My Background

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Ngoc Hien

My name is Hien, Nguyen Ngoc. I am writing this blog to serve the purpose of sharing knowledge, best practices and practical experiences in terms of three fundamental interconnected areas – industrial engineering and management, statistics, and data mining. In return, I expect you post your comments and contributive ideas by which we can expand our expertise together.

Before ending this page, I would like to show my academic background, and professional working experience, in some major research projects in which my expertise has been harnessed. For network, we can know each other here:

Lean Six Sigma

Master Black Belt

It is a remakable day in my professional career with the achievement of Lean Six Sigma 💥Master Black Belt certification 💥 recognized by 🚨The Lean Six Sigma Company (TLSSC)🚨 in accordance with ⚡️ISO  18404 and ISO 13053⚡️ (equivalently with TCVN 9602-1:2013).

These ISO standards not only require the examination pass,

⚡️BUT ALSO call for the concrete proof of competencies in Lean and Six Sigma of more than total **40 CRITERIA** including **100 EVIDENCES**⚡️ At the end, the Master Black Belt certification shows on

–  Evidence of deep knowledge of Lean Six Sigma on a MBB level and Lean expert

–  Evidence of change management skills

–  Good delivery of a training with several waves of GB/BB training delivered and coached in the past

– Deliver Lean six sigma extended and high-profile saving / problem-solving projects with evidence of good use of most (80%) of Lean Six Sigma tools


Academic Background

Master of Science: Global Production Engineering and Management Bachelor: Industrial Management
Vietnamese-German University and Technische Universität Berlin Ho Chi Minh City University of Technology (Viet Nam)

September 2014-October 2016

  • Study emphases: Manufacturing Engineering and Management, Operations Research
  • Teaching language: English
  • Abroad research Technical University of Berlin, Berlin, Germany (06/2016-09/2016)
  • Master Thesis: “Implementation of Lean production systems in small and medium-sized pharmaceutical enterprises.”
  • Honours and awards: Scholarship for conducting four-month research in Technische Universität Berlin, Germany, funded by DAAD
  • Advisor: Prof. Dr.-Ing. Holger Kohl, Technische Universität Berlin
  • Supervisor: M. Sc. Felix Sieckmann, Technische Universität Berlin
  • Thesis Grade: 9.5/10 (1.3 out of 1, based on German scale)
  • GPA: 2.1 (“gut”: an achievement that exceeds the average requirements considerably)

September 2008- January 2013

  • Study emphases: Industrial Engineering and Management, Operations Research
  • Teaching language: Vietnamese
  • Skills acquired: Six Sigma Black Belt (Certified by American Society of Quality), Lean and operations research tools
  • Master Thesis: “Measuring Service Quality and Satisfaction of Patients in Ho Chi Minh Eye Hospital”
  • Advisor: MSc. Huynh Bao Tuan
  • Thesis Grade: 8.8/10
  • GPA: 7.36/10

Master Degree in Global Production Engineerning and Management, certified by Technical University of Berlin

Research Projects

Implementation of lean production systems in small and medium sized pharmaceutical enterprises

Felix Sieckmann (a), Hien Nguyen Ngoc (b), René Helm (c), Holger Kohl (a,c)

  • (a) Technische Universität Berlin, Pascalstraße 8-9, 10587 Berlin, Germany
  • (b) Vietnamese-German University, Le lai, Binh Duong New City, Vietnam
  • (c) Fraunhofer Institute for Production Systems and Design Technology, Pascalstraße 8-9, 10587 Berlin, Germany


This paper is based on work from the project “LeanProductionPharma” (IGF project 18890 N) in cooperation with the German Research Association of Pharmaceutical Manufacturers (FAH). The project was supported via the German Federation of Industrial Research Associations (AiF) within the program for promoting the Industrial Collective Research (IGF) of the German Ministry of Economic Affairs and Energy (BMWi), based on a resolution of the German Parliament.


In order to ensure a sustainable supply of the population with affordable medicines, it is necessary to organize pharmaceutical production processes efficiently in terms of costs, process quality, time and flexibility. Lean Production Systems (LPS) have proved as an effective way to respond appropriately to these requirements. To date, the transfer of established models to the pharmaceutical industry and especially small and medium-sized enterprises (SMEs) is difficult, because of special characteristics regarding production processes and regulatory requirements. At the same time, SMEs often lack the necessary knowledge to develop suitable LPS themselves. The paper describes Lean success factors and barriers and proposes an LPS implementation process taking into account the unique features of SMEs in the pharmaceutical industry. Special attention is given to the consideration of human-oriented factors and the appropriate selection of Lean methods, which are aligned with business goals.

Felix Sieckmann, Hien Nguyen Ngoc, René Helm, Holger Kohl (2018). Implementation of lean production systems in small and medium sized pharmaceutical enterprises. Procedia Manufacturing, 21 (2018), pp. 814-821.

Optimizing equipment efficiency: An application of SMED methodology for SMEs

Hien N. Nguyen (a), Nhan H. Huynh (b)

  • (a) Faculty of Engineering, Vietnamese-German University, Binh Duong, Vietnam
  • (b) Scientific Research Management Office, Nong Lam University, Ho Chi Minh City, Vietnam


Competitiveness in the era of globalization is more tougher than ever before where most of small medium-sized enterprises, especially in the manufacturing sector, are mostly vulnerable due to lack of opportunities and resources to harness the economics of scale as well as business activities in research and development. To drive business competitiveness, the small and medium-sized enterprises (SMEs) have to get out the most of resource efficiency in production processes and optimize the overall equipment effectiveness (OEE). The method of single minute exchange of dies (SMED) comes to be an effective approach, which does not require financial investments but only utilize the current human resource, to improve and maximize the OEE. The paper describes the step-by-step approach to apply SMED and shows its results in the increase of 18% OEE in a semi-auto cutting machine.

Nguyen, H. N., & Huynh, N. H. (2019). Optimizing equipment efficiency: An application of SMED methodology for SMEs. The Journal of Agriculture and Development 18(3), 1-9.

Manufacturing Performance System for SMEs: A Prioritization of KPIs with Fuzzy Analytic Hierarchy Process

Hien Ngoc Nguyen (a), Nhan Huu Huynh (b), Cuong Tan Nguyen (c)

  • (a) Department of Sustainable Corporate Development, Technical University of Berlin, Berlin, Germany
  • (b) Faculty of Airport, Vietnam Aviation Academy, Ho Chi Minh City, Vietnam
  • (c) Faculty of Mechanical Engineering, Bach Khoa University (BKU), Ho Chinh Minh, Viet Nam


In today’s increasing competitive global market, large and successful manufacturing enterprises have implemented the system of key performance indicators (KPIs) which drives the performance toward the business objectives; however, this is not the case for small-medium sized enterprises (SMEs) which have been increasingly important for any national economy, especially in manufacturing sector. Although the KPIs can ideally be constructed in accordance with the concept of SMART (Specific, Measurable, Attainable, Realistic, Time-related) or balanced scorecard, but SMEs that are lack of limited resources and expertise could rarely afford to build such systems with the appropriate definition and measurement of KPIs. Therefore, the paper aimed to provide systematically
the system of KPIs adaptable to SMEs, to prioritize the importance of each proposed KPI with the application of a fuzzy analytic hierarchy process (FAHP), and to instruct the comprehensive deployment of the SMEs’ manufacturing performance system.

Nguyen, H. N., Huynh, N. H., & Nguyen, C. T. (2020). Manufacturing performance system for SMEs: A prioritization of KPIs with fuzzy analytic hierarchy process. The Journal of Agriculture and Development 19(3), 1-9.