ABSTRACT
This project work termed “Design And Implementation Of Computerized Sales-Forecasting System” is a sales system using ‘point of sales (POS) technology to achieve its sales, accounting and linear regression model for sales forecast included in the system is the supplier registration and management, product registration and management, user management, customer management sales report, receipt printing and sales forecasting. This work was done as a result to solve the problems usually encountered in most companies. The method of data collection used were primary and secondary collection. The methodology used is Structured System Analysis and Design Methodology (SSADM) because each phase must be complete in its entirety before the next phase can begin. The choice of language adopted is Visual Basic programming language due to the flexibility they provide. The software was tested with windows 10. The result achieved is firstly, a better service that speed-up forecasting system and ensures data security by restricting access to the program and to the database, experience an accurate information to forecasting system researchers, enable stress-free system for staff to easily document to forecasting system, and also ensure data security by restricting access to the program and to the database. Secondly, a system where information and output report will be produced much faster, more accurately and more detailed to the management of Kenis Hub Okpor, Anambra State. In summary, the project work was aimed at developing a Software that will help to document and store to forecasting information, and also allows users to print-out the hard-copy of the report. The system can be entered using a username and password. It is accessible either by an administrator or staffs. The interface is very user-friendly. protected for personal use and the data processing is very fast.
TABLE OF CONTENT
Title Page
Certification i
Approval Page ii
Dedication iii
Acknowledgement iv
Abstract v
Table of Content vii
CHAPTER ONE
- Introduction 1
1.1 Background of Study 2
1.2 Statement of the Problems 4
1.3 Aim and Objectives of the Study 4
1.4 Significance of Study 5
1.5 Scope of the Study 5
1.6 Limitations of Study 5
1.7 Definition of Terms 6
CHAPTER TWO
2.0 Literature Review 7
CHAPTER THREE
3.1 Methodology 12
3.2 System Analysis 15
3.3 Data Collection 16
3.4 Organization and its environment 17
3.5 Problems of the Present System 19
3.6 The Proposed System 24
3.7 High Level Model of the Proposed System 25
CHAPTER FOUR
4.0 System Design 27
4.1 Objectives Of The New System 27
4.2 Dataflow Diagram of the New System 27
4.3 Database Specification 32
4.4 Program Module Specification 33
4.5 Input and Output Specification 33
4.6 Algorithms 38
4.7 System Flowchart 39
4.8 Program Flowchart 40
4.9. Data Dictionary 45
4.10 Choice of Programming Language Platform 46
CHAPTER FIVE
5.0 System Implementation and Documentation 48
5.1 System Implementation 48
5.2 Hardware Requirements 48
5.3 Software Requirements 49
5.4 Installation Arrangements 49
5.5 Testing 49
5.6 Training Arrangements 50
5.7 Conversion Arrangements 51
5.8 System Documentation 52
CHAPTER SIX
6.0 Summary, Conclusion and Recommendations 54
6.1 Summary 54
6.2 Conclusion 55
6.3 Recommendation 55
REFERENCES 57
APPENDIX
CHAPTER ONE
INTRODUCTION
Forecasting is a process of vital part of business organization as it provides the basis for planning and decision making (Jacobs, Chase and lummus, 2021). It is simply a statement about the future. It is clear that we must distinguish between forecast per se and good forecasts. Good forecast can be quite valuable and would be worth a great deal. Long-run planning decisions require consideration of many factors: general economic conditions, industry trends, probable competitor’s actions, overall political climate, and so on.
For prediction, good subjective estimates can be based on the manager’s skill experience, and judgment. One has to remember that a forecasting technique requires statistical and management science techniques.
In general, when business people speak of forecasts, they usually mean some combination of both forecasting and prediction (Daim and Hermandez, 2020). Forecasts are often classified according to time period and use. In general, short-term (up to one year) forecasts guide current operations, Medium-term (one to three years) and long-term (over five years) forecasts support strategic and competitive decisions (Pan. D., 2015).
According to Thomopoulos, (2021), “The desire to forecast rises from the need of predicting future economic conditions and the wish to eliminate uncertainties and risk”. The forecasting discipline is defined as “a planning tool that helps management in its attempts to cope with the uncertainties of the future, relying mainly on data from the past and present and analysis of trends.”
However, the complex nature of the world economy can make it difficult to predict future values for various variable, in spite of sophisticated mathematical models. A great deal of visibility, information sharing and communication between different divisions of a company is thereby a necessity (Granger and Persaran, 2020).
1.1 Background of the Study
Forecasting is a means of predicting what is going to happen in the future, next month, year, decade etc. Accurate forecasting requires high quality data, application of the application of the appropriate forecasting technique and knowledge interpretation. The accuracy of such forecasts depends in large measure on the degree to which the past is a good guide to the future (Synder R., 2017).
Prior to building a forecast model, the first step is to clearly understand the problem in order to establish the forecast range and objective (Granger and Persaran, 2020). Therefore, the four steps of forecasting are:-
- Collect and analyze the data through graphs to determine, if any of the numbers seen inconsistent with the data.
- Make assumptions consistent with the data.
- Test the relationships using statistical analysis.
- Feedback the result .
The research work on sales forecasting system is a sales system using linear regression model to forecasting sales of products in an organization.
Many companies find it so difficult to identify products that will be on demand in the future and this has destabilized their relationship with their customers. In a country where lots of businesses thrive, i am motivated to carry out this study on sale forecasting because It is essential and highly beneficial for companies to develop a forecast of the future values of some important metrics such as demand for its product or variables that some important metrics such as demand for its product or variables that describes the economic climate.
The current system in use by many companies is Casual method. Casual forecasting is a strategy that involves the attempt to predict or forecast future events in the marketplace, based on the range of variables that are likely to influence the future movement within that market. The limitation of the casual model is that the forecast is, in turn, dependent on indicators that must themselves be forecast. Also isolating the influence of each factor on demand is a complex statistical process involving expert intervention and use of computers.
This study centers on the use of regression model for forecasting. Linear regression is time series methods that use basic statistics to project future values for a target variable. All organizations, big or small have at their disposal men, machines and materials, the supply of which may be limited. If the supply of these resources were unlimited, the need for managerial tool like linear programming would not rise at all. Supply of sources being limited, the management must find the best ablation of its resources in order to maximum the profit or minimize the loss or utilize the production capacity to the maximum. However, this involves a number of problems which can be overcome by quantitative methods, particularly the linear programming in different financial institutions. By understanding and implementing a well functioning forecasting process companies can increase their forecast accuracy, thus reduce their stock outs and increase their customer satisfaction (Jacobs, Chase and Lummus, 2021).
A good forecasting process is central for daily operational management and vital for every significant management decision, as it eases business planning and makes it more efficient (Diaz, Talley and Tulpule, 2017). The objective is to provide a continuous flow of information, hence enabling organization to cope with the ever-changing shift in demand and supply, increase operational efficiency and manage mitigate risk within a market (Vlahogianni, Golias and Karlaftis, 2016). The aim of a forecasting process is to provide its executives and management with a proper tool to improve their performance and competitive positive while adjusting to rapid changes in the economy (Pal Singh Toor and Dhir, 2021). By designing a forecasting process that aligns with strategic goals, a company can use the forecasting process as a mean of sustaining competitive advantages.
By embedding the forecasting process in the organizational decision making process, a clearer picture of the forecasting contribution to organizational effectiveness will emerge. The construction of a process that reflect a realistic assessment of current business environment can help companies prepare and respond to dynamic market situation, thus increase effectiveness and competitive advantage.
Conducting a forecasting process that is based on the most important key performance indicators for effective performance of a company will support improvement business process (Janes and Faganel, 2013). Hence, forecasting processes should align with the strategic goals of an organization. Companies that don’t align their forecasting process with strategic planning of their operations can experience lack of clarity regarding the structure and responsibility, and miss opportunities to reallocate resources and take advantage of market opportunities.
According to Diaz, Talley and Tulpule 2021, improved forecasting processes will have a direct and positive impact on several aspects enhance coordination in establishing plans consistent with corporate across an organization will enhance coordination in establishing plans consistent with corporate strategy, hence improve organization alignment and financial performance. A good forecasting process can fail through poor integration. A well-integrated forecasting process is therefore a necessity in order to enhance the results of the process.
1.2 Statement of the Problem
There are many problems concerned with sales forecasting such as:
- Working with incomplete data on sale.
- Neglect of customers adjustment and needs, failing to adjust to current economic and political factors.
- Neglecting weather conditions in forecasts and there are also many failed forecasting techniques and
- Financial loss as a result of producing products that will not be in demand.
1.3 Aims and Objectives
The aim of this research is the design and implementation of a sales forecasting system using linear regression model and with an added point of sale system.
The specific objectives are:
- Manage and forecast sales.
- To automate customers, supplier and product management.
- Analyze forecasting.
- Manage system access and roles on the system
- Manage every users of the system.
1.4 Significance of the Study
This research work will provide a more benefits to users such as:
- Way of handling sales and sales forecasting using linear regression model to forecast sales in financial institutions and also reduce time and stress in transactions.
- It can be beneficial to both the management and the staff of the company.
- The management would experience a tremendous increase in the profit level since the sales of product can be forecast at case.
- The company will enjoy the attraction of investor as the company’s market share will rise.
- The staff of the company will enjoy an increase in their salary structure and other benefits such as incentives.
1.5 Scope of the Study
This research work is to develop a system capable of managing sales on a daily basis using a “Point of Sale” (POS) method and also being able to forecast sale based on the sale history using linear regression model.
This system will not incorporate in its development all the models use for sales forecasting but will focus only on the forecasting of products with historical report of sales.
1.6. Limitation of the Study
Some problems cropping up in the course of carrying out this research are;
- Financial constraint
- Time factor
- Insufficient information from the marketing department.
- Fund: as a student there was limited fund to take care of the research property and training resources for fish farming.
1.7 Definition of Terms
- Business: This is any economic activity oriented towards producing goods and service at a profit for the satisfaction of mankind.
- Forecasting: means of predicting what is going to happen in the future, next month, year, decade etc..
- Linear Regression Model: Linear regression is a statistical procedure for predicting the value of dependent variables from an independent variable when then relationship between the variables can de descried with a linear model.
- Manager: This is a person that makes use of the material and human resource of an organization in achieving their objectives.
- Management: This is the application of human and material resources in achieving the objectives of an organization effectively and efficiently through planning and forecasting.
- Sales forecast: is a projection of the excepted customer demand for products or services at a specific company, for a specific time horizon and with certain underlying assumption.
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