecasts, depending on the speci?c application.

Short-term forecasts are needed for scheduling of personnel, production and transportation. As part of the scheduling process, forecasts of demand are often also required. Medium-term forecasts are needed to determine future resource requirements in order to purchase raw materials, hire personnel, or buy machinery and equipment. Long-term forecasts are used in strategic planning. Such decisions must take account of market opportunities, environmental factors and internal resources.

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Forecasting Models
The forecasting techniques can be classified into qualitative techniques and quantitative techniques.
Qualitative vs. quantitative methods
Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers and experts; they are appropriate when past data are not available. They are usually applied to intermediate- or long-range decisions. Examples of qualitative forecasting methods are informed opinion and judgment, the Delphi method, market research, and historical life-cycle analogy.

Quantitative forecasting models are used to forecast future data as a function of past data. They are appropriate to use when past numerical data is available and when it is reasonable to assume that some of the patterns in the data are expected to continue into the future. These methods are usually applied to short- or intermediate-range decisions. Quantitative techniques are based on historical data. These are more accurate and computers can be used to speed up the process. Examples of quantitative forecasting methods are: Single moving average, Single exponential smoothing, Double moving average, Double exponential smoothing, Simple regression, Semi average method, Multiple regression, Box Jenkins, last period demand, simple and weighted N-Period moving averages, simple exponential smoothing, poisson process model based forecasting  and multiplicative seasonal indexes. Previous research shows that different methods may lead to different level of forecasting accuracy. For example, GMDH neural network was found to have better forecasting performance than the classical forecasting algorithms such as Single Exponential Smooth, Double Exponential Smooth, ARIMA and back-propagation neural network  French (2017)
Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date.  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. In some cases the data used to predict the dependent variable is itself forecasted French (2017)
A CLASSIC CASE OF FORECASTING IN COCA COLA
Forecasting for the Coca Cola in soft drink industry is made using Volume (in gallons) and revenue (in naira). Consumption from a volume perspective is expected to increase as a result of an anticipated increase in consumer spending. A number of factors determine demand for soft drinks; price, income, consumers? lifestyles and tastes. Coca Cola was aware that it required having more flexibility in production and a better visibility to the real consumer and market demand. Consequently, it developed a strategy that consisted of the implementation of networks demand and fulfilment of networks. In addition, it included collaboration of networks, scheduling of production, and extension of demand planning. The plan offered the company an essential shift to how forecasting and scheduling of production in each of the company’s production plant. Under the plan, planning and demand came in the first stage. It was important for the company to know what the consumers needed. It also had to know how much to transport and to which locations. Collaboration was then executed.

From the production ; sales forecast data of the Coca Cola Company below, it is seen that, at the year 2009-2010 the amount of product manufactured in the company that is 4878379 bottles of Coca-cola were completely sold. Since in the next year the Company. produced 5560306 bottles of Coca-cola which was also sold in whole. That means the demand of Coca-cola was either equal or more than the production in that time. In the year 2011-2012 the production was increased to the amount of 6387338 bottles ; the sales was less than the production. In 2012-2013 the produced amount was completely sold again. Next year the company again made over production. It is seen that every time there is a deviation of production with actual sales. To minimize this deviation the company should use proper forecasting technique.

Table 1: Year Production(in bottles) Sales(in bottles) Forecast
1 2009-2010 4878379 4878379
2 2010-2011 5560306 5560306
3 2011-2012 6387338 5856712
4 2012-2013 5684914 5684914
5 2013-2014 7181598 6545886
Weighted Moving Average Method
In the simple moving average each observation is weighted equally. For example, in a three-period moving average each observation weighted one-third. In a five-period moving average each observation is weighted one-fifth. Sometimes a manager wants to use a moving average but gives higher or lower weights to some observations based on knowledge of the industry. This is called a weighted moving average. In a weighted moving average, each observation can be weighted differently provided that all the weights add up to 1.
Ft+1 = ?Ct At = C1A1 + C2 A2 +…. + Ct At
Where,
Ft+1 = next period’s forecast,
Ct = weight placed on the actual value in period t,
At = actual value in period
Assigning Weight to Different Data: In weighted moving average method, different weight has given to different data of different period. Since the data of the very last year is most important for forecasting for that reason most recent data has given more emphasize. Then demand forecasting in the terms of moving average period (n) of 2 years was calculated. So, while assigning weight, a weight valued 0.6 has given to the very last year’s data and 0.4 to the data of previous year of the last year.Here, 5560306 / (5560306+4878379) = 0.53266 ~ 0.6 because the weight assigned to this data must be greater than the weight assigned to 4878379. After calculation using weighted moving average method three forecasts were made. They are 5287535, 578149, 5753633 bottles at the year 2012, 2013, 2014 respectively and the errors were 569177, -53235, 792253with the actual sales.

Simple Exponential Smoothing Method
In the previous forecasting method, the major drawback is the need to continually carry a large amount of historical data. As each new piece of data is added in these methods, the oldest observation is dropped, and the new forecast is calculated. The reason this is called exponential smoothing is that each increment in the pasts decreased by (1-?).This method provides short term forecasts. The simplest formula is, New forecast = Old forecast + ? (Latest Observation – Old Forecast
Measure of Forecast Accuracy
There are many ways to measure forecast accuracy. Some of these measures are the mean absolute forecast error, called the MAD (Mean Absolute Deviation), the mean absolute percentage error (MAPE) and the mean square error (MSE). This error estimate helps in monitoring erratic demand observations. In addition, they also help to determine when the forecasting method is no longer tracking actual demand and it need to be reset. For this tracking signals are used to indicate any positive or negative bias in the forecast. The mean absolute deviation (MAD) is also important because of its simplicity and usefulness in obtaining tracking signals. MAD is the average error in the forecasts, using absolute values. It is valuable because MAD, like the standard deviation, measures the dispersion of some observed value from some expected value. The only difference is that like standard deviation, the errors are not squared. Standard error a square root of a function, it is often more convenient to use the function itself. This is called the mean square error (MSE) or variance. The mathematical formulas may be used while evaluating data are
Error = Actual Observed value – Forecasted value
Absolute Percentage Error = (Error / Actual Observed Value) × 100
Where,
MAD = the average of the absolute errors.
MAPE = the average of the Absolute Percentage Errors.
MSE = the average of the squared errors.
Importance of business forecasting
Forecasting allows businesses to plan ahead of their needs, raising their chances of keeping healthy through all markets. That’s one function of business forecasting that all investors can appreciate.
The correctness of management decisions to a great extent depends upon accurate forecasting.
The accurate forecasting of sales helps to procure necessary raw materials on the basis of which many business activities are undertaken. The accurate sales forecasting becomes the basis for several other budgets. In the absence of accurate sales forecasting, it is difficult to decide as to how much production should be done.

Drawback of Business Forecasting
Business forecasting is very useful for businesses, as it allows them to plan production, financing and so on. However, there are three problems with relying on forecasts:
1. The data is always going to be old. Historical data is all we have to go on and there is no guarantee that the conditions in the past will persist into the future.

2. It is impossible to factor in unique or unexpected events, or externalities. Assumptions are dangerous, such as the assumptions that banks were properly screening borrowers prior to the subprime meltdown, and black swan events have become more common as our dependence on forecasts has grown.

3. Forecasts can’t integrate their own impact. By having forecasts, accurate or inaccurate, the actions of businesses are influenced by a factor that can’t be included as a variable. This is a conceptual knot.
4. In a worst case scenario, management becomes a slave to historical data and trends rather than worrying about what the business is doing now.
Conclusion
Forecasting of Coca-Cola production can be done by using statistical methods, (Moving Average method, Simple Exponential Method and Least Square Method). Statistical methods are chosen because of their rich historic data and ease of their use. Simple Exponential Smoothing method is more accurate than the other methods. The forecasting technique may be different for different industries. It depends upon the variable factors like place, manpower skill, equipment capacity, raw material availability, inventory characteristics and management policies etc. Sales forecasting of beverage products can also be done using Artificial Neural Network (ANN), Genetic Algorithm, ARIMA etc.

REFERENCES
Bello, S. (2016). Lecture Notes on Research Methodology. Ahmadu Be\Bello University, Zaria.

French, J. (2017). “The time traveller’s CAPM”. Investment Analysts Journal. 46 (2): 81–96.

Li, Rita Y, Fong, S., Chong, W.S. (2017) Forecasting the REITs and Stock indices: Group Method of Data Handling Neural Network approach, Pacific Rim Property Research Journal, 23(2), 1-38
Reid, D. and Sanders, N. R (2011). ‘Operation Management’, Fourth Edition, pp.
264-313.
Mahmud, T.; Hasan, M,l; Chakraborty, A.; Roy-Chowdhury, A. (2016). A poisson Process Model for Activity Forecasting IEEE International Conference on Image Processing (ICIP). IEEE.

Munim, Z. and Schramm, H. (2017). “Forecasting container shipping rates for the Far East – Northern Europe trade lane”. Maritime Economics & Logistics. 19 (1): 106–125. doi:10.1057/s41278-016-0051-7.

Sonia A., Asifur R., Rayhan P. and Mosharraf H. (2016). Selection of a Forecasting Technique for Beverage Production: A Case Study. 6. No.3. September. Pp. 148 – 159