Evaluating Forecasting Methods
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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 -
Secure Collaborative Planning, Forecasting, and Replenishment (SCPFR)
Secure Collaborative Planning, Forecasting, and Replenishment (SCPFR) Mikhail Atallah* · Marina Blanton* · Vinayak Deshpande** Keith Frikken* · Jiangtao Li* · Leroy B. Schwarz** * Department of Computer Sciences ** Krannert School of Management Purdue University West Lafayette, IN 47907 May 30, 2005 Extended Abstract 1. Introduction It is well known that information-sharing about inventory levels, sales, order-status, demand forecasts, production/delivery schedules, etc. can dramatically improveme supply-chain per- formance. Lee and Whang (2000) describe several real-world examples. The reason for this improvement, of course, isn’t information-sharing, per se, but, rather, because shared infor- mation improves decision-making. However, despite its well-known benefits, many companies are averse to sharing their so-called “private” information, fearful that their partner(s) or competitor(s) will take advantage of it. Secure Multi-Party Computation (SMC) provides a framework for supply-chain partners to make collaborative forecasts and/or collaborative decisions without disclosing private in- formation to one another; and, most important, without the aid of a “trusted third party”. SMC accomplishes this through the use of so-called protocols. An SMC protocol involves theoretically-secure hiding of private information (e.g., encryption), transmission, and pro- cessing of hidden private data. Since private information is never available in its original form (e.g., if encryption is used to hide the data, it is never decrypted), any attempt to hack or misuse private information is literally impossible. In our research, we apply SMC protocols to facilitate collaborative forecasting and inventory-replenishment decisions between a single supplier and a single retailer. The model is an extension of Clark and Scarf (1960). -
Earned Value Management Tutorial Module 6: Metrics, Performance Measurements and Forecasting
Earned Value Management Tutorial Module 6: Metrics, Performance Measurements and Forecasting Prepared by: Module 6: Metrics, Performance Measurements and Forecasting Welcome to Module 6. The objective of this module is to introduce you to the Metrics and Performance Measurement tools used, along with Forecasting, in Earned Value Management. The Topics that will be addressed in this Module include: • Define Cost and Schedule Variances • Define Cost and Schedule Performance Indices • Define Estimate to Complete (ETC) • Define Estimate at Completion (EAC) and Latest Revised Estimate (LRE) Module 6 – Metrics, Performance Measures and Forecasting 2 Prepared by: Booz Allen Hamilton Review of Previous Modules Let’s quickly review what has been covered in the previous modules. • There are three key components to earned value: Planned Value, Earned Value and Actual Cost. – PV is the physical work scheduled or “what you plan to do”. – EV is the quantification of the “worth” of the work done to date or “what you physically accomplished”. – AC is the cost incurred for executing work on a project or “what you have spent”. • There are numerous EV methods used for measuring progress. The next step is to stand back and monitor the progress against the Performance Measurement Baseline (PMB). Module 6 – Metrics, Performance Measures and Forecasting 3 Prepared by: Booz Allen Hamilton What is Performance Measurement? Performance measurement is a common phrase used in the world of project management, but what does it mean? Performance measurement can have different meanings for different people, but in a generic sense performance measurement is how one determines success or failure on a project. -
Management Science
MANAGEMENT SCIENCE CSE DEPARTMENT 1 JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD Iv Year B.Tech. CSE- II Sem MANAGEMENT SCIENCE Objectives: This course is intended to familiarise the students with the framework for the managers and leaders availbale for understanding and making decisions realting to issues related organiational structure, production operations, marketing, Human resource Management, product management and strategy. UNIT - I: Introduction to Management and Organisation: Concepts of Management and organization- nature, importance and Functions of Management, Systems Approach to Management - Taylor's Scientific Management Theory- Fayal's Principles of Management- Maslow's theory of Hierarchy of Human Needs- Douglas McGregor's Theory X and Theory Y - Hertzberg Two Factor Theory of Motivation - Leadership Styles, Social responsibilities of Management, Designing Organisational Structures: Basic concepts related to Organisation - Departmentation and Decentralisation, Types and Evaluation of mechanistic and organic structures of organisation and suitability. UNIT - II: Operations and Marketing Management: Principles and Types of Plant Layout-Methods of Production(Job, batch and Mass Production), Work Study - Basic procedure involved in Method Study and Work Measurement - Business Process Reengineering(BPR) - Statistical Quality Control: control charts for Variables and Attributes (simple Problems) and Acceptance Sampling, TQM, Six Sigma, Deming's contribution to quality, Objectives of Inventory control, EOQ, ABC Analysis, -
Are Forecasting Models Usable for Policy Analysis? (P
Federal Reserve Bank of Minneapolis Are Forecasting Models Usable for Policy Analysis? (p. 2) Christopher A. Sims Gresham's Law or Gresham's Fallacy? (p.17) Arthur J. Rolnick Warren E. Weber 1985 Contents (p. 25) Federal Reserve Bank of Minneapolis Quarterly Review Vol. 10, No. 1 ISSN 0271-5287 This publication primarily presents economic research aimed at improving policymaking by the Federal Reserve System and other governmental authorities. Produced in the Research Department. Edited by Preston J. Miller, Kathleen S. Rolfe, and Inga Velde. Graphic design by Phil Swenson and typesetting by Barb Cahlander and Terri Desormey, Graphic Services Department. Address questions to the Research Department, Federal Reserve Bank, Minneapolis, Minnesota 55480 (telephone 612-340-2341). Articles may be reprinted if the source is credited and the Research Department is provided with copies of reprints. The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve Systemi Federal Reserve Bank of Minneapolis Quarterly Review Winter 1986 Are Forecasting Models Usable for Policy Analysis?* Christopher A. Sims Professor of Economics University of Minnesota In one of the early papers describing the applications of and, in a good part of the economics profession, has the vector autoregression (VAR) models to economics, position of the established orthodoxy. Yet when actual Thomas Sargent (1979) emphasized that while such policy choices are being made at all levels of the public models were useful for forecasting, they could not be used and private sectors, forecasts from these large models, for policy analysis. -
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. -
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. -
Using Earned Value Data to Forecast Program Schedule
Using Earned Value Data to Forecast the Duration and Cost of DoD Space Programs Capt Shedrick Bridgeforth Air Force Cost Analysis Agency (AFCAA) Disclaimer The views expressed in this presentation are those of the author and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. 2 Overview 1. Summary 2. Background 3. Methodology 4. Results 5. Conclusion 3 Summary AFCAA studied the accuracy of various cost estimating models for updating the estimate at completion (EAC) for space contracts The Budgeted Cost of Work Performed (BCWP) based model was the most accurate The BCWP model assumed the underlying duration estimate was accurate Objective: Assess the accuracy of the duration method used in the AFCAA study and explore additional methods Duration Results: 2.9 to 5.2% overall improvement (mean absolute percent error - MAPE) Cost Results: 7.5% overall improvement (MAPE) Background EACBCWP = (MonthEst Completion – Monthcurrent) * BCWPBurn Rate + BCWPTo Date [# of months remaining * earned value/month + earned value to date] Duration: the Critical Path Method (CPM) is used to determine the duration of a project (contract) Contractor Reported Estimated Completion Date (ECD) “Status quo” Background BCWP vs. Time 900 800 700 Millions 600 500 400 300 200 100 0 0 2 4 6 8 10 12 Months y = 72,401,283x - 45,653,892 R² = 0.996 6 Background -
Customer Relationship Management (CRM): a Technology Driven Tool
Volume IV March 2012 Volume IV March 2012 Research Papers 2 Customer Relationship Management (CRM): A Technology Driven Tool Dr. Mallika Srivastava Assistant Professor, SIBM, Pune E-mail : [email protected] Introduction Customer Relationship Management (CRM) is a management approach that seeks to create, develop and enhance relationships with carefully targeted customers in order to maximize customer value, corporate profitability and thus shareholders’ value. Managing relationship with the customers has been of importance since last many centuries, but with invent of information technology a new discipline in name of CRM has emerged. The CRM is primarily concerned with utilizing information technology to implement relationship marketing strategies. The emergence of CRM is a consequence of a number of trends like shift in business focus from transactional to relationship marketing, transition in structuring organizations on a strategic basis from functions to processes, and acceptance of the need for trade-off between delivering and extracting customer value. The greater utilization of technology in managing and maximizing value of information has also led to modern shape of CRM. The aim of CRM is to acquire and retain customers by providing them with optimal value in whatever way they deem important. This includes the way businesses communicate with them, how they buy, and the service they receive - in addition, of course, getting the best through the more traditional channels of product, price, promotion and place of distribution. Essentially, CRM is a customer focused business strategy which brings together customer lifecycle management, business process and technology. The trend for companies to shift from a product focused view of the world to a customer focused one is the modern strategy of the business, as products become increasingly hard to differentiate in fiercely competitive markets. -
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. -
Reimagine Forecasting: High Stakes Decision Making for Cfos
Unlock data possibilities www.pwc.com/analytics Make better and faster decisions Expand your perspective with trusted and actionable data-driven insights for extraordinary results Reimagine forecasting: High stakes decision making for CFOs May 2016 How can CFOs deliver more value to their organizations? Reimagining forecasting Unlocking the power of may seem like a gratuitous data and analytics to think exercise in crystal ball about what will happen, design. But it is becoming a assessing its likelihood, critical step that CFOs should and contemplating the take to make sure their implications mean the companies are using leading difference between accurate indicators to maintain their or inaccurate forecasts, positions as market-leading with multi-million or billion organizations, strive to dollar impacts. innovate, and protect the brand – instead of lagging the pack with sub-optimal indicators. “The future ain’t what it used to be.” —Yogi Berra It’s time to reimagine forecasting: Get started We are always forecasting - thinking The signals in the noise increasing cost or ceding revenue to about what will happen, assessing It can be very difficult to find the signal competitors with close substitutes. its likelihood, and contemplating the in the noise, and it is becoming more In services industries, the inability to implications. But for CFOs, the stakes are important than ever. Short-termism accurately forecast demand for specific much higher – the difference between and fast news cycles exacerbate the offerings requiring specific skill sets to accurate versus inaccurate forecasts can effects of missing a forecast on company deliver may result in having too much be worth billions of dollars, and a job. -
Forecasting Supply Chain Management
Forecasting A forecast is a prediction of what will occur in the future. Meteorologists forecast the weather, sportscasters and gamblers predict the winners of football games, and companies attempt to predict how much of their product will be sold in the future. A forecast of product demand is the basis for most important planning decisions. Planning decisions regarding scheduling, inventory, production, facility layout and design, workforce, distribution, purchasing, and so on, are functions of customer demand. Long-range, strategic plans by top management are based on forecasts of the type of products consumers will demand in the future and the size and location of product markets. Forecasting is an uncertain process. It is not possible to predict consistently what the future will be, even with the help of a crystal ball and a deck of tarot cards. Management generally hopes to forecast demand with as much accuracy as possible, which is becoming increasingly difficult to do. In the current international business environment, consumers have more product choices and more information on which to base choices. They also demand and receive greater product diversity, made possible by rapid technological advances. This makes forecasting products and product demand more difficult. Consumers and markets have never been stationary targets, but they are moving more rapidly now than they ever have before. Management sometimes uses qualitative methods based on judgment, opinion, past experience, or best guesses, to make forecasts. A number of quantitative forecasting methods are also available to aid management in making planning decisions. In this chapter we discuss two of the traditional types of mathematical forecasting methods, time series analysis and regression, as well as several nonmathematical, qualitative approaches to forecasting.