Utilize the DMAIC methodology (define, measure, analyze, improve, and control) to expand your knowledge of statistics to improve business processes and enhance your career.
Lean Six Sigma is a methodology for increasing organizational productivity and efficiency through a structured problem solving process called DMAIC (define, measure, analyze, improve, and control). The focus is on improving organizational systems and work processes.
The advanced certificate in Lean Six Sigma is for engineers, process-improvement facilitators, and other practitioners looking to increase their effectiveness or enhance their qualifications to broaden their careers. Industry certifications such as lean six sigma green belt and black belt are not the focus of this academic program, but students interested in obtaining these credentials are well prepared to do so after the deep topical coverage offered in this advanced certificate program. See Lean Six Sigma for Students or contact the program office for details.
What is a graduate certificate?
A graduate certificate, also called an advanced certificate, is a selection of up to five graduate level courses in a particular area of study. It can serve as a stand-alone credential that provides expertise is a specific topic that enhances your professional knowledge base, or it can serve as the entry point to a master's degree. Some students complete an advanced certificate and apply those credit hours later toward a master's degree.
Lean Six Sigma, advanced certificate, typical course sequence
Sem. Cr. Hrs.
Lean Six Sigma Fundamentals
This course presents the philosophy and methods that enable participants to develop quality strategies and drive process improvements. The fundamental elements of Lean Six Sigma are covered along with many problem solving and statistical tools that are valuable in driving process improvements in a broad range of business environments and industries. Successful completion of this course is accompanied by “yellow belt” certification and provides a solid foundation for those who also wish to pursue a “green belt.” (Green belt certification requires completion of an approved project and exam, both of which are beyond the scope of this course).
Choose one of the following:
Applied Statistical Quality Control
An applied approach to statistical quality control utilizing theoretical tools acquired in other math and statistics courses. Heavy emphasis on understanding and applying statistical analysis methods in real-world quality control situations in engineering. Topics include process capability analysis, acceptance sampling, hypothesis testing and control charts. Contemporary topics such as six-sigma are included within the context of the course.
Statistical Quality Control
A practical course designed to provide in-depth understanding of the principles and practices of statistical process control, process capability, and acceptance sampling. Topics include: statistical concepts relating to processes, Shewhart charts for attribute and variables data, CUSUM charts, EWMA charts, process capability studies, attribute and variables acceptance sampling techniques.
Choose one of the following:
Design of Experiments
This course presents an in-depth study of the primary concepts of experimental design. Its applied approach uses theoretical tools acquired in other mathematics and statistics courses. Emphasis is placed on the role of replication and randomization in experimentation. Numerous designs and design strategies are reviewed and implications on data analysis are discussed. Topics include: consideration of type 1 and type 2 errors in experimentation, sample size determination, completely randomized designs, randomized complete block designs, blocking and confounding in experiments, Latin square and Graeco Latin square designs, general factorial designs, the 2k factorial design system, the 3k factorial design system, fractional factorial designs, Taguchi experimentation.
Design of Experiments
How to design and analyze experiments, with an emphasis on applications in engineering and the physical sciences. Topics include the role of statistics in scientific experimentation; general principles of design, including randomization, replication, and blocking; replicated and un-replicated two-level factorial designs; two-level fractional-factorial designs; response surface designs.
Total Semester Credit Hours
Data Management for Business Analytics
This course introduces students to data management and analytics in a business setting. Students learn how to formulate hypotheses, collect and manage relevant data, and use standard tools such as Python and R in their analyses. The course exposes students to structured data as well as semi-structured and unstructured data. There are no pre or co-requisites; however, instructor permission is required for students not belonging to the MS-Business Analytics or other quantitative programs such as the MS-Computational Finance which have program-level pre-requisites in the areas of calculus, linear algebra, and programming.
This course addresses project management from a multidisciplinary perspective, covering the fundamental nature of and techniques for managing a broad range of projects. Topics cover the Project Management Life Cycle from Planning to Termination. It also addresses the behavioral and quantitative facets of project management, as well as the use of methods, tools and techniques for the initiation, planning, and execution of projects. Introduces the standard framework, processes and knowledge areas of the Project Management Institute. *Note: Bachelors degree or minimum of 5 years of work experience in a project related business environment. Recommended education or work experience in organizational behavior, mathematics and basic accounting. *Note: BUSI-510 may not be substituted for BUSI-710 in a graduate concentration or the advanced certificate in project management. Additionally, a student may not register for and receive credit for both BUSI-510 and BUSI-710, whether taken as an undergraduate or graduate student.
Advanced Project Management
Advanced Project Management covers the topics necessary for implementation of and excellence in project management. It deals with turning the principles and theory of project management into practice. The course addresses the best practices for project management in the world; project portfolio management and ROI; the project office and Six Sigma; project risk management and integrated projects; corporate cultures, behavior, and cultural failures; informal, adaptive, and extreme project management; and critical chain project management. Integrates aspects of the framework, processes and knowledge areas of the Project Management Institute. *Note: Advanced Project Management is available in on-campus and online formats.
International Project Management
With the increasing frequency of globalization, mergers, and acquisitions, international projects are becoming more prevalent and approaching the norm for many organizations. This course addresses a wide range of international projects—based in different industries and multiple countries. It deals with cultural and social differences within firms; cultural and social differences among countries and within countries; languages and dialect variations; different management practices and structures; religious practices; legal, regulatory, and reporting requirements; technology and infrastructure differences in different regions; and time zone differences. Incorporates aspects of the framework, processes and knowledge areas of the Project Management Institute.
Agile Project Management
Business agility allows organizations to quickly adapt to new markets. In a fast paced ever changing world of highly competitive products and services, organizations need to be able to deliver solutions to market quickly in an uncertain environment. Agile Project Management provides an iterative and incremental framework to explore and deliver high risk solutions efficiently in a rapid response timeframe. We will explore Agile Project Management practices across multiple industries including Agile project roles following the Project Management Institute® Agile Practice Guide.
Operations and Supply Chain Management
Study of the management of operations and supply chain management. Encompasses both manufacturing and services. Topics include operations and supply chain strategy, ethical behavior, forecasting; work systems, inventory management, capacity and materials planning, lean operation, supply chain design and closed-loop supply chains, global operations, quality management, quality control, and quality improvement, project management; and current issues.
A study in the principles of project management and the application of various tools and techniques for project planning and control. This course focuses on the leadership role of the project manager, and the roles and responsibilities of the team members. Considerable emphasis is placed on statements of work and work breakdown structures. The course uses a combination of lecture/discussion, group exercises, and case studies.
Quality Control and Improvement
Study of total quality management (TQM), including Deming’s philosophy, Six Sigma, quality planning, quality cost principles, problem-solving methods and tools, the use of statistical methods for quality control and improvement, supplier relations, and recent developments in quality. The course focus is on the management and continuous improvement of quality and efficiency in manufacturing and service organizations.
Global Business Analytics
This course is designed to help students, regardless their backgrounds, to identify global business opportunities, possess necessary analytical skills to evaluate these opportunities, and understand the strategies to explore these opportunities to serve transnational businesses’ goals. Students will be exposed to a variety of analytical skill sets such as collecting and analyzing institutional and primary international business data, reading the multinational firm-level data and understanding how global expansion impacts firms’ bottom lines, developing foreign exchange hedging strategies, and apprehending the basic practices of international trade and foreign investment.
Contemporary Production Systems
The focus of this course is Lean. Lean is about doing more with less - less human effort, less equipment, less time, less space. In other words, lean is about the application of industrial engineering principles and tools to the entire supply chain or value stream. The focus of this course will be learning and applying the principles and tools of lean such as value, value stream mapping, takt, flow, pull, kaizen, standard work, line design, and others, all in the context of continuous process improvement. By the end of this course, the student will possess the essential tools and skills to apply lean in their production system from either a line (supervisor or manager) or staff role.
Supply Chain Management
Supply chain management is unique in that it is one of the oldest business activities and yet has been recently discovered as a potentially powerful source of competitive advantage. Supply chain system activities planning production levels, forecasting demand, managing inventory, warehousing, transportation, and locating facilities have been performed since the start of commercial activity. It is difficult to visualize any product that could reach a customer without a consciously designed supply chain. Yet it is only recently that many firms have started focusing on supply chain management. There is a realization that no company can do any better than its supply chain and logistics systems. This becomes even more important given that product life cycles are shrinking and competition is intense. Logistics and supply chain management today represents a great challenge as well as a tremendous opportunity for most firms.
This course discusses several strategic, tactical, and operational concepts used in improving the distribution of goods and services by companies worldwide. The course emphasis is on understanding when and how these concepts are applied, as well as on using mathematical programming and optimization methods for their adequate implementation.
This course will cover the role, the steps and the analysis methods to produce goods and services in support of the production and operations management functions. Topics include: forecasting, inventory policies and models, production systems and philosophies (e.g. JIT/Lean), job shop scheduling, aggregate production planning, and Material Requirement Planning (MRP). Students will understand the importance of production control and its relationship to other functions within the organization. Case studies and the design of actual production systems will be emphasized.
Global Facilities Planning
Facilities planning determines how an activity's tangible fixed assets best support achieving the activity's objective. This course will provide knowledge of the principles and practices of facility layout, material handling, storage and warehousing, and facility location for manufacturing and support facilities. Tools for sizing the resources needed, planning, design, evaluation, selection, and implementation will be covered. The focus of the course will cover both management and design aspects, with the focus being more heavily on the management aspects.
Production Systems Management
The focus of this course is Lean. Students who take this course should be interested in building on their basic knowledge of (lean) contemporary production systems and developing the breadth and depth of their understanding, with a focus on the managerial, quantitative, and systems aspects. It will also address value streams beyond manufacturing - specifically logistics. This course should enable the student to practice the application of lean concepts in the context of systems design at the enterprise level.
This course will provide an introduction to concepts and techniques in the design and analysis of production systems. A blend of traditional and modern approaches is brought into the classroom. At the end of the term, the student will be able to assess and analyze the performance of a given manufacturing system as well as to provide a framework for system redesign and improvement. Modern aspects such as lean manufacturing and setup time reduction are included in the context of the course.
Systems and Project Management
Systems and Project Management ensures progress toward objectives, proper deployment and conservation of human and financial resources, and achievement of cost and schedule targets. The focus of the course is on the utilization of a diverse set of project management methods and tools. Topics include strategic project management, project and organization learning, cost, schedule planning and control, structuring of performance measures and metrics, technical teams and project management, information technology support of teams, risk management, and process control. Course delivery consists of lectures, speakers, case studies, and experience sharing, and reinforces collaborative project-based learning and continuous improvement.
Decision and Risk Benefit Analysis
This course addresses decision making in the face of risk and uncertainty. Various methodologies will be introduced that are useful in describing and making decisions about risks, with particular emphasis on those associated with the design of products. Students will be exposed to issues related to balancing risks and benefits in situations involving human safety, product liability, environmental impact, and financial uncertainty. Presentations will be made of risk assessment studies, public decision processes, and methods for describing and making decisions about the societal risks associated with engineering projects. Topics include probabilistic risk assessment, cost-benefit analysis, reliability and hazard analysis, decision analysis, portfolio analysis, and project risk management.
Engineering of Systems I
This course covers the principles of product, manufacturing process and supply chain development in an integrated fashion. It will examine the methodologies and tools to systematically define, develop and produce world-class products. Students will work on a project to put these methodologies and tools into practice. Major topics include: product planning and definition, characterization of user value, lean product development, product requirements and benchmarking, concept generation, design for "X" (manufacturing/ assembly/ service/ environment, etc.), sustainable design, design for lean six sigma.
This course introduces students to the challenges posed when trying to determine the total lifecycle impacts associated with a product or a process design. Various costing models and their inherent assumptions will be reviewed and critiqued. The inability of traditional costing models to account for important environmental and social externalities will be highlighted. The Lifecycle Assessment approach for quantifying environmental and social externalities will be reviewed and specific LCA techniques (Streamlined Lifecycle Assessment, SimaPro) will be covered.
Introduction to Data Analytics and Business Intelligence
This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments.
Analytics in service organizations is based on four phases: analysis and determination of what data to collect, gathering the data, analyzing it, and communicating the findings to others. In this course, students will learn the fundamentals of analytics to develop a measurement strategy for a given area of research and analysis. While this measurement process is used to ensure that operations function well and customer needs are met; the real power of measurement lies in using analytics predicatively to drive growth and service, to transform the organization and the value delivered to customers. Topics include big data, the role of measurement in growth and innovation, methodologies to measure quality, and other intangibles.
This course is an introduction to two statistical-software packages, SAS and R, which are often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions or macros; graphics; matrices and arrays; simulations; select statistical applications.
Applied Linear Models - Regression
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building.
Applied Linear Models - ANOVA
This course introduces students to analysis of models with categorical factors, with emphasis on interpretation. Topics include the role of statistics in scientific studies, fixed and random effects, mixed models, covariates, hierarchical models, and repeated measures.
This course is designed to provide the student with solid practical skills in implementing basic statistical and machine learning techniques for the purpose of predictive analytics. Throughout the course, many real world case studies are used to motivate and explain the strengths and appropriateness of each method of interest. In those case studies, students will learn how to apply data cleaning, visualization, and other exploratory data analysis tools to a variety of real world complex data. Students will gain experience with reproducibility and documentation of computational projects and with developing basic data products for predictive analytics. The following techniques will be implemented and then tested with cross-validation: regularization in linear models, regression and smoothing splines, k-nearest neighbor, and tree-based methods, including random forest.
Principles of Statistical Data Mining
This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality.
To be considered for admission to the advanced certificate in Lean Six Sigma, candidates must fulfill the following requirements:
Hold a baccalaureate degree (or equivalent) from an accredited university or college.
Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
Have a minimum cumulative GPA of 3.0 (or equivalent).
Have a satisfactory background in statistics (at least one course in probability and statistics).
Submit a current resume or curriculum vitae.
Submit one letter of recommendation.
International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 79 (internet-based) is required. A minimum IELTS score of 6.5 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.
Graduate entrance exams are not required. However, students are welcome to submit scores from the GMAT or the GRE to support their application for admission.
Students currently enrolled in master’s degree programs at RIT may add the advanced certificate in Lean Six Sigma as part of their current program of study in consultation with their academic advisor. Students must have a satisfactory background in statistics (at least one course in probability and statistics) to be eligible. Students should use the following form to add the certificate program. The form should be submitted to firstname.lastname@example.org.
Students should have a basic familiarity with MINITAB statistical software. This may be obtained by self-study or through a short course.