Quantitative Research, 5 pages (1100 words)

Quantitative techniques

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QUANTITATIVE TECHNIQUES Business forecasting is the process of using previous data collected from different sources through suitable techniques to analyze and determine strategies and tactics for taking future business decisions. Many managers exist even in today’s corporate world who prefer to manage their businesses by mere guts and go by certain assumptions, which may or may not turn out to aid in the performance of the business strategy adopted thereafter. However, the number of people who succeed in such endeavors are quite minimal and this has necessitated the incorporation of forecasting in business operations. The social and economic scenario is in the constant process of undergoing change and is influenced by a number of external factors. As such, there are numerous controllable and uncontrollable parameters that today’s decision makers need to take care of in order to prepare a careful course that can navigate through all idenfied obstacles. If adopted, qualititative, well-structured and a well-planned business forecasting effort can yield the desired results. As such, apart from feeling the need to forecast, it is also very important to plan the forecasting process with great precision (John E. Hanke, Arthur G. Reitsch, 1989).
The rise in the growth and use of computing power to perform calculations has gained immense popularity among users. As such, with a good forecasting plan, one can easily perform the required analysis in a short time. Forecasting is used to identify a number of patterns that are deemed important to a company’s interests. It could include attributes such as the performance of stocks over a time period, pattern of sales, prices of essential raw materials, employee satisfaction surveys etc. a company can immensely benefit from business forecasting as these enable it to analyze its performance across various domains and also helps it compare itself amongst other competitors in the market. It enables the company to identify the segments that are not performing according to expectations and paves the way for adopting suitable solutions to overcome them. As the economy is dynamic in nature, there is an ncreasing need to prepare the future map in advance, which emphasizes the need for business forecasting.
In the recent years, business forecasting has adopted an extensive scientific flavor that includes the fusion of various business theories and techniques to forecast specific types of data. It can involve procedures as simple as spreadsheets to massive database networks that are performing trillions of calculations per second. A poor forecasting analysis is either not planned or sometimes not well executed. Shortcomings in either of these stages may result in poor result quality, results not being available when required or poor communication of results to the deision makers. This can result in miscalculations and misinterpretations for future strategies, which may prove severe in some cases. On the contrary, a good forcasting plan will not only deliver good results, but will also help in effective planning and strategizing. It can transform into an increase in sales, rise in customer satisfaction, increased turnover or increased market capitalization etc (David Francis Jordan, 1921).
Based on these discussions, three probable future scenarios for a company are detailed below:
1) The company performs poorly during the next 6 months. The company retrieved past sales data and customer satisfaction surveys that are conducted quarterly. Based on these figures, the company set out to identify the sales and income patterns for a 15 year period. However, the company lacks proper information for a few months as some of the data was not stored properly resulting in data loss. As such, forecasting analysts had to make use of assumed figures that were believed to resemble closely to the actual data values. However, due to lack of proper data, the company was unable to understand clearly what the market presented to it in the coming few months and found it hard to take a proper stand on where it stood, resulting in poor decisions being made.
2) The company’s performance was categorized as average. The company had a sales goal to reach a certain figure in terms of sales. It was targeting a certain section of the public and wanted to draw their attention towards the new line of products. However, with no past data on such products (in-house data as the product line was a first for the company), the company relied on data sourced from other players in the market and studied their product promotions and performance. However, the sales figures 6 months after the launch of the product turned out to be average due to a number of reasons. Despite following a sound advertising and promotion strategy that was similar to the ones used by compatitors, the market had consumers who had developed loyalty to certain brands and the company in question did not do much in terms of innovating over existing products, which thereby did not prove effective in attractive customers. As such, sales did not progress as expected.
3) The company was launched fuel efficient cars in a time of economic turmoil which turned out to be a huge success. The company had been in business over the past few decades in the automobile industry and was a reputed brand when it came to cars. It had gained a huge following in the markets. However, as the economy started to show signs of strain, the sales started to plummet forcing the company to take serious steps to reverse the situation. Upon analyzing past problems that the company hasd been challenged with and by interviewing past customers, the company understood that it had started losing porential customers as people were now focused on spending less on travelling and were looking at having more value for every penny that they spent. The current cars produced by the company were certainly lacking against competitors in mileage issues. the company immediately identified this and invested heavily in bringing out new fuel efficient cars. It also eased its credit lines so that users with lesser but stable income could come ahead to purchase a vehicle. These twin measures worked wonders for the company.
As can be seen from the three scenarios, it can be easily understood that the company under scenario 3 made very good use of the resources it had at hand in forecasting the future needs and demands of the consumer and made sincere and quick efforts to effort those demands, thereby enabling it to not only stay in business, but also to weather the difficulties faced by the economy and maintain its sales
1. John E. Hanke, Arthur G. Reitsch (1989), Busines Forecasting, California: Allyn & Bacon.
2. David Francis Jordan (1921), Business Forecasting. New York: Prentice Hall.

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