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.

Objective:

• To improve the transfer efficiency of robot painting.

Desired Outcomes:

• To improve efficiency by 30% through optimisation of the robot programme and spray parameters using vision system based AI/ML logic:
• The vision system fitted on the robot scans the painting jig and captures the image of the parts to be painted.
• Robot path to be generated using AI/ML algorithm based on the image data captured by the vision system.

Current Limitations:

• Two-wheeler parts are painted using a robot painting process, with the parts moving on a conveyor.
• Robot programme and spray parameters are developed by trial and error process based on the shape of the parts to be painted.
• It is an iterative process of modifying robot parameters and checking the paint film built on the parts after baking.
• This results in lower paint transfer efficiency – paint transfer efficiency is the ratio of paint deposited on the part to the total paint sprayed.
• Transfer efficiency for parts is measured by weight method, i.e. weight of the paint on the component to the total paint sprayed from the robot gun.
• The above process is highly skill-oriented (a skilled robot programmer is needed)
• Wide variety of parts to be painted in a single paint plant.
• Robot programmes cannot be optimised for each type of part, as it is a manual process of creating the robot programme.
• Trials and verification are time-consuming as paint thickness can be checked only after parts are baked in the oven.
• High skill requirement for engineers carrying out robot teaching for the painting process.

Objectives:

• Effectively managing solar energy collected over the weekends when the factory’s electric demand is low.
• To find a solution to manage those excess solar energies for further utilisation.

Desired Outcomes:

• Real users are from the Toyota Manufacturing plants in each country.
• The primary outcome is to be sustainable and have good excess energy management, which will, in turn, lead to cost reduction.

Current Limitation:

• No means to manage the loss of energy, which means lost cost.

About the Problem Statement:

One of the future growth pillars for Mitsubishi Chemical Holdings Corporation’s (MCHC) healthcare business is to provide ‘around the pill solutions’ enabled by digital technology, intended to improve the quality of life of patients and their families, in their core therapeutics areas. Therapeutics areas of interest include the central nervous system (especially neuromuscular disease), autoimmune diseases and metabolic diseases.

Objectives:

To establish multiple touchpoints with patients during the patient journey from prevention to prognosis, to collect and analyse healthcare data and expand the value of current treatments.

Examples of such solutions could be digital platforms enabling patient-centric disease management, electronic patient-reported outcomes, digital diagnostics, disease tracking, digital medication management, digital biomarker, etc.

Automatic 3-way invoice match against shipment information & rate cards, for air/ocean freight & customs brokerage/trucking operations.

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).