Development of an Intelligent Freight Forecasting Model for Optimized Vessel Chartering and Bulk Cargo Procurement from overseas to East Coast of India
Ministry of Steel · Transportation & Logistics · Software
A rare low-competition statement with a sponsor who genuinely wants the answer, but the freight rate series is licensed, so decide on day one what public proxy you are forecasting and be honest about that substitution instead of letting a judge find it.
What it actually is
SAIL currently books ships to bring coal and other bulk cargo to eastern Indian ports by checking the market each day, which means it reacts to prices instead of anticipating them. The ask is a forecasting tool that predicts where freight rates are heading and advises when to lock in a charter. It also has to recommend which size of ship actually fits the port at each end.
What to build
A freight decision-support tool combining a time-series forecast of dry bulk rates by vessel class and trade lane over the named origin routes (Australia, US, Mozambique, Russia, Indonesia to the East Coast), a port constraints database encoding maximum LOA, beam, draft and cargo handling rate for Paradip, Vizag, Gangavaram, Gopalpur, Dhamra, Sagar-Sandheads and Haldia plus the loading ports, a vessel-class recommender that filters Handysize through Capesize against those physical limits for a given parcel size, an idle-time and repositioning module, a volatility and congestion early-warning flag, and a manager-facing dashboard where entering cargo tonnage, route and contract duration returns a charter-timing recommendation with a confidence band.
Smallest thing that wins the room
Enter a 75,000 tonne coal parcel from Australia to Paradip and have the tool return both a wait-or-book call with a forecast curve and a vessel class that respects Paradip's draft limit, then show the backtest of that same call against the historical rate.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 15% of the 226 · #193 of 226 by expected field · reaches the 500 cap
Busier than most. Expect several teams to arrive at the same obvious solution.
Why: central ministry statements sat below the average.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
3/5The domain is unglamorous enough to thin the field considerably and the sponsor has a real operational need, but the licensed-data problem is structural rather than solvable by effort, and a proxy-index forecast is a much weaker answer than the statement is asking for.
Feasibility
3/5Port draft and berth limits are published and macro indicators are free, but route-level dry bulk freight rates are a licensed Baltic Exchange product, so the target variable your model is supposed to predict is the one input you cannot legally obtain and you will end up forecasting a public index as a proxy.
Innovation scope
4/5The description specifies four output categories but says nothing about method beyond suggesting time series and regression, so the modelling approach, the feature set and how the port constraint layer interacts with the forecast are all genuinely yours to design.
Clarity
4/5Unusually concrete for an SIH statement — it names the discharge ports, the origin countries, the vessel classes and the specific constraint variables, and the four recommendation types are laid out separately, though it never states what forecast horizon or accuracy would count as success.
Effort
HeavyAssembling a defensible rate series, building and validating a forecast, encoding a port constraints database accurately, writing the vessel matching logic and wrapping it in a usable dashboard is four substantial workstreams with the data assembly quietly the largest.
Demo-ability
MediumA backtest overlay showing your recommendation would have beaten the spot approach is genuinely persuasive to a logistics judge, but it is charts and tables with no physical or interactive moment, so the presentation has to carry it.
In its favour
- Green flag: The description names seven specific East Coast ports and their constraint variables, so the hardest requirement-gathering work has been done for you and your port database is verifiable against published port handbooks
- Green flag: Shipping economics is a domain almost no student team will touch, so the competition for this statement will be a fraction of what an AI-health or agri PS attracts
- Green flag: The vessel-class matching logic is deterministic and demonstrably correct, giving you a component that works reliably even if the forecast underperforms
- Green flag: A SAIL evaluation panel will actually know this problem, so a correct grasp of draft restrictions and deadheading earns credibility that generic dashboards never do
Against it
- Red flag: Route-specific dry bulk rates are commercial Baltic Exchange data — you cannot get them, and forecasting the headline BDI instead is a materially different and much easier problem that a domain judge will spot
- Red flag: Freight markets are close to a random walk over short horizons, so an honest model may barely beat a naive persistence baseline and you must show that comparison rather than hide it
- Red flag: Real-time port congestion needs an AIS feed whose useful tiers are paid, and free AIS coverage of Indian anchorages is patchy
- Red flag: Four separate recommendation types are asked for and idle-scenario management in particular requires fleet data you have no route to, so scope down explicitly rather than faking that module
What you will be writing
- Prophet / SARIMAX / Temporal Fusion Transformer forecasting
- AIS vessel position feed for congestion features
- Baltic Dry Index sub-index series as rate proxy
- PostgreSQL port constraints model
- Python + FastAPI backtesting service
- Plotly Dash or React decision dashboard
- Maritime logistics
- Commodity procurement
- Time-series forecasting
Prior art to read before you start
dry bulk freight rate forecasting · vessel chartering optimisation · port draft and berth constraint matching
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.