SYNCHRO-NET is a powerful and innovative SYNCHRO-modal supply chain eco-NET which destresses the supply chain through the intelligent use of slow steaming and synchro-modality, guaranteeing effective robust solutions which reduce emissions and costs for logistics operations while simultaneously increasing reliability and service levels for logistics users.
Synchromodal: Synchronised Multi-modal Logistics
The core of the SYNCHRO-NET solution is an integrated optimisation and simulation eco-net, incorporating: real-time synchro-modal logistics optimisation (e-Freight-enabled); slow steaming ship simulation & control systems; synchro-modal risk/benefit analysis statistical modelling; dynamic stakeholder impact assessment solution; and a synchro-operability communications and governance architecture.
Perhaps the most important output of SYNCHRO-NET is the demonstration that slow steaming, coupled with synchromodal logistics optimisation delivers amazing benefits to all stakeholders in the supply chain: massive reduction in emissions for shipping and land-based transport due to modal shift to greener modes AND optimised planning processes leading to reduced empty kms for trucks and fewer wasted repositioning movements.
Smart Steaming: Reduced Emissions & Increased Service
This leads to lower costs for ALL stakeholders - shipping companies and logistics operators will benefit from massive reduction in fuel usage, faster turnaround times in ports & terminals and increased resource utilisation/efficiency. Customers and end users will have greater control of their supply chain, leading to more reliable replenishment activity and therefore reduced safety stocks and expensive warehousing.
Authorities and governmental organisations will benefit from a smoother, more controlled flow of goods through busy terminals, and reduction of congestion on major roads, thus maximising the utilisation of current infrastructure and making the resourcing of vital activities such as import/export control, policing and border security less costly.