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DEDICATION
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ACKNOWLEDGEMENT
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TABLE OF CONTENT
Certification
Dedication
Acknowledgement
Abstract
Table of content
CHAPTER ONE: GENERAL INTRODUCTION
- Introduction
- Aims and objectives
- Significant of the study
- Research methodology
- Scope of the study
- Organization of the report
- Definition of terms
2.1 Review of past works
2.2 Overview of sales forecasting
2.3 Direct extrapolation of sales
2.4 Casual approaches to sales methodology
2.5 Choosing the right forecasting methodology
CHAPTER THREE
3.1 Method of data collection
3.2 Analysis of existing system
3.3 Problem of existing system
3.4 Proposed of system specification
3.5 Advantages of the proposed system
3.6 Design and implementation methodologies
CHAPTER FOUR: DESIGN, IMPLEMENTATION AND DOCUMENTATION OF THE SYSTEM
4.1 Design of the system
4.1.1 Output design
4.1.2 Input design
4.1.3 File design
4.1.4 Procedure design
4.2 Implementation of the systems
4.2.1 Hardware support
4.2.2 Software support
4.3 Documentation of the system
4.3.1 Program documentation
4.3.2. Operating the system
4.3.3 Maintaining the system
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 Summary
5.2 Experienced gained
5.3 Conclusion
5.4 Recommendation
References
Appendix
- System flowchart
- Program Flowchart
- Source listing
- Computer output
1.0 INTRODUCTION
1.1 GENERAL INTRODUCTION
Forecasting is the process of making predictions of the future based on past and present data and analysis of trends. Sales forecasting is the ability to forecast future sales base on past months sales. Sales forecasting system uses several proven models such as qualitative versus quantitative model, average approach model, casual /econometrics forecasting model, linear regression model etc., these model are proven models use over the years by different financial institutes.
Chapter one introduces the research work and the problem definition of the research, and to know the aim of this research work, research justification tells us why it is important to research on this topic; it also covers areas like scope and limitation of study which entails the boundary of this work. Definition of terms gives summarize what each chapter entails.
1.2 PROBLEM DEFINITION
This research work was undertaken to uncover some of the problems with conventional sales forecasting management systems. Where accountant has to calculate using a specific sales model formula in forecasting on a regular basis. Using these conventional method pose lots of constraint on sales forecasting as it takes lots of time forecasting using which ever model is adopted by the sales manager or accountant or financial adviser.
1.3 SIGNIFICANT OF THE STUDY
- Produce intelligent sales forecasts, more quickly, more effectively and with one of the tedious manual processes associated with using spreadsheets for the forecasting of demand.
- Rapid Implementation: The application can be implemented very quickly. Data can be sourced from any ODBC/OLEDB data source or from flat files.
- Forecast revenue and profit as well as quantities: The computer based sales forecasters allow forecasts to be made not just for volume, but also for selling prices, cost of goods etc.
- A versatile software tools: Empower your forecasters to productively forecast, plan and re-plan sales, prepare budgets, monitor, monitor, review and report, all within a single, easy to use system.
Sales forecasting is the prediction of future sales performance based on previous sales history, upcoming events, statistical analysis or anything else that may influence sales. The project is aimed at developing a computer based application that plan purchasing and inventory system of a company. Sales forecasting is an important tool used by many business to fulfill several objectives.
- To develop software that serves as the basis of marketing or sales planning.
- To design a system that helps in financial planning and reporting or budgeting.
1.5 SCOPE OF THE STUDY
What is required is a system that support judgment forecasting, which takes the drudge out of retrieving and analyzing data, and which allows sales marketing managers, finance managers and general manager to work cooperatively on their components of the forecasting, planning and review process, within a common, shared but secure systems environment.
1.6 OVERVIEW OF SALES FORECASTING
Forecasting involves methods that device primarily from judgmental sources versus those from statistical source, judgment and statistical procedure are often used together, and since 1985, much research has examined the integration of statistical and judgmental (Armstrong, 2011). Going down the figure, there is an increasing amount of integrated between judgment and statistical procedures. A brief description of the methods is provided here. Makreidakis and Wheelwright (2018) provide details on how to apply many of these methods.
A person’s role may be a dominant factor in some situations, such as predicting how someone would behave in a job related situation. Role-playing is useful for making forecasts of the behavior of individuals whoa re interacting with others, and especially in situations involving conflict.
Another way to make forecasts is to ask experts to predict how others will believe in given situations. The accuracy of expert forecasts can be improved through the use of structured methods, such as the Delphi procedure. Delphi is an iterate survey procedure in which experts provide forecast for a problem, receive anonymous feedback on the forecast made by other experts, and then make another forecast, for a survey of the evidence on the accuracy of Delphi versus unstructured judgment, Rowe and Wright (2011). One principle is that experts” forecasts should generally be independent of one another. Focus groups always this principle. As a result, they should not be used in forecasting.
Intentions can be explained by relating the “predictions” to various factors that describe the situation. By asking consumers to state their intentions to purchase for a variety of different situation, it is possible to infer how the factors relate to intended sales. This is often done by regressing their intentions against the factors, a procedure known as “conjoint analysis”. As with conjoint analysis, one can develop a model of the expert. This approach, judgmental bootstrapping, converts subjective judgments into objective procedures. Experts are asked to make a series of predictions. For example, they could make forecasts for the next year’s sales in geographical regions. This process is then converted to a set of rules by regressing the forecasts against the information used by the forecaster. Once developed, judgmental models offer a low-cast procedure for making forecasts. They almost always provide an improvement in accuracy in comparison to judgmental forecasts, although these improvements are typically modest (Armstrong, 2011).
Extrapolation methods use only historical data on the series of interest. The most popular and cost effective of these methods are based on exponential smoothing, which implements the use of principle that the more recent data are weighted more heavily. Another principle for extrapolation is to use long time series when developing a forecasting model. Yet, focus forecasting, one of the most widely-used time-series methods in business firms, does not do this. As a result, it forecasts are inaccurate (Gardner and Andersen 2017).
Still another principle for extrapolation is to reliable data. The existence of retail scanner data means that reliable data can be obtained for existing products. Scanner data are detailed, accurate timely and inexpensive. As a result the accuracy of the forecasts should improve, especially because of the reduction in the error of assessing the current status. Not knowing where you are starting form has often been a major source of error in predicting where you will wind up. Scanner data are also expected to provide early identification of trends.
Empirical studies have led to the conclusion that relatively simple extrapolation methods perform as well as more method. For example, the boc-jerkins procedure, one of the more complex approaches, has produced no measurable gains in forecast accuracy relative to simpler procedures (Makridakis et al. 2018; Armstrong 2001). Although distressing to statisticians this finding should be welcome to managers.
Quantitative extrapolation methods make no use of management” knowledge of the series. They assume that the causal forces that have affected a historical series will continue over the forecast horizon. The later assumption is sometimes false. When the casual forces are contrary to the trend in the historical series, forecast errors tend to be large (Armstrong, 2011). While such problems may occur only in a small minority of cases in sales forecasting, their effects can be disastrous. One useful guideline is that trends should be extrapolating only when coincide with managements” prior expectations.
Judgmental extrapolations are preferable to quantitative extrapolations when there have been large recent changes in the sale level and where there is relevant knowledge about the item to be forecast. Quantitative extrapolations have an advantages over judgment method when the large (Armstrong 2011).more important than these small gains in accuracy, however, is that the quantitative methods are often less expensive. When one has thousands of forecasts to make every month, the use of judgment is seldom cost effective.
Experts can identify analogous situations. Extrapolation of results from this situation can be used to predict for the situation that is interest. For example, to assess the loss in sales when the patent protection for a drug is removed, one might examine the results for previous drugs, indecently the first year loss is substantial.
Rule-based forecasting integrates judgmental knowledge about the domain. Rule-based forecasting is a type of expert system that is limited to statistical time series. Its primary advantages is that the incorporates the manger’s knowledge in an inexpensive way. Expert opinion, conjoin analysis, bootstrapping and econometric models can aid in the development of expert systems.
Despite an immense amount of research effort, there is little evidence that multivariate time-series provide any benefits to forecasting. As a result, these methods are not discussed here.
Econometric models, the use of data to estimate the parameters of a model given various constraints. Which is nearly always in management problem, one can draw upon prior research to determine the direction, functional form, and magnitude or relationship, and in addition, they can integrate expert opinion, such as the forma judgmental bootstrapping model. Estimated of relationships can then be updated by suing time-series or cross-sectional data. Here again, reliable data are needed. Scanner data can provide data from low cost field experiments where key features such as advertising or price are varied to assess how they affect sale. The outcomes of such experiments can contribute to the estimation of relationship. Econometric models can also use inputs form conjoint models. Econometric models allow for extensive integration of judgmental planning and decision making. They can incorporate the effects of marketing mix variables as well as variables representing key aspects of the market and the environment. Econometric methods are appropriate when one need to forecast what will happens using different assumption about the environment or different strategies. Econometric methods are most useful when;
- Strong causal relationships with sales are expected;
- These casual relationship can be estimated;
- Large changes are expected to occur in the casual variables over the forecast horizon; and
- These changes in the casual variables can be forecast or controlled, especially with respect to their direction.
1.7 ORGANIZATION OF THE REPORT
Chapter one contains the general introduction which is the chapter that sheds a clear light on what the project is all about. It also contains state of the problems, significant of the study, objectives of the project as well as the scope of the study.
1.8 DEFINITION OF TERMS
Amiss: Is inappropriate or out of place.
Contingency: Is a future event or circumstance which is possible but cannot be predicted with certainty.
Chronological: A record of events following the order in which they occurred.
Linear Regression: Is a statistical analysis that shows the relationship between two variables.
Methodology: Is a system of methods used in a particular area of study.
Minimize: Is to reduce something to the least possible amount.
Maximize: Is to increase to the greatest possible amount or degree.
Minimum: Is the lowest known or lowest possible number, quantity or degree.
Optimistic: Is hopeful and confident about the future.
Product: Is anything that can be offered to a market that might satisfy a want or need. In retailing, products are called merchandise. In manufacturing, products are bought as raw materials and sold as finished goods.
Prediction: Is a statement about an uncertain event.
Pragmatism: Is a philosophical movement that includes those who claim that an ideology or proposition is true if it works satisfactorily, that the meaning of a proposition is to be found in the practical consequences of accepting it and that unpractical ideas are to be rejected.
Quantitative: It refers to a type of information based in quantities or else quantifiable data (objective properties) as opposed to qualitative information which deals with apparent qualities (subjective properties).
Sales: - Is the act of taking goods or service which has value and contributes to the utility of individual to the market.
Variable: It is a characteristic, number or quantity that increases or decreases over time, or takes different values in different situations.
CHARPTER FIVE
SUMMARY AND CONCLUSION
5.1 SUMMARY
In this project, we have been able to design and implement a water distribution, sales and billing system, which is to help Kogi State Water Board in its daily water transaction activities.
PROBLEMS ENCOUNTERED:
The major problem faced was time constraint.
5.2 CONCLUSION:
The new system is easy to use, very interesting, it makes sales easier and more accurate, and eliminates fraudulent practices. This makes predicting the amount people will purchase, given the product features and the conditions of the sale. It will help investors make decisions about investments in new ventures.
5.3 RECOMMENDATION:
Government should engage the services of professional software developers in getting operations of commercial organizations sales in a computerized means.
The sales forecast should be free of political considerations in a firm. To help ensure this, emphasis should be on agreeing about the forecasting methods, rather than the forecasts. Also, for important forecasts, decisions on their use should be made before the forecasts are provided. Scenarios are helpful in guiding this process.
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CHAPTER THREE: The complete chapter three of "design and implementation of sales forecasting system" is available. Order full work to download. Chapter three of "design and implementation of sales forecasting system" consists of the methodology. In this chapter all the method used in carrying out this work was discussed.
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