Management Science
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Project Management © Adrienne Watt
Project Management © Adrienne Watt This work is licensed under a Creative Commons-ShareAlike 4.0 International License Original source: The Saylor Foundation http://open.bccampus.ca/find-open-textbooks/?uuid=8678fbae-6724-454c-a796-3c666 7d826be&contributor=&keyword=&subject= Contents Introduction ...................................................................................................................1 Preface ............................................................................................................................2 About the Book ..............................................................................................................3 Chapter 1 Project Management: Past and Present ....................................................5 1.1 Careers Using Project Management Skills ......................................................................5 1.2 Business Owners ...............................................................................................................5 Example: Restaurant Owner/Manager ..........................................................................6 1.2.1 Outsourcing Services ..............................................................................................7 Example: Construction Managers ..........................................................................8 1.3 Creative Services ................................................................................................................9 Example: Graphic Artists ...............................................................................................10 -
Abstract and Background Project Management Has Been Practiced Since Early Civili- Zation
Business Theory Project Management: Science or a Craft? Abdulrazak Abyad Correspondence: A. Abyad, MD, MPH, MBA, DBA, AGSF , AFCHSE CEO, Abyad Medical Center, Lebanon. Chairman, Middle-East Academy for Medicine of Aging http://www.meama.com, President, Middle East Association on Age & Alzheimer’s http://www.me-jaa.com/meaaa.htm Coordinator, Middle-East Primary Care Research Network http://www.mejfm.com/mepcrn.htm Coordinator, Middle-East Network on Aging http://www.me-jaa.com/menar-index.htm Email: [email protected] Development of Project Management thinking Abstract and background Project management has been practiced since early civili- zation. Until 1900 civil engineering projects were generally managed by creative architects, engineers, and master build- Projects in one form or another have been undertaken for ers themselves. It was in the 1950s that organizations started millennia, but it was only in the latter part of the 20th cen- to systematically apply project management tools and tech- tury people started talking about ‘project management’. niques to complex engineering projects (Kwak, 2005). How- Project Management (PM) is becoming increasingly im- ever, project management is a relatively new and dynamic portant in almost any kind of organization today (Kloppen- research area. The literature on this field is growing fast and borg & Opfer, 2002). Once thought applicable only to large receiving wider contribution of other research fields, such as scale projects in construction, R&D or the defence field, psychology, pedagogy, management, engineering, simulation, PM has branched out to almost all industries and is used sociology, politics, linguistics. These developments make the as an essential strategic element for managing and affect- field multi-faced and contradictory in many aspects. -
The Recent Trends in Production: 1
The Recent Trends in Production: 1. Flexibility: The ability to adapt quickly to changes in volumes of demand, in the product mix demanded, and in product design or delivery schedules, has become a major competitive strategy and a competitive advantage to the firms. This is sometimes called as agile (move quickly) manufacturing. 2. Total Quality Management: TQM approach has been adopted by many firms to achieve customer satisfaction by a never ending quest for improving the quality of goods and services. 3. Time Reduction: Reduction of manufacturing cycle time and speed to marker for a new product provide a competitive edge to a firm over other firms. When companies can provide products at the same price and quality, quicker delivery (short lead time) provide one firm competitive edge over the other. 4. Worker Involvement: The recent trend is to assign responsibility for decision making and problem solving to the lower levels in the organization. This is known as employee involvement and empowerment. Examples of employee’s empowerment are quality circle and use of work teams or quality improvement teams. 5. Business Process Re-engineering: BPR involves drastic measures or break-through improvements to improve the performance of a firm. It involves the concept of clean- state approach or starting from a scratch in redesigning in business processes. 6. Global Market Place: Globalization of business has compelled many manufacturing firms to give operations in many countries where they have certain economic advantage. This has resulted in a steep increase in the level of competition among manufacturing firms throughout the world. 7. -
Open Tebi Thesis.Pdf
The Pennsylvania State University The Graduate School College of Engineering STUDY OF PROCESS IMPROVEMENT TOOLS IMPLEMENTED IN A CUSTOM ENGINEERED MANUFACTURING ENVIRONMENT A Thesis in Industrial Engineering by Tebi Mathew Cheeran © 2013 Tebi Mathew Cheeran Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science December 2013 The thesis of Tebi Mathew Cheeran was reviewed and approved* by the following: Robert Voigt Professor of Industrial and Manufacturing Engineering Thesis Co-adviser Christopher Saldana The Harold and Inge Marcus Career Assistant Professor Thesis Co-adviser Jeya Chandra Professor and Graduate Program Coordinator of Industrial and Manufacturing Engineering *Signatures are on file in the Graduate School. ii ABSTRACT The objective of this research project is to reduce the overall lead time for the manufacture and assembly of Blow Out Preventer stacks, having a highly customized design. The research project is executed for a multi-national company, specialized in the manufacture of mechanical components in the oil and gas industry. The work reviews some of the manufacturing strategies that are employed in today’s industries and tries to identify strategies that could be employed in a custom engineered, Make to Order environment. It then outlines some of the preliminary analysis done using the existing data from the facility. This analysis uses Six Sigma tools to develop an overview of the Blow Out Preventer production process and to identify key focus areas of the project. The preliminary analysis next focuses on the downstream final assembly process and identifies the assembly delays caused due to preassembly operations as the reason for long assembly lead time. -
Topic 6: Projected Dynamical Systems
Topic 6: Projected Dynamical Systems Professor Anna Nagurney John F. Smith Memorial Professor and Director – Virtual Center for Supernetworks Isenberg School of Management University of Massachusetts Amherst, Massachusetts 01003 SCH-MGMT 825 Management Science Seminar Variational Inequalities, Networks, and Game Theory Spring 2014 c Anna Nagurney 2014 Professor Anna Nagurney SCH-MGMT 825 Management Science Seminar Projected Dynamical Systems A plethora of equilibrium problems, including network equilibrium problems, can be uniformly formulated and studied as finite-dimensional variational inequality problems, as we have seen in the preceding lectures. Indeed, it was precisely the traffic network equilibrium problem, as stated by Smith (1979), and identified by Dafermos (1980) to be a variational inequality problem, that gave birth to the ensuing research activity in variational inequality theory and applications in transportation science, regional science, operations research/management science, and, more recently, in economics. Professor Anna Nagurney SCH-MGMT 825 Management Science Seminar Projected Dynamical Systems Usually, using this methodology, one first formulates the governing equilibrium conditions as a variational inequality problem. Qualitative properties of existence and uniqueness of solutions to a variational inequality problem can then be studied using the standard theory or by exploiting problem structure. Finally, a variety of algorithms for the computation of solutions to finite-dimensional variational inequality problems are now available (see, e.g., Nagurney (1999, 2006)). Professor Anna Nagurney SCH-MGMT 825 Management Science Seminar Projected Dynamical Systems Finite-dimensional variational inequality theory by itself, however, provides no framework for the study of the dynamics of competitive systems. Rather, it captures the system at its equilibrium state and, hence, the focus of this tool is static in nature. -
Management Science (MSC) 1
Management Science (MSC) 1 Management Science (MSC) MSC 287 - BUSINESS STATISTICS I Semester Hours: 3 Introduction to probability & business statistics. Covers: tabular, graphical, and numerical methods for descriptive statistics; measures of central tendency, dispersion, and association; probability distributions; sampling and sampling distributions; and confidence intervals. Uses spreadsheets to solve problems. Prerequisite: Any 100 level MA course. MSC 288 - BUSINESS STATISTICS II Semester Hours: 3 Inferential statistics for business decisions. Topics include: review of sampling distributions and estimation; inferences about means, proportions, and variances with one and two populations; good of fit tests; analysis of variance and experimental design; simple linear regression; multiple linear regression; non parametric methods. Prerequisite: MSC 287. MSC 385 - OPERATIONS ANALYSIS Semester Hours: 3 Survey of the firm's production function and quantitative tools for solving production problems, quality management, learning curves, assembly and waiting lines, linear programming, inventory, and other selected topics (e.g., scheduling, location, supply chain management). Uses the SAP software. Prerequisite: MSC 288. MSC 410 - TRANSPORTATION & LOGISTICS Semester Hours: 3 An analysis of transportation and logistical services to include customer service, distribution operations, purchasing, order processing, facility design and operations, carrier selection, transportation costing, and negotiation. Prerequisite: MKT 301. MSC 411 - SUPPLY CHAIN MANAGEMENT Semester Hours: 3 Supply chain management focuses on networks of companies that deliver value to customers. The course focuses on understanding integrated supply chains and examines how product development and design, demand, marketing, globalization, customer locations, distribution networks, suppliers and ERP systems impact a company's supply chain design. Prerequisite: MSC 287. MSC 412 - ARMY SENIOR LOGISTICIAN-ADV Semester Hours: 3 The Senior Logistician Advanced Course (SLAC) is part of the U.S. -
Management Science 1
Management Science 1 MANAGEMENT SCIENCE Department Code: MAS Introduction Management Science uses the ideas and methods of science, mathematics, statistics, and computing, which collectively are often referred to as Business Analytics, to help managers make better business decisions. Management science techniques have been applied in a wide variety of areas including financial modeling, marketing research, organizational theory, transportation and logistics, health care, environmental protection, and manufacturing. Almost any decision can benefit from the methods of management science/analytics. Educational Objectives The Department of Management Science offers a major area of specialization in Business Analytics as well as a minor in Business Analytics. The Business Analytics curriculum is designed to give Miami Herbert Business School students the necessary educational background and experience to allow them to work as successful business analytics professionals. Students pursuing the Bachelor of Business Administration (BBA) degree with a major area of specialization in Business Analytics are trained to combine quantitative, statistical, and computational tools and techniques to help companies understand, predict, and act on large amounts of data, improving decision-making in increasingly complex and interconnected business environments. For students pursuing the Bachelor of Science in Business Administration (BSBA) degree program, the major in Business Analytics requires a solid background in the sciences and mathematics. Additionally, students are required to take sequences of courses in optimization, decision science, and data analytics. A number of the courses in the Business Analytics curriculum require projects, in which the student evaluates a real-world system or process. As the system is studied and modeled, the student applies management science methods to find ways to improve the process. -
Industrial Engineering Industrial Engineering Is a Branch of Engineering Which Deals with the Optimization of Complex Processes Or Systems
Industrial engineering Industrial engineering is a branch of engineering which deals with the optimization of complex processes or systems. It is concerned with the development, improvement, and implementation of integrated systems of people, money, knowledge, information, equipment, energy, materials, analysis and synthesis, as well as the mathematical, physical and social sciences together with the principles and methods of engineering design to specify, predict, and evaluate the results to be obtained from such systems or processes.[1] While industrial engineering is a traditional and longstanding engineering discipline subject to (and eligible for) professional engineering licensure in most jurisdictions, its underlying concepts overlap considerably with certain business-oriented disciplines such as operations management. The various topics concerning industrial engineers include: accounting: the measurement, processing and communication of financial information about economic entities operations research, also known as management science: discipline that deals with the application of advanced analytical methods to help make better decisions operations management: an area of management concerned with overseeing, designing, and controlling the process of production and redesigning business operations in the production of goods or services. project management: is the process and activity of planning, organizing, motivating, and controlling resources, procedures and protocols to achieve specific goals in scientific or daily problems. -
Management Science (MS)
Management Science (MS) MS 5363. Pricing and Revenue Management. (3-0) 3 Credit Hours. MANAGEMENT SCIENCE (MS) Revenue Management is about “providing the right product to the right customers at the right time at the right price.” The main goal of Management Science (MS) Courses this course is to apply revenue management practices to appropriate MS 5003. Quantitative Methods for Business Analysis. (3-0) 3 Credit industries successfully. Specifically, the course will provide tools to Hours. forecast customer demand successfully, identify pricing and revenue Prerequisites: MAT 1033 and MS 1023, their equivalents, or consent opportunities, understand the impact of constrained capacity, opportunity of instructor. Introduction to managerial decision analysis using costs, customer response, demand uncertainty and market segmentation quantitative and statistical tools. Course includes a general framework on pricing decisions, and accordingly formulate and solve pricing for structuring and analyzing decision problems. Some of the topics optimization problems for revenue maximization. The material covered in include decision theory, statistical techniques (such as analysis of the course assumes a basic understanding of probability and probability variance, regression, nonparametric tests), introduction to linear distributions, some knowledge of spreadsheet modeling, and using Excel programming, and introduction to time series. Uses applicable decision Solver or similar optimization tools to get a solution. Differential Tuition: support software. Differential Tuition: $387. $387. MS 5023. Decision Analysis and Production Management. (3-0) 3 Credit MS 5383. Supply Chain Analytics. (3-0) 3 Credit Hours. Hours. The main goal of this course is to integrate data analytics with supply Prerequisite: MS 5003 or an equivalent. Study of applications of chain management. -
Optimization Methods in Management Science and Operations Research
15.053/8 February 5, 2013 Optimization Methods in Management Science and Operations Research Handout: Lecture Notes 1 Class website + more Class website – please log on as soon as possible – Problem Set 1 will be due next Tuesday Lots of class information on website Piazza.com used for Q&A and discussions No laptops permitted in class, except by permission laptops 2 We will use clickers from Turning Technologies. If you own one, please bring it to class with you, starting Thursday. If you don’t own one, we will lend you one for the semester. 3 Videotapes of classes Current plan: start videotaping lectures starting Today (I think). In addition, PowerPoint presentations will all be available. 4 Excel and Excel Solver During this semester, we will be using Excel Solver for solving optimization problems. We assume some familiarity with Excel, but no familiarity with Excel Solver. Homework exercises involve Excel. – Versions A and B (experiment starting this year). – Excel Solver tutorial this Friday 5 An optimization problem Given a collection of numbers, partition them into two groups such that the difference in the sums is as small as possible. Example: 7, 10, 13, 17, 20, 22 These numbers sum to 89 I can split them into {7, 10, 13, 17} sum is 47 {20, 22} sum is 42 Difference = 5. Can we do better? Excel Example 6 What is Operations Research? What is Management Science? World War II : British military leaders asked scientists and engineers to analyze several military problems – Deployment of radar – Management of convoy, bombing, antisubmarine, and mining operations. -
Value Stream Mapping Adapted to High- Mix, Low-Volume Manufacturing Environments 2012:120
Value Stream Mapping adapted to High- Mix, Low-Volume Manufacturing Environments 2012:120 JUAN MANUEL ARAYA Master of Science Thesis Stockholm, Sweden 2011 VALUE STREAM MAPPING adapted to HIGH-MIX, LOW-VOLUME Manufacturing Environments Juan Manuel Araya Master of Science Thesis INDEK 2011:x KTH Industrial Engineering and Management Industrial Management SE-100 44 STOCKHOLM Examensarbete INDEK 2011:x {Rapporttitel} {Namn1} {Namn2} Godkänt Examinator Handledare 2011-mån-dag {Namn} {Namn} Uppdragsgivare Kontaktperson {Namn} {Namn} Sammanfattning Nyckelord Master of Science Thesis INDEK 2011:x {Title} {Name1} {Name2} Approved Examiner Supervisor 2011-month-day {Name} {Name} Commissioner Contact person {Name} {Name} Abstract This research work proposes a new methodology for implementing Value Stream Mapping, in processes that feature a High-Mix, Low-Volume product base. The opportunity for adapting the methodology singularly for these types of environments was identified because implementing Value Stream Mapping as proposed in Learning to See features several drawbacks when implemented in High-Mix, Low-Volume. Although Value Stream Mapping has been proven to enhance many types of processes, its advantages are shrunk if they are implemented in High- Mix, Low-Volume processes. High-Mix, Low-Volume processes are types of processes in which a high variety of finished goods are produced in relatively low amounts. The high variety of finished goods causes several complications for the implementation of flow. The difficulties that prevent the flow are the following: • The variance in the products: With hundreds, or sometimes thousands of possible finished goods, the number of products causes a non-repetitive process. • The variance in the routings: All of the products that are produced can have completely different process routings, or order of stations it has to visit. -
The Art of Project Leadership: Delivering the World's Largest Projects
The art of project leadership: Delivering the world’s largest projects McKinsey Capital Projects & Infrastructure Practice September 2017 Retail February 2014 The art of project leadership: Delivering the world’s largest projects McKinsey Capital Projects & Infrastructure Practice September 2017 Preface The performance of large capital projects has been historically poor and prone to overruns despite extensive research, literature, and practice. Previous work has analysed why these projects have deviated from plan, and many root causes have been identified, largely around the issues related to systems, process, and technical mastery. However, we believe a critical element for successful large project delivery has so far been neglected: specifically the “soft” issues of project delivery such as leadership, organisational culture, mindsets, attitudes, and behaviours of project owners, leaders, and teams. In this report we refer to this blend of soft organisational topics as “the art of project leadership”, as opposed to standards, systems, processes, and technical subject matter expertise which we refer to as project management “science”. We assert that a better understanding of how to get this art right will materially improve delivery of large capital projects, and moreover, that it is especially true in the context of the largest and most complex capital projects (those with budgets exceeding US $5 billion). We set out to answer a simple question: Why do so many large projects continue to fall short of expectations despite so much global experience, learning, discussion, and analysis? To answer this question we researched the existing literature on the topic and conducted in-depth interviews with 27 large project practitioners, who collectively have more than 500 years of project delivery experience.