Steel Import Management System (SIMS) Data

It is the gate every steel import into India passes through on paper before the cargo moves. The Steel Import Management System (SIMS) is the online portal through which importers of iron and steel items under ITC (HS) 2017 chapters 72, 73 and 86 must register in advance - quantity, value, origin, grade and stated purpose - before importing. The Ministry of Steel then publishes what the portal accumulates, in two forms. The first is the raw registration extract: a single-sheet Excel file per month, roughly 4 to 5 megabytes, carrying about 42,852 rows and 18 columns of transaction-level registrations for January 2025 alone - CIF value in US dollars, product category and sub-category, type of steel, application area, and the domestically-manufactured-grade declaration. The second is the monthly analytical PDF, 'SIMS Report <Month> <Year>' at roughly thirteen pages, which breaks the same registrations down by country, ITC code, category (Semis, Finished Steel, Scrap), quality and standard, port and end-use application, and adds BIS licence availability.

The pairing matters because each artefact answers a different question. The extract lets you count shipments, rank origins and distribute declared values yourself; the report shows the ministry's own cuts, including port-level and BIS-licence views that the spreadsheet does not precompute.

Within Datadory's catalog of 1,744 datasets across 159 viable industries, this record scores 7/10 - one of more than twenty steel records, and the only one resolving India's steel imports to individual registered transactions.

Exactly as they land after normalization - three registered shipments from the January 2025 extract:

# one registered shipment per row - January 2025 extract shape
sims_no=MOSSIMS010125000020   origin=UNITED STATES   itc_hs=72044900   desc=OTHER WASTE AND SCRAP
category=Scrap   sub_category=MS scrap   application_area=Retailing   cif_usd=8778.29

sims_no=MOSSIMS010125000008   origin=VIET NAM   itc_hs=72193490   type=Stainless Steel
sub_category=Flat Products - CR - Strip   is_code=IS 6911: 2017   cif_usd=37753.76

sims_no=MOSSIMS010125000025   origin=POLAND   itc_hs=72044900   category=Scrap
application_area=Raw Material for Iron or Steel making   cif_usd=117546.98

# shape of the slice
grain=transaction-level registrations   cadence=monthly extract (~42k+ rows/month)   chapters=ITC(HS) 72, 73, 86

Read what those rows already say. The identifier encodes its own filing date - MOSSIMS, then 01/01/25, then a sequence number - so deduplication needs no external key. Three rows span three continents and two product families: American and Polish scrap bound for melting, Vietnamese stainless strip against an Indian Standard specification. The declared values differ by two orders of magnitude within the same month, 8,778 dollars beside 117,547 dollars, which is why shipment-level distributions beat averages when anyone models import values. And the application-area column - Retailing beside Raw Material for Iron or Steel making - is the importer's own declared end use, a field customs outcome tables never carry. Request a sample naming your ITC(HS) codes and months and the same shapes come back filled.

Eighteen columns per registration, written against the published extract rather than inferred. Every observation keys on the SIMS No, a unique identifier built as MOSSIMS plus filing date plus sequence, alongside the row's serial number and the registration date. The commodity side contributes the ITC(HS) code - six to eight digits, e.g. 72044900 - with its official description, the Indian Standard specification referenced where one applies (IS 6911:2017, or NA), the broad product category (Scrap, Finish Products), the finer product sub-category down to MS scrap or Flat Products - CR - Strip, and the type of steel - stainless, alloy, or blank for scrap. The geography side pairs country of origin with the exporter's country, which differ whenever a trader intermediates. Timing comes as the date of export and the expected date of import, each declared by the importer. Value arrives as CIF in US dollars with the USD conversion rate applied, recorded exactly as filed ('1 USD =85.95 INR'). Two fields close the set and exist nowhere else in India's public trade data: whether the importer declares a similar grade manufactured in India, and an open-form reason for import stating why not.

Geography: India as the importing market, worldwide as the origin frame. Every row names both the country of origin and the exporter's country, so flows resolve per partner at shipment grain - the United States, Viet Nam, Poland and Italy all appear in a single month's slice - and the PDF reports aggregate the same registrations into country rankings without losing the underlying rows.

Temporal: publication runs from October 2019 to the present listing. Earlier years arrived as weekly XLSX snapshots (files like 'SIMS 29Oct2019'); later years settle into monthly extracts and monthly summary PDFs, running through October 2025 at research time. Because registration precedes import, each month's file describes shipments still ahead of their landing - a lead the settled customs series structurally cannot offer.

Granularity: the finest public grain there is for this flow - one row per registered transaction, tens of thousands per month - reconciling upward through the PDF reports' monthly aggregates by country, ITC code, category, port and end-use application. Registration data is importer-supplied and unverified at source, a caveat worth keeping attached: it measures intent to import, not yet goods cleared.

API, files, or your warehouse. Daily, weekly, or hourly.

Pick the channel your stack already speaks. Every registration arrives typed - codes kept as strings so leading zeros survive, CIF values numeric, declarations boolean - so nothing in your pipeline re-parses an eighteen-column spreadsheet to answer which grades crossed which ports last month.

Every delivery ships with the field dictionary above plus the sample rows for validation, and the declaration caveat rides with the data: these are importer-supplied advance registrations, not verified customs outcomes, and any workflow that treats them as cleared entries should say so downstream.

• Trade-policy and competitive-intel analysts read import pressure while it is still prospective - ranking origins and sub-categories across a month's 40,000-plus registrations separates a scrap surge from a flat-products surge weeks before settled trade statistics say the same thing; worked flows continue on our competitor tracking page.
• Market researchers and consultants size product-level demand off declared application areas and CIF values instead of chapter totals - automobile components, retailing and raw-material-for-steelmaking are separable populations inside one month's file; see market sizing.
• Investors and quant researchers build leading indicators for Indian steelmakers and steel-consuming sectors: registration volumes by origin country front-run the settled print, and the CIF distribution supplies price texture without touching a spreadsheet; see investors & quants use cases for steel.
• Supply-chain and procurement teams trace which countries feed which sub-categories - MS scrap for melting, CR strip for fabrication - and benchmark declared unit values against supplier quotes before contracting; see supply-chain mapping.
• Journalists, academics and students cite a national ministry's registration record with identifiers attached - every row carries its SIMS number, so one shipment can be quoted and reproduced; see journalists & academics use cases for steel.

Trade-policy and competitive-intel analysts get the earliest public read on India's steel import mix, at shipment grain, with origin and product named together; the fuller workflow lives on competitive-intel product teams in steel. Market researchers and consultants get declared end-use application areas and CIF distributions that turn a registration ledger into a product-level demand model; see market researchers in steel. Investors and quants get a forward-leaning indicator series whose monthly cadence matches the cycle they trade; see investors and quants in steel. Data scientists and ML engineers get forty-thousand-row monthly tables whose columns arrive typed and identifier-keyed - ready for feature pipelines rather than cleaning sessions; see data scientists in steel.

Provenance note — published by the Government of India's Ministry of Steel, which operates the SIMS registration portal for imports under ITC (HS) 2017 chapters 72, 73 and 86. The transaction extract and the monthly SIMS Report derive from the same registrations, so the two reconcile by construction.

Methodology note — every row is an importer-supplied advance registration: values, grades and dates are declared before landing, not verified customs outcomes, and the ministry's own report pages state the figures are based on unverified filings. Treat CIF values as declared values, the expected import date as intent rather than arrival, and keep that provenance attached to any derived indicator.

Completeness note — Datadory scores this record 7/10 and the eighteen-field dictionary above verifies against the observed January 2025 extract. Port-level and BIS-licence breakdowns live inside the monthly PDF reports; deeper history before October 2019 and any field beyond the published eighteen are confirmed at sampling rather than asserted here.

Where to go next — set the transaction view inside wider frames: Ministry of Steel Monthly Summary reports the same trade flow as national monthly totals with prices and CPSE output beside them; monthly crude steel production for 71 countries widens the supply picture to roughly 98 percent of global output; EUROFER European steel data covers the European counterpart; best steel datasets ranks the whole vertical; and the steel data hub pools the industry view.