ERP MODULE 4

• ERP integrates business of an organization through a centralized database. The organizational data and transaction data are stored in the database. This data is a rich source of information.
• There are many software tools that would process the data and discover useful patterns. These techniques are referred to as data mining. The data from an ERP system may not be directly usable by data mining tools. The data may have to be pre-processed and made ready for data mining.
• Managers cannot generate custom reports or queries without help from a programmer and this inhibits them from obtaining information quickly, which is essential for maintaining a competitive advantage.
• ERP systems provide current status only, such as open orders. Managers often need to look past the current status to find trends and patterns that aid better decision-making.
• The data in the ERP application is not integrated with other enterprise or division systems and does not include external intelligence.
• ERP Related Technologies

  • Business Process Reengineering (BPR)

  • Management Information System (MIS)

  • Decision Support Systems ( DSS)

  • Executive Information Systems (EIS)

  • Data warehousing

  • Data Mining

  • On-line Analytical Processing (OLAP)

  • Supply Chain Management
• Business processes are: simply a set of activities that transform a set of inputs into a set of outputs (goods or services) for another person or process using people and tools.
• And if we do not receive what we want from one supplier, we have many others to choose from (hence the competitive issue for businesses). Many companies began business process improvement with a continuous improvement model. This model attempts to understand and measure the current process, and make performance improvements accordingly.
• The most common definition used in the private sector comes from the book entitled, Reengineering the Corporation, a Manifesto for Business Revolution, by MIT professors Michael Hammer and James Champy.
• The fundamental rethinking and radical redesign of business processes to bring about dramatic improvements in critical, contemporary measures of performance, such as cost, quality, service, and speed. (Reengineering the Corporation, Hammer and Champy, 1993)
• The major emphasis of this approach is the fact that an organization can realize dramatic improvements in performance through radical redesign of its processes. This is in contrast to the notion of streamlining processes in order to achieve a measured level of performance.
• Another aspect to the Hammer/Champy definition is the notion of breakthroughs. This approach to reengineering assumes the existing process is not sound and therefore needs to be replaced. A properly reengineered process will provide quantum leaps in performance, achieving breakthroughs in providing value to the customer.
• Even though these definitions focus on different strategies of implementing change, the common element is that the change occurs across the whole process.
• Business Process Reengineering (BPR) is based on a vision of the future that is increasingly shared by enterprises around the world. It is evolving into the sum total of everything we've learned about management in the industrial age recast into an information age framework.
• The two cornerstones of any organization are the people and the processes. If individuals are motivated and working hard, yet the business processes are cumbersome and nonessential activities remain, organizational performance will be poor.
• Management Information Systems (MIS), are information systems, typically computer based, that are used within an organization. WordNet described an information system as "a system consisting of the network of all communication channels used within an organization".
• The MIS integrates the information flow between functional areas (accounting, marketing, manufacturing, etc.) whereas data processing systems tend to support a single functional area.
• MIS caters to the information needs of all levels of management whereas data processing systems focus on departmental-level support.
• In the course of their decision activities managers work with many pieces of knowledge. Some of this knowledge is descriptive, characterizing the state of past, present, future, or hypothetical worlds
• Such knowledge is commonly called information or data. Other pieces of knowledge are procedural in nature, specifying how to accomplish various tasks.
• In addition to "know what" (information) and "know how" (procedures), a manager may work with reasoning knowledge on the way toward reaching a decision.
• This third kind of knowledge indicates that certain conclusions are valid under particular circumstances.
• Two other kinds of knowledge are very much concerned with communication. One is linguistic knowledge which enables a manager to understand incoming messages. Conversely, a manager works with presentation knowledge when constructing outgoing messages.
• Managers are first and foremost knowledge workers who are involved in the making of decisions.
• Both individual and distributed decision making are susceptible to support by systems that facilitate, expand, or enhance a manager's ability to work with one or more kinds of knowledge. Such knowledge-based systems are called decision support systems (DSSs).
• Decision support systems; emphasize a knowledge management perspective. With the relentless advances in the technology and economics of computers, we are rapidly reaching the point where a manager's success depends on his or her understanding of DSS possibilities and skill in DSS application.
• Executive Information Systems (EIS) a computerized system intended to provide current and appropriate information to support executive decision making for managers using a networked workstation.
• They are tools to provide canned reports or briefing books to top-level executives. They offer strong reporting and drilldown capabilities. An early term for a sophisticated datadriven DSS targeted to senior executives.
• Executive information systems (EIS) provide a variety of internal and external information to top managers in a highly summarized and convenient form. EIS are becoming an important tool of top level control in many organizations.
• Data warehousing - increasingly, organizations are analyzing current and historical data to identify useful patterns and support business strategies. Emphasis is on complex, interactive, exploratory analysis of very large datasets created by integrating data from across all parts of an enterprise; data is fairly static.
• Three Complementary Trends:

  • Data Warehousing: Consolidate data from many sources in one large repository:

  • Loading, periodic synchronization of replicas.

  •Semantic integration.
• Data Mining: Exploratory search for interesting trends and anomalies.
• Data mining is the process of identifying valid, novel, potentially useful and ultimately comprehensible information from databases that is used to make crucial business decisions.
• The primary concept of data warehousing is that the data stored for business analysis can be accessed most effectively by separating it from the data in operational systems. The most important reason for separating data for business analysis, from the operational data, has always been the potential performance degradation on the operational system that can result from the analysis processes.
• High performance and quick response time is almost universally critical for operational systems.
• Data mining permits our companies to profile customers, predict sales trends, and enable customer relationship management (CRM), among other BI initiatives.
• Mining must therefore be integrated with the warehouse data structures and supported by warehouse processes to ensure both effective and efficient use of the technology and related techniques.
• As shown in the BI architecture, the atomic layer of the warehouse as well as data marts is excellent data sources for mining. Those same structures must also be recipients of mining results to ensure availability to the broadest audience.
• Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data.
• While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries.