...Evolution of management thought and patterns of management analysis. Scientific management school •A theory is simply a blueprint or roadmap that guides towards achieving the goal. In other wards, it provides a systemic framework for actions. •Study of management only dates for the last century, although there has been concerns about effective management practice for over centuries Scientific management school The two earliest pioneers of management theory are: -Robert Own and -Charles Babbage Scientific management school Robert Own (1771 -1858) A British industrialist who valued the organization’s human resources. Scientific management school He advocated ideas such as, - better working condition - meals for employees - reduced working hours He claimed that people deserve more respect and dignity. Scientific management school *Charles Babbage (1792 -1871) An English Mathematician who encouraged the application of mathematics to solve efficiency problems Scientific management school His work put the basic lines of both classical and quantitative management theories. He was also the originator of modern management theory and practice The classical management theory Includes two approaches: Scientific management Classical management Scientific management Concerned with the management of work and workers. it grew from researches of five people: Scientific management *Fredrick W.Taylor(1856 -1915) He was interested in...
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...MANAGERS AND MANAGING What is Management? "Management is the organizational process that includes strategic planning, setting objectives, managing resources, deploying the human and financial assets needed to achieve objectives, and measuring results. Management also includes recording and storing facts and information for later use or for others within the organization. Management functions are not limited to managers and supervisors. Every member of the organization has some management and reporting functions as part of their job." (Knowledge Management Terms, 2009) Essential Managerial Tasks A manager's job uniquely describes the functions of management, which are most commonly cited as planning, organizing, leading, and controlling, although some managers' jobs identify additional functions. The process of management is defined by the functions of management, which are distinct from accounting, finance, marketing, and other business functions. " These functions provide a useful way of classifying information about management, and most basic management texts since the 1950s have been organized around a functional framework." (Cengage, 2006) Levels and Skills of Managers Most organizations have three levels of management. First-line, middle, and top managers. While first-line managers are responsible for the day-to-day supervision of non-managerial employees, middle managers are responsible for developing and utilizing organizational resources efficiently...
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...Organizational Patterns A case study in a large utility company Software Engineering Project Management Course Presentation - Fall 2008 Introduction 1. a. A little bit history Early work by Alfred L. Kroeber Kroeber, 1963 Milestone work by Christopher Alexander Alexander, 1979 pattern researches actively conducted in the organization domain in terms of software development (1991 - ) b. c. 2. What is organizational pattern? “Organizational patterns are recurring structures of relationship, usually in a professional organization, that help the organization achieve its goals” Wikipedia 2008 2 Key research directions 1. Social network analysis Coplien, 1995 2. 3. 4. 5. 6. 7. Requirements acquisition Whitenack, 1995 Kerth, 1995 Harrison, 1996 Berczuk et. al., 2003 Evolution from analysis to design Formation and function of teams Episodes (or Agile) Scrum Configuration management patterns Cunningham, 1996 Sutherland, 2007, Sutherland, 2008 3 Organizational patterns, Agile, and Scrum 1. There are patterns in Agile development methodology Scrum is the first formal organizational pattern to describe a complete Agile process Sutherland, 2008 2. 4 Case study – Organizational Environment 1. a. Organization Environment Description The author is working with an utility holding company that provides electric and natural gas services to more than 1 million customers The IT department focuses on integrating...
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...Data Mining D t Mi i Module 1 Introduction to Data Mining Dr. Jason T.L. Wang, Professor Department of Computer Science New Jersey Institute of Technology / Data Management: Its Evolution 1960s: – File management and network DBMS 1970s: – Relational DBMS 1980s: 980s – Non-first normal form, extended-relational, OO, deductive databases and application-oriented DBMS pp (spatial, scientific, CAD/CAM, etc.) 1990s - present: p – Data mining, digital library, and Web databases – Cloud databases, data science, and Big Data Data Mining © Jason Wang 2 Data Mining: Its Definition Data mining (knowledge discovery in databases): ) – Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Alternative names: – Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, analysis data archeology, data dredging archeology dredging, information harvesting, etc. Data Mining © Jason Wang 3 Data Mining: A Multidisciplinary Field Pattern Recognition Machine Learning Databases St ti ti Statistics Information Visualization Data Mining © Jason Wang 4 Data to be mined Text databases Web databases Scientific and biological databases Transactional databases Data Mining © Jason Wang 5 Knowledge to be discovered K l d t b di d Association (correlation) ...
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...What is data mining: * Data mining (knowledge discovery from data) * Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data * data processing using sophisticated data search capabilities and statistical algorithms to discover patterns and correlations in large preexisting databases; a way to discover new meaning in data. 2. KDD process * General functionality * Descriptive data mining * Predictive data mining * Different views lead to different classifications * Data view: Kinds of data to be mined * Knowledge view: Kinds of knowledge to be discovered * Method view: Kinds of techniques utilized * Application view: Kinds of applications adapted Data mining issues * Mining methodology * Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web * Performance: efficiency, effectiveness, and scalability * Pattern evaluation: the interestingness problem * Incorporation of background knowledge * Handling noise and incomplete data * Parallel, distributed and incremental mining methods * Integration of the discovered knowledge with existing one: knowledge fusion * User interaction * Data mining query languages and ad-hoc mining * Expression and visualization of data mining results * Interactive mining of knowledge at multiple...
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...Journal of Operations Management 29 (2011) 329–342 Contents lists available at ScienceDirect Journal of Operations Management journal homepage: www.elsevier.com/locate/jom Qualitative case studies in operations management: Trends, research outcomes, and future research implications Mark Barratt, Thomas Y. Choi ∗ , Mei Li Department of Supply Chain Management, W. P. Carey School of Business, Arizona State University, Tempe, AZ 85287-4706, United States a r t i c l e i n f o a b s t r a c t Our study examines the state of qualitative case studies in operations management. Five main operations management journals are included for their impact on the field. They are in alphabetical order: Decision Sciences, International Journal of Operations and Production Management, Journal of Operations Management, Management Science, and Production and Operations Management. The qualitative case studies chosen were published between 1992 and 2007. With an increasing trend toward using more qualitative case studies, there have been meaningful and significant contributions to the field of operations management, especially in the area of theory building. However, in many of the qualitative case studies we reviewed, sufficient details in research design, data collection, and data analysis were missing. For instance, there are studies that do not offer sampling logic or a description of the analysis through which research outcomes are drawn. Further, research protocols for doing...
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...mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions. K E Y W O R D S ■ ■ Healthcare management ■ Customer relationship management ■ Healthcare applications Data mining methodology and techniques ■ Data mining applications ■ Predictive modeling Introduction Data mining can be defined as the process of finding previously unknown patterns and trends in databases and using that information to build predictive models.1 Alternatively, it can be...
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...convergence to bring information and access to their customers in the moment. As a result, it’s become critical for operators to have the same type of fast access and interaction with their customer data and analytics. The current competitive landscape demands it. Are traditional analytical models enough? No. If operators are changing the way they do business, this shift must apply to analytics as well. It’s become imperative to monitor the instant changes in customers’ behaviors and match them with the most relevant offer as soon as the customer needs it. This is achieved with a sophisticated blend of analytics and business sense. This white paper explores the opportunities of two dynamic analytical capabilities: transactional behavioral analysis and capturing data potential. These tools give operators real-time insight about their customer activity so they can take action to be as agile as possible. The New Frontier in Telecom Analytics: Get Better Insight Faster The dynamic nature of today’s telecom customers requires an equally dynamic use of analytics to understand customers and make decisions. Executive Summary Table of Contents Executive Summary................2 Today: Descriptive and Predictive Modeling.................3 Tomorrow: The New Frontier in...
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...by other articles in PMC. Go to: Abstract Knowledge about users and their information needs can contribute to better user interface design and organization of information in clinical information systems. This can lead to quicker access to desired information, which may facilitate the decision-making process. Qualitative methods such as interviews, observations and surveys have been commonly used to gain an understanding of clinician information needs. We introduce clinical information system (CIS) log analysis as a method for identifying patient-specific information needs and CIS log mining as an automated technique for discovering such needs in CIS log files. We have applied this method to WebCIS (Web-based Clinical Information System) log files to discover patterns of usage. The results can be used to guide design and development of relevant clinical information systems. This paper discusses the motivation behind the development of this method, describes CIS log analysis and mining, presents preliminary results and summarizes how the results can be applied. Go to: INTRODUCTION The availability of clinical information to the clinician at the point of care is essential to the health care process. Inability to locate needed information can be costly in terms of time and quality of care. Clinical information systems have been developed to assist clinicians with their decisions; however, these systems need to ensure that they provide the information in optimal ways. In order...
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...MEDWAY SCHOOL OF ENGINEERING Programme: Msc. Information Technology Management for Business Course: Knowledge Management and Exploitation Course Tutor: Dr. A.A.F. Al-Shawabkeh Topic Using Data Mining and Knowledge Management to Improve Business Performance By Nurudeen Babatunde Lawal 000620744 Table of Contents Content Page No. Table of Contents 2 List of Figures 3 Abstract 4 Chapter One 5 1.1 Overview of Business 5 1.2 Nature of Business 5 1.3 Business Challenges 6 Chapter Two 2.1 Knowledge and Knowledge Management 8 2.1.1 Knowledge 8 2.1.2 Knowledge management 9 2.1.3 Knowledge Management Process 9 2.1.4 Knowledge Discovery from Database 10 2.2 Data Mining 12 2.2.1 Data Mining Tasks in Knowledge Management 12 2.2.2 Data Mining and Knowledge Management in Business 14 Chapter Three 17 3.1 Implementation Challenges of KM in Business 17 3.2 Limitations of Data Mining Applications 18 3.3 Conclusion 18 References 19 List of Figures Figure No. Description Page No. Figure 1 Forms of Knowledge Organisation 8 Figure 2 Integration of KM Technologies with KM Process Cycle 10 Figure 3 DM and KDD Process 11 Figure 4 Intersection of DM and KM 14 Abstract ...
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...A Discourse Analysis of Decision-Making Meetings Jolanta Aritz Robyn C. Walker University of Southern California Measuring culture is a central issue in international management research and has been traditionally accomplished using indices of cultural values. Although a number of researchers have attempted to identify measures to account for the core elements of culture, there is no consensus on those measures. This article uses an alternative method—discourse analysis—to observe what actually occurs in terms of communication practices in intercultural decision-making meetings, specifically those involving U.S.-born native English speakers and participants from East Asian countries. Previous discourse studies in this area suggest that differences in communication practices may be attributed to power differentials or language competence. Our findings suggest that the conversation style differences we observed might be attributed to intergroup identity issues instead. Keywords: intercultural communication; intercultural communication; group communication; discourse analysis; intercultural management; group decision making; communication accommodation theory In an increasingly global economy, multicultural work teams are becoming more commonplace, and fostering teamwork in multicultural teams is a growing challenge. The growing body of intercultural research suggests important Jolanta Aritz is an Associate Professor of Clinical at the Center for Management Communication...
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...(Statistical Analysis System) was originally developed as a project to analyze agriculture from 1966-1976 at North Carolina State University. As demand for such software grew, SAS Institute was founded in 1976. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and they provide more advanced options through the SAS programming language. On August 13 2014, SAS sponsored a web seminar titled “Analytically Speaking” the topic of the webcast was data mining techniques. Michael Berry and Gordon Linoff were the featured speakers, they have written a leading introductory book (on data mining) titled “Data Mining Techniques”. They discussed a lot of the current data mining landscape, including new methods, new types of data and the importance of using the right analysis for your problem (as good analysis is wasted doing the wrong thing). They also briefly discussed using ‘found data’ – text data, social data and device data. Michael Berry is the Business Intelligence Director at TripAdvisor and co-founder of Data Miners Inc. Gordon Linoff is co-founder of Data Miners Inc. and a consultant to financial, media and pharmaceutical companies. Data mining is the analysis step of the “KDD” (Knowledge Discovery in Databases). Data mining is an interdisciplinary sub-field of computer science and of management information...
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...(Statistical Analysis System) was originally developed as a project to analyze agriculture from 1966-1976 at North Carolina State University. As demand for such software grew, SAS Institute was founded in 1976. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. SAS provides a graphical point-and-click user interface for non-technical users and they provide more advanced options through the SAS programming language. On August 13 2014, SAS sponsored a web seminar titled “Analytically Speaking” the topic of the webcast was data mining techniques. Michael Berry and Gordon Linoff were the featured speakers, they have written a leading introductory book (on data mining) titled “Data Mining Techniques”. They discussed a lot of the current data mining landscape, including new methods, new types of data and the importance of using the right analysis for your problem (as good analysis is wasted doing the wrong thing). They also briefly discussed using ‘found data’ – text data, social data and device data. Michael Berry is the Business Intelligence Director at TripAdvisor and co-founder of Data Miners Inc. Gordon Linoff is co-founder of Data Miners Inc. and a consultant to financial, media and pharmaceutical companies. Data mining is the analysis step of the “KDD” (Knowledge Discovery in Databases). Data mining is an interdisciplinary sub-field of computer science and of management information...
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...except where fully reference direct quotations have been included, no aspect of this submission has been copied from any other source. I declare that all other works cited in this submission have been appropriately referenced. I understand that any act of Academic Dishonesty such as plagiarism or collusion may result in the non-award of my degree. Signature…………………………………………………………………………………… Date………………………………………………………………………………………….. Table of Contents Abstract 3 1. Introduction 3 2. General process of customer requirements management 3 2.1. Requirement Elicitation 5 3. Kansei mining 5 3.1. Kansei Engineering 5 3.2. Kansei mining method 6 3.3. Case study 7 3.4. Method analysis 8 4. Picture Sorting and ART2 Network 8 4.1. Sorting technique 9 4.2. The ART2 Network 10 4.3. Case study 11 4.4. Method analysis 12 5. Conclusion 13 6. References 14 Figure Index Figure 1 - Customer requirement management process (Jiao & Chen, 2006) 4 Figure 2 - Perceptual design elements for mobile phones (Jiao, Zhang, & Helander, 2006) 7 Figure 3 -Procedure of specified picture sorts for product design (Shieh, Yan, & Chen, 2008) 10 Figure 4 - Customer attributes hierarchy derived from the sorting process (Shieh, Yan, & Chen, 2008) 12 Table index Table 1- Perceived by testing groups (Jiao, Zhang, & Helander, 2006) 8 Table 2 - Definitions of terminology used in sorting techniques (Shieh, Yan, & Chen, 2008) 9 Table...
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...study on Customers perception on air conditioners : A study with reference to shopkeepers 5. Analysis of it services requirements In the organizations. 6. A study on Awareness and attitude about modular switches among mid size builder segment 7. A Study on Investors Preference towards equity broking. 8. A study on Consumer buying behavior on FMCG 9. A study on Absenteeism among the employees 10. A study on Training Effectiveness in the organization 11. A study on Credit Appraisal for Car loan financing in the finance companies. 12. A study on Consumer buying behavior towards two wheeler bikes. 13. A study on Effectiveness of Performance Management system among the employees 14. A study on Effectiveness of Quality Initiativeness among the employees. 15. A study on Safety , Welfare and Health among the employees. 16. A study on Training and Recreation programmes among the employees. 17. A study on the investor behavior towards the mutual funds investments. 18. A study on Induction Training Program among the employees. 19. A study on Quality of Work Life among the employees. 20. A study on potentiality of Auto and Engineering companies. 21. A study on Customer Financial Need Analysis. 22. A study on financial position of the firm and a comparison of cash management product and multicity cheque facility 23. A study on Effectiveness of performance of Management System among the employees. 24. A Study on Working Capital Requirements of the Comapnay. 25...
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