Objective:

• To get some tools that can gather the data/visualise current stock condition and price for further ordering decision-making.

Desired Outcomes:

• Real users are from the Toyota Manufacturing plants in each country, but in using this tool, TDEM R-HQ can also utilise it.
• The primary outcome is to attain cost reduction.
• The tool should help gather data or visualise current stock conditions and prices for further ordering decision-making.

Current Limitation:

• Currently, there is no visualisation of the data and no centralised tool to do so.

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).

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

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

About the Problem Statement:

The diagnosis of non-radiographic axial spondyloarthritis (nr-AxSpA) is complicated and at times protracted, beginning with a clinician’s interpretation of X-rays and requiring an MRI (Magnetic Resonance Imaging) for confirmation.

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.

Objectives:

• Establish integration tools for data extraction, transformation and loading.
• Develop AI data processing platforms.
• Scalable for operations implementation.
• Enable domain experts to perform data analytics independently.

Desired Outcomes:

• Focus on predictive maintenance for automated material handling equipment.
• To package prototypes into a suite of recommended platforms and tools. 
• Develop AI algorithms related to the project scope. 
• AI SMART Discovery: Demonstrate capability for prototype analytics for the layman.

Current Limitations:

• Problem centric data analysis: Engineers must analyse a voluminous amount of data for actionable insights through various data sources (OEE, yield, product data & recipes, machine alarms, etc.).
• High effort, time and subject matter knowledge are required for effective cause and effect analysis.

Objectives:

• Automate tasks assignment and prioritisation based on technical requirements and technicians’ profiles.
• Optimise execution with a grouping of tasks to improve efficiency.
• Predict the demand vs supply and forecast the overtime planning.
• Identify the technical competency gap among technicians.

Desired Outcomes:

• Over 34 technicians with different skillsets for operation and engineering tasks are complex and vary widely, from logistical units collection to equipment setup.
• To have a one-stop solution platform between managers, engineers and technicians.
• Interactive solution on a mobile device for technicians to receive notifications and report efficiently.
• If the execution of tasks can be on auto-pilot mode and optimised continuously driven by data analytics, this would significantly improve work efficiency.

Current Limitations:

• Engineers need to manually book the necessary resources, including equipment and technicians’ availability.
• Independent systems are used to check and book different resources, e.g. equipment booking, technician scheduling, engineering samples, etc.
• Manual and tedious effort on engineers to communicate, cross-check, and set priorities with managers and teammates.

Objectives:

• The purpose of i4.0 implementation is to increase productivity or promote process transparency.
• To convince and attract the decision-makers / top management.
• Provide a cost-benefit analysis in this i4.0 transformation process to address and educate the benefits of i4.0 implementation and that it should be of high priority.
• Lower the cost of implementing new technologies.

Desired Outcomes:

• The reasons for the reluctance to bring operations to the Internet/Cloud are often security and cost.
• There is currently no one-size-fits-all solution that can be easily applied to companies for a large scale internal review to produce the cost-benefit report for consideration to move operations to the Internet/Cloud.
• There is also no ability to effectively visualise these benefits and present data coherently to address cost and security concerns. 

Current Limitations:

• i4.0 implementation can consist of various technologies/services, which can be costly overall.
• Despite the time and effort spent, the solution proposed may not yield positive results or be up to the business owner’s expectations.
• No ability to effectively visualise benefits – business owners cannot gauge the results / ROI.
• Difficult for a large-scale internal review to produce a cost-benefit report.