Chase E. Rainwater
Department Head
4207 Bell Engineering Center

Burak Eksioglu
Graduate Coordinator
4207 Bell Engineering Center

Ashlea Bennett Milburn
Graduate Coordinator

Operations Analytics Website

Degree Conferred:
M.S.O.A. in Operations Analytics (OPAN)

Program Description: The Department of Industrial Engineering offers a graduate program leading to the Master of Science in Operations Analytics (M.S.) for engineering, science, and other non-engineering graduates. The Master of Science in Operations Analytics is an intensive program that will guide students through the theory and practice of the quantitative modeling of enterprise operations via descriptive, predictive, and prescriptive analytics. Students will develop knowledge of the principles and practices of analytics modeling methods, such as optimization, statistical modeling, machine learning, simulation, and computing methods, as they apply to the strategic, operational, and tactical control of operations.

Requirements for M.S. in Operations Analytics

Prerequisites to the M.S.O.A. Degree Program:

  1. There are no prerequisites for students with an undergraduate degree from an ABET-accredited industrial engineering program.
  2. For students with a degree other than an ABET-accredited industrial engineering degree, a number of prerequisite courses may be required. Students are expected to have completed mathematics courses through differential and integral calculus of several variables and vector calculus and linear algebra.  Students are expected to have completed a calculus-based probability and statistics course. In addition, students are expected to have completed a computer programming course.  Specific University of Arkansas courses that meet these prerequisites are available on-line through the INEG departmental web-pages.

Requirements for the Master of Science in Operations Analytics

In addition to the requirements of the Graduate School and the College of Engineering, the following program requirements must be satisfied by candidates for the M.S.O.A. degree.

  1. Candidates for the degree are required to complete 30 semester hours of course work.
  2. All candidates must successfully complete a master’s oral examination that is conducted by the candidate’s faculty committee.

Accelerated Master of Science in Operations Analytics

High-achieving current undergraduate students seeking a BS degree at the University of Arkansas who choose to pursue graduate studies in Operations Analytics may participate in the accelerated M.S.O.A. program. Provided that 6 credit hours of 5000-level OPAN course work can be taken as electives in the student’s current undergraduate program, students may also count those 6 hours towards their M.S.O.A. degree. In addition, students may take another 6 credit hours of graduate degree credit as undergraduate students in order to apply them to their M.S.O.A. degree. These additional 6 hours of courses may not have been used towards the B.S. undergraduate degree and must meet M.S.O.A. degree requirements. The total of 12 credit hours of graduate courses taken as an undergraduate student must be taken during the final 12 month period of their undergraduate degree.

Once fully admitted to the M.S.O.A. program, students request that up to 12 hours of 5000-level or above courses taken in the final 12-month period of their undergraduate degree count toward their graduate degree, if these courses were taken on the University of Arkansas, Fayetteville campus. Students then take an additional 18 credit hours of approved OPAN graduate-level courses in order to meet the M.S.O.A. degree requirements.

Undergraduate students interested in the accelerated M.S.O.A. degree should apply to the program prior to starting the second-to-last semester of their undergraduate program. To be eligible students must have a 3.5 cumulative GPA or higher and submit the normal application materials required by the graduate school for the M.S.O.A. degree program. For students eligible for the accelerated M.S.O.A. program that have a cumulative GPA of 3.5 or higher, the submission of GRE scores is waived.

Required Courses
OPAN 5003Introduction to Operations Analytics3
OPAN 5013Applied Predictive Analytics3
OPAN 5023Applied Prescriptive Analytics3
OPAN 5903Operations Analytics Capstone3
or OPAN 5913 Operations Analytics Industrial Practicum
Students must select course electives from both of the following course topic areas for a total of 18 credit hours.
Operations Analytics (choose 4 or 5 courses)
Introduction to Modern Statistical Techniques for Industrial Applications
Engineering Applications of Probability Theory
Engineering Applications of Stochastic Processes
Decision Models
Introduction to Database Concepts for Industrial Engineers
Nonlinear Programming
Heuristic Optimization
Simulation Analytics
Engineering and Operations Management (choose 1 or 2 courses)
Introduction to Engineering Management
Tradeoff Analytics for Engineering Management
Systems Thinking and Systems Engineering
Supply Chain Management for Operations Managers
Quality Management
Project Management for Operations Managers
Advanced Project Management
Engineering Statistics
Design of Industrial Experiments
Advanced Engineering Economy
Analysis of Inventory Systems


OPAN 5003. Introduction to Operations Analytics. 3 Hours.

An introduction to operations analytics providing an understanding of the role of analytics within operational settings. Builds basic skill instruction in descriptive analytics and the communication of analytics. An overview of introductory techniques within the field of analytics and their application. (Typically offered: Fall, Spring and Summer)

OPAN 5013. Applied Predictive Analytics. 3 Hours.

This course focuses on the fundamental theory, methodologies, algorithms and software tools for predictive analytics. The main goal is to equip the students with the basic knowledge and skills to solve common predictive analytics problems arising from various applications. Methodologies covered in this course include linear and non-linear regression, additive models, ensemble trees, model assessment and selection, Artificial Neural Network. Students will learn how to implement the methods using popular statistical computing and analytics tools. Working knowledge of multi-variate calculus based probability and statistical inference is expected. Prerequisite: OPAN 5003. (Typically offered: Fall, Spring and Summer)

OPAN 5023. Applied Prescriptive Analytics. 3 Hours.

Methods, algorithms, and techniques for optimization models used in analytics applications. Coverage includes model formulation, solution methods and the use of optimization software. Prerequisite: OPAN 5003. (Typically offered: Fall, Spring and Summer)

OPAN 5713. Simulation Analytics. 3 Hours.

An overview of Monte Carlo computer simulation methods and their application within analytics. Generation of random variates from univariate and multi-variate distributions. Probability model representation and fitting methods. Computing methods for simulating and estimating random processes. Bootstrapping procedures. Statistical reasoning and decision making under uncertainty. Working knowledge of calculus-based probability and statistics and computer programming is expected. (Typically offered: Fall and Summer)

OPAN 5903. Operations Analytics Capstone. 3 Hours.

Comprehensive analytics project. Conduct background research, data collection, and preliminary analysis; define objectives, performance measures, and deliverables; apply analytics methods, develop recommended solutions, and document solution and benefits. Course should be taken in the term prior to meeting degree requirements. Students cannot receive credit for both OPAN 5903 and OPAN 5913. Prerequisite: Instructor consent. (Typically offered: Fall, Spring and Summer)

OPAN 5913. Operations Analytics Industrial Practicum. 3 Hours.

Student must apply to enroll in this course. Students must be employed within an analytics organization in industry. Prior approval to use an organization's analytics project as the basis of the student's course project must be obtained. A project report documenting the application of analytics performed by the student within the organization is required. An evaluation by the student's supervisor on the technical aspects of the student's work will be required in addition to an evaluation by the course instructor. The student's supervisor must be an analytics professional. Course should be taken in the term prior to meeting degree requirements. Students cannot receive credit for both OPAN 5903 and OPAN 5913. Prerequisite: Instructor consent. (Typically offered: Fall, Spring and Summer)