DOCSLIB.ORG

Forecasting Management

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Forecasting Management FORECASTING MANAGEMENT Authors: Andreas Jessen Carina Kellner Supervisor: Professor Hans Jansson Program: Growth Through Innovation & International Marketing Subject: International Marketing Level and semester: Masterlevel, Spring 2009 Baltic Business School Master Thesis, VCE Region International, Spring 2009 i Abstract Abstract In a world that is moving faster and faster, a company’s ability to align to market changes is becoming a major competitive factor. Forecasting enables companies to predict what lies ahead, e.g. trend shifts or market turns, and makes it possible to plan for it. But looking into the future is never an easy task. “Prediction is very difficult, especially if it’s about the future.” (Niels Bohr, 1885-1962) However, progress in the field of forecasting has shown that it is possible for companies to improve on forecasting practices. This master thesis looks at the sales forecasting practices in MNCs primarily operating in emerging and developing countries. We examine the whole process of sales forecasting, also known as forecasting management, in order to develop a comprehensive model for forecasting in this type of companies. The research is based on a single case study, which is then later generalized into broader conclusions. The conclusion of this master thesis is that forecasting is a four-step exercise. The four stages we have identified are: Knowledge creation, knowledge transformation, knowledge use and feedback. In the course of these four stages a company’s sales forecast is developed, changed and used. By understanding how each stage works and what to focus on, companies will be able to improve their forecasting practices. Key Words: Sales Forecasting, Sales Forecasting Process, Forecasting Management, Forecasting Methods, Performance Measurement, Domain Knowledge, Forecasting Systems, Emerging and Developing Country Markets, Construction Equipment, VCE Region International, Volvo ii Master Thesis, VCE Region International, Spring 2009 iii Acknowledgements Acknowledgements With this acknowledgement, we would like to express our deep gratitude to all people that contributed time and effort in supporting us when writing this thesis. Without these people this thesis would not have achieved such a high level of quality that we believe it has. First, we would like to sincerely acknowledge our supervisor Professor Hans Jansson for his guidance, supervision and support throughout the entire research. Moreover, we would like to thank our fellow students for profitable discussions and feedback. Furthermore we would like to address special thanks to Terese Johansson for all of her assistance and support during this research. Also, we would like to thank our case company Volvo Construction Equipment Region International and its management for making this research possible. In particular, we would like to thank Anders Sjögren who provided us with excellent supervision from the initial kick- off meeting in Eskilstuna until the final hand-in of this master thesis. We would also like to thank all participating respondents at VCE Region International for taking their time to give us valuable inputs and information for this study. Finally, we would like to thank our families and friends for their encouragement during the last five months, as well as their patience and great support. Kalmar, SWEDEN Spring 2009 Andreas Jessen Carina Kellner iv Master Thesis, VCE Region International, Spring 2009 v About the Authors About the Authors Andreas and Carina are both studying in Sweden at Baltic Business School, University of Kalmar, completing the master degree program: Growth through Innovation and International Marketing. In the course of our studies at BBS, we accomplished several assignments for Volvo Construction Equipment with great results. When the opportunity to write a Master Thesis for Volvo Construction Equipment occurred, we were immediately interested. We are thankful for this opportunity and put our best effort into achieving a good thesis – and we can honestly declare that we are proud of the result! Andreas Jessen was born in May 1985 and grew up in Odense, Denmark. After finishing his engineering degree in business development at Århus University – Herning Institute of Business Administration and Technology, Andreas started on a Master of Technology Based Business Development. In the course of this master he went a year to Kalmar to study his second master. Andreas has not worked with sales forecasting previously but has experience in technology forecasting, which do have some similarities. His motivation for this thesis lies in the opportunity to work with a company such as Volvo Construction Equipment, and to get real life experience in working with a subject of great interest to him. Carina Kellner was born in November 1985 and grew up in Tulln, Austria. She graduated with a Bachelor of Business Administration degree majoring in Business Administration and E-Business Management from IMC FH Krems, University of Applied Science, Austria in 2008. In the course of her Bachelor studies, she spent half a year as an exchange student in Helsinki/Finland, as well as completed an internship in Vancouver/Canada for half a year. Her motivation for this thesis lies within her natural passion for market research, and her marketing-, management- and project planning background in her Bachelor’s degree. Her interest has been in applying this knowledge in the real business world. She would like to further develop her career in business consulting and international marketing. This thesis concludes their studies for a Master of Science in Growth through Innovation and International Marketing at Baltic Business School, University of Kalmar, Sweden. vi Master Thesis, VCE Region International, Spring 2009 vii Abbreviations Abbreviations 4C New VCE Forecasting system and Process, “Foresee” Act Actuals, i.e. units sold AH Articulated Haulers ASM Area Sales Manager BC Regional Business Coordinator CCC Communication, Cooperation, and Collaboration DBM Dealer Business Meeting EtA Expected date of arrival to destination port EtD Expected date of delivery from port EWI-Report Early Warning Indicator Report F0x Forecast month x Fc Forecast - When talking about forecast, we mean sales forecasting, not to mix up with other types of forecasting, e.g. technology forecasting. HQ Head Quarter IOA Islands of analysis KPI Key Performance Indicator MAPE Mean Absolute Percentage Error MAS Machine Administration System, for machine ordering & reporting MNC Multinational Company MOM Master Order Management, Order System OOH Orders On Hand R12 F0x Rolling 12 months forecast from month x RBF Rule Based Forecasting ROH Retails on Hand, i.e. all end-customer orders known by Dealer (also referred to as Orders on Hand or Order Backlog) S&OP Sales and Operations Planning S&OP Leader Responsible person for a product group SAP Systems, Applications, and Products in Data Processing TM Total Market TOD Target for Operational Development VCE Volvo Construction Equipment VDN Volvo Dealer Network VFO Volvo Front Office VP Vice President for each sub-region VP Sales Vice President of Sales Region International VPT Volume Planning Team YTD Year-to-Date viii Master Thesis, VCE Region International, Spring 2009 ix Table of Contents Table of Contents 1 Introduction ..................................................................................................................2 1.1 Background .............................................................................................................2 1.2 The Case Company .................................................................................................2 1.3 Research Problem ...................................................................................................4 1.3.1 Problem Definition ..............................................................................................5 1.4 Purpose of the Thesis ..............................................................................................7 1.5 Delimitations...........................................................................................................8 1.6 Contributions ..........................................................................................................8 1.7 Outline of the Thesis ...............................................................................................9 1.8 Time-Plan for Research ......................................................................................... 10 2 Methodology ................................................................................................................ 12 2.1 Scientific Approach ............................................................................................... 12 2.2 Research Strategy .................................................................................................. 13 2.2.1 Strengths and Weaknesses of a Case Study ........................................................ 14 2.3 The Case Study Research Design .......................................................................... 16 2.3.1 Overall Intent .................................................................................................... 16 2.3.2 The Single Embedded Case Study Design ......................................................... 17 2.3.3 Sampling ........................................................................................................... 19 2.3.4 The Researcher’s Role ......................................................................................
Load more

Recommended publications
  • 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
    [Show full text]
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • 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,
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]