Overview of FE Industrial and Systems Engineering
The Industrial and Systems Engineering discipline on the FE exam tests your ability to optimize systems and processes using quantitative methods. This discipline emphasizes probability and statistics, engineering economics, manufacturing, quality engineering, operations research, and human factors. Unlike other FE disciplines that focus on physical systems, industrial engineering focuses on designing and improving complex systems involving people, machines, materials, and information.
Key Topics
Probability and Statistics
Probability and statistics is heavily weighted on the FE Industrial exam. Key topics include probability distributions (binomial, Poisson, normal, exponential, uniform), descriptive statistics (mean, variance, standard deviation), confidence intervals, hypothesis testing (z-test, t-test, chi-square, F-test), regression analysis (simple and multiple linear regression, coefficient of determination), ANOVA, and design of experiments (factorial designs, blocking, randomization). Understand how to select the appropriate statistical test and interpret results.
Engineering Economics
Engineering economics covers time value of money (present worth, annual worth, future worth), interest rate calculations, cash flow analysis, depreciation methods (straight-line, declining balance, MACRS), benefit-cost analysis, rate of return analysis, and replacement analysis. Understand how to compare alternatives using present worth, annual cost, and incremental rate of return methods. Know how to use interest factor tables from the FE Reference Handbook.
Manufacturing Processes and Systems
Manufacturing topics include machining processes (turning, milling, drilling: cutting speed, feed rate, depth of cut, tool life via Taylor's equation), forming processes (forging, rolling, extrusion, drawing), casting, welding, assembly line balancing (cycle time, efficiency, number of workstations), facility layout design, and material handling. Understand process capability indices (Cp, Cpk) and how manufacturing tolerances relate to quality.
Quality Engineering
Quality topics include statistical process control (X-bar and R charts, p-charts, c-charts, control limits), acceptance sampling (OC curves, AQL, LTPD, producer's and consumer's risk), total quality management principles, Six Sigma methodology (DMAIC), and reliability engineering (series and parallel systems, MTBF, MTTF, failure rate, bathtub curve). Understand how to construct and interpret control charts and make decisions about process stability.
Operations Research
Operations research covers linear programming (formulation, graphical solution, simplex method, sensitivity analysis, duality), queuing theory (M/M/1 and M/M/s models: arrival rate, service rate, utilization, average wait time, average queue length), inventory models (EOQ, reorder point, safety stock), network analysis (shortest path, minimum spanning tree, maximum flow), simulation basics, and decision analysis (decision trees, expected value, utility theory).
Human Factors and Ergonomics
Human factors topics include workplace design, anthropometry, work measurement (time study, predetermined time systems, work sampling), learning curves, and occupational safety (OSHA standards, hazard identification). Understand how to apply the learning curve formula and conduct a basic time study to set production standards.
Study Strategy
Probability and statistics and engineering economics together make up a large portion of the exam, so master those first. For operations research, practice formulating and solving linear programming problems and queuing calculations. Quality engineering problems often involve constructing or interpreting control charts, so practice those calculations. Use FE Exam Prep flashcards for formulas and distributions, and mini exams for timed problem-solving.