Logistics Startup of the Month: We4Sea

we4sea

Every month we select one Logistics Startup which represents a positive example of innovation in logistics and has the potential to alter the way the industry operates. This month, Transmetrics selected We4Sea, a fuel-efficiency solution for ships, as the March “Logistics Startup of the Month” for its outstanding usage of data analytics to save fuel, decrease emissions and accelerate the sustainability of the shipping industry. In order to learn more about the company and what they do, we talked with Dan Veen, CEO of We4Sea, and asked him several questions about the business.

Congratulations, We4Sea!

Blockchain in Logistics – Will It Change the Industry? (Part 1)

Blockchain in Logistics – Will It Change the Industry? (Part 1)

Since its first implementation with bitcoin back in 2008, blockchain has shown extreme promise to upend a number of industries – and most recently, the industry in the spotlight has been transport and logistics. In December, logistics leader UPS joined the Blockchain in Transport Alliance. In January, Maersk and IBM announced their intention to form a blockchain joint venture. And in February, Warren Buffet’s BNSF Railway joined the Blockchain in Transport Alliance as the first of seven major railroads to do so.

To understand more about this industry transition, Transmetrics’ Co-Founder and CCO Anna Shaposhnikova spoke with Martijn Siebrand, expert and advisor in blockchain technology in Supply Chains.

Is Data Quality an Obstacle for Predictive Optimization in Logistics?

predictive optimization in logistics

First of all, If you try to put any data that you have into the predictive algorithm, it is going to predict some results, but they’re not going to be what you need. Instead, the algorithm is going to deliver you a set of very low-quality predictions. In other words, it follows the principle “garbage in, garbage out”, in which the decision-making might be flawed due to incomplete, or imprecise data. Improving the quality of the historical data is extremely difficult but it is a must before you even start thinking about predictive optimization in logistics operations.