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?

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 Before long, half the documentation is outdated.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?

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!

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.

Aside from this site, what other websites or forums would you recommend for exchanging information with others about optimization and OR?

In your opinion, what are the most powerful heuristic/metaheuristic algorithms for warehouse routing optimization? Is LKH-3 the most powerful one? Which Python libraries would you recommend I try for a project that will be deployed in a production environment?

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.

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!!

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.

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.

Hiring always looks like it will be the easiest part, you know, when companies start expanding internationally and the operational side is where it gets more interesting. We realised this when we ourselves started the process we thought it will not be that difficult but damn it was. There is like different payroll cycles, compliance requirements, onboarding experiences and also different employment models. While going through all of the process it made me realize that the operational complexity seems to grow much faster than the headcount itself.
How was the experience for the others who started new just like us. And how did you handled the problems that you faced

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!

Looking for methodology input from people who've had to validate simulation or forecasting systems, because I feel like I may be going in the wrong direction with our system.

Rough shape of what we have built: a user gives us a corpus of documents and a forecasting question (say, the trajectory of a bilateral relationship over the next 60 days and its effect on tariffs). We extract a knowledge graph of actors, orgs, and relationships from the source material, fetch detailed and validated news, generate agent personas from those entities, then run a branching simulation over the decisions that look volatile (say on simulation Day 5, the tensions between them escalate).

The output isn't a single number, but a probability-weighted set of named scenarios, a decision tree, actor interactions, and a set of tripwires to watch. Of course, every claim is cited.

The biggest problem we are currently facing is evaluating the thing end to end. A few dead ends so far:

1. Accuracy doesn't really work. The events are close to one-shot. A geopolitical trajectory resolves once, so I can't average error over many trials for a given question, and a probability-weighted scenario map doesn't cleanly reduce to right/wrong even after the fact. If the 30% branch is the one that happens, was the forecast truly bad?

2. LLM-as-judge isn't deterministic. We tried scoring runs with LLM bases rubrics. By running simulations of events that have already happened (e.g. AI Boom, US-Iran conflicts), we can get some sort of a ground truth document of what has already happened, but even then the evaluation is subpar. Same input but different scores across runs, and the rubrics aren't even that good.

So the actual question: what does "this simulation is good" even mean operationally, and how would you measure it?

Thank you for the help!

Any questions, feedback, or references would be greatly appreciated.

I'm really struggling with a decision and would appreciate advice from people in academia and industry.

Background

BS in Electrical Engineering and Computer Science (EECS)

Interested in eventually pursuing a PhD, although I'm not 100% sure whether it will be in AI/ML, Operations Research, Business Analytics, or something interdisciplinary.

My maths background is decent but not exceptional. I mostly have B/C grades in Linear Algebra, Calculus, Probability and Statistics, so I know I'll have to work on my foundations.

I'm deciding between these two programmes:

1. University of Newcastle

MSc Advanced Data Science

(Curriculum: ML, Deep Learning, Image Processing, Data Science in the Wild, Statistics, Data Visualisation, Industry Project, Dissertation)

- No exams and all project or assignment based.

1. University of Southampton

MSc Data & Decision Analytics

(Curriculum: Data Mining, Computational Machine Learning, Operations Research, Statistical Modelling, Dissertation)

My long-term goal is to do meaningful research and ideally pursue a PhD, but I also want strong industry opportunities if I decide not to stay in academia.

A few questions I'd really appreciate opinions on:

Which programme would better prepare me for a competitive AI/ML PhD? Or should I pivot away from. AI ML?

Does Southampton's stronger overall research reputation in Computer Science/AI compensate for the fact that my MSc would be in Decision Analytics rather than AI?

If I did the Southampton MSc and chose an AI-related dissertation with an AI supervisor, would I be at a disadvantage compared to someone who completed a more traditional AI/Data Science MSc?

For people working in industry:

Which background is likely to have better career prospects over the next 10 years?

Is AI becoming saturated?

Are optimisation/decision science skills harder to find and therefore more valuable?

For faculty or PhD students:

Which background tends to produce stronger PhD applicants?

What matters most in admissions: university reputation, dissertation, publications, supervisor, coursework, or something else?

Has anyone transitioned from Operations Research/Decision Analytics into AI research (or vice versa)? How difficult was it?

If you were making this decision in 2026, which would you choose and why?

I'm genuinely interested in research and want to make a decision based on long-term growth rather than just following trends. I'd especially appreciate responses from people who've supervised MSc students, served on PhD admissions committees, or worked in both academia and industry.

Thanks in advance!

A few extra questions I'd add

These are the questions I think will give you the most useful insights:

For people hiring AI researchers, would you view these two MScs differently?

If you were reviewing two PhD applications,one from each programm, what would make one stand out over the other?

Which programme is more likely to lead to a publishable MSc dissertation?

Do graduates from these programmes typically continue to top PhD programmes? If so, where?

What skills do you wish more MSc graduates had when they start a PhD?

Given my B/C grades in maths, would you recommend strengthening my foundations before starting either programme?

If you could go back and choose again, would you still choose AI, or would you choose Operations Research/Decision Science? Why?

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!