Quantitative Management Techniques

Lecturer: Dr Adrian Boucher FRSA

Introduction and Aims

This core Module of both Graduate Programmes is intended to equip all students of management with the necessary technical, evaluative and logical tools to enable them to operate effectively in the fast-moving world of Internet and web-enabled global business.  Regardless of any individual student’s particular interest in specific functional areas or application fields in business, one of the most challenging problems facing management today is the Knowledge Explosion.

Modern business is rapidly developing in many previously underdeveloped countries.  Worldwide research and development activities intensify, markets become increasingly competitive and global, with the Internet/world-wide web and telecommunications revolutions advancing, knowledge development is occurring at a frightening rate.  We need to develop expertise to capture and exploit this knowledge to engage in productive competitive activities to help develop sustainable competitive advantage, to ensure the long-term survival and growth of enterprises and contribute to the economic welfare of our homelands.

Introduction and Aims Continued

This combinatorial explosion of data and communications can be made worse by inappropriate data collection procedures. Too much data, having too great redundancy, can clog organizations’ information and administration systems. The resulting effects on business performance lead to process and workflow problems, causing the information flow equivalent of coronary heart disease.

As a student on the Graduate Diploma Programme and the First Year of the full-time MBA Programme, you will be expected to become proficient in a range of Business Management, Information Management and Knowledge Management skills. This Module, Quantitative Management Techniques, is intended to provide you with a structural analytical framework to help you to cope with the organization and interpretation of business data.

All businesses need data in order to function effectively. One of the most important aspects of this process is to build the analytical framework into a useful adjunct to other business activities. To do so, it is essential to turn data into information. Subsequent transformation of the information into knowledge allows one to improve the quality and effectiveness of business decision-making.

Development of the Internet/world-wide web has added another dimension to data gathering and analysis. By use of cookies, web server logfiles and relational databases, businesses are able to extract extremely useful and high-value added information on the behaviour of website visitors. Using techniques such as data-mining, collaborative filtering, online analytical processing (OLAP) and related statistical techniques, marketers are able to build up precise profiles of consumer behaviours, and thereby to tailor marketing campaigns closely to specified customer groups. This allows for more precise and cost-effective targeted marketing campaigns, so that budgets are spent more effectively than ever before. This Core Module in Quantitative Management Techniques will provide you with exposure to a range of analytical techniques designed to help you gain maximum benefit from all of the other Modules in the Programme. Emphasis will be placed on the application of these techniques and interpretation of results obtained from the analysis, rather than on mathematical rigour. However, some mathematics and the ability to undertake appropriate calculation are required.

This course aims to equip students with the basic mathematical and statistical techniques that underpin business analysis and decision-making.

Many business decisions (some authors estimate over 70% of business decisions) turn out, with hindsight, to be wrong. Any means of reducing this percentage is obviously extremely important to improving managerial decision-making performance, and is the major justification for including these techniques in DBA, MBA and other business studies programmes.

Many of the techniques will be used again, developed, or referred to in other course modules in both years of the programme.

Subject areas in which the concepts may be used explicitly or implicitly include:

  • Operations Management,
  • Accounting,
  • Finance,
  • Economics,
  • Project Management,
  • International Business,
  • Strategic Management
  • Marketing
  • Dissertation writing.

Objectives and Learning Outcomes

On completion of the course, students are expected to:

  • Understand the elementary concepts and techniques of Business Research Methods, and to appreciate how Statistical and Mathematical analysis can materially help in business decision-making. The concept of models of business processes, and the scope and limitations of commonly-adopted models;
  • Understand the concepts of populations and samples and the use of descriptive statistical tools for population and sample description. To use descriptive statistics to categorise and describe the shapes of data distributions; [measures of Centrality and Variation in Data]
  • Understand and be able to communicate best-practice in objective Data Visualization and Presentation

Objectives and Learning Outcomes Continued

  • To appreciate the different sampling procedures adopted in Business Analysis, Simple random sampling; quota sampling; systematic sampling; stratified sampling; sampling from processes, and the contexts in which these methods are adopted in marketing and other business activities.
  • Understand the nature and rules of objective and subjective probability and how to apply probability theory to the analysis and solution of simple business processes and decision problems.
  • Understand the concept of sampling distributions of sample statistics (means and proportions) and the behaviour of these distributions according to sample size [Central Limit Theorem] and its importance in statistical Hypothesis Testing
  • Appreciate the relationships between sample size and the choice of Test Statistic in determining the validity of conclusions concerning estimates of unknown population parameters, based on calculation of a statistic, calculated from a sample drawn from the population.
  • Use objective probability distributions in classical statistical decision problems involving confidence intervals and hypothesis testing in the context of Operations, Quality Assurance, Finance and Marketing
  • Understand the methods of structuring decision problems, selecting relevant management information, and representing sequential business decision-making problems graphically. This will also include discussion of suitable decision-making software applications.
  • Understand and use simple forecasting techniques (regression analysis and time series decomposition) and be able to articulate their use and limitations in business and economic analysis.
  • Formulate problems from narrative business descriptions, select and deploy the appropriate analytical techniques.
  • Be able to undertake calculations relevant to the above using simple calculators and Microsoft EXCEL (and similar spreadsheet packages, such as XLSTAT; DATADESK; MegaStat and other Statistical Packages available on the University computer networks).                

Module Assessment

Continuous Assessment: Assignment to count for 50% of total mark.

Examination Component: One 3-hour exam to count for 50% of total mark.

Your assignments to be handed in no later than 12 noon on Wednesday the 12th December 2012. (Two copies are required; one hard copy and one submitted via WebCT. A printed receipt from WebCT is required when you hand in your hard copy).