For supply chains to run smoothly, they need savvy, data-driven decision makers who can communicate well. Our curriculum is built to help you be that person. You'll find the ideal balance of hard, analytical data skills and generalized supply chain domain knowledge, as well as leadership and management opportunities with practical application. Some courses also offer you the opportunity to earn professionally-recognized certificates that will help advance your career.
You'll master analytics, learn to navigate the industry and become a data-driven leader.
Offered in lock-step format for full-time students, and flexible scheduling for part-time students, you'll be able to finish your degree in as quickly as 18 months.
Students must complete 36 credits (12 courses) including:
- 5 analytics core courses
- 5 business and supply chain core courses
- 2 elective courses
Credit waiver: Up to 6 credits can be requested – see business and supply chain core courses section below for more details
Full-time students can graduate in 2-3 semesters (1-1.5 years) depending on the number of courses taken per semester. Part-time students are suggested to take 2 courses per semester, and graduate in 3 years.
Analytics Core Courses (5 courses, 3 credits each)
- Operations Analysis (22:799:580)
- Supply Chain Analytics (22:799:585)
- Data Analysis and Decision Making (22:960:575) or Introduction to Statistical Linear Models (26:960:577)
- Analytics for Business Intelligence (22:960:641) or Data Mining (26:198:644)
- Industry Client Projects (22:799:650)
Business and Supply Chain Core Courses (5 courses, 3 credits each)
- Management Skills for Supply Chain Management (22:620:550)
- Global Procurement and Supply Management (22:799:608) or Supply Chain Finance (22:799:640)
- Supply Chain Solution w/ SAP I (22:799:659)
- Introduction to Project Management (22:799:661)
- Lean Six Sigma (22:799:676)
Note: Subject to approval by the program director(s), students can be granted a waiver of the above courses totaling up to 6 credits
Students can be granted a waiver of up to 6 credits, for the following:
- Prior comparable course work at the graduate level (preferably from AACSB-accredited schools).
a. The student who request to waive a course should provide a detailed syllabus of the comparable course and prove the similarity in contents covered. A transcript showing the grade of the comparable course should also be provided.
b. Exception: RBS UG students who passed Management Skills (29/33:620:302) will be automatically waived for Management Skills for Supply Chain Management (22:620:550).
- At least five years of relevant work experience or professional certification in relevant supply chain functional areas.
a. Work experience: the student should provide a detailed resume and a support letter from his/her employer to prove the relevant work experience.
b. Professional certification: the student should provide a relevant professional certificate and the contents covered in the certificate exam/training, to prove the similarity in contents covered by the correspondingly course.
(choose any 2 of the following courses, 3 credits each)
|22:799:675||Advanced Project Management|
|22:198:603||Business Data Management|
|22:960:646||Data Analysis and Visualization|
|22:799:663||Demand Management for Value Chain|
|22:799:677||Fundamentals of Project Management Professionals|
|22:799:679||Global Logistics Management|
|22:799:662||Global Supply Chain Law and Contracts|
|16:960:586||Interpretation of Data|
|26:960:575||Introduction to Probability|
|22:799:634||SAP Certification: Integration of Business Processes|
|22:799:607||Supply Chain Management Strategies|
|22:799:672||Supply Chain Sustainability|
|NJIT CS661||Systems Simulation|
22:799:580 - Operations Analysis
Covers fundamentals of performance analysis for various operational issues encountered in real-life supply chain processes. The major topics include demand forecasting techniques, sales and operations planning (SOP), mathematical programming applications and spreadsheet solutions, supply chain inventory planning, uncertainty, safety stock management, project resource allocation and risk analysis, network design and facility location selections, and computer simulation and quality management. Uses Harvard Business Cases in developing cost-effective solutions for continuous improvement of a company's operational efficiency and strategic position in today's highly dynamic and competitive marketplace. The objective of the course is to help students to develop analytical thinking skills and to build the knowledge of business performance optimization toward operational excellence of supply chains.
22:799:608 - Global Procurement and Supply Management
Supply Management is the overarching cross-functional management framework that integrates all activities related to the acquisition and management of resources for the organization. It includes global sourcing, supplier relationship management, procurement and purchasing. Supply Management is now recognized as a key strategic initiative to create value for the corporation. This course reviews the demands placed on today's procurement and supply management from the firm's stakeholders and demonstrates their impact on the competitive success and profitability of the organization. Furthermore it describes ethical, contractual and legal issues faced by procurement, and recognizes the expanding strategic nature of supply management. The major areas covered are procurement as a functional activity, and how effective supply management impacts on total quality, cost, delivery, technology, and responsiveness to the needs of a firm's external customers (insourcing/outsourcing, supplier evaluation, supplier development, and global sourcing). We introduce the tools, techniques, and approaches for managing the procurement and sourcing process (cost/price analysis, negotiations, and contract management). Case studies and outside speakers will be used to illustrate the issues discussed in lectures.
22:799:659 - Supply Chain Solution w/SAP I
Provides a technical overview of Enterprise Resource Planning Systems and their role within an organization. It introduces key concepts of integrated information systems and explains why such systems are valuable to businesses. SAP ECC is introduced to illustrate the concepts, fundamentals, framework, general information, technology context, technological infrastructure, and integration of enterprise-wide business applications. In addition to lectures, students will be guided through several hands-on activities of various business processes in SAP ECC. The objective of this course is to help students: 1) master the basic concepts, architecture and terminology of an ERP system; 2) understand the need and examine the capabilities of ERP systems; and 3) illustrate how integrated information systems can help a company prosper.
22:799:661 - Introduction to Project Management
Project Management is one of the most critical elements in the competitiveness and growth of organizations. Projects are the drivers of innovation and change and no organization can survive today without projects. Effective leaders in today's leading companies must be effective project managers. Furthermore, almost every MBA graduate may sooner or later be asked to manage a project. This course presents the classical foundations of project management and introduces students to the world of real-life project problems. Upon completion of this course, students will understand the basic concepts and critical factors of initiating, planning, organizing, controlling, and running a project. They will be able to develop a project plan, build a project team and adapt their project management style to the unique project characteristics. Course topics will include: project initiation, project success dimensions, integration, scope, planning, controlling and monitoring, time, cost and risk management, project organization, project teamwork, and project adaptation. The course will also advise students how they could prepare themselves for the PMP Exam of the Project Management Institute in order to become Professional Project Managers.
22:799:677 - Fundamentals of Project Management Professionals
This course is a complementary course to the Introduction to Project Management (22:799:661), or its equivalent. It focuses on the development of knowledge for professional project managers, and will prepare students to take the Project Management Institute (PMI)'s PMP Examination. The PMI's PMP Certification has become an attractive qualification to many employers, who are looking to hire experienced project managers. Recent surveys found out that even in today's tight economic environment, over 50% of project managers reported getting salary increases during last year. The survey also showed that having a PMP Certification added 11% to the average salary of project managers who are non-PMPs. Students should have prior PM experience or education, typically, 3 years industry experience in project management, and instructor's consent, or Introduction to Project Management (22:799:661), or equivalent. Students can take the course concurrently with the introduction course (in the same semester).
22:799:676 - Lean Six-Sigma
Lean six sigma is an application of the quantitative six sigma quality management techniques within a lean enterprise. The goal is to create an efficient organization that continuously reduces waste and operates at the most efficient levels possible. In addition to covering the fundamentals of Lean and Six Sigma, this course will equip students with other important tools and strategies to improve the performance of business processes. Students will practice solving business problems and improving processes through case studies, team exercises and simulations, self-assessments, and guest lectures. Topics covered will include: six sigma improvement methodology and tools, lean manufacturing tools and approaches, dashboards and other business improvement techniques. Students will also gain an understanding of: the strategic importance of business improvement, the need for fact based management, the significance of change management, and how to deploy these tools in different parts of the value chain.
22:799:585 - Supply Chain Analytics
This course showcases real life applications of data analytics (descriptive, predictive and prescriptive) in various fields of supply chain management, such as forecasting and inventory management, sales and operations planning, transportation, logistics and fulfillment, purchasing and supply management, supply chain risk management, etc. in manufacturing, trade and service industries. Students learn to define the right data set, ask the right questions to drive supply chain efficiency and business value, and use the right models and tools to develop data-driven decisions. Topics includes demand forecasting for new products, product/service-line selection and rationalization, transportation analytics, fulfillment diagnostics in logistics systems, sales and operations analytics in production, inventory and resource management, spend analytics and supplier selection, supply chain risk management, and product development analytics. Software packages such as R and Python will be utilized.
22:960:575 - Data Analysis and Decision Making
Introduces statistics as applied to managerial problems. Emphasis is on conceptual understanding as well as conducting statistical analyses. Students learn the limitations and potential of statistics, gain hands-on experience using Excel, as well as comprehensive packages, such as R or SAS. Topics include descriptive statistics, continuous distributions, confidence intervals for means and proportions, and regression. Application areas include finance, operations, and marketing. Introduces the basic concepts of model building and its role in rational decision making. Knowledge of specific modeling techniques, such as linear and nonlinear programming, decision analysis, and simulation, along with some insight into their practical application is acquired. Students are encouraged to take an analytic view of decision making by formalizing trade-offs, specifying constraints, providing for uncertainty, and performing sensitivity analyses. Students form groups to collect and analyze data, and to write and present a final report.
22:960:641 - Analytics for Business Intelligence
This course is intended for business students of data mining  techniques with these goals: 1) To provide the key methods of classification, prediction, reduction, and exploration that are at the heart of data mining; 2) To provide business decision-making context for these methods; 3) Using real business cases, to illustrate the application and interpretation of these methods. The course will cover Classification (e.g. helps banks to determine who will default on a loan, or email filters to determine which emails are spam), Clustering (like classification, but groups are not predefined, as in legitimate vs. spam email, so the algorithm will try to group similar email together for instance), Regression (e.g. how ad campaigns in offline media such as print, audio and TV affect online interest in the advertiser's brand), Association Rule Learning (enables merchants, for example Amazon, to determine which items customers tend to buy together and make suggestions for further purchase, otherwise known as "market basket analysis"); and Neural Nets (helps financial agents to model complex markets for high frequency trading; helps Pandora adapt to your personal radio station). The pedagogical style will use business cases so the student can follow along and implement the algorithms on his or her own with a very shallow learning curve. In addition, students will work in teams to mine their own data. Individual students may request to work on their own company data. The computation platform with be the R Programming language and the specialized packages in data mining.
22:620:550 - Special Topics: Management Skills – Professional Development
This course explores human dynamics by examining the role of management and learning styles in the effective functioning of organizations. Topics include personality types, motivation, cognition and learning, communication, team development, and leadership. Through class discussions, case analyses, simulations, and group projects, students learn critical managerial skills such as communication, decision making, conflict resolution, and team building.
22:799:607 - Supply Chain Management Strategies
Provides an understanding of the variety and the importance of critical decisions encountered in the practice of an integrated supply chain. Offers important quantitative techniques needed for continuous improvement of a company's operation efficiency, product/service quality, and strategic position in the global marketplace. Team projects based on real-world supply chain managerial issues will be assigned.
22:799:640 - Supply Chain Finance
Supply chain management is an interdisciplinary field in which knowledge of techniques of financial analysis as they relate to the SC context is one valuable aspect of a manager's toolkit. This course is not an overall introduction to financial management. Rather, it is a focused examination of SC in the finance context in general and the financial services and operations context in particular, with an emphasis on cash management strategies, working capital management strategies, and financial management software applications. While some quantification is important, it is not a predominantly quantitative course.
22:799:634 - SAP Certification: Integration of Business Processes
This course is mainly for the preparation of the SAP consultant certification exam C_TERP10_66, SAP Certified Associate - Business Process Integration with SAP ERP 6.0 EhP6. Students will become SAP Certified Associates upon successful completion of this course and passage of the certification exam. In this course, we explain the organizational structures used in each business process, such as accounting, procurement, sales, project management, plant maintenance and human capital management. We then identify the key master data which must be maintained to execute each business process and discuss the ERP transactions required to complete each business process cycle. We further identify the key integration points between the different business disciplines supporting each business process cycle.
22:799:662 - Global Supply Chain Law and Contracts
Supply chain managers must be cognizant of the way that law structures their business decisions. International and domestic law impacts the a) costs and risks of entering into transactions, and b) decisions on sourcing from a given country or supplier. This course will examine a wide variety of legal subjects, primarily international, that shape and impact domestic and global sourcing decision-making. These topics include but are not limited to: contracts, trade law, transportation, tort, dispute settlement, international investment law, international marketing law, and labor and environmental supply chain regulation. Students will be able to apply the credits earned in this class towards their supply chain or marketing concentration.
22:799:663 - Demand Management for Value Chain
This course focuses on the applications of forecasting models and methodologies throughout supply chains, for use in business related activities, including operations, sales, marketing and finance. The course aims to help students understand the significance of matching supply and demand and the development of managerial insights aimed at improving that balance. Several relevant techniques for forecasting, inventory management, and production planning are developed and illustrated. The students are asked to build forecasting and inventory models in Microsoft Excel. The course will focus on by combining theory, examples, practical applications and case studies and consist of a combination of lectures, case presentations, and class discussion.
22:799:672 - Supply Chain Sustainability
Global experience and examples show how sustainability criteria (using Sustainable Development as a point of reference) in the ‘upstream’ supply chain management and procurement process and decision-making of public and private agencies, organizations and corporate entities can both improve environmental performance, while addressing ethics, social regeneration and economic concerns (e.g. the ‘triple bottom-line’). This course will allow students to participate in applied research (real-time programs currently being developed in the U.S., and around the world) to explore the application of environmentally responsible supply chain and ‘green’ procurement principles across multiple public and private sectors which includes: designing supply chain management and procurement schemes which address environmental, social and ethical considerations in organizational policy development as well as the procurement process. Our goal in this class is to bring business students (and other academic students) together with different experiences to examine environmental management from a supply chain management perspective. It is our intention to provide each of you with an opportunity to apply your general background knowledge and specific expertise of your foci to a single issue that is relevant not only to the local area but also to the global environment as well.
22:799:675 - Advanced Project Management
Building on the foundations established in the Introduction to Project Management Course, this course will bring students to the next level of project management by developing a business-focused approach to project management. Many organizations realize that the traditional approach to project management is insufficient to deal with today's business requirements and that meeting time and budget goals is no longer enough. This course will help students deal with the complexity and dynamics of modern projects and show them how to manage projects for building competitive advantage and achieving business results. The course will introduce the principles of Strategic Project Leadership, which is an integrated approach to project management. It will combine the strategic, business-related aspects of projects, the operational needs of getting the job done, and the leadership sides of inspiring and leading the project team. Upon completion of the course students will be able to create their strategic business-focused project plan and align their project with the company's business strategy.
22:799:679 - Global Logistics Management
Logistics and Transportation Management is designed to provide students with an understanding of the strategic and tactical elements of logistics and transportation management. This course will examine various forms of transportation and how the supply chain can be structured to supply logistics and transportation solutions. In addition to studying transportation modal choices, logistics and transportation infrastructure in the U.S. and around the world will be discussed. This course will provide students with a thorough overview of the concepts of logistics management and transportation. We will take a total systems approach to the management of all those activities involved in physically moving and storing materials and information through the supply chain. Improved management of logistics activities offers significant potential for improving corporate profitability and return on assets. Students will learn how to make tradeoffs using logistics cost modeling techniques. The course focuses on strategic, management, and operational design issues. While some quantification is important, it is not a predominantly quantitative course.
26:198:644 - Data Mining
The key objectives of this course are two-fold: (1) to teach the fundamental concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real world applications. The core topics to be covered in this course include classification, clustering, association analysis, and anomaly/novelty detection. This course consists of about 13 weeks of lecture, followed by 2 weeks of project presentations by students who will be responsible for developing and/or applying data mining techniques to applications such as intrusion detection, Web usage analysis, financial data analysis, text mining, bioinformatics, systems management, Earth Science, and other scientific and engineering areas. At the end of this course, students are expected to possess the fundamental skills needed to conduct their own research in data mining or to apply data mining techniques to their own research fields.
22:198:603 - Business Data Management
The purpose of this course is to provide students with an understanding of database technology and its application in managing data resources. The conceptual, logical, and physical design of databases will be analyzed. A database management system will be used as a vehicle for illustrating some of the concepts discussed in the course.
22:960:646 - Data Analysis and Visualization
The course will enable students to develop critical business data presentation skills to ensure that the visualizations add to the effective interpretation and explanation of the underlying data without undue strain to the consumer of the information; ensure the visualizations enable the effective detection of trends that can be easily connected to real world events to help explain relationships and interrelationships; learn appropriate and minimal use of color to maximize its impact. Spatial data analysis tools will be introduced and advanced graphical programming skills will be developed using R graphics packages.
22:960:608 - Business Forecasting
Innovative businesses are using data to make better predictions about their business environment, their business future, and the future of their global competitors. “Big Data” is a business term frequently used these days. Businesses are storing and collecting more data than ever before to gain a competitive edge. McKinsey predicts that data will grow 10-fold by 2015 and 100-fold by 2020. This will result in businesses looking for better data scientists to help them leverage “Big Data” and gain a competitive edge. In this class, students will use the level R programming language to become data scientists and business forecasters. Specifically, students will learn how to: Understand Data, Analyze Data, Apply various forecasting methods, Leverage forecasts to make decisions. Communicate forecasts and recommendations to management. No prior knowledge of R programming is required. You will learn and become proficient in R and obtain hands-on experience of its forecasting package through case studies and real-life examples during each lecture. You will also learn to better communicate your forecast and strengthen your analytical skills. The practical knowledge gained upon completion of this course will help in careers ranging from business analytics to marketing, accounting, financial services, and more.
22:630:586 - Marketing Management
The purpose of the course is to offer an understanding of the nature and role of marketing in the firm and in the society. Students will gain knowledge regarding the marketing decisions of price, place, promotion, product, develop an understanding of consumer behavior, market research, social and cultural factors affecting marketing. The course will expose students to a series of marketing principals, frameworks, and analyses. These techniques will be applied to a series of case studies to reinforce the concepts. At the end of the course the students should be able to develop effective marketing plans for products and services.
22:630:606 - Business-to-Business Marketing
Introduces business-to-business marketing from the perspective of both the seller and the buyer. Covers marketing strategy and product/ market planning systems; selling and management of the sales force; marketing research and competitive intelligence; pricing and promotion; management of auxiliary services; and industrial buying behavior.
22:620:617 - Negotiation
Provides an introduction to the principles, practice, and processes of negotiations as a management skill with bosses, subordinates, peers, clients, and customers. Discussion of the preparation and planning for negotiation, the strategy and tactics of negotiation, issues regarding both distributive and integrative bargaining, and ethics in negotiation.
16:960:586 - Interpretation of Data
Modern methods of data analysis with an emphasis on statistical computing: univariate statistics, data visualization, linear models, generalized linear models (GLM), multivariate analysis and clustering methods, tree-based methods, and robust statistics. Expect to use statistical software packages, such as SAS (or SPSS) and Splus (or R) in data analysis.
16:198:536 - Machine learning
An in-depth study of machine learning, to impart an understanding of the major topics in this area, the capabilities and limitations of existing methods, and research topics in this field. Inductive learning, including decision-tree and neural-network approaches, Bayesian methods, computational learning theory, instance-based learning, explanation-based learning, reinforcement learning, nearest neighbor methods, PAC-learning, inductive logic programming, genetic algorithms, unsupervised learning, linear and nonlinear dimensionality reduction, and kernels methods.
26:711:651 - Linear Programming
A survey of linear programming and its applications. Topics include linear programming models, basic simplex method, duality theory and complementary slackness, sensitivity analysis, degeneracy, matrix notation and revised simplex method, special linear programs such as transportation and network flow theory, applications in statistics, economics and finance models of linear programming, game theory, and introduction to interior point methods. Prerequisite: undergraduate linear algebra.
26:711:653 - Discrete Optimization
Combinatorial and discrete optimization problems on graphs and networks, knapsack, cutting stock, set covering and packing problems: theoretical properties, algorithms, complexity. Branch and bound methods, cuts, lifting. Applications.
26:960:575 - Introduction to Probability
Foundations of probability. Discrete and continuous simple and multivariate probability distributions; random walks; generating functions; linear functions of random variable; approximate means and variances; exact methods of finding moments; limit theorems; stochastic processes including immigration-emigration, simple queuing, renewal theory, Markov chains.
NJIT CS661- Systems Simulation
This course covers the use of simulation as a tool for analyzing business and engineering problems. The two primary goals of the course are to learn how to plan, build and use simulation models and to develop an understanding of when simulation is an appropriate tool for analysis. Much of the work in the course will involve learning the mathematical and software tools for building simulation models, performing experiments with them, and interpreting the results.
22:799:650 - Supply Chain Analytics Industry Project
This course builds upon academic SCM learnings by working on "real life" supply chain management projects requested by our Rutgers Center for Supply Chain Management Advisory Board companies and corporate partners. Students in this course must identify and understand the key issues, formulate models, complete analyses, and apply SCM course learnings to solve real-world problems. Faculty members whose expertise lies in a particular area are available to assist students with complexities of the projects. The projects change each semester depending on the current requirements of the clients, but always focus on specific issues within the supply chain. Client visits may be included to better understand the project scope and work with the company executives. The culmination of the project will be a formal presentation to the client's SCM executives and management team along with delivery of a final report. The presentation and report will include the team's approach, data analysis, findings and recommendations.
26:960:577 - Introduction to Statistical Linear Models
Linear models and their application to empirical data. The general linear model; ordinary-least-squares estimation; diagnostics, including departures from underlying assumptions, detection of outliners, effects of influential observations, and leverage; analysis of variance, including one-way and two-way layouts; analysis of covariance; polynomial and interaction models; weighted-least squares and robust estimation; model fitting and validation. Emphasizes matrix formulations, computational aspects and use of standard computer packages such as SPSS. Prerequisite: Undergraduate or master’s-level course in statistics.