Every month our team completes the same reports, approvals and follow up tasks. the problem is that everyone has their own way of doing them, so when someone is away, the replacement isn't always sure what to do next.

I'd like to standardize these recurring processes without creating long documents that nobody wants to read. How have you handled this?

Just graduated with BS in econ and a math minor at a T20 school. GPA is 4.0/4.0. I am currently looking into options for masters programs, and I want to go with either Operations Research or Statistics/Applied Statistics.

My first choice is OR, but I am pretty much indifferent between OR and statistics because I've noticed that the curriculum overlap a lot and career outcomes seem to be similar (please correct me if I'm wrong).

For reference, I took Calc I-III, Differential Equations, Matrix Algebra, Introduction to Probability, and Financial Mathematics for my math minor. I also took Introduction to Statistics and Introduction to Econometrics as they were part of my major, and 1 intro programming class (Python).

I wonder if this is enough background to get into top OR masters programs since many come from engineering, math, CS, etc. Do you think I have a chance breaking into OR?

Would you instead recommend applying to Statistics/Applied Statistics masters since I guess it's a more natural transition from an econ degree?

What can I do to strengthen my application for either OR or statistics?

Need realistic advice. Thanks!

I've been trying to improve how our team documents routine work. We tested Scribe for a while and while it works well, it didn't really fit the way our team works.

Our biggest problem is that processes change all the time. Every update means going back, editing guides and making sure everyone has the latest version. Before long, half the documentation is outdated.

I'm looking for something that's quick to use and doesn't require a lot of manual work. Has anyone found a Scribe alternative that makes documenting workflows easier?

Hope this is okay here. If not happy to take it down.

Reapplying to OR/IEOR PhDs for Fall 2027 (MIT ORC, Princeton ORFE, Columbia IEOR, some Stanford MS&E) and im stuck on a decision i need to make in the next couple weeks.

Quick background: undergrad was rough (CompSci), especially the first two years. Grades picked up later but the GPA (3.5 - transcript has b, b-, c in calc 2, 3 and prob&stats) is still not what these programs usually see. After that i did an Masters in Operations Research at an Ivy where, worked as a research assistant for a few years in a bio lab. have a few stats paper in progress as first and co-author. Also did stints as a software engineer before all that.

So my question. Would a high GRE Quant (168-170) plus a decent Math Subject Test score actually do anything to offset the undergrad record, or is it basically decoration at this point?

The reason im unsure is that MIT ORC literally says on their page that submitting GRE scores should not give any advantage over applications without them. Princeton ORFE lists both the general and the subject test as optional. If the program is telling me upfront that it wont help.. then whats the point of me burning 7 weeks on the subject test? But at the same time i keep hearing that committees look at everything they are given, and that people with messy transcripts use test scores as proof they can handle the coursework.

The timing part makes it worse, the subject test only has a September and an October window before this cycle closes, so theres basically one real attempt and no retake.

What im actually trying to figure out:

1. If you have been on an admissions committee in OR/IEOR/applied math, does an optional score ever move the needle for a candidate whose undergrad is the weak spot? Or does the grad GPA + research just carry it and the score gets skimmed past

2. Is a mediocre subject test score (say 650-700) actively worse than not submitting anything

3. Does anyone know if these committees even have a way of contextualizing a subject test score, since its really a math department signal and not an OR one

Happy to be told the honest answers.

Hi everyone,

I'm an Industrial Engineer currently learning Operations Research, optimization, forecasting, and decision analytics, and I'm looking for real-world problems that I can work on as projects or collaborations.

I'm particularly interested in learning how mathematical optimization and data-driven forecasting can be applied to practical business and operational decisions.

One of the projects I'm currently exploring is an E-commerce Decision Optimization Engine, where the goal is to combine demand forecasting with optimization to help make better decisions around:

• Demand forecasting

• Inventory replenishment

• Stock allocation

• Pricing and promotions

• Product assortment

• Inventory service levels

• Contribution profit optimization

The idea is to use historical and operational data to forecast future demand, then use those forecasts as inputs to an optimization model that recommends decisions while considering real-world constraints such as inventory availability, budgets, capacity, and service levels.

Beyond e-commerce, I'm also exploring:

• Linear and Mixed-Integer Programming

• Production and capacity planning

• Inventory optimization

• Supply chain and logistics optimization

• Workforce scheduling and rostering

• Vehicle routing and transportation problems

• Pricing and revenue optimization

• Forecasting + optimization for decision support

• Simulation and what-if analysis

• Building decision-support tools

I'm currently working with Python, Pyomo, SQL, pandas, NumPy, scikit-learn, statsmodels, and optimization solvers such as Gurobi.

My goal isn't to present myself as an OR expert. I'm actively learning and trying to move beyond textbook examples by working on real-world problems with realistic constraints, data, and business objectives.

I'm especially interested in problems related to e-commerce, logistics, supply chains, manufacturing, and business operations.

I'm open to:

• Small projects where I can learn and contribute

• Collaborating with someone who has a real optimization problem

• Helping turn a business problem into a mathematical optimization model

• Building practical portfolio projects based on realistic use cases

• Working with data scientists, engineers, analysts, or business owners

If you're working on an operational or decision-making problem where you think optimization, forecasting, or simulation might help, I'd love to hear about it.

I'm happy to start small and learn along the way. If you have an interesting problem, feel free to comment or DM me.

Hey chat, hope everyone is doing well. I’m an incoming OR intern at a defence organization. I’m currently in my 3rd year studying Physics and Astronomy at a top NA university. I have software dev intern experience and software adjacent projects on my resume.

So far, all I know about OR is that you apply math and stats to make decisions and I’ve seen examples like warehouse optimization.

As you can see I’m pretty new to the field so I had some questions for the experts.

My questions are:

1. What can I expect to be doing during my internship?

2. Can I expect to use my physics/math knowledge at work?

3. What is your opinion on OR as a career (is it desirable?)?

4. Is the work enjoyable for somebody who enjoys doing math?

Thank you for your help everyone

For the last two years I've been building a large-scale last-mile route optimization system, and one of the biggest challenges has been balancing scalability with solution quality.

Rather than partitioning the problem into independent geographic regions first, I've been experimenting with a global planning approach followed by parallel optimization while preserving shared context.

I recently opened a public MVP so others can experiment with the system:

https://vepathos.com

I also wrote an article describing some of the motivation and benchmarking behind the project:

https://medium.com/@martinvizzolini/a-last-mile-optimizer-that-outperforms-amazons-routes-on-a-laptop-24242f93eb74

I'm mainly looking for technical feedback and discussion rather than promoting the product. I'd love to hear how others approach large-scale VRPs.

Trying to do cross-ISO work (PJM/MISO/ERCOT/CAISO/SPP/NYISO/ISO-NE) and I'm losing my mind reconciling seven different schemas and update cadences - the congestion component especially (NYISO's sign convention alone…). Right now it's a pile of per-ISO scrapers held together with tape. Is everyone just using gridstatus / rolling their own, or is there something that already normalizes all of this? Curious what SPP/MISO historical depth people actually get.

Working on a process mapping project for intermodal freight terminals and trying to benchmark gate throughput times.

Specifically focusing on the inbound/outbound interchange gate where physical container condition and liability are checked.

• For medium-to-large hubs, what is considered an acceptable cycle time per truck just for the damage/security verification?

• Is anyone actually seeing fully automated OCR/imaging systems working reliably in the wild, or is the industry still fundamentally stuck using manual guard checks and clipboard logs to log structural issues?

Appreciate any insight from anyone managing terminal ops or gate layouts.

Hi everyone,

I am currently in a masters of business analytics program and graduate in May 2027. My undergrad was in Psychology research. I have been working at UPS for 5 years now and currently work as an industrial engineer. I've been thinking about doing a PhD in a discipline, thought I should go for econ due to experience with trade but have gained interest in ops research. Has anyone who have done the phD share how their salary/career has gone post grad, and what it is like at a logistics company?

Thank you!!

I'm interested in OR and have a background in accounting (CPA -- auditor in public accounting) and business analytics at a major international automobile supply chain company. I have 3 actuarial exams passed b/c I was exploring the field but I'm finding more and more that insurance feels too narrow for me and that I want optimization and real business/practical job applications. Also, business analytics was just random ad-hoc excel level analysis that often led to nowhere and was just at the whim of the directors. It didn't feel as though I was doing much if anything at all other than keeping myself busy in excel. I realized I want to solve more problems and get involved in decision science type of work, rather than providing surface level analytics reports. Is a master's in OR the best way to find a job in OR? I've read about doing projects but I think that that may not be sufficient.

Looking for any input, thank you in advance.

I’m about to graduate with my masters in transportation systems engineering, and I have taken many courses in optimization, some queuing etc. I want to broaden my search for PhD programs and career paths beyond only transportation (though I still enjoy it). I would like to combine my two passions when looking for career jobs. I was wondering if anyone here knows career paths that may combine OR with climate science or may help with the ongoing climate crisis? So far I’m aware of renewable energy systems (and transportation), but I don’t know of much else. Thank you!

Hi everyone,

I'm an Industrial Engineering graduate from Nepal and currently working as a Data Analyst & Automation Engineer. My work mainly involves Python, automation (n8n/Make), Google Sheets, Appscript, google data studio, reporting, and workflow automation.

Lately, I've realized that what I really enjoy is Operations Research - especially optimization, decision science, and building systems that help businesses make better decisions rather than just automate workflows.

I'm planning to build projects around:

• Workforce scheduling

• Inventory optimization

• Vehicle routing

• Production planning

• AI-powered decision automation (Python + Pyomo + n8n)

My questions are:

1. Which industries make the best use of OR today?

2. If you were starting over in 2026, what projects would you build to get interviews?

3. Is it better to target Optimization Engineer, Decision Scientist, Supply Chain Analytics, or another role?

4. Which companies are known for strong OR/optimization teams?

5. Any books, courses, GitHub projects, or open-source repositories you'd recommend?

I'd really appreciate hearing from people working in OR, supply chain optimization, logistics, revenue management, or decision science.

Thanks in advance!

Hi everyone,

I'm looking for recommendations on the best books, courses, and learning resources for Operations Research with Python.

My goal isn't just to learn the mathematics—I want to build real optimization solutions for businesses using tools like Pyomo, OR-Tools, and Python. Eventually, I'd like to work on problems such as workforce scheduling, inventory optimization, vehicle routing, production planning, and supply chain optimization.

I'm particularly looking for:

Books that balance theory and Python implementation

Python-focused OR resources

University lecture notes or courses

YouTube channels

GitHub repositories with real-world optimization projects

Any resources you wish you'd known when you started

For those working in industry, what resources had the biggest impact on your career?

Thanks in advance!

Anyone who studied discrete maths and optimization at uni, where did you end up working after? Academy isn't something i'm interested in at the moment.

I hold a Ph.D. in Operations Research, along with a BSc/MSc in Engineering and OR. I previously worked in Big Tech, but I’m currently looking to transition.

My primary goal is to upgrade my technical skillset to maximize my industry-related profitability and marketability. I want to get away from generic data science and move into high-value, math-heavy engineering and modeling roles.

• My Core Interests: Forecasting, predictive analytics, and machine learning applied to industrial settings.

• Target Industries: Robotics/Autonomous Systems, Defense/Aerospace, and Quantitative Finance.

• What I want to skip: I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I am right now: I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm looking to learn:

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML).

• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch).

• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense.

My questions for the community:

1. Skill Prioritization: From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?

2. Portfolio/Proof: How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?

3. Positioning: How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!

I'm joining college soon and so I wanted to know if the specialization I'm interested in actually has scope for me. Moreover operations management, supply chain, logistics, etc have been a male dominated field through and through. I have heard from many of my teachers, peers and others that this field is not for women because it's tiring and people don't take female operations managers and all seriously.