MAGPIE Smart green logistics tool

Introduction

The Smart Green Logistics Tool (SGLT) is a decision-support tool for planning containerised hinterland transport across road, rail and inland waterway. It has two connected components. The operational module is offered by the port authority to shipment planners, who upload their shipments and set the relative importance of cost, lead time and emissions; the tool returns an optimised multimodal plan over a 24–48 hour horizon and supports synchromodal re-planning when better options appear later. The tactical module runs the same optimisation engine over a rolling horizon of up to a year on aggregate demand, so the port authority can evaluate system-level performance rather than individual shipments. Port authorities are expected to facilitate the green transition of freight transport, but the decisions determining hinterland emissions, namely mode, timing and route, are made by shippers and freight forwarders. SGLT connects those two levels. It was developed and demonstrated for HAROPA Port.

Value proposition

Optimised shipment plans
Shippers receive multimodal plans tailored to their own weighting of cost, lead time and emissions, with synchromodal flexibility.
Scenario testing
Port authorities can quantify the long-term effect of demand growth, seasonal or river-level capacity variation, new or withdrawn services, and shifts in how users weigh emissions.
Gap analysis
Comparing stakeholder-optimal with system-optimal plans shows where behaviour diverges from policy targets, and therefore where incentives would have most effect.

Port authorities hold a central coordinating position but no control over shipment-level choices. They manage the consequences of decentralised decisions such as congestion, underutilised rail and barge infrastructure, avoidable emissions without visibility into the trade-offs driving them. A new rail connection may stay underused simply because it does not fit the decision logic of its intended users.

Port applicability

Applicability depends on a genuine multimodal choice existing in the hinterland. Where rail and barge alternatives are marginal, optimization is less of value. Large container / hub ports: The primary use case. volume, multiple terminals and a scheduled rail and barge network give both the data density and the decision space required. Le Havre is the demonstration case. Industrial ports: Applicable where containerised or unitised hinterland flows are significant. Less so for bulk bound to pipelines or dedicated rail. Smaller / regional ports: technically applicable, but value declines with the number of scheduled intermodal services available. Other: inland ports are best treated as nodes within a deep-sea port’s network.

Impact

Impact level per aspect
Impact	Level	Remark
GHG emissions	Large impact	The principal impact area. Emissions are dominated by road in all scenarios. Across shipper objective profiles the spread between lowest- and highest-emission scenarios exceeded 15% annually, driven by truck usage; expanding hinterland rail and barge capacity reduced modelled emissions by more than 30%.
Congestion	Medium impact	Every TEU moved intermodally is a truck movement removed from port access roads and hinterland corridors.
Cost	Medium impact	In the operational runs, prioritising speed nearly doubled daily shipment costs against the cost-optimal plan; balanced optimisation cost roughly 30% more. Over an annual rolling horizon, capacity constraints compress that gap to around 10%. Capacity expansion reduced total transport cost by over 20%.
Port city and community	Limited impact	Lower truck volumes on port access roads mean less noise and local air pollution.

Port characteristics

The port authority becomes tool operator and network data steward, requiring data-sharing with rail and barge operators and, for the operational module, willingness of shippers to plan through a port authority platform.

Barriers and enablers

Enablers

Reporting obligationsEnabler
Tightening emissions reporting obligations on shippers.
Port's positionEnabler
The port authority’s neutrality as a non-competitor.
Modal shift targetsEnabler
This gives tactical output a policy anchor.

Barriers

Commercial sensitivity of shipmentBarrier
Including capacity and pricing data.
Reluctance to share planning dataBarrier
Reluctance to share planning data outside the commercial chain.
MandateBarrier
No mandate to require use.
Established routines and incumbent toolsBarrier

How to implement?

Step 1
Populate the data model
Map the hinterland: routes, services with capacities and timings, connection distances and emission factors, terminals. The largest effort, and it determines the quality of everything downstream.
Step 2
Agree data feeds
Arrangements with rail and barge operators for schedule and capacity updates, so the network does not go stale.
Step 3
Calibrate parameters
Cost per TEU-km, CO₂ cost and value of time to local conditions; the demonstration used EU sector approximations.
Step 4
Start tactical, then operational
Tactical scenarios can use generated demand and need no data sharing, so run these first to build the evidence base; open the operational module once network data is trusted.

Stakeholder overview

Port Authority: Owns and operates the tool, is the tactical user, stewards the network data. Shippers and Freight Forwarders: The operational users. They supply shipment data and set the weights; their adoption determines whether the operational module has any effect. Rail and Barge Operators: Provide schedules, capacities and emission factors; the intended beneficiaries of improved utilisation. Terminal Operators: Provide terminal details and transfer characteristics. Technology Providers and R&D Institutions: Hosting, integration with operator and forwarder systems, and model maintenance, calibration and methodological development. Local & Regional Government: Set the modal shift and air quality targets against which scenarios are assessed.