About the Problem Statement:

Sustainability is a top priority in Yusen Logistics as many customers are working towards public green initiatives. They are looking for service partners who prioritise corporate responsibilities as well.

About the Problem Statement:

The logistics supply chain generates waste at every turn. The types of waste include transport packaging materials, lashing materials, cushioning materials, cooling materials, and desiccant agents produced in logistics operations. Society is demanding that we reduce and promote recycling of these wastes.

Currently, our supply chain does not have a holistic, efficient and optimal waste management system and solution. Yusen Logistics hopes to reduce internal costs and prioritise ESG goals by optimising our supply chain through waste reduction and sorting technology.

About the Problem Statement:

Freight transport is a large contributor to emissions of CO2 and customers look for supply chain partners who prioritise Environmental, Social and Governance (ESG) goals. Customers often ask us to optimise logistics concerning cost and efficiency in their supply chains. Recently, we have been asked not only to optimise logistics but also to propose ways to reduce the environmental impact of logistics. In other words, they are looking for the best balance between logistics optimisation and environmental impact. To meet this new demand, it is necessary to visualise the CO2 emissions from air, ocean, and land transportation with transportation data.

For air and ocean freight forwarding, the estimated amount of CO2 emissions can be calculated through current shipment data from the organisation’s operations system.

Yusen Logistics owns its trucks for land transportation and works with many partner companies in many countries. The number of subcontractors is so large that it takes a considerable amount of time to collect the necessary data from all the subcontractors and a great deal of effort to organise the data. This is because each company has its transportation management methods and IT systems. Some of them even manage the data manually. In addition, the transportation data is handled in various foreign languages, which takes time to translate.

Green transport platform to implement shared resources, minimise empty transport legs & protect our planet.

Solutions that consider customer demands that may change rapidly (such as delivery destination, delivery window) to improve logistics efficiency.

A platform to synchronise different transportation, yard & warehousing systems to optimise resources & unlock efficiency with speed.

Objective:

• Improve vehicle build quality using vision systems, AI & Machine Learning algorithms.

Desired Outcomes:

• Delivery of visually defect-free vehicle to the customer.
• Inspection of fit and finish, painting defects, missing parts and mismatch of parts as per checklist.
• Quantifying in terms of AQI score as per the provided standard.
• Statistical analytics using the obtained data and escalation in case of deviations as defined.
• The system needs to be capable of connecting defects to the stage where the defect is happening and sending alerts/escalations.
• The system should be able to give results immediately after inspection, clearly indicating the zones and places where visual defects need to be corrected.
• The system needs to interact with the ERP system to understand the variant of the vehicle to inspect. 

Current Limitations:

• Skilled men are deployed in the line to inspect the finished vehicle and give dispatch clearance on every shift.
• Inspection needs to be done within the cycle time of <20 seconds on a moving conveyor.
• Inspection is subjective, and perception varies between inspectors and between shifts too.
• Data is manually recorded and not in a form for consumption or analysis.
• Data is entered manually in SAP.
• Occasionally, errors escape to the next stage due to monotony in the inspection.
• More information on existing system constraints:
• Type A and Type B errors – disagreement between man and machine
• Unable to cover all checkpoints (220) in < 20 seconds.
• A vehicle needs to be loaded in a particular orientation to get better images for giving ok and not ok decisions – loading difficulty to an operator to be avoided.
• Difficulty in getting repeatability in results due to allowed variations in fabricated parts.
• The camera cannot clearly differentiate minor dust particles (>1.5 mm) and colour shade differences.
• Some defects will come once in a while, and it is challenging to teach the system frequently.

To automate this highly manual process & improve the standardisation & visibility of the booking process.

About the Problem Statement:

Teijin Pharma Limited (Teijin) is looking for solutions to treat neurological diseases in the Japanese population.

Hardware MedTech startups for neuromodulation and software startups experience in healthcare for a mobile app would be relevant.

Objectives:

To save and reduce patients with neurological diseases relating to the central nervous system, including psychiatric and neurodegenerative diseases. Solution examples include user-friendly therapeutic modalities like non-invasive neuromodulation and/or digital therapeutics at home.

Working Model:

Teijin is open to discussing the collaboration model with startups but typically takes responsibility for technology development for the Japanese market, conducting clinical studies, regulatory / reimbursement tasks and finally commercialisation in Japan.

To achieve the goal, Teijin will invest with dilutive (equity) and/or non-dilutive funding in the partner(s).

About the Problem Statement:

Teijin Pharma Limited (Teijin) is looking for solutions for the ageing population to correctly understand their own health conditions.

Startups with capabilities to develop AI algorithms based on health / vital data for chronic diseases and startups with technologies for sensing vital signals for chronic diseases would be relevant.

Objectives:

To provide an easy solution for the ageing population with chronic diseases to proactively understand their own health conditions from the patients’ homes. This includes, but should not be limited to, innovative technology to predict exacerbation before their illness gets worse.

Working Model:

Teijin is open to discussing the collaboration model with startups but typically takes responsibility for technology development for the Japanese market, conducting clinical studies, regulatory / reimbursement tasks and finally commercialisation in Japan.

To achieve the goal, Teijin will invest with dilutive (equity) and/or non-dilutive funding in the partner(s).