The Logistics Maturity Model for Steel Plants

The Logistics Intelligence Maturity Model: Five Levels for Steel and Metal Plants

Table of Content :

Why Maturity, Not Software
The Five Levels
How to Locate Yourself Honestly
Where SuperProcure Fits
What Each Jump Is Actually Worth
Frequently Asked Questions
Where to Start

Two steel plants of roughly the same size, making roughly the same products, can run their logistics in completely different centuries. One dispatches on the strength of a logistics manager who knows every transporter by name and keeps the real plan in a notebook. The other runs the same volume with fewer people, better rates, and a system that reassigns a load before anyone has noticed the truck isn’t coming.

The gap between them is not budget. It is maturity. And most plants have never actually located themselves on that curve, which makes it very hard to know what to fix next.

This is a five-level model for doing exactly that. It is written specifically for metal and steel operations, because the sequence of problems in this industry is distinctive: inbound raw material and outbound dispatch behave like two different businesses, plant TAT is a bigger cost than most CFOs realise, and freight settlement volumes are high enough that small errors compound quickly.

In one line: The Logistics Intelligence Maturity Model describes five stages a steel plant’s freight operation moves through — Manual, Digitised, Integrated, Predictive and Agentic — defined by how much of the decision-making the system does rather than the people.

Key Takeaways

Maturity is defined by decision ownership, not by how much software you own.
Level 1 Manual: freight runs on calls and spreadsheets. Records are created after the fact.
Level 2 Digitised: data is captured but siloed. Reporting explains yesterday.
Level 3 Integrated: one platform across the lifecycle, shared with transporters in real time.
Level 4 Predictive: the system forecasts arrivals, rates and vendor risk, and recommends action.
Level 5 Agentic: the system decides and acts inside guardrails. People handle exceptions, not routine.
Most Indian steel plants sit between Level 1 and Level 2. The largest single gain is usually the jump to Level 3.

Why Maturity, Not Software

It is tempting to measure progress by what you have bought. An ERP, a tracking app, a rate spreadsheet with pivot tables that only one person understands. But tools do not determine maturity. What determines it is how many decisions the system makes without a human in the loop.

A plant with three logistics tools where every decision still routes through a WhatsApp group is less mature than a plant with one platform that reassigns loads on its own. The question is not what you own. It is what happens when a transporter refuses an indent at 8pm on a Saturday.

Every level in this model is defined by the same question, asked at increasing difficulty: when something goes wrong, does a person have to notice first?

The Five Levels

Level	What it looks like	Who decides
1. Manual	Freight sourced by phone and WhatsApp. Rates in spreadsheets. Plant TAT known anecdotally. Records written after the event.	People, entirely
2. Digitised	Indents and rates recorded in systems. Some GPS tracking. Data exists but sits in separate places. Reporting is retrospective.	People, with better records
3. Integrated	One platform across sourcing, allocation, in-plant and settlement. Transporters on the same system. Status is live, not requested.	People, with real-time facts
4. Predictive	System forecasts arrival times, rate direction and vendor reliability. Flags problems before they land. Recommends the action.	System advises, people approve
5. Agentic	System acts within set rules: reassigns, escalates, reschedules, computes penalties. Humans handle exceptions only.	System decides, people govern

Level 1: Manual

Everything works because someone remembers. Sourcing is a round of calls to four transporters, the rate is whatever the third one said, and the record of it exists in a spreadsheet updated when there’s time. Plant TAT is discussed rather than measured. Freight bills are checked in batches at month end, when disputes are already stale.

The tell is not the absence of software. It is that if your logistics manager took two weeks of leave, nobody could reconstruct why last month’s freight cost what it did.

Level 2: Digitised

Records now exist in systems rather than notebooks. Indents are raised in the ERP, there’s a GPS app, rates live in a shared sheet. This feels like progress and it is, but the data sits in separate silos that don’t talk. You can produce a report explaining what happened last week. You still cannot act on what is happening this afternoon.

Most Indian steel plants are here, or somewhere between here and Level 1. The frustration at this stage is real: you have invested in tools and the problems haven’t gone away.

Level 3: Integrated

This is the jump that changes the economics, and it is less about intelligence than about connection. Sourcing, allocation, in-plant movement, tracking and freight settlement run on one platform. Crucially, transporters are on it too, which means status stops being something you phone someone to obtain.

At this level the invisible costs become visible for the first time. Stage-wise plant TAT. Which transporters actually place versus which ones promise. Where the spot premium is being paid. You cannot fix what you cannot see, and Level 3 is where you start seeing.

Level 4: Predictive

Once the data is connected and clean, forecasting becomes possible. The system tells you which trucks will actually reach the plant in the next four hours, which transporter is likely to fail on tomorrow’s indent, whether the rate you’re being quoted is above or below the live market. The work shifts from chasing information to acting on it.

The distinguishing feature of Level 4 is that problems announce themselves before they arrive. A delay surfaces while there is still time to reschedule the bay rather than after the truck is idling in the yard.

Level 5: Agentic

At the top of the curve, the system does not just predict and recommend. It acts. Within rules you set, it reassigns a refused indent to the next transporter, escalates to spot sourcing when a response window lapses, recomputes penalties on non-performance, and adjusts the plant schedule when an ETA slips.

People are still very much involved, but their job changes. Instead of running the routine, they govern the rules and handle the genuine exceptions. This is what an AI-native platform makes possible, and it is worth being precise about what is real today: rule-bound autonomous action across sourcing, allocation, in-plant and settlement is in production now. Fully self-directing procurement, where the system sets its own objectives, is not, and anyone telling you otherwise is selling a roadmap.

How to Locate Yourself Honestly

Six questions, answered without generosity:

When a transporter refuses an indent, how long before something happens? If the answer involves someone noticing, you are at Level 2 or below.
Can you produce stage-wise plant TAT for last Tuesday, broken into gate, weighbridge, yard and loading? If only a monthly average exists, Level 2.
Do your transporters see the same screen you do? If they call for updates, Level 2.
Do you know whether today’s spot rate is above or below market, before you accept it? If not, Level 3 at best.
Does anything in your freight operation happen automatically at 2am? If not, you are below Level 5.
When a freight invoice is wrong, is it caught before payment or after? Before means Level 3 or higher.

Most plants score unevenly, and that is normal. A plant can be Level 3 on outbound dispatch and Level 1 on inbound raw material, because inbound rarely gets the same attention. The uneven pattern is itself useful information about where the next gain is hiding.

Where SuperProcure Fits

SuperProcure is built to take a metal or steel operation from wherever it currently sits to Levels 3 through 5, and the modules map onto the curve fairly directly.

The Level 3 integration comes from running the lifecycle on one platform: SP Freight Sourcing for competitive digital sourcing, SP Indent Allocation for allocation and auto-reassignment, SP In-Plant Logistics for movement inside the plant, SP Shipment Tracking for the journey, and SP Freight Accounting for settlement. Transporters operate on the same platform rather than at the end of a phone line, which is what makes the data complete enough to be useful.

Level 4 comes from what that connected data enables. Live ETAs from GPS, FASTag and ULIP integration instead of driver self-reporting. Rate benchmarking through the SP Indian Freight Index so a quote can be judged rather than accepted. Carrier recommendations ranked on actual placement history rather than reputation.

Level 5 is where the platform starts acting on its own. Auto-reassignment when a transporter refuses. Automatic penalty debits on non-performance. Escalation to spot sourcing when a response window closes. These run inside the guardrails you configure, which is what makes autonomy safe rather than alarming.

80%

Improved plant TAT

97%

On-time vehicle placement

Up to 9%

Freight cost reduction

25%

Faster vehicle finalisation

Source: SuperProcure — Metal & Steel Industry, verified performance metrics

Delivered results across SuperProcure’s enterprise customer base of 300+ manufacturers.

Metal and steel companies working with SuperProcure include Shyam Steel, Shyam Metalics, Goodluck, APL Apollo, Hindalco, Sunflag Iron & Steel and Hansa Metallics.

What Each Jump Is Actually Worth

The returns are not evenly distributed, which matters when you are deciding where to spend effort.

Getting from Level 1 to Level 2 mostly buys you better hindsight. Useful, not transformative. The genuinely large gain is Level 2 to Level 3, because that is where hidden costs become visible and where the same team starts handling more volume without more people. Level 3 to 4 converts visibility into foresight, which is where placement reliability and rate discipline improve. Level 4 to 5 is where headcount stops scaling with tonnage, because routine decisions no longer need a human.

Plants that try to leap from Level 1 to Level 5 usually fail, because autonomy without clean data is just fast mistakes. The order matters more than the speed.

Frequently Asked Questions

What is a logistics maturity model?

A logistics maturity model is a framework that describes the stages an organisation’s logistics operation passes through as it becomes more capable, measured by how much decision-making the system handles rather than people. This model defines five levels for steel and metal plants: Manual, Digitised, Integrated, Predictive and Agentic.

What are the five levels of logistics intelligence maturity?

Level 1 Manual, where freight runs on calls and spreadsheets. Level 2 Digitised, where data is captured but siloed. Level 3 Integrated, where one platform covers the lifecycle and transporters share it. Level 4 Predictive, where the system forecasts and recommends. Level 5 Agentic, where the system acts within configured rules and people handle exceptions.

Where do most Indian steel plants sit on the maturity curve?

Most sit between Level 1 and Level 2, with records digitised but siloed and decisions still made by people reacting to phone calls. Many are uneven, running at Level 3 on outbound dispatch while inbound raw material movement remains at Level 1, because inbound typically receives less attention despite carrying comparable tonnage.

Which maturity jump delivers the biggest return?

Usually Level 2 to Level 3, the move to a single integrated platform shared with transporters. That is where previously invisible costs such as stage-wise plant TAT, spot premiums and placement failure rates become measurable, and where the same team begins handling more volume without additional headcount.

What does agentic logistics mean in practice?

It means the platform takes action inside rules you configure rather than waiting for instruction: reassigning a refused indent, escalating to spot sourcing when a response window lapses, computing penalties on non-performance, rescheduling a bay when an ETA slips. Rule-bound autonomous action is available today; fully self-directing procurement is not.

Can a steel plant skip maturity levels?

Partially, but not safely. Predictive and agentic capabilities depend on connected, reliable transaction data, which is what Level 3 establishes. Attempting autonomy on incomplete or siloed data produces confident automated decisions built on bad inputs, which is worse than manual work. The sequence matters more than the pace.

Where to Start

Score yourself against the six questions, and do it with the logistics team in the room rather than alone, because the answers tend to differ depending on who you ask. That disagreement is usually the most informative part of the exercise.

Then pick the single weakest link rather than attempting the whole curve. For most steel plants that turns out to be inbound raw material scheduling or stage-wise plant TAT, both of which sit lower on the maturity curve than the dispatch operation everyone watches. The next level is rarely a leap. It is one place where the system starts knowing something before a person tells it.