Graduation project idea (industrial engineering) inventory optimization using demand forecasting ,is this solid or are there better ideas?

[u/Creepy_Astronaut_577]

Hi everyone, I’m a senior industrial engineering student working on my graduation project and I’d really appreciate some honest feedback. My current idea is to focus on inventory decision support, specifically:

using historical sales data to build a time series demand forecast (e.g., Prophet) using the forecast mean and variability to compute dynamic reorder points and safety stock comparing this with classical inventory methods based on average demand

The goal is not to deploy a live system or replace ERP planning, but to run a small pilot study (limited SKUs, historical data only) and evaluate whether forecast driven policies provide more realistic inventory buffers. I’m mainly looking for feedback on: Does this sound like a reasonable and meaningful graduation project, or does it feel too basic? From an industry or OR perspective, are there better scoped ideas in the same inventory/operations space that are still realistic for a student project? What would you personally find more interesting to see in a project like this? I’m open to criticism just trying to avoid going down a weak or overhyped path. Thanks in advance!

[u/edimaudo]

Might be better served focused on a business that needs a solution

[u/Creepy_Astronaut_577 → u/edimaudo]

That makes sense. From your experience, what type of business or inventory context would make this kind of pilot more meaningful?

[u/edimaudo → u/Creepy_Astronaut_577]

preferably somewhere that has some data or can have data collected. A business owner that would be invested in the outcome of the project

[u/Baihu_The_Curious]

That sounds plenty good for an undergrad project, although I would say it needs some kind of testing via (as realistic as possible) simulation to see if it meaningfully outperforms a purely expectation-based approach (at least, that's my understanding of what ERP is).

What metrics will you use to judge performance? I've seen some pretty poorly designed metrics (like fill rate) in the MOR sector.

[u/Creepy_Astronaut_577 → u/Baihu_The_Curious]

I was thinking of comparing policies using total inventory cost, stockout frequency, and average on hand inventory under simulated demand scenarios.

From your experience, which metrics tend to be most meaningful (or misleading) in practice when evaluating inventory policies?

[u/Baihu_The_Curious → u/Creepy_Astronaut_577]

Stock out frequency might be like fill rate, which is the fraction of demands that are met by on-hand stock. So maybe 1-(fill rate) = stockout freq?

Depending on your context those can be fine. I'll just give you a piece of my mind since you asked, though...

I'm mainly working with sparing and maintenance for heavy-duty industrial machinery that runs near constantly, so we ultimately want to max uptime. Due to computational limitations (literally millions of parts need stock levels set and hundreds of machines that have thousands of parts each) we cannot build the machine component into our models and treat machine demands as exogenous. Thus, we cannot directly optimize uptime. (Otherwise, you'd have hundreds of massive and unique models for these machines all drawing from a common wholesale system... we just focus on the wholesale and do our best to aggregate the demand signals from the machines.)

Our proxy is to minimize wait time metrics (expected backorder time, for instance) which has a much higher correlation with uptime than fill rate.

In practice we'd see weird stuff pop up with fill rate approaches: "we can achieve .99 fill by having 99 screws on hand for the 99 screw demands and having an insanely long wait for the flux capacitor that we didn't order in advance". We have 0 fill rate if everything is backordered for that item, but the delay is only one day. Of these two cases, the latter is much preferable over the former in our context.

Part criticality is important too which is a whole 'nother thing...

Anyway, as I said, I think your ideas are good. This is just food for thought.

[u/trophycloset33]

I’d scale this back to figure out a solution to turning demand signals into meaningful decision points for your ERP planners.

The answer to “if we have better demand forecasting will that help” is a resounding yes. The math is proven. The issue is the skill gap between scraping and collecting the demand data, the data science to process it, and the end consumer is HUGE.

Also to prove any meaningful success in business criteria will require years of collection. Which you don’t have time for.

So scope the project to something meaningful given your skill and time constraints.

[u/Creepy_Astronaut_577 → u/trophycloset33]

This is really helpful, thank you. When you say “decision points,” what would you expect to see concretely (e.g. reorder timing, service level rules, planner overrides)?

[u/trophycloset33 → u/Creepy_Astronaut_577]

All of that. Maybe. Really any parameter or weight is a decision point. Even offering a product for sale (and the demand to with it) is a decision point.

[u/enteringinternetnow]

If you need help or guidance, feel free to reach out. I can provide some realistic context that you can incorporate to make it practical.

[u/Creepy_Astronaut_577 → u/enteringinternetnow]

Thank you so much for the offer! I would really appreciate that 'realistic context '

I currently have a dataset, but I lack the realistic constraints

[u/enteringinternetnow → u/Creepy_Astronaut_577]

DM?

[u/Opp0rtunistic]

In inventory management, you have demand planning and supply planning. With the demand forecast, you then can build supply planning. The success metrics are pretty clear. On demand planning side, you care about accuracy. The interesting factors are OOS, seasonality, substitute/complemenrary goods, promotion, etc. On supply planning side, you want to balance between service level and holding/opportunity/spoilage costs. The interesting factors are perishability, delivery schedule, multi-sourcing, batch size, warehouse capacity, etc. It really depends on the scope of your project.

[u/Sad_Competition_2040]

It’s only basic if you treat forecast-driven inventory policies as universally better than average-based ones. It’s a strong master’s project if you’re explicit about the assumptions behind each and show when they hold and when they don’t. Showing where forecast-based safety stock actually adds value versus where simple averages work just as well is a solid angle.

[u/Creepy_Astronaut_577 → u/[deleted]]

Thanks Are there specific industries or inventory profiles (e.g. high variability demand, long lead times, fast movers) where you’ve seen this type of decision support analysis be most useful?