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introduction

LEAN is an operational philosophy and systematic approach that strives to continuously improve productivity by focusing on what is important to the customer, through the elimination or reduction of non-value-added activities. This helps to reduce cost and helps organizations to be more efficient and competitive. It is also an important foundation for Industry 4.0 by streamlining operations and ensuring only value-added activities are digitized or automated. 

LEAN can be applied to all corners of the enterprise by every employee and for all industries. It helps to build continuous improvement culture and to break down silo within the organisation. It can also allow for coordinated effort by management to drive improvement to help meet overall organisational objectives.

benefits

WHAT YOU WILL LEARN

The programme engages the Learn-Practise-Implementâ„¢ (LPIâ„¢) pedagogy, through which fundamental knowledge and principles taught will be reinforced in practice, as well as during the implementation process using real business scenario and data. It covers:

• Introduction to LEAN and Principles
• Wastes identification and value stream mapping 
• KAIZEN and continuous improvement approach 
• LEAN Tools including relevant basic Industry 4.0 tools
• LEAN Sustainability

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Introduction

Business owners often show a smile of delight when they see metrics that confirmed their hunch of hidden capacity despite the machines being operated over full planned hours. Measuring by quantities delivered on time is not quite enough. A holistic measurement of effective utilization is needed. 

about the programme

This intensive two-day hands-on course provides participants with the fundamentals in applying OEE to improve machine productivity. This course is specifically developed for local industry needs and taught by industry practitioners in the field. It includes latest knowledge on OEE in Industry 4.0. Case studies are used to highlight the applications in industry.

Course outline

This programme consists of 4 OEE Fundamentals Learning Units that are sequenced-based to increase the participants’ level of understanding of the fundamentals and skills so as to improve their productivity.

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Introduction

Data mining is a technique used to extract useful information from a large number of datasets. A good understanding of the updated data mining techniques and the ability to use effectively is increasingly important for data-intensive manufacturing operations. This training is targeted towards manufacturing companies and companies in other industries which are data rich but information poor. It aims to provide the trainees understanding of up-to-date data mining technologies, build a fundamental about data analytics and data mining techniques for various applications such as process optimisation, correlation analysis for major factor identification, product quality improvement and many more.

about the programme

This course aims to provide participants with up-to-date technologies in data mining. Through extensive hands-on and sharing of successful case studies, it allows the participants to have the confidence and ability to use data mining techniques to help them in their daily work.

The course will provide the participants with a set of methodology for conducting problem-solving using data mining. From the basics of methodologies in data collection, pre-process data from multiple sources, cleaning of the data, to finally using data mining techniques to analyse the data and solve actual industrial problems.

At the end of the course, the participants can:

• Understand the fundamentals of data mining technologies
• Gain knowledge of actual industry case studies of how data mining can be used to solve actual industrial problems
• Understand data collection and pre-process methodologies
• Understand the K-means clustering method and its application
• Able to apply correlation analysis to identify the major factors for root cause analysis
• Understand and apply predictive modelling by multiple regression and neural networks
• Apply smart design of experiment with What-If analysis through predictive models

Course Outline

The programme consists of 4 learning stages.

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CERTIFICATIONS

There are 3 modules for completion under the SCTP: Manufacturing Shopfloor Excellence course.

Participants will be awarded a SIMTech Certificate of Attendance (COA) for passing each of the individual modules listed below:

• Implement LEAN to Improve Operational Efficiency (I-LEAN) (16 hours)
  
• Overall Equipment Effectiveness Fundamentals & Calculation (OEE-LITE) (16 hours)
• Data Mining for Correlation Analysis (DM-Lite) (16 hours)

Participants will be awarded with a certificate for each individual module, if they meet the following criteria:

• Achieve at least 75% course attendance;
• Take all assessments; and
• Pass the course.
  

Pre-Requisites

• Only òòò½ÍøCitizens, òòò½ÍøPermanent Residents and Long-Term Visit Pass Plus Holder (LTVP+) aged at least 21 years old (based on the year of course commencement) who can commit to the SCTP courses on a part-time basis are eligible to apply.
  
  
• Applicants should possess a degree in any discipline or a diploma with a minimum of 3 years of related working experience.
  
  
• Applicants who do not have the required academic qualifications are still welcome to apply, but shortlisted candidates may be required to attend an interview for special approval.

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John, a 40-year-old Engineer, aspires to pivot to a new job role in the automation sector that demands skills and competency in digital leadership and digital business flow. Seeking support for this mid-career job pivot, John explores the train-and-place programme with SIMTech for training suitability and funding eligibility.

After a series of consultations and confirmation of training suitability and eligibility, John registers and begins a three-month SCTP on a part-time basis. During this period, SIMTech supports John with job-specific skills trainings, job placement facilitation, including job referrals, interview opportunities, and career coaching to improve his placement prospects.

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