• Production Systems Engineering for Industrial Audience

Analytical Methods and Software for Analysis, Continuous Improvement, and Design of Production Systems

 

To ensure high productivity, managers of production systems are well aware that they must:

  • Identify, protect, and improve bottlenecks
  • Maintain leanness of work-in-process
  • Select raw material release rates, which ensures the desired production lead time
  • Determine the number of carriers in closed lines so that the throughput is maximized
  • Evaluate effects of the product-mix on the performance of multi-job production systems
  • Above all, design effective continuous improvement projects with rigorously predicted results
But how can all this be accomplished? Which machine is the bottleneck? How should the buffer capacity be allocated in order to maximize the throughput? What is the optimal number of carriers? How do release rates affect the lead time? What is the coupling between the product-mix and the bottleneck position? How can the outcome of a continuous improvement project be quantitatively predicted?

While the Toyota Production System and lean techniques offer conceptual answers to these questions, the goal of this five-day course is to provide quantitative methods and analytical formulas that enable their practical implementation.

The material is based on the theory recently developed at the University of Michigan of Production Systems Engineering (PSE) and will be presented for the industrial audience—with logical justifications and emphasis on application and without lengthy mathematical derivations. In addition, the course introduces a suite of software called PSE Toolbox, which enables application of PSE methods in small, medium, and large manufacturing organizations.
 

Learning Objectives

Participants will learn analytical methods and software tools for addressing the following issues:

  • Analytical methods for performance analysis of production systems
  • Mathematical modeling of production systems
  • Bottleneck identification and elimination
  • Lean buffering design and optimal buffer capacity allocation
  • Selecting the number of carriers in closed systems leading to throughput optimization
  • Calculating the raw material release rate, which ensures the desired production lead time
  • Analysis of multi-job production systems as a function of product-mix
  • Analysis of product quality and identification of quality bottlenecks
  • Fundamental laws of production systems and their utilization for production management
  • Smart production systems and their design using PSE methods
 

In-Class Continuous Improvement Project

Under the guidance of the course instructors, participants will design a continuous improvement project for a production system of importance for their manufacturing organization. The system will be analytically evaluated and improved using PSE Toolbox. It is expected that the developed continuous improvement projects will be suitable for application in the participant's organization.

The project will commence on the first day and continue throughout the course. The results of each project will be summarized in a report and presented in class at the end.

 
 

Syllabus

Each of the topics from the Learning Objectives includes 1.5 hours of lecture and case studies and up to 1.5 hours of hands-on lab and project work.

 
Day 1- Mathematical Modeling and Performance Evaluation

Types of production systems and their performance evaluation

  • Block diagrams of production systems
  • Evaluation of machine and buffer parameters
  • Performance metrics and their evaluation
  • Case studies
  • PSE Toolbox Lab: Production systems performance evaluation

Mathematical modeling of production systems

  • Structural modeling
  • Parametric modeling
  • Model validation
  • Case studies
  • PSE Toolbox Lab: Mathematical modeling of production systems

Continuous Improvement Project Stage 1: Selecting production systems of interest and structural modeling

Day 2- Fundamental Laws of Production, Bottlenecks, and Leanness

Fundamental laws of production systems

  • Throughput vs. work-in-process
  • Uptime vs. downtime
  • Reversibility
  • Monotonicity
  • Improvability
  • Case studies
  • PSE Toolbox Lab: Analysis and improvement of production systems

Managing bottlenecks 

  • Bottleneck definition, identification, and potency
  • Measurement-based management of production systems
  • Case studies
  • PSE Toolbox Lab: Bottleneck identification and elimination

Managing leanness

  • How lean can lean be?
  • Definition of lean buffering
  • Calculation of lean buffers capacity
  • Case studies
  • PSE Toolbox Lab: Design of lean buffering

Continuous Improvement Project Stage 2: Parametric modeling and model validation

Day 3- Carriers, Release Rates, and Multi-Job Production

Managing production systems with carriers

  • Issues of production systems with carriers
  • Calculating unimpeding number of carriers 
  • Bottlenecks in systems with carriers
  • Case studies
  • PSE Toolbox Lab: Design of production systems with carriers

Managing raw material release and production lead time

  • Throttling release rates
  • Calculating lead time as a function of release rates (characteristic curves)
  • Calculating raw material release rates to endure the desired lead time
  • Case studies
  • PSE Toolbox Lab: Calculating hourly or daily release rates

Managing multi-job production (MJP) systems

  • Definition of MJP systems
  • Work-based model
  • Performance and bottleneck analysis in MJP systems
  • Throughput and bottlenecks as a function of product-mix
  • Product-mix performance portrait of MJP systems
  • Case studies
  • PSE Toolbox Lab: Design, analysis, and continuous improvement of MJP systems

Continuous Improvement Project Stage 3: Bottleneck identification and “what if” analyses

Day 4- Quality and Smart Production Systems

Managing product quality

  • Performance analysis of systems with non-perfect quality machines
  • Quality bottlenecks
  • Case studies
  • PSE Toolbox Lab: Analysis and continuous improvement of systems with non-perfect quality machines 

Smart production systems (SPS)

  • Definition of SPS
  • Structure of SPS
  • Analytics and artificial intelligence blocks of SPS
  • Case studies
  • PSE Toolbox Lab: SPS advising tool

Continuous Improvement Project Stage 4: Developing continuous improvement projects

Day 5- Project Work Completion and Presentation

Continuous Improvement Project Stage 5:

  • Completion of the continuous improvement project design
  • Quantification of the expected outcomes
  • Project report and presentation preparation
  • Presentation of results

Return to Work Prepared to Achieve Improvement

Participants will acquire theoretical and hands-on knowledge of rigorous quantitative methods for mathematical modeling of single- and multi-job production systems, their performance analysis, bottleneck identification, lean buffer design, lead time control, and fundamental laws governing production systems.

These methods have been used by the instructors and their associates in dozens of continuous improvement projects in large, medium, and small manufacturing organizations. These include GM, Ford, Chrysler, Toyota, MillerCoors, Kraft Foods, Kroger, Lexmark, Nexteer, FormTech, Subzero, Ruud Lighting, and HellermannTyton. Significant improvements in productivity, quality, and customer demand satisfaction have consistently been obtained. Learning and using these methods, course participants will be able to do the same.

 
 
 
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Prerequisites

A bachelor's degree is preferred, but substantial manufacturing experience is sufficient.

 



 
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Software and Hardware

Participants will learn and use the PSE Toolbox developed by the instructors and are encouraged to bring a laptop to run this software.

 



 
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Course Materials

Participants will receive the preliminary version of the textbook titled Production Systems Engineering for Industrial Audience written by the course instructors.

 
 

Program Faculty

The program is developed and taught by top faculty and researchers in this emerging area of engineering.

 
Semyon Meerkov
Semyon M. Meerkov
Professor, College of Engineering
University of Michigan
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Liang Zhang
Liang Zhang
Professor, College of Engineering, University of Connecticut
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Pooya Alavian
Pooya Alavian
Research Assistant, College of Engineering
University of Michigan
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