Amongst Public Listed Companies in FTSE Bursa Malaysia EMAS assessed by FTSE Russell
Consecutive year being
included in FTSE4Good Bursa Malaysia indices
Year-on-year revenue growth
Female composition on Board of Directors
Confirmed incidents of corruption
Completed Anti-Bribery and Corruption e-Quiz
Major information security incidents
Participation in cybersecurity awareness training
New products introduced
New Product Introduction revenue
R&D expenditure invested
Customer trustworthiness level
Customer Net Promoter Score
Customer Experience Recovery Rate
Communication with new suppliers on Supplier Code of Conduct
Active suppliers signed the Supplier Code of Conduct
Assessment on bribery and corruption risks for new suppliers
Suppliers communicated on participation in Self-Assessment Questionnaire
Our Sustainability Goals
In 2022, we set short-term sustainability targets for the year 2023 and long-term sustainability goals for the year 2030 to drive ESG performances. The targets and goals are set to guide our efforts in addressing our sustainability matters and realising the UN SDGs.
Innovation and Product Excellence
Number of new product introduction (“NPI”)
At least two (2) NPIs per annum by each business unit
Not applicable; we started tracking revenue from new products in 2021.
Not applicable; we started
collecting the data in 2021.
Corporate Governance and Ethical Practices
Number of confirmed incidents of corruption
Female composition on the Board of Directors
Practice 5.9 of the Malaysian Code on Corporate Governance: At least to have 30.0% females on the Board
Supply Chain Management
Active suppliers signed Supplier Code of Conduct (“SCOC”)
Not applicable; we initiated SCOC in 2021.
Communication with active suppliers on Self-Assessment Questionnaire (“SAQ”)
Not applicable; we initiated SAQ in 2022.
In setting our target and goal for RoHS-compliant materials, ViTrox will move into self-declaration and monitoring of RoHS-compliant material
starting in 2023. We target to achieve zero (0) use of hazardous material and achieve 100% RoHS-compliant in all ranges of our products by
2024 with the exception of Advanced 3D X-ray Inspection (“AXI”) machines.
This data represented 100% diversion of wooden waste from landfill from July 2021 to December 2021 as we started collecting data from July
In 2022, we communicated with 50 key suppliers on Supplier SAQ. For 2023 and 2030, we strive to communicate at least 40% and 80% of
active suppliers on the Supplier SAQ.
Incident rate refers to the number of occupational incidents occurring in a workplace every 1 million hours worked.
Lost time injury frequency rate refers to the number of lost time injuries occurring in a workplace every 1 million hours worked.
We launched CERA in October 2020. As such, 2020 CERA represents a three (3)-month rating from October 2020 to December 2020.
Driving sustainable manufacturing with smart solution in Industry 4.0 era
The manufacturing industry is currently undergoing a technological transformation through Industrial Revolution 4.0 (“IR4.0”). This revolution is in line with the UN Sustainable Development Goals (“SDGs”), especially Industry, Innovation and Infrastructure (Goal 9), Responsible Consumption and Production (Goal 12) and Climate Action (Goal 13). IR4.0 is fundamentally changing the way products are designed and produced, as well as the operations, processes, supply chain management and energy footprint of factories. The revolution is driven by a range of advanced technologies, including artificial intelligence (“AI”), big data analytics, augmented reality, additive manufacturing, system integration, autonomous robots, cybersecurity, simulation, advanced materials, the Internet of Things (“IoT”) and cloud computing.
ViTrox strives to identify gaps, create solutions for our customers and bring positive contributions that genuinely matter to society and humankind. Ultimately, our goal in technology and innovation is to reduce the global electronic waste generated from manufacturing processes.
Among the IR4.0 advanced technologies, ViTrox’s manufacturing intelligence suite, V-ONE, focuses on industrial IoT, data analytics and AI to help manufacturing companies achieve digital transformation and prepare their businesses for IR4.0. To provide a more tangible and comprehensive solution, V-ONE also works with ecosystem partners to enable system integration, cloud computing, manufacturing execution system (“MES”), manufacturing operations management (“MOM”), quality management system (“QMS”) and enterprise resource planning (“ERP”).
Providing sustainable approaches to factories
Many common issues faced by manufacturing companies are correlated to a lack of data visibility. V-ONE targets to tackle these challenges, such as loss of hidden operational costs and frequent production downtime, by helping customers identify and address bottlenecks.
Factories often operate below their optimum level because they don't identify problems early enough to fix them easily, or because they spend too much time and money on manual tasks. Even today, many factory workers perform routine jobs manually, such as recording periodic data and printing job orders, which takes time away from more valuable tasks. Digitisation and digitalisation of V-ONE solution can analyse vast amounts of production data to identify areas for improvement, such as reducing energy consumption or streamlining material usage.
V-ONE AIoT on the cloud is a plug-and-play solution that saves businesses time and money on developing their own hardware and software solutions. It is easily adaptable to changing business needs, and customers can customise the standard template to meet their specific requirements, which facilitates the implementation of sustainable practices that are aligned with their environmental goals.
In short, V-ONE simplifies IoT deployment for factories, helping them achieve digital transformation and adopt smart manufacturing processes. Besides improving production efficiency and output, the solution helps reduce resource footprints from the production process and minimise waste because the operators can solve the problem before it harms or damages the other product or wastes more materials and resources. By ensuring better operational efficiency, fewer defective products are manufactured, leading to a decrease in electronic waste. Moreover, enabling better decision-making helps companies avoid overproduction or production of faulty items that might end up as waste.
The implementation of digitalisation with this smart solution follows a few key stages. First, the data is surfaced to enable visibility across the production line. This gives an immediate overview of the production process through traceability. After at least one month of data collection, the data is visualised to provide insights into machine utilisation, production capacity, output quality, etc. These insights can then be used for resource planning and allocation, which minimises the resource wastage of companies. Eventually, reduced material waste and more efficient energy usage contribute directly to sustainable manufacturing practices.
In the long term, with sufficient data, more advanced decision-making becomes possible. V-ONE machine learning can be deployed to enable preventive and predictive analytics on maintenance plans across the production line to avoid unforeseen issues and downtime. This not only improves overall efficiency but also reduces the need for emergency repairs or replacements, thereby decreasing resource consumption and waste.
Through its ability to provide data insights and recommend maintenance needs, V-ONE contributes to better product lifecycle management. It extends the life of machinery and equipment, reducing the frequency of replacements and, consequently, the environmental impact associated with manufacturing new equipment.
Helping customers achieve digital transformation
One of the V-ONE solution’s notable successes was assisting a customer in converting their retrofit painting operation line into a fully digital painting 4.0 control line. Prior to V-ONE solution’s implementation, the operators manually monitored indicators like water and pH levels, recording them hourly on paper. This outdated process resulted in failed output whenever operators were absent for monitoring. Additionally, the manual operation required operators to manually set the oven temperature control whenever an engineering change order (“ECO”) was issued.
To address these challenges, the painting line was outfitted with IoT sensors in the first stage, enabling data collection and shifting the operation away from its heavy reliance on manual monitoring. Subsequently, the system processed the collected data with machine learning and quality standard ratios to generate comprehensive analyses presented in dashboards. The table below shows how V-ONE innovative features address the customer’s challenges and lead them to success.
The entire painting line did not have any system traceability and automation features.
The pretreatment chemical tank required manual measurement without any datalog.
The curing process required manual setting on oven temperature control without any datalog.
Automatically collect machine data through direct connection, edge devices or sensors to achieve machine-to-machine connectivity
Achieved smart automation traceability by improving connectivity across the painting line through the deployment of IoT-enabled sensors and edge devices and system integration.
Enabled automatic capture datalog of water and pH levels and alkaline/phosphate conductivity with the sensor installation.
Upgraded the existing painting line control panel with temperature control functionality to enable smart temperature reading capture and automatic set-point value adjustment based on job order parameters scanned at the kiosk system.
Provide meaningful insights to users on a real-time basis through a fully flexible digital analytics dashboard with drill-down charts
Obtained immediate access to accurate sensory data and statistical analysis, including machine utilisation, overall equipment efficiency, and daily job orders, on a real-time basis in a user-friendly dashboard view.
Notify users of potential problems before occurrences through an alert system empowered with deep learning and AI processing technologies to avoid downtime and lost production
Enabled the autonomous alert-triggering system with auto-ticketing issuance for equipment status monitoring, predictive and preventive maintenance and abnormal machine activities.