AI Open Innovation Challenge

BACKGROUND OF THE PROBLEM

In the hospitality industry, creating personalised and seamless guest experiences is key to fostering loyalty and increasing customer satisfaction. As a PARKROYAL COLLECTION hotel we seek to improve operational efficiency and provide guests with a personalised experience and we believe technology such as facial recognition offers a compelling opportunity. Facial recognition can streamline check-in processes, enhance personalised greetings, and help staff anticipate guest needs by identifying them upon arrival.

This challenge seeks a solution that leverages facial recognition technology to optimise guest interactions, while ensuring compliance with data privacy laws and easy integration with our existing hotel management systems such as Opera and (mobile) check-in platforms. The right solution would not only improve guest satisfaction but also reduce operational burden on staff, offering a more streamlined, cost-effective, and personalised hotel experience.

We are looking for a solution that can be deployed at the following critical touchpoints:

• Hotel Reception: Receptionists able to greet guests by their last name upon arrival.
• Restaurant: Food & Beverage personnel able to greet guests and identify breakfast entitlement, be aware of allergies and dietary restriction of our guests.
• Club lounge, Spa and Wellness: Personnel able to greet guests, guest access and eligibility to be known upon arrival based on membership level of our loyalty programs or booking information.

We are looking for a solution that respects the data privacy of our guests and doesn’t store more information than strictly necessary, integrates with legacy systems and is cost-effective. Identification, of course, needs to be real-time and be able to extract data from multiple systems such as the reception- and mobile check-in systems, loyalty program database, passport scanner, and others.

Currently, no off-the-shelf solution fully meets our needs. Existing systems either lack the accuracy, necessary integration capabilities or fail to comply with stringent data privacy regulations.

TECHNICAL REQUIREMENTS / PERFORMANCE CRITERIA

Technical Requirements:

• Facial Recognition Accuracy: The system must be able to accurately identify guests upon arrival with minimal errors, even in high-traffic areas.
• Integration with Existing Systems: The solution must integrate seamlessly with systems like Opera (hotel management) and mobile check-in platforms, enabling automatic updates of guest data.
• Data Privacy and Security: The solution must ensure compliance with local privacy regulations, such as PDPA, by collecting minimal guest data (e.g., last name, photo, eligibility, allergies) and securely storing or processing this information, preferably using on-premise systems. If cloud based solutions are used, they must be secure.
• Operational Efficiency: The system should reduce the need for manual data entry and help staff serve guests faster by automatically pulling up relevant guest information.

Performance Requirements:

• Guest experience: The main driver for this challenge is to improve the guest experience. Solutions will therefore be evaluated based on their impact on the experience as evidenced by being mentioned in guest reviews (for example, mentioning the greetings by last name at check in and dietary awareness) as well as an uptick in review scores from our guests.
• Cost effectiveness: Please see below section.

COST TARGET

Cost targets will be determined on a case-by-case basis. In general we evaluate solutions based on their return on investment but the specific target will depend on the solution. Separate from the investment needed for the system we have a cost target of S$10,000 per year, per hotel for operational expenses.

TIMEFRAME FOR DEVELOPMENT

Phase 1: POC development: Q2- Q3 2025 at our PARKROYAL COLLECTION Pickering hotel in Singapore.

Phase 2: Commercial roll-out: to be determined on a case-by-case basis.

POTENTIAL MARKET / BUSINESS OPPORTUNITY

If the solution is successful, PARKROYAL COLLECTION Pickering, Singapore is willing to support a further roll-out across other locations in Singapore. Furthermore PARKROYAL COLLECTION Pickering, Singapore is part of the Pan Pacific Hotels Group with about 50 hotels in our portfolio where the solution can also be implemented.

Additionally, we are open for solution providers to deploy their solution with other players.

RESOURCES

OTHER CONSIDERATIONS

We are looking for SMEs and startups with solutions that can be implemented in a relatively short time frame (TRL of 5 and higher).

For Background IP (BIP), both parties will retain their respective IPs brought into the project. In the event of new Foreground IP (FIP) creation, ParkRoyal is agreeable to the FIP being retained by the solution provider.

BACKGROUND OF THE PROBLEM

As the world’s leading infrastructure consulting firm, our clients come to us for solutions that meet their ESG targets. While the environmental and governance parts are well developed the social part is not as developed yet. Governments planning, developing and upgrading large infrastructure projects such as cities, neighbourhoods, transportation systems, utilities, and public spaces are looking for solutions to effectively measure the social value during the planning, execution, and operational phases.

Large infrastructure projects generate social value, such as community well-being, social cohesion, accessibility, and economic opportunities. Current evaluation methods often rely on subjective assessments, such as surveys, Therefore, it is currently challenging to make informed design decisions during the planning phase and have an objective measure of the social value impact of that decision. As an example, decisions that we are looking to optimise are: where to place a hospital or school, what should the square metre size be, and what type of healthcare is needed given the surrounding social conditions and demographics.

To make these types of decisions, we are looking for a tool that uses publicly available data and location-specific data sets that calculates the social value impact of design decisions in real time during the planning phase. The solution should ideally express the social value as an objective number so decisions can be compared. Furthermore, the tool should also be able to measure the actual social value after project completion using all data available (including a.o. survey data and social media).

The methods currently used most are pre- and post-construction surveys, community consultations, and economic impact studies. The nature of the questions in the surveys is qualitative and subjective. Making the data more objective is part of the goals of this challenge. Some indicators we currently use for expressing social value are savings in public health costs, job creation, community well-being and educational outcomes. We are open to other data and indicators for use in creating the social value score. Besides expressing the social value as a score, the ideal solution should also make design recommendations to improve the social value created.

We are not interested in solutions that focus exclusively on economic impact assessments without considering broader social impacts, or those that are not scalable or cannot provide insights throughout the project's lifecycle. Additionally, any solution that lacks AI-based data analysis and relies solely on manual data collection will not meet our needs.

TECHNICAL REQUIREMENTS / PERFORMANCE CRITERIA

Technical Requirements:

• Pattern recognition: The AI models should be capable of identifying and analysing patterns in community sentiment and well-being related to infrastructure projects.
• Predictive: the solution should use predictive analytics to estimate potential social value outcomes during the planning stages.
• Subjectivity: Social value is a subjective measure and different databases use different values for economic- and social opportunities for example. The solution should pick the best from what’s available and customise for each situation but justify why particular indicators have been selected
• Real-time: The solution should provide (near) real-time feedback on the impact of design decisions on the social value created (in the planning phase). This includes social value dashboards for stakeholders, enabling visualisation of metrics like community satisfaction, accessibility improvements, and economic inclusion.
• Automatic data collection: data collection should be automatic through integration with digital platforms (e.g., government’s open data initiatives, social media, mobile applications, etc.).
• Data privacy: Compliance with data privacy regulations, including anonymisation of community feedback and adherence to local and international privacy laws.

• Performance criteria for the solutions will be evaluated on a case-by-case basis.
• Ideally we should be able to take the tool to our clients as part of our go-to-market.
• We are open to exploring different business models together with solutions providers to not only use the solution in our own work but to also make the solution available to our clients as part of our broader offering. Depending on the business model chosen, the performance criteria will vary.
  

COST TARGET

Cost targets will be determined on a case-by-case basis.

TIMEFRAME FOR DEVELOPMENT

Phase 1: POC development: Q3-Q4 2025.

Phase 2: Commercial roll-out: To be determined on a case-by-case basis, target implementation by Q1 2026.

POTENTIAL MARKET / BUSINESS OPPORTUNITY

If the solution is successful, AECOM is willing to support further deployment across other locations worldwide. We believe the solution could be applied to a wide range of infrastructure projects in Asia and globally to help infrastructure providers prioritise projects according to needs. The solution also has potential in adjacent markets like real estate, urban development agencies, and community-focused organisations to ensure social equity, get project buy-in and avoid costly delays.

As stated under performance criteria, we are open to exploring different business models together with potential solution providers and leverage our global network to create a win-win for everyone involved.

AECOM might be requesting for a period of exclusivity to bring the solution to AECOM clients first.

RESOURCES

OTHER CONSIDERATIONS

We are looking for SMEs and startups with solutions that can be implemented in a relatively short-time frame (TRL of 5 and higher).

Intellectual Property (IP): For Background IP (BIP), both parties will retain their respective IPs bought into the project. In the event of new Foreground IP (FIP) creation, FIP ownership will be discussed on a case-by-case basis

BACKGROUND OF THE PROBLEM

As an EPC company, for Seatrium, tracking what is going on in our construction sides is important since it helps us to monitor key safety, productivity and efficiency parameters. Currently, Seatrium works with many different large contractors in different yards across the world. These contractors can include many different parties such as welders, cable layers etc. Currently, we have an existing practice where our safety managers and or project managers will conduct weekly (or sometimes daily) walkarounds to ensure work is done in compliance with the relevant safety, productivity and efficiency requirements and standards. While doing these manual checks can be sometimes useful, there are several limitations to their effectiveness including:

• Consistency and Accuracy: Supervisors can be inconsistent in their detection abilities, leading to variability in results. Factors like fatigue, attention, and experience can affect accuracy.
• Scalability: As the volume of workforce to be detected is very large, manual methods become impractical. Automated systems can handle large datasets more efficiently.
• Subjectivity: Manual detection can be subjective, with different people potentially interpreting or detecting things in different ways.
• Labour Costs: Relying on Safety/QA/Ops manpower for detection tasks can be expensive and resource-intensive compared to automated solutions. 

• Track if workers wear their PPE vests and helmets
• People counting
• Checking the productivity and general presence of workers
• Checking the occurrence of sparks or flames when welding
• etc

TECHNICAL REQUIREMENTS / PERFORMANCE CRITERIA:

1. Yard activities – grinding, welding, cutting, cable pulling, scaffolding etc
2. Yard Safety risk – drop objects, tripping, sparks, flame, fire, wet surface
3. Productivity evidence – Weldment defects and completion, tag recognition, pressure recorder and gauge, sensor reading
4. Of the aforementioned use cases, we would expect a solution provider to be able to implement at least 3-4 use cases
5. Ideally, the solution would also provide an integration with LLM’s so that our staff can interact with the system by using natural language
6. We would expect the system to be able to provide real-time intervention for certain (safety related) use cases
7. Ideally, the solution should include a dashboard to monitor key KPI’s for safety, quality and productivity. If this is not feasible, we are open for this to be included in a further commercial rollout post POC
8. The solution should eventually be able to be deployed on a variety of IOT devices such as smart glasses, robot docs, smart cameras etc
9. The data source of these are from the various IOT devices such as smart glass, robot dogs or smart cameras. The AI detection must be able to provide analytic insights for process improvements
10. We are looking for a solution that can provide us operational insights to improve our operations

• We are looking for an efficiency gain of 20% more in terms of productivity increase
• We would be looking to save approximately S$100-200K on personnel doing ad-hoc patrolling

COST TARGET

Cost targets will be determined on a case-by-case basis.

TIMEFRAME FOR DEVELOPMENT

Phase 1: POC development: Q3-Q4 2024.

Phase 2: Commercial roll-out: to be determined on a case-by-case basis, target implementation by Q1 2025.

POTENTIAL MARKET / BUSINESS OPPORTUNITY

If the solution is successful, Seatrium is willing to support further deployment across other yards worldwide. Seatrium has businesses all over the globe. Seatrium main customers include major energy companies, vessel owners and operators, shipping companies, and cruise and ferry operators. Seatrium operates shipyards, engineering & technology centres and facilities in Singapore, Brazil, China, India, Indonesia, Japan, Malaysia, the Philippines, Norway, the United Arab Emirates, the United Kingdom and the United States.

RESOURCES

OTHER CONSIDERATIONS

We are looking for SMEs and startups with solutions that can be implemented in a relatively short-time frame (TRL of 5 and higher). For Background IP (BIP), both parties will retain their respective IPs bought into the project. In the event of a new Foreground IP (FIP) creation, FIP ownership will be discussed on a case-by-case basis.

BACKGROUND OF THE PROBLEM

TECHNICAL REQUIREMENTS / PERFORMANCE CRITERIA

Technical Requirements:

• We are looking for a decrease in damages and incidents of 20% or more.
• We are looking for a productivity gain of 10% or more for the packaging line (speed / reduction in wrong packaging resulting in reduction of redelivery)
  

COST TARGET

Cost targets will be determined on a case-by-case basis.

In a post proof-of-concept phase and a solution that is implemented over several of our warehouses, we are looking for a cost target of not more than S$50 per video stream per site for a total of S$2500 per month cost (running cost).

TIMEFRAME FOR DEVELOPMENT

Phase 1: POC development: Q2- Q3 2025.

Phase 2:  Commercial roll-out: to be determined on a case-by-case basis

POTENTIAL MARKET / BUSINESS OPPORTUNITY

If the solution is successful, Toyota Tsusho is willing to support a further roll-out across our 100-200 sites in the Asia-Oceania region. Additionally, we are open for solution providers to deploy its solution with other players.

RESOURCES

OTHER CONSIDERATIONS

We are looking for SMEs and startups with solutions that can be implemented in a relatively short time frame (TRL of 5 and higher). 

For Background IP (BIP), both parties will retain their respective IPs bought into the project. In the event of new Foreground IP (FIP) creation, Toyota Tsusho is agreeable to the FIP being retained by the solution provider.

BACKGROUND OF THE PROBLEM

Urban infrastructure is increasingly exposed to climate risks such as rising sea levels, extreme heat, intense rainfall, and storms. Traditional approaches to climate resilience often rely on static models and historical data, making them inadequate for predicting and adapting to the evolving nature of climate change. Coastal defences, drainage systems, and building codes are all based on these models and are often not adapted to these risks. Even the most advanced models do not optimally support decision making in what to build in defence of these risks, that’s the main goal of this challenge. We are, therefore, looking for a (AI-driven) solution to assess vulnerabilities, predict local climate risks, and optimise the design, maintenance, and retrofitting of infrastructure to improve climate resilience.

Current practices include computer models and expert meetings to discuss risks, probabilities, and opinions on potential solutions. The accuracy of these methods for predictions on the level of the specific infrastructure element is limited. Plus, current processes are often reactive to the actual climate risks.

Predictive models are currently unable to process real-time weather data (so the models are always up-to-date) and link the probability of climate risks to the performance of specific infrastructure elements. Digital twins developed for climate resilience have so far failed due to limitations in data integration and the lack of scalability to the level of larger urban areas. Limitations in data integration do not relate to the data not being available but rather to integrating the data in a way that creates a comprehensive overview, a complete story of the risk so it delivers actionable insights in how to prioritise potential infrastructure adjustments.

This challenge seeks a solution that both increases the accuracy of the climate risk prediction models for infrastructure, but mainly a solution that suggests and assesses the best way to protect communities and assets against these risks. When a good risk prediction is currently available it is still unclear what infrastructure to build in response to the specific risk, such as coastal walls, drainage systems, reservoirs and elevated roads. The solution should make it easy for infrastructure planners and asset owners to quickly assess alternatives. Furthermore, with a solution available the assessment can be performed more frequently and at lower costs, based on the updated data.

The model should incorporate the vast amounts of data available, such as governmental open data, weather data, historic datasets, satellite, asset types, asset values, existing sensor data, and hydraulic models.

The ideal solution should help us and our governmental clients understand the actual climate risks for their infrastructure, support in the infrastructure planning phase and with monitoring the risks on a frequent basis. The solution should also support our large real-estate asset owners in understanding the impact of climate risks on their portfolio.

Solutions that focus only on monitoring weather patterns without linking them to specific infrastructure impacts, or those that cannot adapt their models over time to reflect changing conditions, would not be of interest. Similarly, solutions that rely solely on manual data input and do not utilise machine learning or AI to process large datasets will not meet the challenge's requirements.

TECHNICAL REQUIREMENTS / PERFORMANCE CRITERIA

Technical Requirements:

• Compatibility: with existing infrastructure data platforms such as [include relevant examples] and climate data sources (e.g., satellite data, weather stations).
• Infrastructure: Cloud-based with options for edge processing to enable real-time risk assessment for remote or critical infrastructure.
• Scalability: Scalable to different types of urban infrastructure, such as transportation networks, water supply and drainage systems, and energy grids.
• Real-time: The solution should be capable of evaluating adjustments to infrastructure plans in real-time, based on up-to-date climate data and infrastructure performance.
• Predictive capabilities: To forecast extreme weather impacts on infrastructure over different time horizons (e.g., immediate, short-term, long-term).
• Suggestions: The solution should support in decision making by recommending adaptive measures (e.g., prioritising maintenance, retrofitting suggestions, building new somewhere else).
• Integration: Integration with city management dashboards to provide actionable insights for city planners and infrastructure managers.

Performance Criteria:

• Performance criteria for the solutions will be evaluated on a case-by-case basis.
• Ideally we should be able to take the tool to our clients as part of our go-to-market.
• We are open to exploring different business models together with solutions providers to not only use the solution in our own work but to also make the solution available to our clients as part of our broader offering. Depending on the business model chosen, the performance criteria will vary.
  

COST TARGET

Cost targets will be determined on a case-by-case basis.

TIMEFRAME FOR DEVELOPMENT

Phase 1: POC development: Q3-Q4 2025.
Phase 2: Commercial roll-out: To be determined on a case-by-case basis, target implementation by Q1 2026.

POTENTIAL MARKET / BUSINESS OPPORTUNITY FOR THE PRODUCT/SOLUTION: