The Digital Transformation and Innovation™ (DTI™) Programme is to train and guide key personnel of organisations to be digital transformers in leveraging digital technologies to accelerate business model changes and achieve meaningful digital transformation. Using the Digital Transformation and Innovation™ (DTI™) Methodology, the participants will learn to analyse and (re-)design company's strategies, business model, value streams, and system architecture to ensure greater alignment, unlock new business growth and achieve sustainable competitive advantage.
This programme consists of 10 training modules. By completing this programme successfully, the participants will be awarded Certificates of Attendance (COAs) in Digital Transformation and Innovation, Digital Manufacturing and Industrial 4.0 Enabling Technologies and Statement of Attainments (SOAs) in OEE for Productivity Improvement & Manufacturing Data Mining Techniques. A lab-based practise training module and 2 project-based training modules are designed to provide the unique opportunities to the participants to learn and apply the cutting edge technologies and skills in data mining techniques, overall equipment effectiveness, production planning and scheduling for smart manufacturing and data analytics driven inventory planning and equipment condition monitoring techniques.
* Trainees who are concurrently receiving COVID-19 Support Grant (CSG) or Self-Employed Person Income Relief Scheme (SIRS) payouts will receive a lower training allowance as they are already receiving income relief.
Participants will be awarded with Statement of Attainments (SOA) certificates and/or Certificates of Attendance (COAs) for each individual module, if they meet the following criteria:
This unit focuses on immersive learning at Model Factory@SIMTech by allowing participants to experience advanced manufacturing technologies and learn the DTI™ Methodology in an actual smart production environment.
Data mining techniques are increasingly important for data-intensive manufacturing operations as the industry faces a number of challenges such as equipment and material condition variations, trial-and-error in process parameter setting, product quality inconsistencies, low capability of root cause discovery, process performance prediction and process parameters/recipe auto tuning. By applying data mining techniques, a company can improve its product quality and manufacturing productivity.This WSQ course aims to provide a good understanding of the fundamentals of data analytics and data mining techniques for different manufacturing applications. Participants will learn techniques for advanced clustering methods for product quality management, correlation modelling, and data pattern methods for root cause analyses and neural networks for process performance prediction.
OEE (Overall Equipment Effectiveness) is a key machine performance metric to identify hidden capacities and improve manufacturing productivity. The three key OEE factors include availability, performance, and quality. By analysing its OEE losses including machine breakdown, machine slow-down, and scrap parts, a manufacturing company can optimise the performance of their existing equipment.
The digitalisation journey to Industry 4.0 is fundamentally transforming the traditionally siloed supply
chains into integrated digital supply networks, in which supply and demand signals are originated at any
point and travel immediately throughout the supply networks making data analytics a powerful tool in
This programme is to train participants in inventory planning knowledge and principles, and equip them
with skills and tools that are required in inventory performance analysis and planning.
Harvesting the Low Hanging Fruit
Reaching right-sized inventory by applying the knowledge and skills learned through the course is a low
hanging fruit that company can achieve in the early stage of the digitalisation journey. Meanwhile, the
course will pave the way for continuous inventory performance improvement in the journey to Industry 4.0.
To start companies on their digitalisation journey and equip them with the knowledge on advanced planning and scheduling (APS), this course aims to equip participants with the essential understanding of Production Planning and Scheduling, by providing practical sessions such as hands-on modelling of the participants’ existing planning & scheduling practices into SIMTech’s Smart Manufacturing Operations Management (S-MOM) software.
Participants get to gain knowledge and skills to:
Machine breakdowns and unplanned downtime affects equipment availability and interrupts the delivery of services. Monitoring equipment’s condition and alerting its impending failure can help to minimise disruptions and costly repairs. This course provides participants with training in implementing an equipment condition and alert system using Industrial Internet-of-Things (IIoT) devices for remote monitoring of machine conditions. This course is specifically developed for local industry needs and taught by industry practitioners in the field. Case studies are discussed to highlight the applications in industry.
The project-based modules provide opportunities for the participants to embark on an industry project either in-house at SIMTech or work with a participating company.
For more information about the SkillsFuture Credit, please visit its webpage here.