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MB0047 | 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?

5.  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
Enterprise Resource Planning (ERP) is an integrated management information system connecting internal and external systems that facilitate the flow of information between all business functions inside the boundaries of the organization and manage the connections to outside stakeholders.

·         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.

·         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 experienced rapid growth in the 1990s because the year 2000 problem and introduction of the Euro disrupted legacy systems. Many companies took this opportunity to replace such systems with ERP.

·         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.

ERP Before and After

Before ERP, systems of various departments in an organisation were isolated.

·         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, say HRMS, would typically contain information on the department, reporting structure, and personal details of employees.
·         The payroll department would typically calculate and store pay check 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 HRMS 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.

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 of ERP
·         In the absence of an ERP system, a large manufacturer may find itself with many software applications that do not talk to each other.
·         They provide a comprehensive enterprise view with no "islands of information".
·         They eliminate the need to synchronize changes between multiple systems—consolidation of finance, marketing and sales, human resource, and manufacturing applications.
·         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 by not maintaining separate systems and personnel to manage them.
·         Improved customer service.

Disadvantages –
·         Customization is problematic.
·         Cost factor - ERP can cost more than less integrated and/or less comprehensive solutions.
·         Integration of truly independent businesses can create unnecessary dependencies.
·         Extensive training requirements take resources from daily operations.
·         Harmonization of ERP systems can be a mammoth task (especially for big companies) and requires a lot of time, planning and money.

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 is "the study and design of intelligent agents". And intelligent agents are systems that perceive its environment and takes actions that maximize its chances of success.  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.

·         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.
·         Artificial Intelligence can be classified into various branches like Natural Language Processing (NLP), Speech Recognition, Automated Programming, Machine Learning, Pattern Recognition and Probabilistic Networks.
·         Artificial Intelligence is used in many areas such as problem solving, knowledge representation, learning, perception, social intelligence, gaming etc.

Artificial Intelligence and Neural Networks
Both are interconnected fields. Artificial Intelligence is the science and technology based on various functions to develop a system that can think and work like a human being. Neural networks are a systems designed based on the way human neurons work – to process data and learn from it. The more examples, the more the experience and thus more capability to solve future problems on its own.

·         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.
·         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.

An Artificial Neural Network, often just called a neural network, is a mathematical model inspired by biological neural networks.
·         A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation.
·         In most cases a neural network is an adaptive system that changes its structure during a learning phase. Neural networks are used to model complex relationships between inputs and outputs or to find patterns in data.
·         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.
Genetic algorithm software uses Darwinian (survival of the fittest), randomizing and other mathematics functions to simulate evolutionary processes that can generate increasingly better solutions to problems.

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