Reference no: EM131768053
Using below Monash Video Library Case Study, you are to prepare User Requirements Document.
It is suggested that a framework for the document could be based on the following structure:
1. Business Overview (Goals, Business Objectives, Current Business Situation, Problems, Gaps, Opportunities)
2. Data Gathering and Data Analysis
3. Logical Process Modelling,
4. Data Modelling and Normalisation
5. Feasibility Analysis
6. Business requirement specification (Functional, Non- Functional)
7. Business proposed solution.
8. Implementation Issues
The Monash Video Library purchases videocassettes and loans them to its members for a charge in order to make a profit. The business currently uses a rather old PC package to manage its loans. Since this software was purchased, the library has expanded to three new stores, each of which uses a stand-alone copy of the system. In order to compete with the major video store chains, management has decided to develop an updated information system with a shared database in order to improve customer service and gain a competitive advantage.
The proposed new system will include a quick and accurate catalogue to inform members of videos help in each store by a number of different categories (e.g. Action, comedy, etc.) or which videos are held featuring their favourite actors. The catalogue will also show if a particular video is available that day at the store. Members will be able to put a "hold" on a particular video if they desire.
Accurate information about which members have borrowed which videos, and when videos are due to be returned will also be available. This should encourage borrowers to return their videos promptly. Keeping track of loans using the current membership system has proved to be slow and prone to error. Improved turnaround of videos should increase profit.
Management will also be able to analyse the borrowing patterns of members in order to focus better on the videos that are likely to be most appropriate for the store. They are also interested in predicting the members who are most likely to return videos late. They may be able to offer cheaper memberships to reliable members.
In order to keep track of the costs involved in purchasing videos, details of purchase orders will be stored for all videos. This information will help to select suppliers,negotiate cheaper prices for future purchases, and help with auditing.
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