Business Forecasting: What You Need to Know

Business forecasting is a powerful tool that uses historical data to predict future occurrences. The ability to determine the future events with accuracy gives a firm an opportunity to plan their activities in advance. Companies predict their core activities like revenue and sales volume or use national data on gross domestic products, consumer price index, economic growth and inflation to plan their future activities. Different techniques are used to forecast and they fall into qualitative and quantitative models. Qualitative approaches to forecasting are useful for short-term guesses where there is a limited scope. The techniques are expert driven and rely on external information for consensus. They are appropriate for predicting products and services and the success of a business. The main qualitative models are market research, focus groups, expert opinion, historical analogy, panel consensus and Delphi methods. Quantitative models use data for prediction purposes and therefore eliminate the human element in the analysis of the data. They are used for long-term prediction. Some of the techniques include trend analysis, graphic method, seasonal adjustment, econometric modelling, and lifecycle modelling, decomposition and indicator approach. 

Forecasting and decision making 

Business forecasting is an important task that helps to inform decisions on transportation, scheduling, and personnel. It helps in developing a roadmap for strategic planning. Forecasting is the process of predicting the future using available data and knowledge of future events. Forecasting should be part and parcel of management decision making. Short, medium and long-term forecasts are essential ingredients of decision making. Business needs to develop forecasting systems that use different approaches for predicting certain variables depending on the need of the company. Expertise is required in identifying the business problem and refining the different methods that can be used for prediction purposes. Strong support from the company is also required for the successful use of formal forecasting techniques (Hyndman & Athanasopoulos, 2013). Forecasting has the ability to generate accurate predictions that can guide businesses on issues like new product development, market growth, investment, and sales target and personnel requirements. It assists managers in making appropriate decision that can affect the strategic, functional and operational level of an organization. 

What to Forecast 

The forecaster must decide on what they need to predict. In a fast moving consumer goods company, it could be difficult to develop a complete guess of everything. It would, therefore, be useful to establish whether the forecasts are required for the entire product line of the company, all sales outlets or regional outlets, the required data for example weekly, monthly or annually. The forecasting horizon must also be considered where monthly, quarterly, annually or five to ten years time can be used. The frequency of the forecast also determines the system to be used. A manager who needs periodic forecasts for example weekly or monthly should consider an automated system. Careful manual work can be used for long-term forecasting. Forecasting must involve the users of the information to be able to understand what they require and how they can use the forecast. Data collection for the forecast will then follow once it is known what type of forecast is required. Some of the forecasting data is readily available and the forecaster only needs to identify the appropriate data that meet their needs (Hyndman & Athanasopoulos, 2013). The selected data must be adequate and useful for forecasting. It must meet the needs of the prediction and present conclusive results that can be used to make informed decisions. The forecaster must, therefore, understand the different approaches that they can manipulate the collected data to make an informed guess of future events. 

Forecasting and the Methods 

The type of data determines the forecasting method used. Forecasters can use qualitative techniques if there is no data or where the available data is irrelevant for addressing the problem. Such methods have well developed structured approaches for prediction yet it does not depend on the historical data. The technique is therefore not a complete guesswork. Quantitative techniques are used when the forecaster can access historical numerical data and some of the past events will continue into the forecasting period. Different techniques of quantitative forecasting methods have been developed in rious disciplines for unique purposes. The methods have unique characteristics, properties, accuracies and costs and therefore they form part of the decision on the most appropriate technique. Most of the techniques use time series or cross-sectional data (Agarwal, Singh & Agarwal, 2006; Hyndman & Athanasopoulos, 2013). Forecasters must understand the entire process including the type of forecasting techniques that they should use. Similarly, they should evaluate the available data to determine whether it meets their requirements and the needs of the identified model. 

Why Businesses Should Forecast 

There are many reasons as to why enterprises predict. They are interested in understanding the future using current and historical data so that they can develop appropriate responses based on the outcome of the prediction. According to Wheelwright (2008), there are time lags when the management becomes aware of the impending event and when the event occurs. The lead time is the cause for forecasting. Short lead times lead to short-term predictions whereas long lead time leads to long-term forecasting. Forecasting is helpful to planning and will help an organization to establish when an event will occur and the appropriate actions that should be taken. Planning is useful for the management and helps companies to make short term and long term decisions. Forecasting is therefore useful for decision making and planning purposes. There has been substantial progress in business forecasting which has led to the accurate prediction of some of the phenomenon. Science and information technology has supported the development of prediction models and therefore the predictability of different events. Forecasting is an integral part of decision making in organizations. The goals and objectives of the company can easily be met if environmental factors can easily be predicted and the appropriate actions selected. The management desires to reduce their dependence on chance as they try to move to scientific techniques that can be helpful in dealing with the environmental changes (Evans, 2010). The interconnectedness of the different areas of an enterprise makes it appropriate to develop good forecasts as any prediction will affect the entire organization. Forecasting plays an integral part in scheduling, resource acquisition and determining resource requirements. 

Forecasting 

This report forecast the events using Trend analysis and graphical method. Trend analysis uses historical data to make a prediction of the expected events. It is appropriate where there is in definite upward or downward pattern the technique can use regression, exponential smoothing and triple smoothing. This report used exponential smoothing to forecast GDP growth. Graphical method is the second forecasting technique used in this report. The data is plotted in a graphical form by converting the data in the excel file to graph to display the information in a visual manner. The trend is then determined. The data is extrapolated to predict future events. The relationship between GDP and personal consumption of the U.S are closely related and influence each other and can easily be used to predict future trends 

The above data was obtained from the U.S Bureau of Economic analysis and shows the GDP and personal consumption as a percentage change from the previous quarter in real Gross Domestic Product. Exponential smoothing was established by selecting data analysis in the excel file then exponential smoothing. The input and output range was then selected by selecting the GDP and then personal consumption as the input range and the desired cells for the output then clicking OK. The graphical method involved the selection of the GDP and personal consumption and then selecting insert and then line. The trend line of each data series is inserted by right clicking on the individual series and selecting trend line which shows the movement of personal consumption in the different quarters from 2016 to 2019. The prediction covered four quarters three in 2018 and one in 2019 as the data showed information up quarter one of 2018. The percentage change in the personal consumption as the GDP continues to increase. The trend lines show that GDP will continue to increase in the next one year while personal expenditure as a percentage of the gross domestic product will continue declining in the four predicted quarters. The prediction, however, will be affected by seasonality and sudden increase or decrease in consumer spending which makes it is difficult to accurately determine the actual changes in the quarters. 

The exponential smoothing shows that personal expenditure keeps on changing relative to the changes in the gross domestic product. The changes are seasonal but it is possible to pred.ict future movement based on the available data. The GDP can, therefore, be used to predict personal expenditures in the future based on the historical data for the three years. Exponential smoothing and graphical approach is ideal for situations where the predicted data has a close relationship with the GDP and therefore a change in the predictor contributes to a similar change in the predicted data. 

It is possible to predict future business operation using qualitative and quantitative techniques. Such predictions allow companies to make an informed decision on the best course of action and to plan their operations according to the predicted information. Business forecasting is a powerful tool for the management and their work can be enhanced if they capitalize on the strength of forecasting. It allows the use of historical data and expertise to determine future trends eliminating the use of guesswork. 

References 

Agarwal, N., Singh, B., & Agarwal, S. (2006).  Forecasting techniques . Jaipur, India: RBSA Publishers. 

Evans, M. (2010).  Practical Business Forecasting . Hoboken: John Wiley & Sons, Ltd. 

Hyndman, R., & Athanasopoulos, G. (2013).  Forecasting: principles and practice . Heathmont: OTexts. 

U.S. Bureau of Economic Analysis (BEA). (2018). Retrieved from https://www.bea.gov/iTable/iTable.cfm?reqid=19&step=2#reqid=19&step=3&isuri=1&1921=survey&1903=1 

Wheelwright, S. (2008).  Forecasting methods and application . New York, N.Y.: Free Press/Simons & Schuster. 

Appendix 

Raw data table