What is ERP? Explain its existence before and its future after? What are the advantages & Disadvantages of ERP? What is Artificial Intelligence? How is it different from Neural Networks?

Enterprise Resource Planning
To be considered an ERP system, a software package must provide the function of at least two systems. For example, a software package that provides both payroll and accounting functions could technically be considered an ERP software package. However, the term is typically reserved for larger, more broadly based applications. The introduction of an ERP system to replace two or more independent applications eliminates the need for external interfaces previously required between systems, and provides additional benefits that range from standardization and lower maintenance to easier and/or greater reporting capabilities. Examples of modules in an ERP which formerly would have been stand-alone applications include: Manufacturing, Supply Chain, Financials, Customer Relationship Management (CRM), Human Resources, Warehouse Management and Decision Support System.

Enterprise Resource Planning is a term originally derived from manufacturing resource planning that followed material requirements planning . 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.

ERP Before and After
Before  

Prior to the concept of ERP systems, departments within an organization (for example, the human resources (HR)) department, the payroll department, and the financial department) would have their own computer systems. The HR computer system (often called HRMS or HRIS) would typically contain information on the department, reporting structure, and personal details of employees. The payroll department would typicallycalculate and store paycheck information. The financial department would typically store financial transactions for the organization. Each system would have to rely on a set of common data to communicate with each other. For the HRIS to send salary information to the payroll system, an employee number would need to be assigned and remain static between the two systems to accurately identify an employee. The financial system was not interested in the employee-level data, but only in the payouts made by the payroll systems, such as the tax payments to various authorities, payments for employee benefits to providers, and so on. This provided complications. For instance, a person could not be paid in the payroll system without an employee number. 

After 

ERP software, among other things, combined the data of formerly separate applications. This made the worry of keeping numbers in synchronization across multiple systems disappears. It standardized and reduced the number of software specialties required within larger organizations. 

Advantages and Disadvantages
Advantages – In the absence of an ERP system, a large manufacturer may find itself with many software applications that do not talk to each other and do not effectively interface. Tasks that need to interface with one another may involve: 

• A totally integrated system 

• The ability to streamline different processes and workflows

• The ability to easily share data across various departments in an organization 

• Improved efficiency and productivity levels 

• Better tracking and forecasting 

• Lower costs 

• Improved customer service 

Disadvantages – Many problems organizations have with ERP systems are due to inadequate investment in ongoing training for involved personnel, including those implementing and testing changes, as well as a lack of corporate policy protecting the integrity of the data in the ERP systems and how it is used. While advantages usually outweigh disadvantages for most organizations implementing an ERP system, here are some of the most common obstacles experienced:
Usually many obstacles can be prevented if adequate investment is made and adequate training is involved, however, success does depend on skills and the experience of the workforce to quickly adapt to the new system. 

• Customization in many situations is limited 

• The need to reengineer business processes 

• ERP systems can be cost prohibitive to install and run 

• Technical support can be shoddy

• ERP's may be too rigid for specific organizations that are either new or want to move in a new direction in the near future. 

Artificial Intelligence
Artificial Intelligence is the science and technology based on various functions to develop a system that can think and work like a human being. It can reason, analyze, learn, conclude and solve problems. The systems which use this type of intelligence are known as artificial intelligent systems and their intelligence is referred to as artificial intelligence. It was said that the computer don‘t have common sense. Here in AI, the main idea is to make the computer think like human beings, so that it can be then said that computers also have common sense. More precisely the aim is to obtain a knowledge based computer system that will help managers to take quick decisions in business. Artificial Intelligence can be classified into various branches like Natural Language Processing (NLP), Speech Recognition, Automated Programming, Machine Learning, Pattern Recognition and Probabilistic Networks. Most of the software developed for AI have been through Prolog, C++, Java and LISP. These programming languages provide facility of creating various functions of business activity, extension of a function, handling dynamic situations in business, providing uniformity in application etc. A business decision making process depends upon the level of risk and uncertainty involved in the problem. To model the uncertainty and risk of natural language used in developing a AI for business application the concept of fuzzy logic is used. For problems related finance applications apart from fuzzy logic concepts, two other concepts of AI are being researched. These are genetic algorithm and chaotic models. AI is also being applied to the functions of marketing like – Selling, Forecasting, and Communication etc.

Artificial Intelligence and Neural Networks
Artificial intelligence is a field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics and engineering. The goal of AI is to develop computers that can simulate the ability to think, see, hear, walk, talk and feel. In other words, simulation of computer functions normally associated with human intelligence, such as reasoning, learning and problem solving.
AI can be grouped under three major areas: cognitive science, robotics and natural interfaces.
Cognitive science focuses on researching on how the human brain works and how humans think and learn. Applications in the cognitive science area of AI include the development of expert systems and other knowledge-based systems that add a knowledge base and some reasoning capability to information systems. Also included are adaptive learning systems that can modify their behavior based on information they acquire as they operate. Chess-playing systems are some examples of such systems. Fussy logic systems can process data that are incomplete or ambiguous. Thus, they can solve semi-structured problems with incomplete knowledge by developing approximate inferences and answers, as humans do. Neural network software can learn by processing sample problems and their solutions. As neural nets start to recognize patterns, they can begin to program themselves to solve such problems on their own. Neural networks are computing systems modelled after the human brain‘s mesh like network of interconnected processing elements, called neurons. The human brain is estimated to have over 100 billion neuron brain cells. The neural networks are lot simpler in architecture. Like the brain, the interconnected processors in a neural network operate in parallel and interact dynamically with each other. This enables the network to operate and learn from the data it processes, similar to the human brain. That is, it learns to recognize patterns and relationships in the data. The more data examples it receives as input, the better it can learn to duplicate the results of the examples it processes. Thus, the neural networks will change the strengths of the interconnections between the processing elements in response to changing patterns in the data it receives and results that occur. For example, neural network can be trained to learn which credit characteristics result in good or bad loans. The neural network would continue to be trained until it demonstrated a high degree of accuracy in correctly duplicating the results of recent cases. At that point it would be trained enough to begin making credit evaluations of its own.