DESIGN AND IMPLEMENTATION OF COMUPTERIZED BUSINESS FORECASTING SYSTEM USING REGRESSION ANALYSIS
As the strategic planner bogs is mind with trying to predict the future performance of some available variable qualitatively, at the mercy of human limitations and time constraint.
The COMPUTERIZED FORECASTING TECHINIQUES comes not only to increase his speed of operation but to save him the trouble of determining which method to use and if so applied assures of reliability of such forecast.
The features of this package include
1. Ability to choose from the various quantitative forecasting techniques.
2. Ability to select the method by menus.
3. finally a help path, which includes documentation on the software loading the Pascal computer.
TABLE OF CONTENT
TABLE OF CONTENTS
1.1 OVERVIEW OF FORECASTING TECHNIQUES
1.2 STATEMENT OF PROBLEMS
1.3 PURPOSE OF THE STUDY
1.4 AIMS AND OBJECTIVES
1.5 SCOPE OF STUDY
1.7 DEFINITION OF TERMS
REVIEW OF RELATED LITERATURE
2.0 FORECASTING TECHNIQUES
2.1 QUANTITATIVE TECHNIQUES
2.1.1 N-PERIOD MOVING AVERAGE
2.1.2 EXPORENTIAL SMOOTHING METHOD
2.1.3 LEAST SQUARE REGRESSION METHOD
2.2 QUANTITATIVE FORCASTING TECHNIQUES.
2.2.1 JURY OF EXECUTIVE OPINION
2.2.2 THE DELPHI TECHNIQUES
2.2.3 SALES FORCE COMPOSITE.
DESCRIPTION AND ANALYSIS OF THE EXISTING SYSTEM.
3.1 FACTS FINDING AND METHOD USED.
3.2 ORGANIZATIONAL STRUCTURE
3.3 OBJECTIVE OF THE EXISTING SYSTEM
3.4 INPUT, PROCESS, AND OUTPUT ANALYSIS
3.5 INPUT ANALYSIS
3.6 OUTPUT ANALYSIS
3.7 INFORMATION FLOW DIAGRAM.
3.8 PROBLEMS OF THE EXISTING SYSTEM.
3.9 JUSTIFICATION OF THE NEW SYSTEM.
DESIGN OF THE NEW SYSTEM.
4.1 OUTPUT SPECIFICATION AND DESIGN
4.2 INPUT SPECIFICATIONS AND DESIGN
4.3 FILE DESIGN
4.4 PROCEDURE CHART
4.5 SYSTEM FLOWCHART
4.6 SYSTEM REQUIREMENT.
4.6.1 HARDWARE REQUIREMENT.
4.6.2 SOFTWARE REQUIREMENT
5.1 PROGRAM DESIGN
5.2 PROGRAM FLOWCHART
5.4 SOURCE PROGRAM
5.5 TEST RUN
5.6 SYSTEM CHANGEOVER
6.1 SYSTEM DOCUMENTATION
6.2 PROGRAM DOCUMENTATION
6.3 USER DOCUMENTATION
SUMMARY, CONCLUSION AND RECOMMENDATION.
1.1 OVERVIEW OF FORCASTING TECHNIQUES.
Right from the time life started, till now, man ahs sought to forecast the future. Things that happened before are used to justify what will take place in the future. Most of the times, it becomes true while at other time it facts. The ability to forecast the consequence of actions and events is one of the defining properties of the mind.
In any business situation, it is necessary to be able to make some predictions about the future in order to make some predictions about the future in order to plan the business operations well-Arriving at such an climate of the future is the purpose of the process of forecasting.
In the service sector, a forecast of demand for the service being offered is necessary to determine the number of staff that will be sufficient enough for the business and the quantity of raw materials to be bought.
In the retailing sectors, forecast of sales will be needed to decide staffing levels and also to determine what qualifies of stock should be purchased. Excessive levels of stock tie up working capital and storage space, further expenses can be incurred through such process as theft, insurance and possibly, deterioration. On the other head, inadequate re-order quantities such as high, ordering cost and the inability to meet customers demand.
Forecasting is simply the scientific name for guessing, what the will bring. There are several standard techniques available for forecasting and each techniques has it’s own assumption, benefits and pitfall.
This project is concerned with the evaluation of the different forecasting techniques used by Anammco Ltd Emene, Enugu to determine how to manage their business operation.
Consider the following problems.
Problems 1: A sales manager collects together records of the past sales for particular product how does he find a forecasting techniques that gives good forecasts for this data?
Problems 2: A member of his staff suggests that a particular method of forecasting. How does the sales manager test this method of forecasting to see if it can yield any solution?
The ability to find answers to questions such as these is obliviously basic to the whole exercise of practical forecasting. It is necessary for me to know which forecasting techniques is best for any particular problems.
1.2 STATEMENT OF PROBLEMS.
The investigation done revealed that workers manually do the record keeping for forecasting.
The above manual system is outdated in terms of speed of processing and accuracy. This results into wastage of time and in efficiency in productions.
These inefficiencies call for the computerization of the operations of the forecast.
1.3 Purpose of study.
The purpose of this research is to present the different forecasting techniques used by Anammco ltd in manufacturing their cars. It is however to make analysis based in definite statistical data, which will enable an executive to take advantage of future condition to a greater extent than he could do without them.
1.4 Aims and objectives.
The objective include the following.
A. To help the managers and all those interacted in obtaining forecast for practical purposes to be able to make decision on which forecasting techniques to use at different situation.
B. To demonstrate the different methods which Anammco uses in their business forecast.
C. To point out that all forecast used by Anammco have errors and that the measurement of the errors is critical.
D. To help the managers and forecasters to know the internal values of forecalls and the degree of confidence they need to have in the forecast.
1.5 SCOPE OF STUDY.
This project uses the simple linear repression analysis that concerns itself with just one explanatory variable and a linear form of relationship. The explanatory variable could be time of periods.
The other forecasting methods are the exponential smoothing and the moving average methods, which are used, should a time series observation exhibits a trend. This exhibition of trend can be verified with a scatter diagram of the observation against observed points.
Forecast can be made from such models built using methods above after analysis by the software such forecast most likely will be in the short to medium term range.
The methods though appear complex is simplified as will be seen in the user friendly user interfaces. The complex or the multi-variable regression analysis was avoided in this project as well as the logarithm exponential smoothing for the purpose of the project.
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