Alpek Polyester
How the digital twin turned an existing yard into a system 40% faster — without buying a single machine.
Alpek Polyester produces PET at industrial scale. The logistics operation — silos, yard, gatehouse, scales, invoicing — moves dozens of trucks per shift.
Each truck runs a cycle: arrival, check-in, loading, check-out, invoicing, exit. It sounds simple. In practice, it meant 110 minutes per cycle — most of that time adding no value to the operation whatsoever.
Alpek knew there was slack in the process. They just didn't know where, or how much. And they had a tough decision ahead: buy new yard infrastructure, or bet that the problem was in the method — not in the machinery?
Invisible bottlenecks, a CAPEX decision on the table — and data fragmented across eight systems that didn't talk to each other.
The logistics cycle had four stages: check-in, loading, check-out, invoicing. At each stage, idle time was accumulating — but no one could measure precisely where the clock stopped unnecessarily. The data existed: fleet GPS, silo PLCs, SAP, gatehouse spreadsheets, scales, e-checklists. But each system lived in isolation.
Without integrated visibility, decisions were reactive. The operations team knew the yard was a bottleneck — but couldn't quantify the loss or safely simulate alternatives. Pressure was mounting: loading capacity stuck at 30 trucks per 12-hour shift, invoicing backed up, and the inevitable question in the boardroom: do we buy new infrastructure, or is there another way?
Bringing more trucks into the circuit without fixing the flow would only spread the congestion to other points. Mekatronik was brought in to find the other way — if one existed.
Measure first. Simulate second. Change only when the model says it will work.
Our method follows a clear sequence: instrumentation → modeling → simulation → controlled implementation. We don't propose a solution before measuring. We don't invest in change before running the model.
- Instrumentation and data integration. We unified eight sources — fleet GPS, silo PLCs, SAP, the gatehouse system, scales, spreadsheets, and e-checklists — into a single data layer via Meka Edge (our proprietary Edge platform built on Siemens Industrial Edge). For the first time, Alpek had full real-time visibility of the complete cycle.
- Discrete simulation calibrated with real data. We built the logistics flow model using Siemens Tecnomatix Plant Simulation and our proprietary MK Bottleneck Analyzer Lib. The model was fed with real operational data — not estimates. The first simulation revealed something unexpected: the main bottleneck was not in the silos, but in truck availability and the check-in process.
- 10,000+ simulations run together with the team. We tested optimization scenarios in detail — process changes, operator allocation, arrival sequencing — before touching anything in the real plant. Alpek's team actively participated in every sensitivity analysis session. Every decision was based on model data, not intuition.
- Live bidirectional Digital Twin. With the validated model in place, we deployed the Digital Twin as the "conductor" of the logistics operation — receiving real-time data and recommending actions. The DT monitors flow, predicts bottlenecks, and keeps incoming truck volume synchronized with silo loading speed. Without this sustaining mechanism, operational gains revert within weeks.
Every number below was measured in the real plant — not in simulation, not in projection.
- Logistics cycle: 110 min → 65.2 min (−40.7%). The time compression didn't come from "moving faster." It came from eliminating idle time — check-in wait, yard downtime, check-out delays. Total cycle time dropped by 45 minutes.
- Loading capacity: 30 → 42 trucks per 12-hour shift (+40%). No new silo built. No new scale. No gatehouse expansion. The same yard, running smarter.
- 75 orders invoiced by 6:30 PM — versus 60 in 24 hours before. The logistics flow gain translated directly into earlier invoicing, freeing the night shift and creating room to grow.
- Zero equipment CAPEX. The decision on whether to buy new infrastructure was answered by the model: it wasn't necessary. The investment went into method, modeling, and the digital twin.
Why wasn't this obvious before? Because the gain depends on three levers working simultaneously: the right truck availability, a dedicated pre-check-in operator, and an optimized process. Any single lever in isolation produces nothing. And what keeps all three working together, shift after shift, is the Digital Twin.
Without the active digital twin, the optimization reverts within weeks. The DT is not the final deliverable — it's the mechanism that sustains the gain. "The extra engine is installed. The technical infrastructure is the easy part. The real challenge is the decision to operate differently."
"It means that the factory can produce 10% more, can make 10% more profit, with the same infrastructure, same factory, same machinery, same people."
Dênis Leite · CEO, Mekatronik · Hannover Messe 2026
Technologies selected for the problem — not the other way around.
Each tool was chosen because it solved a specific challenge in this case: real-time data visibility, discrete-event simulation, bidirectional twin, operational interface. Nothing extra. No gaps.
More results measured in real plants.
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If you suspect there's hidden capacity in your operation — but don't know where it is or how much — the path starts with measurement. We don't sell a solution before understanding the problem. Start with a conversation.