Total Quality Management (TQM) is a management strategy that puts awareness of quality at the heart of all organizational processes. This is combined with a strong philosophy of lowering costs by eliminating waste and defects. So TQM can be described as a management system that aims at a continual increase in customer satisfaction while continually lowering real costs. The father of TQM was William Deming‚ an American college professor‚ author‚ and consultant. Deming played a major role in improving
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TYPES OF DATA AND COMPONENTS OF DATA STRUCTURES Data types 1. Primitive: is a data type provided by a programming language as a basic building block 2. Composite: is any data type which can be constructed in a program using its programming language’s primitive data types and other composite types 3. Abstract: is a mathematical model for a certain class of data structures that have similar behavior; or for certain data types of one or more programming languages that have similar semantics
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IT433 Data Warehousing and Data Mining — Data Preprocessing — 1 Data Preprocessing • Why preprocess the data? • Descriptive data summarization • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation • Summary 2 Why Data Preprocessing? • Data in the real world is dirty – incomplete: lacking attribute values‚ lacking certain attributes of interest‚ or containing only aggregate data • e.g.‚ occupation=“ ”
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suitable for persons who: • are aspiring to‚ or embarking on‚ a career in HR/L&D • are working in the field of HR/L&D in a support role and wish to develop their knowledge and skills • have responsibility for HR/L&D activities and decisions within an organisation without a specialist function • are employees or independent consultants within the field of HR/L&D • engage in CPD to enhance and maintain their professional practice and membership. Learning outcomes On completion
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TOPIC: Poor Financial Management Behavior of Students as a Factor why they are facing Financial Difficulty I. INTRODUCTION 1.1 Theoretical Background While students are getting busy developing and learning about their chosen professions foreseeing themselves doing successful individuals in the near future‚ we can’t deny the fact of the increasing costs of college education‚ that make students find themselves in a world of uncertainties regarding financial situations. What makes it more complicated
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11:Strategic Quality and Management Systems Instructor Information | | ------------------------------------------------- ------------------------------------------------- ID: …………………………………………….. ------------------------------------------------- Class (Course): Extended Diploma in strategic Management and leadership ------------------------------------------------- Section …………………………………………. ------------------------------------------------- Unit title: (11) Strategic Quality & Management
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IMC PROJECT Group 2 Cadbury Dairy Milk Cadbury Dairy Milk Submitted by: Swarna Renu (11EX-051) Udit Vikram Singh (11EX-052) Umacharan (11EX-053) Utkal Garg (11EX-054) Varenya Maheshwari (11EX-055) Submitted by: Swarna Renu (11EX-051) Udit Vikram Singh (11EX-052) Umacharan (11EX-053) Utkal Garg (11EX-054) Varenya Maheshwari (11EX-055) Section-1: Brand campaign descriptors Brand Name- Cadbury Dairy Milk Campaign Name- “Shubh Aarambh”
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reviewed. It was found that BSC provides valuable information that supports managers in taking strategic as well as tactical decisions. In addition‚ BSC appears to be positively associated with profitability and managers’ job satisfaction. In terms of real world application‚ BSC was found to be widely applied among businesses. It is used mainly as a tool of ‘Strategic Decision Making’ but exerts greatest influence on ‘Business Actions’. Contents
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qualitative analysis Chapter 19 Decision Analysis LEARNING OBJECTIVES Chapter 19 describes how to use decision analysis to improve management decisions‚ thereby enabling you to: 1. Learn about decision making under certainty‚ under uncertainty‚ and under risk. 2. Learn several strategies for decision-making under uncertainty‚ including expected payoff‚ expected opportunity loss‚ maximin‚ maximax‚ and minimax regret. 3. Learn
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measures widely used to measure complexity in manufacturing systems. With reference to this second framework‚ two indexes were selected (static and dynamic complexity index) and a Business Dynamic model was developed. This model was used with empirical data collected in a job shop manufacturing system in order to test the usefulness and validity of the dynamic complex index. The Business Dynamic model analyzed the trend of the index in function of different inputs in a selected work center. The results
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