Date of Award


Degree Type


Degree Name

Master of Science in Manufacturing Systems Engineering


Industrial and Manufacturing Engineering

First Advisor

Valerie Maier-Speredelozzi


This thesis will focus on resource consumption models of productivity, cost of quality, and cycle time, to describe and select preferred configurations as a function of the system and operating parameters. Consider a production system which consists of workstations or machines that can be arranged in different configurations, where each configuration can affect the performance of the system. This thesis will model and analyze four main configurations, namely serial, parallel, and serial-parallel with and without crossover.

In a production system, parts are processed at each workstation or machine, where a value is added to the part. Inspection is performed to classify a quality characteristic of a part at each workstation or machine to be accepted, reworked, or scrapped. The short term probability of accept, rework, and scrap are utilized to model the long term probabilities using an absorption Markov Chain methodology. Each configuration will result in unique long term probabilities depending on the number of processes, order and location of each process. The long term probabilities and process flow are used to develop the resource consumption models for each alternative. At first, a two process production system is analyzed using pure serial and parallel configurations, and their performance is evaluated using sensitivity analysis. Then, a four process production system is analyzed using the four configurations, and the performance of each alternative is evaluated using a numerical example. Finally, general models for the resource consumption of an n-process production system are developed for the serial, parallel, and serial-parallel without crossover. Mathematica® is utilized to develop the matrix calculations and equations for the n-process models. A case study of a biopharmaceutical company is used to apply the proposed models. All required data for three production systems are collected then analyzed to be used in identifying the productivity, quality cost, and cycle time for different configurations.

It is shown that there is a relationship between system configuration and its performance measured by productivity, quality cost, and cycle time. The proposed methodology in this thesis can be used to select the preferred configuration, where production systems with different parameters can result in different conclusions. The selection of the best configuration can be done by evaluating the resource consumption models for each possible alternative considering the different operating constrains, where the models in this thesis allow the manufacturer to select the best alternative based on specific performance targets. The developed models can also be used in discrete and continuous manufacturing systems, such as the biopharmaceutical industry case study.



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