Innovative strategy

TELS CARGO ‘s innovation activity is aimed at competency development and the use of digital technologies to improve the quality of customer service, increase operational efficiency and create new business models. We focus on the three key topics determining company’s innovation strategy.

Digitalization of asset management

The company produces a huge amount of data daily – they contain many potential ideas for optimizing asset management and increasing profitability. Transforming big data into information about asset life cycle will increase their efficiency and reliability while automation will eliminate redundant tasks and manual processes.

Areas of improvement:

Availability of up-to-date data on all business assets.
Improving asset efficiency by enhancing digital capabilities.
Real-time analytics for strategic, tactical and operational asset management.

Creating new business models

The global trend towards digital ecosystems opens the opportunity for transport companies to participate in end-to-end logistics of business partners. Collaboration with global logistics ecosystems will promote diversification of the transport solutions offered by TELS CARGO and will allow to obtain an ever-growing share of revenues.

Areas of improvement:

Connecting global logistics digital ecosystems.
Integration with potential business partners and regulation authorities.
Using digital ecosystem infrastructure to transfer data on the implementation of transport solutions.
Exchanging vital information (customs, foreign trade, security) depending on the logistics industry standards of the Customer.

Customer experience

In international logistics, Customers are increasingly relying on the transport companies not just offering transportation rates for delivery of goods from point A to point B, but being able to provide potential transport scenarios, adaptive service options and flexible prices quickly and with a thorough understanding of Customer’s needs.

Areas of improvement:

Understanding Customer’s business and supply chain needs.
Using machine learning and artificial intelligence to analyze historical customer-related data and the market to predict delivery options.
Improving the speed and quality of the provided transport solutions.