Enterprise resource planning and

Related TechnologiesSemester 5th

BSc (IT)

• ERP is an abbreviation for Enterprise resource planning and means the techniques and concepts for the integrated management of business as a whole, from the viewpoint of the effective use of management resources, to improve the efficiency of an enterprise.

• ERP systems serve an important function by integrating separate business functions-materials management, product planning, sales, distribution, finance and accounting and others-into a single application.

• However, ERP systems have three significant limitations:• 1. 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.

• 2. 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.

• 3. the data in the ERP application is not integrated with other enterprise or division systems and does not include external intelligence.

• There are many technologies that help to overcome these limitations. These technologies, when used in conjunction with the ERP package, help in overcoming the limitations of a standalone ERP system and thus, help the employees to make better decisions. Some of these technologies are:

• 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

1. Business Process Reengineering (BPR)

• 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. We all do them, and at one time or another play the role of customer or supplier.

• So why business process improvement? • Improving business processes is paramount for

businesses to stay competitive in today's marketplace. Over the last 10 to 15 years companies have been forced to improve their business processes because we, as customers, are demanding better and better products and services.

• 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.

• Definition of BPR. • Corporate Reengineering• 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. Hammer and Champy defined business process reengineering as:

• 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.

• THE BUSINESS PROCESS REENGINEERING (BPR) VISION

• 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 impact of BPR on organizational performance

• 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 non-essential activities remain, organizational performance will be poor. Business Process Reengineering is the key to transforming how people work. What appear to be minor changes in processes can have dramatic effects on cash flow, service delivery and customer satisfaction. Even the act of documenting business processes alone will typically improve organizational efficiency by 10%.

2. Management Information System (MIS)• 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".

• As an area of study it is commonly referred to as information technology management.

• The study of information systems is usually a commerce and business administration discipline, and frequently involves software engineering, but also distinguishes itself by concentrating on the integration of computer systems with the aims of the organization.

• The area of study should not be confused with Computer Science which is more theoretical and mathematical in nature or with Computer Engineering which is more engineering.

• In business, information systems support business processes and operations, support decision making, and support competitive strategies.

• 2. MIS: How does the company "mine" its relational database systems for information and trends to be used in the management of the business?

• The major differences between a management information system and a Data Processing system are:

• 1. The integrated database of the MIS enables greater flexibility in meeting the information needs of the management.

• 2. The MIS integrates the information flow between functional areas (accounting, marketing, manufacturing, etc.) whereas data processing systems tend to support a single functional area.

• 3. MIS caters to the information needs of all levels of management whereas data processing systems focus on departmental-level support.

• 4. Management’s information needs are supported on a more timely basis with the MIS (with its on-line query capability) than with a data processing system.

• The main characteristics of the management information system are:

• 1. The MIS supports the data processing functions of transaction handling and record keeping.

• 2. MIS uses an integrated database and supports a variety of functional areas.

• 3. MIS provides operational, tactical and strategic levels of the organization with timely, but for the most part structured information (ad-hoc query facility is not available0.

• 4. MIS is flexible and can be adapted to the changing needs of the organization.

3. Decision Support Systems ( DSS)

• 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.

• Sometimes, a manager makes decisions individually. In other cases, decision-making may be distributed, involving the combined and coordinated efforts of many knowledge workers.

• 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.

• Many DSSs are oriented toward individual decision support. There is growing interest in DSSs that directly support distributed decision making at the group, organization, and inter-organization levels.

• Decision support systems also differ with respect to the kinds of knowledge they help manage.

• The majority of conventional DSSs have been devised to help manage primarily descriptive and procedural knowledge. In contrast, there is a class of artificially intelligent DSSs concerned mainly with the representation and processing of reasoning knowledge.

• The main characteristics of DSS are:

• 1. A DSS is designed to address semi-structured and unstructured problems.

• 2. The DSS mainly supports decision-making at the top management level.

• 3. DSS is interactive, user-friendly can be used by the decision-maker with little or no assistance from a computer professional.

• 4. DSS makes general-purpose models, simulation capabilities and other analytical tools available to the decision-maker.

• A DSS does not replace the MIS; instead a DSS supplements the MIS. There are distinct differences between them. MIS emphasizes on planned reports on a variety of subjects; DSS focuses on decision-making. MIS is standard, scheduled, structured and routine; DSS is quite unstructured and is available on request. MIS is constrained by the organizational system; DSS is immediate and user-friendly.

4. Executive Information Systems (EIS)

• Definitions for Executive Information Systems• A computerized system intended to provide current

and appropriate information to support executive decision making for managers using a networked workstation.

• The emphasis is on graphical displays and an easy to use interface that present information from the corporate database.

• They are tools to provide canned reports or briefing books to top-level executives. They offer strong reporting and drill-down capabilities. An early term for a sophisticated data-driven 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.

• They help an executive spot a problem, an opportunity, or a trend.

Executive information systems have these characteristics:

• 1. EIS provide immediate and easy access to information reflecting the key success factors the company and of its units.

• 2. AUser-seductive@ interfaces, presenting information through color graphics or video, allow an EIS user to grasp trends at a glance. 3. EIS provide access to a variety of databases, both internal and external, through a uniform interface.

• 4. Both current status and projections should be available from EIS.

• 5. An EIS should allow easy tailoring to the preferences of the particular users or group of users.

• 6. EIS should offer the capability to drill down into the data.

• DSS are primarily used by middle and lower level managers to project the future, EIS's primarily serve the control needs of higher level management.

• 1. EISs primarily assist top management in uncovering a problem or an opportunity. Analysts and middle managers can subsequently use a DSS to suggest a solution to the problem.

• 2. At the heart of an EIS lies access to the data. EISs may work on the data extraction principal, as DSSs do, or they may be given access to the actual corporate databases or data warehouses.

• 3. EISs can reside on personal workstations or servers.

• Developing EIS

• EIS's should make it easy to track the critical success factors (CSF) of the enterprise, that is, the few vital indicators of the firm's performance.

• With the use of this methodology, executives may define just the few indicators of corporate performance they need. With the drill-down capability, they can obtain more detailed data behind the indicators.

• Strategic business objectives methodology of EIS development takes a company-wide perspective of the strategic business objectives of the firm where the critical businesses are identified and prioritized.

• Then the information needed to support these processes is defined, to be obtained with the EIS that is being planned. Finally, an EIS is developed to report on the CSFs. This methodology avoids the frequent pitfall of aligning an EIS too closely to a particular sponsor.

• An EIS takes the following into consideration:

• 1. The overall vision and mission of the company and the company goals.]

• 2. Strategic planning and objectives

• 3. Crisis management/Contingency planning

• 4. Strategic control and monitoring of overall operations

5. Data warehousing

• Introduction

• 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.

•

6. Data Mining:

• Exploratory search for interesting trends and anomalies.

• 1. Definitions for Data Warehousing

• The ability of a system to store data resulting from Data Mining to be used in future inquiries of that database. 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 syatem that can result from the analysis processes.

• High performance and quick response time is almost universally critical for operational systems.

• The main reasons for needing automated computer systems for intelligent data analysis are:

• 1. Enormous volume of existing and newly appearing data that require processing.

• 2. The inadequacy of the human brain when searching for complex multifactorial dependencies in the data.

• 3. The lack of objectiveness in analyzing the data • 4. The automated data mining systems is that this

process has much lower cost than hiring an army of highly trained professionals’ statisticians.

• Data mining. 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

• Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both.

• Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Continuous Innovation

• Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.

What can data mining do?

• 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.

• With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.

• For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures.

• WalMart is pioneering massive data mining to transform its supplier relationships. WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. WalMart allows more than 3,500 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries.

• The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scout software analyzes the movements of players to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995 reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game.

• By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick's defense and then finds Williams for an open jump shot.

• How does data mining work?

• 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. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:

• Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.

• Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.

• Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.

• Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.

• Data mining consists of five major elements:

• Extract, transform, and load transaction data onto the data warehouse system.

• Store and manage the data in a multidimensional database system.

• Provide data access to business analysts and information technology professionals.

• Analyze the data by application software.

• Present the data in a useful format, such as a graph or table.

7. OLAP

• OLAP is an acronym for On Line Analytical Processing. It is an approach to quickly provide the answer to analytical queries that are dimensional in nature.

• It is part of the broader category business intelligence, which also includes Extract transform load (ETL), relational reporting and data mining.

• The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business performance management (BPM), budgeting and forecasting, financial reporting and similar areas.

• The term OLAP was created as a slight modification of the traditional database term OLTP (On Line Transaction Processing).

• Databases configured for OLAP employ a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time.

• Nigel Pendse has suggested that an alternative and perhaps more descriptive term to describe the concept of OLAP is Fast Analysis of Shared Multidimensional Information (FASMI). They borrow aspects of navigational databases and hierarchical databases that are speedier than their relational

Functionality• OLAP takes a snapshot of a set of source data and

restructures it into an OLAP cube. The queries can then be run against this. It has been claimed that for complex queries OLAP can produce an answer in around 0.1% of the time for the same query on OLTP relational data.

• The cube is created from a star schema or snowflake schema of tables. At the centre is the fact table which lists the core facts which make up the query. Numerous dimension tables are linked to the fact tables. These tables indicate how the aggregations of relational data can be analyzed. The number of possible aggregations is determined by every possible manner in which the original data can be hierarchically linked.

• For example a set of customers can be grouped by city, by district or by country; so with 50 cities, 8 districts and two countries there are three hierarchical levels with 60 members.

• These customers can be considered in relation to products; if there are 250 products with 20 categories, three families and three departments then there are 276 product members.

• With just these two dimensions there are 16,560 (276 * 60) possible aggregations. As the data considered increases the number of aggregations can quickly total tens of millions or more.

• The calculation of the aggregations AND the base data combined make up an OLAP cube, which can potentially contain all the answers to every query which can be answered from the data (as in Gray, Bosworth, Layman, and Pirahesh, 1997). Due to the potentially large number of aggregations to be calculated, often only a predetermined number are fully calculated while the remainder are solved on demand.

Types of OLAP

• There are three types of OLAP.

• Multidimensional or MOLAP

• MOLAP is the 'classic' form of OLAP and is sometimes referred to as just OLAP. MOLAP uses database structures that are generally optimal for attributes such as time period, location, product or account code. The way that each dimension will be aggregated is defined in advance by one or more hierarchies.

• Relational or ROLAP

• ROLAP works directly with relational databases. The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. Depends on a specialized schema design.

• Hybrid Or HOLAP

• There is no clear agreement across the industry as to what constitutes "Hybrid OLAP", except that a database will divide data between relational and specialized storage.

• For example, for some vendors, a HOLAP database will use relational tables to hold the larger quantities of detailed data, and use specialized storage for at least some aspects of the smaller quantities of more-aggregate or less-detailed data.

Comparison• Each type has certain benefits, although there is

disagreement about the specifics of the benefits between providers.

• Some MOLAP implementations are prone to database explosion. Database explosion is a phenomenon causing vast amounts of storage space to be used by MOLAP databases when certain common conditions are met: high number of dimensions, pre-calculated results and sparse multidimensional data. The typical mitigation technique for database explosion is not to materialize all the possible aggregation, but only the optimal subset of aggregations based on the desired performance vs. storage trade off.

• MOLAP generally delivers better performance due to specialized indexing and storage optimizations. MOLAP also needs less storage space compared to ROLAP because the specialized storage typically includes compression techniques.

• ROLAP is generally more scalable. However, large volume pre-processing is difficult to implement efficiently so it is frequently skipped. ROLAP query performance can therefore suffer.

• Since ROLAP relies more on the database to perform calculations, it has more limitations in the specialized functions it can use.

• HOLAP encompases a range of solutions that attempt to mix the best of ROLAP and MOLAP. It can generally pre-process quickly, scale well, and offer good function support.

8. Supply chain management (SCM)

• Supply chain management (SCM) is the process of planning, implementing, and controlling the operations of the supply chain with the purpose to satisfy customer requirements as efficiently as possible. Supply chain management spans all movement and storage of raw materials, work-in-process inventory, and finished goods from point-of-origin to point-of-consumption.

• According to the Council of Supply Chain Management Professionals (CSCMP), a professional association that developed a definition in 2004, Supply Chain Management "encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all logistics management activities.

• Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers. In essence, Supply Chain Management integrates supply and demand management within and across companies.

• Supply chain event management (abbreviated as SCEM) is a consideration of all possible occurring events and factors that can cause a disruption in a supply chain. With SCEM possible scenarios can be created and solutions can be planned.

• Some experts distinguish supply chain management and logistics management, while others consider the terms to be interchangeable.

• From the point of view of an enterprise, the scope of supply chain management is usually bounded on the supply side by your supplier's suppliers and on the customer side by your customer's customers. Supply chain management is also a category of software products.

• Opportunities enabled by Supply Chain Management• The following strategic and competitive areas can be used to

their full advantage if a supply chain management system is properly implemented.

• Fulfillment. “Ensuring the right quantity of parts for production or products for sale arrive at the right time.”(Haag, Cummings, McCubbrey, et al., 2006, p. 46). This is enabled through efficient communication, ensuring that orders are placed with the appropriate amount of time available to be filled. The supply chain management system also allows a company to constantly see what is on stock and making sure that the right quantities are ordered to replace stock.

• Logistics. “Keeping the cost of transporting materials as low as possible consistent with safe and reliable delivery.” (Haag, Cummings, McCubbrey, et al., 2006, p. 46).

• Here the supply chain management system enables a company to have constant contact with its distribution team, which could consist of trucks, trains, or any other mode of transportation.

• The system can allow the company to track where the required materials are at all times. As well, it may be cost effective to share transportation costs with a partner company if shipments are not large enough to fill a whole truck and this again, allows the company to make this decision.

• Production: “Ensuring production lines function smoothly because high-quality parts are available when needed.” (Haag, Cummings, McCubbrey, et al., 2006, p. 46).

• Production can run smoothly as a result of fulfillment and logistics being implemented correctly. If the correct quantity is not ordered and delivered at the requested time, production will be halted, but having an effective supply chain management system in place will ensure that production can always run smoothly without delays due to ordering and transportation.

• Revenue & profit. “Ensuring no sales are lost because shelves are empty.”(Haag, Cummings, McCubbrey, et al., 2006, p. 46).

• Managing the supply chain improves a company’s flexibility to respond to unforeseen changes in demand and supply.

• Because of this, a company has the ability to produce goods at lower prices and distribute them to consumers quicker than companies without supply chain management thus increasing the overall profit.

• Costs. “Keeping the cost of purchased parts and products at acceptable levels.” (Haag, Cummings, McCubbrey, et al., 2006, p. 46). Supply chain management reduces costs by “… increasing inventory turnover on the shop floor and in the warehouse” (&ldquo Supply chain management,” 2006) controlling the quality of goods thus reducing internal and external failure costs and working with suppliers to produce the most cost efficient means of manufacturing a product.

• Cooperation. “Among supply chain partners ensures 'mutual success.'” (Haag, Cummings, McCubbrey, et al., 2006, p. 46). Collaborative planning, forecasting and replenishment (CPFR) is a “longer-term commitment, joint work on quality, and support by the buyer of the supplier’s managerial, technological, and capacity development.” (Klassen, Krajewski, Ritzman, 2004, p.293) higher quality goods provided at a lower cost.

• This relationship allows a company to have access to current, reliable information, obtain lower inventory levels, cut lead times, enhance product quality, improve forecasting accuracy and ultimately improve customer service and overall profits.

• The suppliers also benefit from the cooperative relationship through increased buyer input from suggestions on improving the quality and costs and though shared savings. Consumers can benefit as well through