Innovation Challenges

Challenge Owner(s)
Bosch Rexroth, Infineon Technologies, Toyota Daihatsu Engineering & Manufacturing, TVS Motor Company
Organiser(s)
SGInnovate, Economic Development Board (EDB)
Industry Type(s)
Circular Economy & Sustainability, Digital/ICT, Electronics, Land Transport, Logistics
, Sustainable Energy
Opportunities and Support Opportunities to connect with open innovation leaders in Singapore and the region
Application Start Date 1 September 2021
Application End Date 1 November 2021
Website Click here to learn more

About Challenge

In this digital age, the concept of Open Innovation is to better use shared resources among corporates and startups to solve complex problems today. The manufacturing industry has thus recognised the need for more innovative solutions to keep up with the highly digitised world or widely termed Industry 4.0. Artificial Intelligence, advanced robotics, IIOT and digital twin have significantly reduced maintenance costs and quality improvement. To better pivot themselves to embrace these transformational technologies, manufacturers thus seek to collaborate with the ecosystem, particularly with startups.

Presented by SGInnovate and EDB, the Reverse Pitch event series aims to spotlight market demands for frontier technologies to better inform the startup community to support their go-to-market efforts. At these Reverse Pitch events, corporate leaders will present their problem statements and innovation needs that they are open to working with startups. This session on Advanced Manufacturing featured business needs from leading manufacturing companies such as Bosch Rexroth, Infineon Technologies, Toyota Daihatsu Engineering & Manufacturing and TVS Motor Company.

Learn More



Challenge Owner(s)Bosch Rexroth
Industry Types(s)
Digital/ICT, Electronics

Bosch Rexroth

Objectives:

  • To help establish fully interconnected and digitised smart factories.
  • Data can be extracted from legacy machining data/throughput.
  • Data within legacy machines/systems can integrate with external condition monitoring systems into one complete dashboard.

Desired Outcomes:

  • Key users are management and shop-floor personnel.
  • One current method to obtain data from legacy machines is to introduce sensors in a non-invasive way to extract data for visualisation and analysis.
  • However, there are still crucial data of these legacy machines or data from their Manufacturing Execution system that are still not retrievable.
  • The limitation to the add-on sensors is that they are not able to fully generate data to the needs of business owners. 

Current Limitations:

  • Costly and unjustifiable to replace existing legacy machines/systems (e.g. CNC/laser cutting).
  • Lack of common IoT communication protocols like MQTT and OPCUA.
  • External sensors can only fulfil a partial wish list of business owners – mainly on reducing downtime and saving wastage costs.
  • Crucial data like cutting speed, machine uptime, throughput are also needed for OEE.

Conditions to Apply:

  • Series A startups and above, at the very least with offices in Singapore to provide technical support and business development (but Series B preferred due to limited development capacity from Bosch Rexroth).
  • Startups must have a ready solution or product to be tested with relevant inputs from Bosch Rexroth.
  • Open to sensor solutions that are robust and can cater to a wide variety of applications. If not, specialised in a certain sector. Sensor solution can integrate seamlessly with our IoT Edge Data system.
  • Wireless sensor solutions with multiple parameters in one sensor are preferred. As condition monitoring cuts across various industries, Bosch Rexroth currently is more interested in sensor solutions for automation, chemical and marine industries.

For more information and to submit your solution, please click here

Challenge Owner(s)Bosch Rexroth
Industry Types(s)Digital/ICT

Bosch Rexroth

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 in a coherent manner to address cost and security concerns.

Current Limitations:

  • i4.0 implementation can consist of various technologies/services so it 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.

Conditions to Apply:

  • Series A startups and above, at the very least with offices in Singapore to provide technical support and business development (but Series B preferred due to limited development capacity from Bosch Rexroth).
  • Startups must have a ready solution or product to be tested with relevant inputs from Bosch Rexroth.
  • Open to sensor solutions that are robust and can cater to a wide variety of applications. If not, specialised in a certain sector. Sensor solution can integrate seamlessly with our IoT Edge Data system.
  • Wireless sensor solutions with multiple parameters in one sensor are preferred. As condition monitoring cuts across various industries, Bosch Rexroth currently is more interested in sensor solutions for automation, chemical and marine industries.
For more information and to submit your solution, please click here
Challenge Owner(s)Infineon Technologies
Industry Types(s)
Digital/ICT, Logistics

Infineon

Objectives:

  • Automate tasks assignment and prioritisation based on technical requirements and technicians’ profile.
  • 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 both operation and engineering tasks. 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 to report efficiently.
  • If the execution of tasks can be on auto-pilot mode and optimised continuously driven by data analytics, this would greatly 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, manually cross-check, and set priorities, with managers and teammates. 
For more information and to submit your solution, please click here
Challenge Owner(s)Infineon Technologies
Industry Types(s)Digital/ICT

Infineon

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. 

For more information and to submit your solution, please click here

Challenge Owner(s)Toyota Daihatsu Engineering & Manufacturing
Industry Types(s)Digital/ICT

Toyota Daihatsu Engineering & Manufacturing

Objective:

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

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 main outcome is to attain cost reduction.
  • The tool should help to gather data or visualise current stock condition and price for further decision making of ordering.

Current Limitation:

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

For more information and to submit your solution, please click here

Challenge Owner(s)Toyota Daihatsu Engineering & Manufacturing
Industry Types(s)
Sustainable Energy, Urban Solutions

Toyota Daihatsu Engineering & Manufacturing

Objectives:

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

Desired Outcomes:

  • Real users are from the Toyota Manufacturing plants in each country.
  • The main 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.

For more information and to submit your solution, please click here

Challenge Owner(s)Toyota Daihatsu Engineering & Manufacturing
Industry Types(s)
Circular Economy & Sustainability, Environmental Services, Urban Solutions

Toyota Daihatsu Engineering & Manufacturing

Objective:

  • To achieve zero CO2 emission by having a solution for some kind of oven that can bake the vehicle and not emit CO2 e.g. electricity or others.

Desired Outcomes:

  • Real users are from the Toyota Manufacturing plants in each country.
  • The main outcome is to be sustainable and achieve zero CO2 emission as part of company policy.

Existing Limitation:

  • Currently, the burners are still using gas thus resulting in a high volume of CO2 emission.

For more information and to submit your solution, please click here

Challenge Owner(s)TVS Motor Company
Industry Types(s)Digital/ICT

TVS Motor Company

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:
    • 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 build 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 a time-consuming process 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.

Conditions to Apply:

  • The solution can be developed as a POC and proved for sample parts in our paint plant.
  • Selection of vision system capable of functioning in paint spray line environment.
  • Startup should have proficiency in AI/ML programme development, vision system interfacing and robot programming.
  • Collaboration with robot OEMs like Fanuc and Yasakawa will be an added advantage.
  • Successful proving of POC can be scaled up for all parts coverage in paint plant and also deployment to other TVS paint plants.

Overseas Office Requirements:

  • Preference for startups with office(s) in India or a tie-up with an Indian supplier, as multiple iterations may be needed to develop the learning algorithm and robot teaching and validation.
  • The system should be designed for remote assistance from the startup’s locatios with assistance from our engineers at the site.
  • Source code sharing and training to the TVSM team to be done for ease of service.

For more information and to submit your solution, please click here

Challenge Owner(s)TVS Motor Company
Industry Types(s)
Digital/ICT, Land Transport

TVS Motor Company

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 send 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 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 in 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 of 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 operator to be avoided.
    • Difficulty in getting repeatability in results due to allowed variations in fabricated parts.
    • The camera is not able to clearly differentiate minor dust particles (>1.5 mm) and colour shade differences.
    • Some defects will come once in a while and difficulty in teaching to the system frequently.

Desired Outcomes:

  • To improve efficiency by 30% through optimisation of the robot programme and spray parameters using vision system based AI/ML logic:
    • 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.

Conditions to Apply:

  • It is preferable if the supplier is able to establish the system ready for inspecting vehicles in mass production on a conveyor running at a speed of 5m/sec.
  • Desired Skills of Startup:
    • Vision system with AI/ML capabilities.
    • Suitable camera and lighting choosing skills.
    • Suitable camera and lighting choosing skills.
    • Capability to integrate vision system with conveyor, robot or slides.
    • User Interface screens.
    • Knowledge of statistical analysis parameters and KAPPA analysis.
    • Knowledge for taking inputs using API or similar input methods from ERP/Intranet.
    • Able to design the algorithm for ease of inputs for re-teaching new defects.

Overseas Office Requirement:

  • Startups with overseas offices are desirable as multiple iterations to be done while resolving Type A and Type B errors.
  • However, the system should be designed for remote assistance and our engineers can support online.
  • Source code sharing and training to TVSM team to be done for ease of service. 

For more information and to submit your solution, please click here