Periodic Reporting for period 2 - Transmetrics (Augmented Intelligence for logistics)
Reporting period: 2021-01-01 to 2022-04-30
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.
● 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.
During the project the efforts of Transmetrics were focused on:
● Setting up partnerships with external data providers
● Attracting beta users for trial usage of the asset management tool-set
● Collecting feedback, testing implementation feasibility, evaluating results and measuring benefits during beta user tests in real-life scenarios
● Setting up a user review board to support the commercialization, business plan and product roadmap development
● Developing end-to-end external-data-based AI predictive optimization solution, including proprietary forecasting, cost and maintenance scheduling optimization modules.
● Developing data standardization and cleansing tools for fast integration of new data sets
● Developing and testing proprietary meta-language for data integration
● UI ease of use development
● Introduction of external authentication methods to facilitate integration with customer IT systems (active directory integration, Google authentication, Microsoft authentication)
● Demo system development
● Improving security with HTTPS and FTPS protocols (HTTPS secure cloud-based solution)
● Development of detailed IPR strategy and risk management
● ISO 27001 certification for Information Security Management System (ISMS)
● Communication of the project objectives and results via TV Spot and YouTube Video.
● Dissemination of the results of the project and their impact in blog articles, press releases, webinars, and industry and investor events both online and offline.
The development efforts financed by the EIC grant have resulted in the rising uptake of our predictive solutions in the logistics market. We are now perfectly positioned to support an industry desperately looking for tools that reduce planning uncertainty in times of extremely fluctuating demand.
● Setting up partnerships with external data providers
● Attracting beta users for trial usage of the asset management tool-set
● Collecting feedback, testing implementation feasibility, evaluating results and measuring benefits during beta user tests in real-life scenarios
● Setting up a user review board to support the commercialization, business plan and product roadmap development
● Developing end-to-end external-data-based AI predictive optimization solution, including proprietary forecasting, cost and maintenance scheduling optimization modules.
● Developing data standardization and cleansing tools for fast integration of new data sets
● Developing and testing proprietary meta-language for data integration
● UI ease of use development
● Introduction of external authentication methods to facilitate integration with customer IT systems (active directory integration, Google authentication, Microsoft authentication)
● Demo system development
● Improving security with HTTPS and FTPS protocols (HTTPS secure cloud-based solution)
● Development of detailed IPR strategy and risk management
● ISO 27001 certification for Information Security Management System (ISMS)
● Communication of the project objectives and results via TV Spot and YouTube Video.
● Dissemination of the results of the project and their impact in blog articles, press releases, webinars, and industry and investor events both online and offline.
The development efforts financed by the EIC grant have resulted in the rising uptake of our predictive solutions in the logistics market. We are now perfectly positioned to support an industry desperately looking for tools that reduce planning uncertainty in times of extremely fluctuating demand.
There is an untapped market for high-quality data-cleansed predictive logistics using big data. This is mainly true in the shipping and trucking industries globally. Only the largest logistics companies like DHL have built in-house predictive analytics systems. Such systems are unfeasible to develop for mid-sized logistics service providers.
The state-of-the-art includes also strategic optimization solutions (complimentary to our tactical planning systems), real-time optimization solutions that lack predictive components (are therefore limited in the scope of solutions that they offer) and general-purpose optimization engines with poor success in asset management (due to their developers’ lack of in-depth logistics industry know-how). All existing solutions also suffer from the chronic low data quality in the industry.
With the support of the EIC grant, we have developed a one-of-a-kind cleansing and enrichment module that deals with the data quality issues in the industry and supports our predictive planning solutions with a high-quality data lake. We are also pioneers in the Augmented Intelligence space for logistics planning. The artificial intelligence components of our platform handle the data-heavy lifting: automatically improving data quality, analyzing the historic demand patterns and the effects of external factors, and suggesting optimal asset positioning, asset maintenance and network planning decisions that reduce repositioning, storage, maintenance and network costs. The planner is still in the driving seat, making the final decisions, but his choices are now informed by high-quality AI-boosted information. This results in an increase in margins increased planning horizon and reductions in fleet size.
Besides improving decision-making for transportation, our solutions impact the larger society by attracting top talent in logistics (the next generation of planners and asset managers is interested in working with innovative solutions) and reducing carbon emissions (optimal asset positioning and network management reduce the environmental pollution produced by empty movements). This supports the larger European Green Deal strategy of the European Union.
The state-of-the-art includes also strategic optimization solutions (complimentary to our tactical planning systems), real-time optimization solutions that lack predictive components (are therefore limited in the scope of solutions that they offer) and general-purpose optimization engines with poor success in asset management (due to their developers’ lack of in-depth logistics industry know-how). All existing solutions also suffer from the chronic low data quality in the industry.
With the support of the EIC grant, we have developed a one-of-a-kind cleansing and enrichment module that deals with the data quality issues in the industry and supports our predictive planning solutions with a high-quality data lake. We are also pioneers in the Augmented Intelligence space for logistics planning. The artificial intelligence components of our platform handle the data-heavy lifting: automatically improving data quality, analyzing the historic demand patterns and the effects of external factors, and suggesting optimal asset positioning, asset maintenance and network planning decisions that reduce repositioning, storage, maintenance and network costs. The planner is still in the driving seat, making the final decisions, but his choices are now informed by high-quality AI-boosted information. This results in an increase in margins increased planning horizon and reductions in fleet size.
Besides improving decision-making for transportation, our solutions impact the larger society by attracting top talent in logistics (the next generation of planners and asset managers is interested in working with innovative solutions) and reducing carbon emissions (optimal asset positioning and network management reduce the environmental pollution produced by empty movements). This supports the larger European Green Deal strategy of the European Union.