Automated human tasks, and automated product handling with small changeover time.

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.

Alternative transport modes, fuels, packing materials & end-to-end services.

Technology and best practices to turn our middle managers into data citizens.

Perfect low-code platforms to capture more relevant data and improve efficiency.

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.

As part of the Capitaland Master Sustainability Plan, Ascott is committed to reducing waste over the next few years. There is an increasing need for the group to find solutions that can address the needs of the properties that Ascott operates globally. The properties range from Serviced Residences, Rental Housing, Hotels, Senior Living Estates and Student Accommodations.

Objectives:

Because each Ascott property is on average 200 rooms, and does not operate F&B, the scale of the property does not justify the purchase of expensive equipment that costs tens of thousands of dollars.

The Ascott is looking for a solution that will help Ascott-operated properties with at least two out of four of the following:

• Waste Measurement
• Waste Tracking
• Recycling
• Waste Reduction

Working Model:

The solution should be lightweight and if possible, offered on the as-a-service model.