The Pitch Day and Prize Winners
The Enterprise Track saw 13 challenge sponsors, 25 challenge statements and 100 submissions. The challenge saw start-ups worldwide participating, including from Singapore, Chile, Argentina, France, India, Brazil, Kazakhstan, Greece, Austria, United Kingdom, Iran, Slovenia, Oman, Canada, the Philippines and New Zealand.
After over 3 months of the TCC journey, we are pleased to announce the prize winners of the TCC 2020 Enterprise Track, who were awarded on 3 November 2020. We had 20 startups shortlisted to virtually pitch their innovative ideas on how they will solve challenge statements in aviation, maritime, land transport, logistics, and trade. Each startup was given only 5 minutes to pitch. TCC 2020 had a panel of 15 judges consisting of representatives from the challenge sponsors, Enterprise Singapore, and IPI Singapore.
With startups showcasing innovative solutions competitively, the judges had a tough decision to make. But with their help, the prize winners of the TCC 2020 Enterprise Track are decided. Congratulations to all the prize winners! The Winner, VesselBot, has also earned a place at SLINGSHOT 2020's Virtual Finals as part of SFFxSWITCH.
|
VesselBot uses Artificial Intelligence (AI), Big Data and Machine Learning (ML) in its system to make decisions on bunker utilisation, addressing a challenge statement by Pacific International Lines. The system allows for real-time synchronisation of data between the vessel and other sources such as ports and vessel sensors. With data collected in real-time, the system identifies the optimal operational plan for the vessel. With this, shipping companies can proactively reduce and monitor bunker consumption, improve bunker purchase process and reduce greenhouse gas emissions.
Both Conundrum and DataKrew developed solutions to support predictive maintenance of ships for Tristar Group. Ships typically go through scheduled maintenance where data is logged manually. This process is prone to error and limits data captured in real-time. Conundrum's software collates machinery data through ML and can detect abnormal behaviour and pinpoint causes of malfunction. With AI, it is able to predict performance of the machinery and make recommendations for fine-tuning. Deep technology startup DataKrew, a spinoff from Nanyang Technological University (NTU), developed an Internet of Things platform collating data from various news feeds, which will then monitor, analyse and predict performance of the ship's machinery in real-time.
Hand Plus Robotics a software robotics startup, also a spinoff from NTU, developed a solution to automate kitting and co-packing processes for Teckwah Value Chain. The company was looking to improve output and efficiency, and further streamline and optimise its manpower resources through automation. The startup drew from its expertise across industries such as healthcare, logistics and manufacturing to create a customised robotic picking solution for unstructured picking.
Koireader Technologies developed a solution to improve the efficiency in scanning carton labels for YCH Group. Today, there are a variety of labels used in the shipment of goods such as barcodes, QR codes and text labels. This poses a challenge for logistics companies to efficiently scan the various labels and identify the necessary information. The KoiScan mobile application can capture information accurately regardless of the size and type of labels, thus improving efficiency of the manual task. |
Links