Q.1 Describe the role of sensitivity analysis in decision support systems.

Ans. Sensitivity analysis is the study of how model output varies with changes in model inputs. A model is said to be sensitive to an input if changing that input variable changes the model output. This output variability (numerical or otherwise) can be apportioned, qualitatively or quantitatively, to different sources of variation in the inputs.
Sensitivity Analysis can be used to determine:
- The model resemblance with the process under study
- The quality of model definition
- Factors that mostly contribute to the output variability
- The region in the space of input factors for which the model variation is maximum
- Optimal - or instability - regions within the space of factors for use in a subsequent calibration study
- Interactions between factors

Sensitivity Analysis is popular in financial applications, risk analysis, signal processing, neural networks and any area where models are developed. Sensitivity analysis can also be used in model-based policy assessment studies
In a decision support system, the analyst may want to identify cost drivers as well as other quantities for which we need to acquire better knowledge in order to make an informed decision. On the other hand, some quantities have no influence on the predictions, so that we can save resources at no loss in accuracy by relaxing some of the conditions.
Sensitivity analysis can help in a variety of other circumstances which can be handled by the settings illustrated below:
1. to identify critical assumptions or compare alternative model structures
2. guide future data collections
3. detect important criteria
4. optimize the tolerance of manufactured parts in terms of the uncertainty in the parameters
5. optimize resources allocation
6. model simplification or model lumping, etc.

However there are also some problems associated with sensitivity analysis in the business context:
1. Variables are often interdependent, which makes examining them each individually unrealistic. eg: changing one factor such as sales volume, will most likely affect other factors such as the selling price.
2. Often the assumptions upon which the analysis is based are made by using past experience/data which may not hold in the future.
3. Assigning a maximum and minimum (or optimistic and pessimistic) value is open to subjective interpretation. For instance one person’s 'optimistic' forecast may be more conservative than that of another person performing a different part of the analysis. This sort of subjectivity can adversely affect the accuracy and overall objectivity of the analysis.









Q.2 Differentiate between what-if analysis and goal seeking analysis.
Ans.
- Goal-Seeking Analysis: Making repeated changes to selected variables until a chosen variable reaches a target value. Represents a "backward" solution approach. it attempts to find the value of the inputs necessary to achieve a desired level of output.
- Optimization Analysis: Finding an optimum value for selected variables in a mathematical model, given certain constraints.
- Sensitivity Analysis: Observing how repeated changes to a single variable affect other variables in a mathematical model.
- What-If Analysis: Observing how changes to selected variables affect other variables in a mathematical model. Attempts to check the impact of a change in the assumptions (input data) on the proposed solution. For example, what will happen to the total inventory cost if the originally assumed cost of carrying inventories is not 10 percent but 12 percent?









Q. 3 Compare and contrast file management system and database management system.

Ans. Files vs. DBMS
Application must stage large datasets between main memory and secondary storage (e.g., buffering, page-oriented access, 32-bit addressing, etc.). There is a need to protect data from inconsistency due to multiple concurrent users. Crash recovery, security and access control should be taken into account. Database management systems were developed to handle the following difficulties of typical file-processing systems supported by conventional operating systems.
• Data redundancy and inconsistency
• Difficulty in accessing data
• Data isolation - multiple files and formats
• Integrity problems
• Atomicity of updates
• Concurrent access by multiple users
• Security problems
When a computer user wants to store data electronically they must do so by placing data in files. Files are stored in specific locations on the hard disk (directories). The user can create new files to place data in, delete a file that contains data, rename the file, etc -- all known as file management; a function provided by the Operating System (OS).
If the user wishes to perform some operation on the data he has placed in the file, such as viewing a list of his friends that celebrate their birthday in June, he has to scroll through all the data by himself in order to see the data he is interested in. Moreover, he has to know where he put the files that contain the data, and if there are multiple files he has to remember to go through each one of them.
A Database Management System is intended to remove this burden of manually locating data, and having to scroll through it by allowing the user to create a logical structure for the data beforehand, and then allowing the user to place the data in the database that the DBMS is managing. In this way the DBMS abstracts away the physical concerns of organising files, and provides the user with a logical view of the data.
Note, that the DBMS will still -- behind the scenes though -- place the data in files on the hard-disk.








Q.4 Describe Transaction Processing System.
Transaction processing systems were among the earliest computerized systems. Their primary purpose is to record, process, validate, and store transactions that take place in the various functional areas of a business for future retrieval and use. It is an information system that records company transactions (a transaction is defined as an exchange between two or more business entities).
- Transaction processing systems (TPS) are cross-functional information systems that process data resulting from the occurrence of business transactions.
- Transactions are events that occur as part of doing business, such as sales, purchases, deposits, withdrawals, refunds, and payments.
- Transaction processing activities are needed to capture and process data, or the operations of a business would grind to a halt.
For example, let McDonald's, which sells a large number of hamburgers every day, orders raw materials from its suppliers. Each time the company places an order with a supplier, a transaction occurs and a transaction system records relevant information, such as the supplier's name, address, and credit rating, the kind and quantity of items purchased, and the invoice amount.

Types of Transactions
Transactions can be internal or external.
• Internal Transactions: Those transactions, which are internal to the company and are related with the internal working of any organization. For example, when a department orders office supplies from the purchasing department, an internal transaction occurs.
• External Transactions: Those transactions, which are external to the organization and are related with the external sources, are regarded as External Transaction. For example sales, purchase etc. and when a customer places an order for a product, an external transaction occurs.

Characteristics of Transaction Processing Systems
1. A TPS records internal and external transactions for a company. It is a repository of data that is frequently accessed by other systems
2. A TPS performs routine, repetitive tasks. It is mostly used by lower-level managers to make operational decisions
3. Transactions can be recorded in batch mode or online. In batch mode, the files are updated periodically; in online mode, each transaction is recorded as it occurs.

Features of TPS
1. A TPS supports different tasks by imposing a set of rules and guidelines that specify how to record, process, and store a given transaction. There are many uses of transaction processing systems in our everyday lives, such as when we make a purchase at retail store, deposit or withdraw money at a bank, or register for classes at a university.
2. A TPS is the data lifeline for a company because it is the source of data for other information systems, such as MIS and DSS (Decision Support Systems). Hence, if the TPS shuts down, the consequences can be serious for the organization
3. A TPS is also the main link between the organization and external entities, such as customers, suppliers, distributors, and regulatory agencies.
4. TPS exist for the various functional areas in an organization, such as finance, accounting, manufacturing, production, human resources, marketing quality control, engineering, and research and development.

Process of Transaction Processing System
The seven steps in processing a transaction are:
a. Data entry
b. Data Capture
c. Data validation
d. Processing and revalidation
e. Storage
f. Output generation
g. Query support








Q.5 Explain the following (short notes): Critical Factor Analysis, Customer Relationship Management.

Ans. Critical Success Factor Analysis:
- A method developed at MIT’s Sloan school by John Rockart to guide businesses in creating and measuring success
- Widely used for technology and architectural planning in enterprise I/T
- A top-down methodology that is especially suitable for designing systems as opposed to applications
- A reductionist method for going from an abstract vision to concrete requirements


What Is a Critical Success Factor?

- A key area where satisfactory performance is required for the organization to achieve its goals
- A means of identifying the tasks and requirements needed for success
- At the lowest level, CSFs become concrete requirements
- A means to prioritize requirements


The CSF Method

- Start with a vision: mission statement
- Develop 5-6 high level goals
- Develop hierarchy of goals and their success factors
Leads to concrete requirements at the lowest level of decomposition (a single, implementable idea)
Along the way, identify the problems being solved and the assumptions being made
- Cross-reference usage scenarios and problems with requirements

Conclusion
CSF analysis:
· Produces results that express the needs of the enterprise clearly and (hopefully) completely
· Allows us to measure success and prioritize goals in a sensible way
· When used together with traditional usage scenarios, ensures that the needs of both the user and the enterprise are being met.









(ii) CRM
Customer relationship management (CRM) is a broad term that covers concepts used by companies to manage their relationships with customers, including the capture, storage and analysis of customer, vendor, partner, and internal process information.
There are three aspects of CRM which can each be implemented in isolation from each other:
· Operational - automation or support of customer processes that include a company’s sales or service representative
· Collaborative - direct communication with customers that does not include a company’s sales or service representative (self service)
· Analytical - analysis of customer data for a broad range of purposes
Operational
Operational CRM provides support to "front office" business processes, including sales, marketing and service. Each interaction with a customer is generally added to a customer's contact history, and staff can retrieve information on customers from the database when necessary.
One of the main benefits of this contact history is that customers can interact with different people or different contact channels in a company over time without having to describe the history of their interaction each time.
Consequently, many call centers use some kind of CRM software to support their call center agents.
Collaborative
Collaborative CRM covers the direct interaction with customers, for a variety of different purposes, including feedback and issue-reporting. Interaction can be through a variety of channels, such as web pages, email, automated phone (Automated Voice Response AVR) or SMS.
The objectives of collaborative CRM can be broad, including cost reduction and service improvements.
Analytical
Analytical CRM analyzes customer data for a variety of purposes:
· Design and execution of targeted marketing campaigns to optimize marketing effectiveness
· Design and execution of specific customer campaigns, including customer acquisition, cross-selling, up-selling, retention
· Analysis of customer behavior to aid product and service decision making (e.g. pricing, new product development etc.)
· Management decisions, e.g. financial forecasting and customer profitability analysis
· Prediction of the probability of customer defection (churn).
Analytical CRM generally makes heavy use of predictive analytics.













Q. Short note : Enterprise Resource Planning (ERP)

Ans. Enterprise Resource Planning (ERP) systems integrate (or attempt to integrate) all data and processes of an organization into a unified system. A typical ERP system will use multiple components of computer software and hardware to achieve the integration. A key ingredient of most ERP systems is the use of a unified database to store data for the various system modules.

Some organizations — typically those with sufficient in-house IT skills to integrate multiple software products — choose to implement only portions of an ERP system and develop an external interface to other ERP or stand-alone systems for their other application needs. For instance, the PeopleSoft HRMS and financials systems may be perceived to be better than SAP's HRMS solution. And likewise, some may perceive SAP's manufacturing and CRM systems as better than PeopleSoft's equivalents. In this case these organizations may justify the purchase of an ERP system, but choose to purchase the PeopleSoft HRMS and financials modules from Oracle, and their remaining applications from SAP.

This is very common in the retail sector , where even a mid-sized retailer will have a discrete Point-of-Sale (POS) product and financials application, then a series of specialized applications to handle business requirements such as warehouse management, staff rostering, merchandising and logistics.

Ideally, ERP delivers a single database that contains all data for the software modules, which would include:

Manufacturing
Engineering, Bills of Material, Scheduling, Capacity, Workflow Management, Quality Control, Cost Management, Manufacturing Process, Manufacturing Projects, Manufacturing Flow

Supply Chain Management
Inventory, Order Entry, Purchasing, Product Configurator, Supply Chain Planning, Supplier Scheduling, Inspection of goods, Claim Processing, Commission Calculation

Financials
General Ledger, Cash Management, Accounts Payable, Accounts Receivable, Fixed Assets

Projects
Costing, Billing, Time and Expense, Activity Management

Human Resources
Human Resources, Payroll, Training, Time & Attendance, Benefits

Customer Relationship Management
Sales and Marketing, Commissions, Service, Customer Contact and Call Center support

Data Warehouse
and various Self-Service interfaces for Customers, Suppliers, and Employees
Enterprise Resource Planning is a term originally derived from manufacturing resource planning (MRP II) that followed material requirements planning (MRP).[2] MRP evolved into ERP when "routings" became a major part of the software architecture and a company's capacity planning activity also became a part of the standard software activity. ERP systems typically handle the manufacturing, logistics, distribution, inventory, shipping, invoicing, and accounting for a company. Enterprise Resource Planning or ERP software can aid in the control of many business activities, like sales, marketing, delivery, billing, production, inventory management, quality management, and human resource management.

ERP systems saw a large boost in sales in the 1990s as companies faced the Y2K problem in their legacy systems. Many companies took this opportunity to replace their legacy information systems with ERP systems. This rapid growth in sales was followed by a slump in 1999, at which time most companies had already implemented their Y2K solution.

ERPs are often incorrectly called back office systems indicating that customers and the general public are not directly involved. This is contrasted with front office systems like customer relationship management (CRM) systems that deal directly with the customers, or the eBusiness systems such as eCommerce, eGovernment, eTelecom, and eFinance, or supplier relationship management (SRM) systems.

ERPs are cross-functional and enterprise wide. All functional departments that are involved in operations or production are integrated in one system. In addition to manufacturing, warehousing, logistics, and information technology, this would include accounting, human resources, marketing, and strategic management.

ERP II means open ERP architecture of components. The older, monolithic ERP systems became component oriented.

EAS — Enterprise Application Suite is a new name for formerly developed ERP systems which include (almost) all segments of business, using ordinary Internet browsers as thin clients.