2500 University Dr NW
Calgary, T2N 1N4
Canada

©2019 by Operations and Supply Chain Management Association.

Operations Management

Optimization and Analysis of People, Resources, & Processes.

Operations Management oversees all activities directly related to making a product or providing a service. This functional area is responsible for the processes that transform or convert inputs of materials, equipment, energy, information, and human skills into goods and services that satisfy customer needs. This includes business processes (purchasing, order entry, billing, recruiting, training, payroll, etc.) where the immediate customers are primarily internal to the organization. Large portions of the organizational resources (labour, capital, and materials) are devoted to or allocated by the operations function. The curriculum will often include hands on industry-based projects.

 

Operations Management (OPMA) Courses

Instruction offered by members of the Haskayne School of Business through the University of Calgary. 

Prerequisite(s): Admission to the Haskayne School of Business, Operations Management 317 and Management Studies 391.

OPMA 401

Production Planning and Control​

An in-depth treatment of inventory and production within an organization. Topics covered include inventory control, sales and operations planning, materials requirements planning, and lean processes. Recent advances in manufacturing may also be discussed.

OPMA 403 

Managing Quality in Products and Services

An in-depth treatment of quality management practices and techniques for products and services. Topics and techniques covered include designing and assuring quality, quality issues in the supply chain, statistical quality measurement, and continuous process and quality improvement.

OPMA 405

Service Operations Management

The management of service businesses from both a qualitative and quantitative perspective. Topics may include service design and performance measurement, service quality and recovery, managing people in service industries, service demand forecasting, scheduling, managing lineups, yield management, network optimization, and the role of information technology. Industry examples include travel and hospitality, professional services, retail, communication and transportation and banking.

OPMA 407

Project Management

The management of projects in a variety of settings such as software development and installation, disaster relief, new product development, advertising campaigns and financial auditing are examined. Material from the organizational, planning, technical, financial, informational, and logistical aspects of project management will emphasize the interdisciplinary nature of projects. Use of commercial computer software for planning and scheduling projects is learned.

OPMA 409 

Computer Simulation for Business

Companies encounter numerous problems that are characterized by uncertainties for which they need to find a solution. Simulation provides a means for imitating the behavior of real-life situations in a computer environment, allowing for "what-if" analyses of different scenarios. Hands-on experience in creating simulation models and obtaining reliable results for decision making with the use of different simulation techniques is gained.

OPMA 411

Field Investigations in Operations Management

Field investigation concerned with operational improvements with an off-campus organization. Students work in teams on a single project. Both oral and written reports are required.

OPMA 415

Prescriptive Models in Business Analytics

Identification of data requirements for analytical decision-making. Case studies used throughout to develop insight for decision making while dealing with incomplete and ambiguous data. Introduction to VBA-enabled spreadsheet modelling to design and implement optimization models without/with uncertainty as well as advanced Monte Carlo simulation.

OPMA 419

Predictive Models in Business Analytics

Decision making with big data and predictive analytics methods. Students learn to describe and visualize business data, as well as to make predictions and classifications with data. Methods include regression models, regression trees, association rules and cluster analysis.