The transport and logistics industry accounts for 14% of Europe’s GDP and 2.8M tonnes of all annual CO2 emissions. One-fifth of all trucks are moved empty, while 50% of EU trucks travel empty on their return journeys. The logistics industry moves the world economy but it suffers from significant inefficiencies to the tune of nearly 500 billion euros per year. Considerable contributing factors are major weaknesses in asset management, such as:
● inefficient positioning
● overstocking
● repair and maintenance in high-cost locations
Inefficient operations are impacted by macroeconomic factors (trade imbalances, business cycle fluctuations and even COVID-19) but a third of the costs can be reduced with technological solutions. Most logistics planning is still based on gut feeling and manual calculations in excel. Such decisions are conservative, resulting in bloated fleets, huge storage and maintenance costs and low margins.
Transmetrics is a predictive optimization company that helps logistics service providers increase their operational efficiency with artificial intelligence, data mining, predictive analytics, and computer optimization. Supported by a 1.67 million euro grant from the European Innovation Council Accelerator Pilot, Transmetrics has developed an end-to-end predictive optimization solution for four key use cases tailored toward the needs of the mid-sized logistics companies - asset repositioning, linehaul planning, asset maintenance, and last-mile prediction.
Mid-sized companies account for nearly 30% of the global logistics market, but are very price-conscious and are looking for an off-the-shelf product that supports them in the optimal positioning of assets, supplier management, and operations efficiency oversight. To support the upscaling of the Transmetrics predictive optimization products in this segment, the EIC-funded project’s objectives include:
● Objective 1: The development of an aggregated external database with more than 100+ predictive variables
● Objective 2: Productized predictive end-to-end asset management solution, tested for a period of at least 6 months, including external data supplemented forecasts per geographic sector, optimal positioning of assets, minimize storage costs and efficient maintenance scheduling.
● Objective 3: Productized data cleansing and enrichment module, focused on reducing implementation efforts by handling frequently encountered data quality issues such as low-quality location data, missing events and measurements.
● Objective 4: Large demo sets, based on obfuscated historical data of donor customers (synthetic data sets cannot replicate the complexity inherent in real logistics data) to showcase the functionality and benefits of the solution.
● Objective 5: Try-before-you-buy package including the features developed under Objectives 1-4: a simple end-to-end rapid proof-of-concept package that is easy to install and use at a new, smaller firm.
● Objective 6: HTTPS secure cloud-based solution developed to support the needs of SMEs that are interested in easily integrable, HTTPS, secure network, and WAN solutions.
These objectives as a whole amount to a solution fast to deploy within mid-sized logistics companies, reducing the barrier to trying that lengthy and costly implementation periods produce and benefiting from a state-of-the-art planning platform that has an economical and environmental impact.
