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Business Forecast This Paper examines and compares various forecasting techniques used for qualitative and quantitative business forecasting and their use in Firstlogic Inc., to forecast the demand under conditions of uncertainty. Time series and Delphi forecasting methods are considered for this research to evaluate their ability to make effective decisions regarding the future. Business Forecasting Business forecasting is the process of studying historical performance for the purpose of using the knowledge gained to project future business conditions so that decisions can be made today that will aid in the achievement of established goals. Forecasting plays a crucial role in today's uncertain global marketplace. Forecasting is traditionally either qualitative or quantitative, with each offering specific advantages and disadvantages. Qualitative and Quantitative Forecasting Techniques Forecasting can be classified into qualitative and quantitative. Qualitative techniques are subjective or judgmental and are based on estimates and opinions. The Delphi technique, a common form of qualitative forecasting, allows experts to create an effective forecast under conditions of extreme uncertainty. Time?s series forecasting, a quantitative technique, uses a statistical analysis of past sales in order to effectively predict future outcomes, but can be limited under conditions of uncertainty (Chase, 2003, p.364). Business forecasting can be used in a wide variety of contexts, and by a wide variety of businesses. For example, effective forecasting can determine sales based on attendance at a trade show, or the customer demand for products and services (Business and Economic Forecasting, p.1). One of the most important assumptions of business forecasters is that the past acts as an important guide for the future. It is important to note that forecasters must consider a number of new information, including rapidly changing economic conditions and globalization, when creating business forecasts based on past sales. Globalization and economic slowdown has made businesses subject to a great deal of uncertainty. In this time of rapid change, economies worldwide change rapidly, new markets open up and old ones change, and demand for products is often uncertain. As such, businesses must be flexible and adaptable in the types of methods that they use... ... middle of paper ... ...forecasts. Given the high degree of uncertainty in today's marketplace, qualitative forecasting techniques like the Delphi technique may help Firstlogic to better-forecast future sales. Conclusion In conclusion, business forecasting methods must be used in order to fit current conditions of uncertainty. Delphi technique and time series forecasting both are valuable forecasting tools when used in the right circumstance. The Delphi technique is useful for short-term forecasts; therefore, it is often a more valuable tool for business forecasting during conditions of uncertainty. References University of Phoenix(Ed.).(2003) Operations management for competitive advantage[University of Phoenix custom edition e-text]. New York: McGraw-Hill. Retrieved February 01, 2005, from university of phoenix, Resource, MGT554- operations management website: https://mycampus.phoenix.edu/secure/resource/resource.asp Business and Economic Forecasting. Retrieved February 24, 2005, from http://www.sbeusers.csuhayward.edu/~acassuto/econ3551/summary/chapter6.htm Namvar, Bob . (2000). Economic Forecasting. Retrieved February 24, 2005, from http://gbr.pepperdine.edu/001/forecast.html
Addressing the trials of operating in a continually changing environment and realizing forecasts can only
Decisions can be made using below mentioned approaches (Various Types Of Decision Making Models, 2009).
They store all of their parts in it factory store. The sales team takes the approach of forecasting sales by using the last two to three months of sales data and also compares that to the same months over the past couple of years. This method of predicting sales has been problematic from the start. Forecasting sales on limited and outdated data never produces accurate results.
One way these businesses seek to do this, is the collection of quantitative data and various sampling methods as a way to help find intuitive ways to solve the problems they have, or to make what they better. There are several ways to collect meaningful data, using both descriptive and inferential statistical methods. For example, in a business setting descriptive statistics can show an increasing trend in profits, however if you look at the inferential statistics of customer satisfaction surveys we may see a decline. This would lead you to say, profits are going to decrease unless a solution is made
One of the forecasting techniques typically used by organizations is the historical analogy. Chase et al. (2005), define that historical analogy "ties what is being forecast to a similar item" (p. 514). This technique is used when the company is planning to launch a new product to market. Since there is no data available for the new product, the organizations try to compensate the uncertainty by using data from product with similar characteristics. Similarly, the market research technique also uses data collection to forecast demand. The data collection is primarily done through direct surveys and interviews. Companies use this technique to be able to come up with better products than the existing ones. The uncertainty of what customers want or dislike is reduce by collecting data directly from them. It is common for organizations to hire external companies to conduct this investigation and to provide the forecast. Since the external organizations are solely dedicated to the forecasting business; they usually provide adequate and accurate information.
He uses trend exploration to determine his forecast. Since he is the sales person of his business he does his own forecasting. Trend exploration involves extending a pattern observed in past data into the future. He simply looks at the past sales of a few months or a year to try to come up with a projection for the next few months or year. This is a simple way of forecasting because trend exploration assumes that sales will remain the same, however, this may not always be the case.
Yankee Fork and Hoe Company have an informal method of forecasting. First, marketing determines the forecast for the next upcoming month by meeting with managers from the sales regions. In these meeting they go over the previous demand, economic changes, and shortages they experienced. Through these meetings the marketing manager develops a forecast for the upcoming year, and then passes it onto production.
Forecasting is crucial in managing of any business; it enables the business to make decisions about purchases, production, raw material, costs and the price of a product and shipping. Forecasting enables a business to be proactive and plan for future demand instead of waiting for demand to emerge and then reacting to it, as this can cause a delay in customer orders. Demand forecast ensures faster order cycle times.
It is undetermined in being able to use the forecast method due to the fact that there is not data to compare it to. Furthermore, in this case, there cannot be any ramification to the inventory that can be identified.
Schonberger, R.J. and E.M. Knod Jr. Operations Management: Continuous Improvement. Richard D. Irwin, 1994, p. 44. 16. Selto, F.H. and D.W. Jasinski. "
Davenport expresses the need for a widespread use of modeling to take the basic statistical data to a new level. By generating predictive models firms become better able to determine the places in the business and the customers that will help to drive the greatest potential profitability while at the same time determining those customers who are most likely to discontinue their relationship with the firm. These tests can theoretically analyze the possible outcomes of business factors on profitability and provide viable options for things such as price point, shipping costs and even product R&D (Davenport,2006).
In business today it is notable that change is no longer a variable but an ever-changing constant. As it relates to “managing change” for companies business models, then the majority of them are substantiating this theory of change. If change is managed properly, then it can be a rewarding experience for company’s employees, managers, shareholders and customers. “In order to do this effectively, there is an element of foresight required, which is a complex and conflicting process of analyzing, experiencing, interpreting, and absorbing uncertainties” (Brown and Eisenhardt, 1997).
Time series analysis would be an advantage to monitor the progress achieved, in order to determine whether the set target is currently being realized or can be achieved in the foreseeable future. By using the historical data over time, we will be able to foresee and estimate the future if the most suitable method is used. The objectives of time series analysis are to identify and describe the underlying structure and the phenomenon as depicted by the sequence observations in the series and to determine the best mathematical model to fit the data series and use the model to generate forecast value.
There are several methods that can be used to perform forecasting, but it is also important to select the suitable one based on the company’s background, system, process and other criteria. One of the criteria to select the suitable forecasting method is to understand the system’s sophistication level and the
Before doing capacity planning we should forecast the future demand of the good or service. Determining future capacity is based on future demand for the product. It is a very complex decision but it can be done by different tools like primary data or previous sale. When demand for goods and services is done then we can proceed to capacity planning,