Portcast raises $3.2 million in financing drive

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Logistics technology start-up Portcast has raised $3.2 million in Pre-Series A financing.

The Singapore-based company provides technology which enables freight forwarders and manufacturers to achieve real-time visibility, reduce operational costs and improve customer experience, thereby improving profit margins, according a company statement.

Its solution predicts estimated time of arrivals (ETAs) of more than 90% of ships globally and forecasting demand for over 30,000 trade routes daily through a SaaS platform and APIs. 

The financing is led by Newtown Partners (via the Imperial Venture Fund) with participation from Wavemaker Partners, TMV and Innoport, who join existing investors SGInnovate, among others.

The capital will go towards international expansion, doubling the team size and product enhancement, including moving from predictive Artificial Intelligence (AI) to prescriptive AI and launching new product features such as order-level visibility and scenario planning.

Nidhi Gupta, CEO and Co-Founder of Portcast, commented, “Global supply chains are under extreme pressure with record delays, unprecedented congestion at ports and constrained capacities.

“This has led to ridiculously high transportation costs which are being transferred to the end consumer yet service reliability remains low.

“We believe that companies with predictive visibility on cargo movements have a significantly higher preparedness to downstream planning and customer service, and we have already seen the impact of this with reductions in overall port fees by 20% and manual work by 80% for our customers.”

Since its launch in 2018, Portcast has already amassed a list of freight forwarding and manufacturing customers across the world and predicts over 300,000 shipment events daily.

Gartner reports that 50% of product-centric supply-chains will invest in real-time transportation visibility platforms by 2023.

Chairman of Portcast, Roger Crook, added, “Global supply chains create a massive opportunity for big data and machine learning to help optimise procurement and pricing and provide better customer service to consumers.”

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