Supply Chain Management
This course is intended for students interested in general management or careers in consulting, operations, or marketing. Understanding how supply chain management impacts business performance is also of value for students aspiring to accounting and finance careers.
Note: this course counts towards the following Majors: Consulting, Management Analytics, Process and Supply Chain Management.
- Understand how to make supply chain design and policy decisions to develop the supply chain capabilities required to support the business strategy and improve the performance of a firm and of an entire supply chain.
- Learn how to examine and improve the flow of materials and information through a network of suppliers, manufacturers, distributors, and retailers in order to help firms get the right product to the right customer in the right amount and at the right time.
- Learn how to make decisions on the following fundamental supply chain performance drivers: facilities, inventories, transportation, information, sourcing and pricing.
- Special emphasis is given to understanding of how supply chain decisions have to account for coordination requirements within and across firms, the impact of uncertainty, and the specific product and customer characteristics that derive from the overall business strategy.
Supply chains are networks of organizations that supply and transform materials, and distribute final products to customers. This course views the supply chain from a general manager’s perspective. Supply chain management represents a great challenge as well as a tremendous opportunity for most firms. If designed and managed properly, supply chains are a crucial source of competitive advantage for both manufacturing and service enterprises. There is a realization that no company can do any better than its supply chain. This becomes even more important as product life cycles are shrinking, product and service variety is growing and competition is intensifying. The course emphasizes the use of qualitative and quantitative analysis in making supply chain management decisions.
Operations Management Strategy
The course is designed to be of interest to students considering positions in the transformative parts of firms. These include leading roles production and manufacturing, in service design and fulfillment, and supply chain management. Most of the topics require systematic thinking and many topics require some mathematical analysis.
- Enable students to formulate an operations strategy for a firm.
- Provide some concepts, tools and experiential input on how to analyze, value and optimize the key decisions involved in an operations strategy. These decisions include benchmarking; capacity expansion, timing, flexibility and location; sourcing and supply management; operational hedging; innovation and learning; and development of relevant indicators and methods for performance management.
Operations Management Strategy builds on the core operations course, focusing on the higher-level issues of strategic operations management discussed in that course. Operations strategy consists of the development and use of operating capabilities, whether they are in service provision, manufacturing, or supply chain fulfillment, to further the competitive position of the firm.
We discuss trade-offs firms make in their operations and the underlying assets and processes they invest in to support their decisions. We consider both external views of the organization to ascertain their competitive choices and internal views to estimate their productivity and competitive threats. We study examples of asset sizing, type, and flexibility. We also study how firms assess the buy vs. make decision in supply chains. We consider innovation and operations development. Finally, we consider practices in Performance Management as a mechanism to maintain strategy execution.
Each topic will be discussed using a combination of models, case-discussions, and readings. The anticipated mix for the course is 60-40 qualitative-quantitative. In a typical session we will cover one major case in-depth, supplemented by mini-lectures, presentations and qualitative discussions of other examples highlighting current material where possible. Active class participation is expected.
Service Operations Management
Students interested in applying the concepts of operations management to the service industry. We consider applications in healthcare, travel/leisure related industries, retail management, retail banking, insurance, and call center management. As such, students with interests in services management or management consulting would benefit from the course.
- Discuss means by which new service processes are designed to match market demand.
- Foster your ability to analyze services with regards to their ability to deliver on promises.
- Provide you with tools to assist in determining appropriate service capacity.
- Provide you with tools to assist in service pricing.
- Provide you with tools that you can apply to the design and improvement of service quality.
- Demonstrate service industry leadership through guest speaker presentations.
- Encourage an active, constructively critical posture as consumers of services whose aim is to stimulate service providers to improve service quality.
The service sector represents the largest segment of most industrial economies. In Canada, it accounts for 70% of GDP and 76% of employment. Yet over the last 30 years, service sector productivity growth has consistently lagged that of the manufacturing sector. In addition, the effects of deregulation, technological change, expanding world trade and increasingly sophisticated consumers are combining to create new competitive pressures in a variety of service industries, ranging from transportation to health care. As a result, issues of operating efficiency and competitiveness are becoming more critical than ever for success in service industries. To succeed as managers in this environment, you must understand how to effectively organize work, analyze and improve operating practices, optimally allocate resources and guide the application of new delivery technologies.
Major service sectors such as health care, banking and financial services, transportation, restaurants, hotels and resorts are examined. The course addresses both strategic analysis and operational decision making, with emphasis on the latter. Among the topics covered are: the service concept and operations strategy, the design of effective service delivery systems, capacity planning, and productivity and quality management.
Modelling and Optimization for Decision Making
The course is intended for students interested in learning how to support decision making using business analytics. Specifically, we will focus on formulating models to enhance analytic decision making in applications such as finance, marketing, and operations. We will learn how to formulate, solve, and analyze models based upon optimization, decision trees, and simulation. These models and language will be required by any effective member of the C-suite in the future. This course is targeted at all MBA students.
- To improve students ability in the area of business analytics decision making supported by fact-based, data-driven, quantitative analysis
- To develop, integrate and reinforce students’ modelling skills in a variety of applications
- To enhance and reinforce students’ ability to intelligently use information in the presence of uncertainty
In this course, we will learn how to structure, analyze, and solve business decision problems using business analytics tools. We will focus on problems involving decision-making and risk analysis. The emphasis of the course will be on systematic, critical and logical thinking, and problem solving and their implementation. We will start with the basic techniques of good modeling and organization, and proceed to introduce a variety of modeling techniques and approaches. All along, we will critically think on how to interpret the results of our analysis process in the context of data driven decision-making. These will be illustrated by building and analyzing problems in finance, marketing, and operations. While the underlying concepts, models, and methods of this course are analytical in nature, we will develop them on intuitive and easy to use spreadsheets, always focusing on the ideas and insights, rather than the underlying mathematical details. We will discuss the application of these methods with other software. We will study four specific techniques: sensitivity analysis (what if analysis), optimization, decision trees, and simulation. The usage of these techniques in practice can improve the decision making process in many situations. In many practical situations these techniques are essential for effective last steps as part of business analytics- prescribing decisions based on available data and its analysis.
Data and Information Management for Business Analytics
This course is for those that want to gain the foundational skills of working with data. 80% of business analysis efforts are spent on finding, extracting, cleaning, and visualizing data prior to performing the actual data analysis. Whether or not you are considering a career in data sciences, this course introduces the essential skills for working with data that can apply to careers in information technology, business, finance, marketing analysis, or data sciences. This is a hands on course focused on developing practical skills across a broad range of the most common data management and analysis concepts, such as relational database design and its implementation with SQL and Python, programming fundamentals, data manipulation and data cleaning and preparation, and data analysis and visualization techniques using Python.
- Apply the principles of Database Management System Design concepts from analysis to full implementation
- Create Structured Query Language (SQL) queries appropriate for data extraction and summarization tasks
- Demonstrate the ability to prepare, explore and validate data for business analysis
- Apply advanced data analysis techniques towards the development of decision-making tools
- Develop Business Intelligence Dashboards to support business decision-making
This course focuses on the fundamentals of data management for the purposes of producing information and supporting advanced techniques of business analytics. This applied learning course will expose the learner to a broad range of technical skills that are required to access, prepare and visualize data for advanced analysis. The course introduces the fundamental skills that form the foundation in the development of advanced business analytics. These include:
- Database Management Fundamentals
- Understanding how data is stored and retrieved in both relational and non-relational data systems
2. Data Preparation for Analysis
- Defining a business objective and determining the data requirements to support it
- Performing assessments of data to determine suitability for use and identify data quality issues and apply mitigating strategies
- Applying data manipulation and transformation techniques to support analysis
3. Data Analysis and Visualization
- Performing exploratory data analysis to delve into the data, examine and discover important interrelationships between attributes, and identify interesting subsets or patterns
- Developing data visualization solutions that support: exploratory analysis, insight generation, and decision making
Using a combination of theory and practical exercises and case studies, the learner will develop the data acquisition, preparation, and preliminary analysis skills that are a necessary pre-requisite to applying advanced business analytics to their data.
Quality Management with Lean Six Sigma
Students interested in learning about lean six sigma and how best to employ the framework and tools in organizations to achieve excellence in operational quality.
- Learn how to achieve quality improvements using lean six sigma methodologies and tools. Knowledge gleaned from the course can prepare students for green belt certification
- Apply the tools and framework to real life business problems
- Understand lean six sigma transformations in organizations
This quality management course will provide students with the foundational elements of the lean six sigma approach. The course will have two main components; a theoretical one followed by a hands-on project. The theoretical component will introduce the Define, Measure, Analyze, Improve, and Control methodology with corresponding process and statistical tools. In the hands-on project students will apply the lean six sigma quality management framework and tools to solve business problems within different organizations. (The course can serve as preparation for the green belt lean six sigma certification.)
Students who are interested in acquiring hands-on skills in data structuring and predictive modelling and analytics to support business decision-making. There are no formal prerequisites for the course beyond first-year courses.
- Expose students to key Predictive Analytics and Machine Learning tools
- Enable students to:
- Structure business decisions as “analytical” problems
- Identify which data sources are needed to provide an answer
- Structure the data for analysis using common data management operations (joins, aggregations, disaggregations, etc.)
- Understand and apply appropriate analytical tools
- Derive managerial insights from the analytical results
- Communicate the findings effectively
- Expose students to some effective applications of predictive modeling across a variety of functional areas and make them aware of both promises and challenges involved.
The course is designed for students interested in advanced analytics and data-driven decision-making techniques. Analytics (data science/ data mining/ machine learning) skills are increasingly important across a wide spectrum of industries and functional areas. In this hands-on course students will be exposed to all aspects of predictive analytics, starting with data acquisition, preparation and structuring, proceeding to data modeling techniques, and then using the results to support effective decision-making. The course will expose student to advanced software tools for data management and manipulation, data visualization and modeling.
The course will be divided into several modules focusing on the main steps in a typical Predictive Analytics project:
- Translation of a business problem to a set of ‘analytical” questions. This involves identifying the key elements of the analytical study to be conducted (unit of the analysis, data to be measured, evaluation of results, implementation issues)
- Acquisition, cleaning and transformation of data using SQL-type tools
- Conducting preliminary exploratory analysis using descriptive tools, as well as data visualization and dimensionality reduction techniques
- Identifying and applying appropriate analytical models. We will use a variety of tools, ranging from multiple and logistic regression to decision trees, random forests, boosting and regularization methods, clustering, neural networks, and other tools.
- Effective communication of analytical findings to business managers.
The class builds on skills acquired in first-year courses on Statistics, Data and Models. It is one of the core courses for the Management Analytics major. While this course is an excellent complement to Modeling and Optimization for Decision Making (another core course), the latter is not a pre-requisite – all analytical techniques employed will be introduced within the course. We will discuss applications of analytical techniques to business issues drawn from many functional areas, including marketing, operations, strategy, etc.