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Emerging concept in information system

Management information system

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Emerging concept in information system

  1. 1. Supply Chain Management
  2. 2. Supply Chain Management(SCM) A firm’s Supply chain consists of all processes and activities that are necessary to bring products to market.  It includes:- 1. Procurement to acquire raw material; 2. Manufacturing to convert raw materials into components and final products; and distribution to respond to market demand; 3. The objective of supply chain management is to coordinate and integrate all these processes and activities so as to meet customers’ expectations in the most cost-effective way SCM is a set of approaches to manage the SC, i.e.,  To efficiently integrate suppliers, manufacturers, warehouses, and stores, so that merchandise is produced and distributed at the right quantity, to the right location, and at the right time.  “Efficiently means “minimizing” the system-wide cost while satisfying service level requirement, or maximizing the total profit.
  3. 3. Definition  SCM is the integration of all activities associated with the flow and transformation of goods from raw materials through to end user, as well as information flows, through improved supply chain relationships, to achieve a sustainable competitive advantage. Handfield and Nichols
  4. 4. SCM Software  SCM software refers to software that supports specific segments of the supply chain, especially in manufacturing, inventory control, scheduling and transportation. This software is designed to improve decision making, optimization, and analysis.
  5. 5. E- Supply Chain  When supply chain is managed electronically, usually with web based software, it is referred to as an e-supply chain.
  6. 6. Seven Principles of Supply Chain Management  Segment customers based on service needs  Listen to signals of market demand and plan accordingly  Develop a supply-chain-wide technology strategy  Customize the logistics network  Differentiate product closer to the customer  Source strategically  Adopt channel-spanning performance measures
  7. 7. Customer Relationship Management (CRM)
  8. 8. Customer Relationship Management (CRM)  “It is a business strategy to select and manage customers to optimize long-term value.”  “It requires a customer-centric business philosophy and culture to support effective marketing, sales, and service processes.”  “CRM applications can enable effective Customer Relationship Management, provided that an enterprise has the right leadership, strategy, and culture.”
  9. 9. Definition  CRM “is the process of managing detailed information about individual customers and carefully managing all customer ‘touch points’ to carefully managing all customer touch points to maximize customer loyalty” Kotler & Keller
  10. 10. Benefits  Instill greater customer loyalty  Increased efficiency through automation  Deeper understanding of customers  Increased marketing and selling opportunities  Identifying the most profitable customers  Receiving customer feedback that leads to new and improved products or services  Obtaining information that can be shared with business partners
  11. 11. Components of CRM 1. People Management:- People Management is nothing but the effective use of people in the right place at the right time. It imperative to adopt the right measures to ensure the people skills their job profiles. 2. Lead management:- Basically involves tracking and distribution of sales leads. This benefits the sales., call centers and marketing industries as well.
  12. 12. 3. Sales forces automation:- Sales forces automation is by far one of the most essential components of customer relationship Management and also of the first. It is nothing but a software solution that includes forecasting, Tracking, potential interaction and processing of sale. 4. Customer service :- the Customer service component in CRM. This is because CRM focuses on collection of customer data, gathering in formation about their purchase patterns and provides this information to every department that requires it. 5. Marketing:- Marketing is nothing but the promotional activities that are involve in promoting a product either to a general public or to specific group.
  13. 13. 6. Work flow automation:- Work flow processes include cutting cost and streaming lings processes. It basically save several people form doing the same jobs again. 7. Business reporting:- This is nothing but being able to identify the exact position of your company at given point of time. 8. Analytics:- It involve the study of data so tat information can used to study market trends.
  14. 14. Process of CRM 1. Clearly identify your target market and value proportion 2. Define your over all strategy and consider cost 3. Define how customer type will be handled 4. Select a CRM software to measurer performance 5. Continue to re-engage software
  15. 15. Enterprise resource planning system (ERP)
  16. 16. Enterprise resource planning system (ERP)  ERP is a set of tools and processes that integrates department and functions across a company into one computer system.  ERP runs off a single database, enabling various depts. to share information and communicate with each other.  ERP system comprise function specific modules designed to interact with other modules, e.g. accounts receivable, accounts payable purchasing etc.
  17. 17. Cross functional approach of ERP Production Planning Customer/ Employee Integrated Logistics Accounting and Finance Sales, Distribution, order Management Human Resources
  18. 18. ERP features: 1. Security 2. Authorization 3. Referencing 4. Responsibility 5. Implementation
  19. 19. Benefits  Help in integrating applications for decision making and planning  Allow departments to talk to each other  Easy to integrate by using processed built into ERP software.  Better management of resources reducing the cost of operations.  Increases in the productivity of the business possible
  20. 20. Implementation of ERP  The Implementation stage of ERP life cycle involve a number of activities that must be managed effectively in order for the project to be success. Those activities are:- 1. Installation 2. Confrigration 3. Customization 4. Testing 5. Change management 6. Training
  21. 21. Data Ware Housing
  22. 22. Data Ware Housing  Data Ware House is a repository which stores integrated information for efficient querying and analysis. “A data warehouse is simply a single, complete, and consistent store of data obtained from a variety of sources and made available to end users in a way they can understand and use it in a business context.” -- Barry Devlin, IBM Consultant
  23. 23. Why Data Warehousing?  Data warehousing can be considered as an important preprocessing step for data mining Heterogeneous Databases Data Warehouse data selection data cleaning data integration data summarization  A data warehouse also provides on-line analytical processing (OLAP) tools for interactive multidimensional data analysis.
  24. 24. Example of a Data Warehouse FACT table timeid pid sales 1 1 2 2 1 4 2 2 1 3 3 2 ... ... ... dimension 1: time timeid day month year 1 11 4 1999 2 15 4 1999 3 2 5 1999 ... ... ... dimension 2: product pid name type 1 chair office 2 table office 3 desk office ... ... Employee US-Database eid name birthdate ... ... ... Transaction tid type date 1 sale 4/11/1999 2 sale 5/2/1999 3 buy 5/17/1999 ... ... ... Department did dname ... ... Data Warehouse Details tid pid qty 1 21 2 2 13 1 3 41 3 ... ... ... HK-Database Supplier sid name birthdate ... ... ... Country sid date time qty pid 1 15:4:1999 8:30 2 11 2 15:4:1999 9:30 2 11 3 ??? 3 56 4 19:5:1999 4 22 ... ... Sales cid cname ... ...
  25. 25. Characteristics of Data Warehouse  Subject-Oriented  Integrated  Non- Volatile  Time Variant
  26. 26. Data Warehouse—Subject- Oriented  Organized around major subjects, such as customer, product, sales.  Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing.  Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.
  27. 27. Data Warehouse—Integrated  Constructed by integrating multiple, heterogeneous data sources ◦ relational databases, flat files, on-line transaction records  Data cleaning and data integration techniques are applied. ◦ Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources  E.g., Hotel price: currency, tax, breakfast covered, etc. ◦ When data is moved to the warehouse, it is converted.
  28. 28. Data Warehouse—Time Variant  The time horizon for the data warehouse is significantly longer than that of operational systems. ◦ Operational database: current value data. ◦ Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years)  Every key structure in the data warehouse ◦ Contains an element of time, explicitly or implicitly ◦ But the key of operational data may or may not contain “time element” (the time elements could be extracted from log files of transactions)
  29. 29. Data Warehouse—Non- Volatile  A physically separate store of data transformed from the operational environment.  Operational update of data does not occur in the data warehouse environment. ◦ Does not require transaction processing, recovery, and concurrency control mechanisms ◦ Requires only two operations in data accessing:  initial loading of data and access of data.
  30. 30. Data Mining
  31. 31. Data Mining  Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. The information that can be used to increase revenue.  Data mining is primarily used today by companies with a strong consumer focus- retail, financial, communication, and marketing organization.
  32. 32. Components of data mining ◦ Data mining—core of knowledge discovery process 32 Task-relevant Data Data Warehouse Data Cleaning Data Mining Data Integration Databases Selection Pattern Evaluation
  33. 33. Process of data mining 1. Problem definition 2. Data exploration 3. Data preparation 4. Modeling 5. Evaluation 6. Deployment
  34. 34. Problem definition  A data mining project starts with the understanding of the business problem. Data mining experts, business experts, and domain experts work closely together to define the project objectives and the requirements from a business perspective. The project objective is then translated into a data mining problem definition. In the problem definition phase, data mining tools are not yet required.
  35. 35. Data exploration  Domain experts understand the meaning of the metadata. They collect, describe, and explore the data. They also identify quality problems of the data. A frequent exchange with the data mining experts and the business experts from the problem definition phase is vital. In the data exploration phase, traditional data analysis tools, for example, statistics, are used to explore the data.
  36. 36. Data preparation  Domain experts build the data model for the modeling process. They collect, cleanse, and format the data because some of the mining functions accept data only in a certain format. They also create new derived attributes, for example, an average value. In the data preparation phase, data is tweaked multiple times in no prescribed order. Preparing the data for the modeling tool by selecting tables, records, and attributes, are typical tasks in this phase. The meaning of the data is not changed.
  37. 37. Modeling  Data mining experts select and apply various mining functions because you can use different mining functions for the same type of data mining problem. Some of the mining functions require specific data types. The data mining experts must assess each model. In the modeling phase, a frequent exchange with the domain experts from the data preparation phase is required.  The modeling phase and the evaluation phase are coupled. They can be repeated several times to change parameters until optimal values are achieved. When the final modeling phase is completed, a model of high quality has been built.
  38. 38. Evaluation  Data mining experts evaluate the model. If the model does not satisfy their expectations, they go back to the modeling phase and rebuild the model by changing its parameters until optimal values are achieved. When they are finally satisfied with the model, they can extract business explanations and evaluate the following questions: Does the model achieve the business objective?  Have all business issues been considered?  At the end of the evaluation phase, the data mining experts decide how to use the data mining results.
  39. 39. Deployment  Data mining experts use the mining results by exporting the results into database tables or into other applications, for example, spreadsheets. The Intelligent Miner™ products assist you to follow this process. You can apply the functions of the Intelligent Miner products independently, iteratively, or in combination.  The following figure shows the phases of the Cross Industry Standard Process for data mining (CRISP DM) process model.
  40. 40. Thank you

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