This course is designed to equip participants with the basic knowledge of analytics technologies behind the digital intelligence and pervasive computing extending from factories all the way to homes and offices in order to leverage the power of Industry 4.0 and the Smart Economy. The course will use actual use cases to cover the following:
Servitization, Circular Economy, Additive Manufacturing and other drivers and enablers shaping Industry 4.0 and the Smart Economy – Use Case of Managed Services
- Smart factories and the transformations wrought by Internet of Thing (IoT) and Industrial Internet of Things (IIoT) – Use Case of Production Constraint Diagnosis
- Big Data, Cloud Computing and the Predictive and Prescriptive Analytics needed for data-driven smart decisions and process automation – Use Case of Demand and Stock Planning
- Human-machine collaborative efforts, Augmented Intelligence and the future of work and skills needed in Industry 4.0 / Smart Economy – Use Case of Smart Retail Implementation
At the end of the course, participants will be able to:
- Recognise the characteristics of advanced analytics which makes them key to Industry 4.0
- Understand the basic underlying concepts of diagnostic analytics in the analysis of factory constraints
- Understand how predictive analytics techniques such as count data regression could be used to forecast demand
- Apply basic prescriptive analytics to recommend stocking level of several products sharing WIP space
Understand how smart systems should be designed to work with humans responsible for business outcomes
- Servitization of Manufacturing & Managed Service Use Case
- Circular Economy, Additive Manufacturing and other drivers and enablers shaping Industry 4.0
- Industrial Internet of Things (IIoT)
- Production Line Design & Diagnosis Exercise
- Big Data, Cloud Computing and the Predictive and Prescriptive Analytics for Industry 4.0
- Demand and Stock Planning Use Case & Exercise
- Man-Machine Collaboration, Augmented Intelligence & Future of Work
- Smart Retail Use Case
There will be two hands-on exercises using Excel 2016 for Windows (or later versions), with the Analysis ToolPak and Solver Add-ins. These exercises will be carried out in groups.
Who Should Attend
This course is designed for business owners, managers and professionals who desires to acquire basic understanding of how advanced analytics is shaping Industry 4.0 and how they can be harnessed for their work.
Mode of Training
On-campus or Online (Live)
Mr. Chan Kum Yew
Chan Kum Yew is a supply chain industry professional and a seasoned manager with over 30 years of experience in the IT hardware and the training and consultancy industries.
He held a range of engineering, leadership and global management positions in Manufacturing, Quality and Research & Development at Hewlett-Packard (HP) for over 26 years. As Director of Quality, he led the development of a Quality Management System for a global manufacturing network which delivered products renowned for their top quality. He was a leadership sponsor of the HP Lean Sigma programme. His last role at HP was the Senior Director of Manufacturing for its laser printers product family.
Mr Chan was also the CEO of TUV SUD PSB Learning Private Ltd, a training and consultancy business unit subsidiary of the TUV SUD group of companies. The company helps local and regional corporate clients in developing their employees’ skills and improving their processes, as well as local individuals in skills training. The company is a recognised training and assessment provider for a number of global professional skills and appointed as a CET Centre for Service Excellence and Employability Skills by the Singapore Workforce Development Agency.
Prior to his association with IDSC, Mr Chan was providing business advisory services in the areas of quality, manufacturing and supply chain, and speaking at technical seminars.
Mr Chan holds a Bachelor Degree (First-Class Honours) and a Master’s Degree in Engineering from the National University of Singapore (NUS). He possesses a Professional Diploma in Data Analytics from the NUS Institute of System Sciences and an Advanced Certificate in Training & Assessment (full ACTA) from the Singapore Workforce Development Agency.
Mr. Sim Cheng Hwee
Sim Cheng Hwee is an experienced practitioner of creative problem solving using advanced predictive and prescriptive analytics with almost 30 years of experience in the public and private sector. He studied in Yokohama National University under Japan’s Mombusho Scholarship awarded through PSC and graduated in 1983 with a B.Eng. in Naval Architecture. He won a Ministry of Defence scholarship in 1988 to study Operations Research at the US Naval Postgraduate School and graduated in 1989 with an M.Sc.(with Distinction). Sim oversaw all aspects of operations research work in Mindef as Head Ops Analysis Dept in the Chief Defence Scientist Office for many years. In this role, he was heavily involved in developing planning policies and processes in order to continuously renew the organisation and better position for the future. He also provided analytical support for many plans and developed a broad understanding of technology and how a large organisation should organise itself to harness technological innovation to stay ahead. His areas of expertise include logistics and supply chain planning, strategic planning and manpower planning. He tackled many complex problems such as optimizing ammunition storage in land scarce Singapore, optimizing anti-terrorism patrols to best meet multiple threats, reducing lost sales and unsold bread for a major bakery, etc. He consulted for MNCs such as ExxonMobil, GE Plastics, Gillette and HP as well as for the public sector in security, healthcare and library services.
Sim is a Past President of the Operations Research Society of Singapore and was Adjunct Professor at Zhejiang University Ningbo Institute of Technology from Jun 2009 to May 2011. He was an Adjunct Lecturer at Institute of System Science -National University of Singapore and the Singapore Civil Service College and served on the Scientific Advisory Committee for National Research Foundation’s Future Resilient Systems research programme.