Analytical Methods & Quantitative Decision Support

Financial and Capital Investment Analysis

Time-value-of-money analysis, present value, annuities, discounted cash flow, NPV and IRR provide ways to compare capital alternatives on a consistent basis. Lease-versus-buy analysis, risk-adjusted discount rates, scenarios and sensitivity testing show which assumptions drive a decision. These methods connect equipment and operating requirements with capital justification without treating an estimate as a realized result.

Investment Economics and Lifecycle Analysis

Evaluate ROI, NPV, IRR, Payback, Breakeven, Cost per Gallon, and Lifecycle/TCO Alternatives

Engineering practice and documented application

Investment Economics and Lifecycle Analysis

Model Cash Flows, Present/Future Value, Annuities, Perpetuities, Lease-Versus-Buy Choices, and Risk-Adjusted Discounting; Financial-Theory Components Include Academic Preparation

Engineering practice and documented application

Investment Economics and Lifecycle Analysis

Evaluate Labor, Media, Chemicals, Electrical Demand, Water, Membrane Life, Repairs, Freight, Tanker Hauling, Downtime, Overtime, and Production Impacts

Engineering practice and documented application

Investment Economics and Lifecycle Analysis

Analyze Repair-Versus-Replace Economics, Deferred Expansion, Depreciation, Write-Offs, and Stranded-Cost Exposure

Engineering practice and documented application

Investment Economics and Lifecycle Analysis

Use Sensitivity, Scenario, and Weighted Decision Analysis to Establish Investment Thresholds

Engineering practice and documented application

Investment Economics and Lifecycle Analysis

Analyze WACC, CAPM, Bond and Equity Valuation, Variance, Covariance, Correlation, and Investment Risk in Academic Financial Models

Academic preparation and analytical method

Risk, Uncertainty, and Decision Support

Identify Technical, Commercial, Safety, Environmental, Quality, Operational, and Schedule Risks

Engineering practice and documented application

Risk, Uncertainty, and Decision Support

Develop Risk Registers, Mitigation Owners, Contingency Triggers, Assumptions, Decision Records, and Residual-Risk Dispositions

Engineering practice and documented application

Risk, Uncertainty, and Decision Support

Use Three-Point Estimates, PERT, and Monte Carlo-Style Schedule-Risk Methods

Engineering practice and documented application

Risk, Uncertainty, and Decision Support

Assess Constructability, Operability, Reliability, Maintainability, and Failure Consequences

Engineering practice and documented application

Risk, Uncertainty, and Decision Support

Compare Options Through Weighted Criteria, Tradeoff Matrices, and Documented Selection Rationales

Engineering practice and documented application

Risk, Uncertainty, and Decision Support

Track Unresolved Technical Questions, Calculation Conflicts, Test Exceptions, and Acceptance Actions

Engineering practice and documented application

Statistical Quality and Process Analytics

Statistical process control, capability indices, measurement-system analysis, hypothesis testing and acceptance sampling help distinguish normal variation from a meaningful change. Pareto and cause-and-effect analysis focus investigation, while calibration and gage repeatability and reproducibility establish whether the measurements are suitable for the decision.

Statistical Quality, Measurement Systems, and Sampling

Use Descriptive Statistics, Variable/Attribute Control Charts, Special-Cause Interpretation, and Process Baselines

Academic preparation and analytical method

Statistical Quality, Measurement Systems, and Sampling

Evaluate Cp/Cpk, Specification Limits, Target Bands, Sigma-Level Concepts, and Process Stability

Academic preparation and analytical method

Statistical Quality, Measurement Systems, and Sampling

Plan Measurement-System Analysis, Gage R&R, Calibration Governance, Reliability, and Validity Checks

Academic preparation and analytical method

Statistical Quality, Measurement Systems, and Sampling

Use Acceptance Sampling, Supplier-Quality Risk, Defect Pareto, and Structured Hypothesis Testing

Academic preparation and analytical method

Statistical Quality, Measurement Systems, and Sampling

Evaluate Statistical Confidence, Sample Adequacy, and Measurement Limitations

Academic preparation and analytical method

Optimization and Operations Research

Linear and integer programming express decisions as variables, objectives and constraints. Resource allocation, production scheduling, network flow and assignment models make tradeoffs between capacity, labor, cleaning windows and utility availability explicit. Sensitivity analysis helps explain the value of relieving a constraint.

Optimization and Operations Research

Formulate Decision Variables, Objectives, Constraints, Resource Limits, and Feasibility Conditions

Academic preparation and analytical method

Optimization and Operations Research

Use Linear Programming, Simplex/Sensitivity Interpretation, Integer Decisions, and Branch-and-Bound Concepts

Academic preparation and analytical method

Optimization and Operations Research

Model Transportation, Assignment, Shortest Path, Maximum Flow, and Minimum-Cost Flow Problems

Academic preparation and analytical method

Optimization and Operations Research

Evaluate Capacity Allocation, Tank/Production Scheduling, Routing, Utility Constraints, and Discrete Upgrade Choices

Engineering practice and documented application

Optimization and Operations Research

Develop Network-Flow and AMPL Planning Models and Interpret Scenario/Sensitivity Breakpoints Within the Capstone Evidence

Academic preparation and analytical method

Optimization and Operations Research

Check Batch/Scenario Consistency, Solver Outputs, Assumptions, Visualizations, and Human-Controlled Limitations

Academic preparation and analytical method

Supply Chain and Inventory Analysis

Forecasting, regression, seasonality and error measures support demand planning. Reorder points, safety stock, economic order quantities and service-level analysis connect inventory policy with lead-time variability and shortage exposure. Transportation and distribution models compare routing, capacity and service requirements on a consistent basis.

Forecasting, Inventory, Supply Chain, and Logistics

Use Moving Averages, Exponential Smoothing, Regression, and Seasonal Forecasting Concepts

Academic preparation and analytical method

Forecasting, Inventory, Supply Chain, and Logistics

Measure MAD, MSE, MAPE, Bias, and Tracking Signals; Review Forecast Method Performance

Academic preparation and analytical method

Forecasting, Inventory, Supply Chain, and Logistics

Evaluate EOQ, Reorder Points, Safety Stock, Service Levels, Fill Rates, and Lead-Time Variability

Academic preparation and analytical method

Forecasting, Inventory, Supply Chain, and Logistics

Use Newsvendor/Critical-Ratio Concepts for One-Time Uncertain Demand and Overage/Underage Tradeoffs

Academic preparation and analytical method

Forecasting, Inventory, Supply Chain, and Logistics

Analyze Carrier Bids, Shipment Consolidation, Routing, Distribution Cost, and Service Performance

Academic preparation and analytical method

Forecasting, Inventory, Supply Chain, and Logistics

Assess Bullwhip Effects, Ordering Cadence, Demand Sharing, Supply Continuity, and Replenishment Alignment

Academic preparation and analytical method

Forecasting, Inventory, Supply Chain, and Logistics

Coordinate Docks, Racking, Shuttle Movement, Yard Logistics, Offsite Storage, Warehouse Capacity, and Shipment Acceleration During Outages

Engineering practice and documented application

Uncertainty, Reliability and Queueing

Probability, expected value, variance and covariance help describe uncertain demand, failures and schedule exposure. Event-arrival models, state-transition models and queueing relationships connect operating variability with staffing, availability, cycle time and work in process. Scenario analysis makes the consequences of assumptions visible before a commitment is made.

Stochastic Models and Uncertainty Analytics

Use Expected Value, Probability, Variance, Covariance, and Event-Arrival Models

Academic preparation and analytical method

Stochastic Models and Uncertainty Analytics

Apply Poisson-Process Reasoning to Arrivals and Workload Variability

Academic preparation and analytical method

Stochastic Models and Uncertainty Analytics

Use Markov-State Transitions and Steady-State Availability Concepts for Operating/CIP/Fault States

Academic preparation and analytical method

Stochastic Models and Uncertainty Analytics

Apply Queueing Concepts and Little’s Law to Throughput, Cycle Time, WIP, Congestion, and Blocking

Academic preparation and analytical method

Stochastic Models and Uncertainty Analytics

Compare Uncertainty Scenarios, Risk Exposure, Staffing Needs, Redundant Capacity, and Expected Avoided Loss

Academic preparation and analytical method

Experimental Design and Model Validation

One-way and two-way ANOVA, factorial designs, blocking and fractional-factorial screening help separate factors and interactions. Regression and correlation quantify relationships, while residual diagnostics, variance checks, autocorrelation review and confirmation runs test whether an inference is dependable. The statistical work connects a model's conclusions with the quality and limits of its inputs.

DOE, Regression, and Empirical Statistical Modeling

Design One-Way/Two-Way ANOVA, Factorial Experiments, Interaction Studies, and Targeted Contrasts

Academic preparation and analytical method

DOE, Regression, and Empirical Statistical Modeling

Use Randomized Complete Blocks, Latin/Graeco-Latin Designs, and Nuisance-Variable Control in Academic or Documented Analytical Scope

Academic preparation and analytical method

DOE, Regression, and Empirical Statistical Modeling

Evaluate Fractional Factorial Screening, Generators, Aliasing, Confounding, Resolution, and Confirmation Runs

Academic preparation and analytical method

DOE, Regression, and Empirical Statistical Modeling

Interpret Sums of Squares, Degrees of Freedom, Mean Squares, F-Tests, P-Values, and Factor Significance

Academic preparation and analytical method

DOE, Regression, and Empirical Statistical Modeling

Use Tukey HSD and Analyze Production-Rate Factors in the De-Identified Industrial Study

Academic preparation and analytical method

DOE, Regression, and Empirical Statistical Modeling

Develop Simple/Multiple/Polynomial Regression and Logistic Models Where Appropriate; Evaluate Correlation and Effect Direction/Magnitude

Academic preparation and analytical method

DOE, Regression, and Empirical Statistical Modeling

Review Residuals, Shapiro-Wilk/P-P Normality Checks, Homoscedasticity, Multicollinearity, Autocorrelation, and Model Fit

Academic preparation and analytical method

DOE, Regression, and Empirical Statistical Modeling

Use Transformations or Alternative Nonparametric/Semiparametric Methods When Assumptions Fail

Academic preparation and analytical method

DOE, Regression, and Empirical Statistical Modeling

Apply Production-Rate Data Preparation, Exploration, Factor Analysis, and Model Validation

Engineering practice and documented application

Production-Rate Driver Analysis

The analytical work includes data preparation, exploration, transformation and baseline analysis for production-rate studies. ANOVA, regression and diagnostic checks connect candidate drivers with measured performance. The purpose is to identify useful operating decisions from the evidence and explain the assumptions behind them.

Explore DOE, regression, and empirical statistical modeling capabilities ↑

Data Analytics, BI, and Operational Reporting

Prepare, Cleanse, Validate, Align, Transform, and Explore Production, Project, Maintenance, and Quality Datasets

Engineering practice and documented application

Data Analytics, BI, and Operational Reporting

Develop Descriptive/Predictive Analysis, Forecasts, Time-Series Views, Scenario Comparisons, and Simulation-Output Interpretation

Engineering practice and documented application

Data Analytics, BI, and Operational Reporting

Analyze Throughput, Capacity, Bottlenecks, Cycle Time, OEE, Downtime, Yield, and Process Performance

Engineering practice and documented application

Data Analytics, BI, and Operational Reporting

Build KPI Dashboards, EVM Views, Variance Trends, Benchmark Reporting, and Corrective-Action Triggers

Engineering practice and documented application

Data Analytics, BI, and Operational Reporting

Use Dashboard Tools, Data Transformation, Data Modeling, DAX, Advanced Spreadsheets, and Reporting Automation

Engineering practice and documented application

Data Analytics, BI, and Operational Reporting

Validate Data Definitions, Calculation Bases, Reconciliation, Exceptions, and Decision Traceability

Engineering practice and documented application

Work Measurement and Methods Engineering

Work sampling, observation planning, sample-size calculations and confidence intervals provide a basis for labor standards and staffing models. Clear activity definitions make comparisons repeatable across shifts and facilities. Worker-machine analysis, line balancing, standard work and automation alternatives connect measured work content with an improved operating method.

Industrial Engineering, Methods, and Work Measurement

Design Work-Sampling and Time-Observation Studies, Activity Taxonomies, Operational Definitions, and Labor Standards

Engineering practice and documented application

Industrial Engineering, Methods, and Work Measurement

Plan Random/Stratified Observations, Collection Frequency, Sample Sizes, Confidence Intervals, Margins of Error, and Standard-Error Analysis

Engineering practice and documented application

Industrial Engineering, Methods, and Work Measurement

Analyze Worker-Machine Interactions, Travel Distance, Facility Layout, Manual Handling, Line Balance, and Staffing

Engineering practice and documented application

Industrial Engineering, Methods, and Work Measurement

Validate Observational Data Through Stability, Equal-Variance, Autocorrelation, Pareto, and Multi-Plot Review

Engineering practice and documented application

Industrial Engineering, Methods, and Work Measurement

Develop Manual-to-Automation Alternatives and Phased Migration Plans Grounded in Observed Baseline Performance

Engineering practice and documented application

Quality Engineering and Structured Improvement

SIPOC and process maps establish boundaries and handoffs. FMEA prioritizes failure modes, while control plans translate requirements into monitoring, triggers and corrective action. DMAIC, structured problem solving, error-proofing and verification support improvements that can be sustained in day-to-day operation.

Lean Six Sigma, Quality Systems, and Governance Maturity

Apply DMAIC, SIPOC, Process Maps, FMEA, Control Plans, and Structured Improvement Methods Within Documented Practice or Preparation

Engineering practice and documented application

Lean Six Sigma, Quality Systems, and Governance Maturity

Use A3, 8D, Kaizen, Root-Cause Analysis, Corrective-Action Verification, and Standard Work

Engineering practice and documented application

Lean Six Sigma, Quality Systems, and Governance Maturity

Apply Documented FIFO and Takt Experience, Scheduling-Team FMEA, First-Hours Production Reviews, Backlog Control, and Changeover Improvement to Bottling-Line Work

Engineering practice and documented application

Lean Six Sigma, Quality Systems, and Governance Maturity

Develop Error-Proofing, Poka-Yoke, Visual Controls, Interlocks, and Layered Procedural Safeguards

Engineering practice and documented application

Lean Six Sigma, Quality Systems, and Governance Maturity

Link Monitoring, Triggers, Escalation, Periodic Review, Recalibration, and Evidence Retention into Closed-Loop Quality Systems

Engineering practice and documented application

Lean Six Sigma, Quality Systems, and Governance Maturity

Apply SPC/Capability Concepts to Delivery KPIs, AFE Cycle Times, SPI/CPI, and Repeatable Governance Processes

Engineering practice and documented application

Lean Six Sigma, Quality Systems, and Governance Maturity

Use PMMM Benchmarking and DFSS Modular Playbook Concepts in Improvement Planning

Engineering practice and documented application

Engineering Option Studies and Operating Models

Cooling-load and pump calculations, steam and pressure analysis, filtration-capacity comparisons, electrical load planning and packaging-flow studies connect quantitative analysis with capital decisions. The project pages show how these methods support equipment selection, interface definition and practical execution.

Explore Investment economics and lifecycle analysis capabilities ↑

Thermal/Hydraulic Analysis and Engineering-Calculation Review

Evaluate Heat Balances, Mass/Flow Relationships, Temperature Differential, Residence/Dwell Time, and Heating Demand

Engineering practice and documented application

Thermal/Hydraulic Analysis and Engineering-Calculation Review

Assess Heat-Regeneration Recovery, Insulation Losses, Hot-Water/Steam Demand, Exchanger Arrangement, and Process-Temperature Control

Engineering practice and documented application

Thermal/Hydraulic Analysis and Engineering-Calculation Review

Review Exchanger Duty, Heat-Transfer Area, LMTD, Hot/Cold-Side Conditions, and Product-Specific Regeneration/Bypass Arrangements as Recorded Design or Vendor Information

Engineering practice and documented application

Thermal/Hydraulic Analysis and Engineering-Calculation Review

Compare Proposed Heat-Exchanger Plate/Gasket/Material/Connection Specifications, Pumps, Control Valves, and Routing Alternatives

Engineering practice and documented application

Thermal/Hydraulic Analysis and Engineering-Calculation Review

Check Unit Conversions, Time Bases, Arithmetic Consistency, Design Targets, and Calculation Assumptions

Engineering practice and documented application

KPI Ownership and Performance Interpretation

A useful measure has an owner, a defined unit, a reporting period and a clear operating scope. Rates, equipment ratings, project results and cross-facility totals answer different questions. Consistent definitions, data checks and action thresholds make dashboards useful for decisions about reliability, capacity, schedule and benefits.

Explore Data analytics, BI, and operational reporting capabilities ↑

Analytical Capability and Practical Application

The portfolio brings together industrial-engineering study, applied analytical work and plant project experience. Method descriptions explain the analytical toolkit; linked projects show specific engineering applications. This connects rigorous analysis with decisions about production, utilities, maintenance, construction and operating readiness.

Explore Industrial engineering, methods, and work measurement capabilities ↑

Explore Optimization and operations research capabilities ↑

Explore Data analytics, BI, and operational reporting capabilities ↑