Machine Learning for Supply Chain Analytics and Operations Management (Spreads)

About the Course Leaders ADVANCED MANUFACTURING Ms Yan Wenjing is a Principal Research Engineer at SIMTech with more than 20 years of SERIES experience in system design, development and implementation for supply chain and enterprise information management and analysis. She has led and participated in both Machine Learning for research and industry projects covering several areas of supply chain management: production Supply Chain Analytics & planning and scheduling, demand forecasting, inventory planning, logistics planning, and Operations Management data analytics. She holds a master degree in engineering. IMMERSIVE LEARNING IN THE MODEL FACTORY@SIMTech Mr Tan Chin Sheng has been with SIMTech since 2012. Some of his works include simulation studies for analysing the impact of business disruption in manufacturing systems and the application of genetic algorithm for sales order allocation to multiple factories and layout planning of manufacturing facilities. He is also a part-time PhD student at the Nanyang Technological University, Singapore, where he researches on the potential synergy between machine learning and metaheuristic optimisation for better operational planning. Dr Wen Rong is a Research Scientist in SIMTech whose expertise lies in data analytics, data mining, data visualization, as well as image data processing and pattern recognition. He has R&D experiences working with industry companies helping them from problem identification to proper technical solution development with machine learning and deep learning technologies. Dr Yang Dazhi, a Scientist with SIMTech, has more than 10 years of experience in data analytics. He obtained his B.Eng, M.Sc., and Ph.D. degrees from the National University of Singapore in 2009, 2012, and 2015, respectively. He has published more than 80 refereed articles in various scientific journals over the past 5 years. In 2018, he won the third place in the IEEE Big Data Analytics Challenges held in Seattle. He is also a guest professor with the Harbin Institute of Technology. When and Where Dates: Please visit our website at KTO.SIMTech.a-star.edu.sg for the course schedule. This programme consists of 10 half-day sessions. Time: 9.00am to 12.30pm Venue: Singapore Institute of Manufacturing Technology Model Factory@SIMTech, 6 Fusionopolis Way, Synthesis, #01-06, Singapore 138636 Registration Please register online at KTO.SIMTech.a-star.edu.sg Contact Us For general enquiries, please contact: Scan the QR code Mr Chai Lai Sing, Industry Development Manager, at [email protected] for more information. For technical information, please contact: Ms Yan Wenjing, Principal Research Engineer, at [email protected] Organised by: Funded by: 02/2020 This programme is part of the training portfolio offered under SIMTech Manufacturing Control Tower™ (MCT™) Programme.

Course Outline The programme adopts the Learn-Practise-Implement model. Participants will acquire knowledge through a gradual learning curve, reinforce the knowledge and skill taught by working on hands-on examples that are related to their work, and apply the knowledge acquired to solve their business problems. In today’s connected world, machine learning has emerged as a key technologies for improving business Data Analytics Machine L earning Supply Chai Supplier/Customer operations in organisations. It extracts meaningful insights from raw business data for better decision Fundamentals (I4MLE0(MIL4MAE0HERMAOHRNERUSOIRNNRIUSVIGSNIREV)GSE) Profiling support & making in the supply chain planning and operations management. Data Demand Pattern Visualization Discovery About this Programme Supervised Learning Order Lead-time This programme focuses on applying relevant machine learning techniques to extract hidden patterns from Analysis either commercial systems such as CRM, ERP or excel files containing large volume of transactions data Unsupervised amassed over the years. Learning Demand Forecasting Some of the examples are listed below: Decision n Activities Managing Analysis Demand Uncertainty Sale Order Hidden Trend & Patterns The machine learning topics covered in this in Demand course are aimed to provide insights for better Sales + decision support in the areas of demand Delivery (Demand) Order Order Fulfilment planning, inventory planning and profile analysis Performance of suppliers and customers. Purchase + Supplier Delivery (Supply) Order Performance Course Fee and Funding Operational Purchase + Uncertainty • The nett course fee for all Singaporeans and SPRs aged 21 years old and above is S$1,500 (before GST). Delivery (Supply) Order + • Employees fully sponsored by SMEs can claim an additional 20% of the full course fee with the Sales Order Enhanced Training Support for SMEs Funding scheme, subject to approval by SSG. • Singaporeans aged 40 years and above can claim an additional 20% of the full course fee with the Who Should Attend Mid-Career Enhanced Subsidy (MCES). This programme is designed for organisations in the manufacturing and service sectors that have substantial • Singaporeans aged 25 years old and above are eligible for SkillsFuture Credit which can be used to amount of operational and transaction data. It aim to equip professionals with data analytics skill that can be utilized to discover insights for better planning of their supply chain activities. offset course fees (for self-sponsored registrations only). • The full course fee before funding is S$5,000 (before GST). The programme is highly suitable for management officers/ directors and professional who works in the area For more information about the funding, please visit SkillsFuture Singapore website at www.ssg.gov.sg of supply chain planning and management, logistics planning, sales and marketing function, production, operation or IT.