Improving Bulk Material Logistics in Steel Manufacturing

Problem:

Traditional tools couldn't handle the complexity of the steel plant’s expanding bulk material logistics system.

Solution:

MOSIMTEC built a dynamic simulation model in AnyLogic that captured real-world operations. This enablied smart routing, stockpile logic, and scenario testing to optimize logistics performance before construction began.

Results:

Identified and corrected design flaws early, avoiding costly rework.
Enabled fast, data-driven decision-making through scenario testing.
Lowered operational costs by reducing equipment strain.
Gave the client confidence in the system’s performance before construction.

Introduction: bulk material logistics in modern steel plants

When an international steel producer aimed to become the largest single-site operation in its country, it needed a robust bulk material logistics system. Their handling network—wagon and truck tipplers, dump hoppers, stockpiles, stacker-reclaimers, and conveyors—had to run without interruption.

To increase operational efficiency and lower operational costs, they teamed with MOSIMTEC and utilized AnyLogic simulation software.

Problem: barriers to increasing operational efficiency

The planned expansion made the material handling system much more complicated. Conveyor routes changed during the day, equipment could break down at any time, and materials arrived at unpredictable times. Because everything in the system was connected, one small change could affect the entire flow.

It was too complex for basic tools like spreadsheets or simple diagrams. If the system didn’t work smoothly, furnaces could run out of material—causing production delays, expensive repairs, or even full shutdowns of the plant.

Solution: AnyLogic simulation to increase operational efficiency

To help the client improve bulk material logistics, increase operational efficiency, and lower operational costs, MOSIMTEC developed a simulation model in AnyLogic using a clear, step-by-step approach.

The process started with gathering real plant data and ended with rapid scenario testing—giving engineers the tools they needed to make better design and planning decisions before construction began. Here's how the solution was built.

Step 1: gathering and organizing data

First, the MOSIMTEC team met with the plant’s engineers to collect every detail about the processes. They cataloged tippler throughputs, conveyor lengths, and stockpile capacities. Then they organized these parameters into simple Excel tables.

This spreadsheet-driven approach meant that anyone, even without coding skills, could review and tweak the layout and rules for the bulk material logistics system.

One of the Excel tables used for data input in the project — Data input via Excel table

Step 2: building the model in AnyLogic

MOSIMTEC then partnered with Goldratt Research Labs to develop a flexible, AnyLogic-based discrete-event simulation model of the plant’s bulk material logistics system.

Using the Excel inputs, the team imported the data into the model. Each conveyor belt, wagon tippler, truck hopper, and stockpile was visually represented, mimicking the real system. The model captured operational logic between components, providing a solid foundation for improving operational efficiency.

Logic behind the model (click to enlarge)

Step 3: smart routing and stockpile management

MOSIMTEC set up the simulation to check if conveyors were available and to send materials along the quickest routes. If a conveyor was busy or broken, the system would automatically find another way to keep materials moving without stopping.

The simulation also made sure stockpiles didn’t get too full or run out by choosing the best option nearby. If any equipment went offline, the system switched to backup routes or stockpiles on its own, helping the whole process run smoothly.

This automated logic improved flow reliability, helped increase operational efficiency, and created a more balanced workload.

To learn more about material handling optimization with simulation modeling read our white paper.

Step 4: validating against real operations

Before testing future scenarios, the team validated the model using historical data. They compared simulated throughput rates, queue lengths, and downtime effects with real performance reports. The close match confirmed that the model accurately reflected the plant’s bulk material logistics. This validation built confidence in the simulation’s ability to support planning decisions.

The chart showing bulk material inventory under different scenarios — Raw material inventory under different scenarios (click to enlarge)

Step 5: exploring what-if scenarios

Finally, with validation complete, MOSIMTEC used the same Excel-driven setup to test expansion scenarios. They added new processing units, increased demand peaks, and experimented with different ore blends.

Results: lower operational costs and optimized logistics

The simulation gave the client clear insights that improved both the design and operations of their bulk material logistics system. Early tests showed missing conveyor connections that would have caused serious flow problems. Fixing these issues during the design stage helped avoid expensive rework and lower operational costs by reducing stress on key equipment.

Engineers used the model to run full-year simulations in under two minutes. This made it simple to test different layouts, check material flow, and predict stockouts. The results helped the client improve system balance and increase operational efficiency.

Most importantly, the AnyLogic based model gave the client confidence. They could see how the system would perform in real conditions.

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