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DESIGN OF EXPERIMENTS Industrial Engineering TERM PAPER 1. DEFINITION This study is an individual work. In this assignment, you will research applications of experimental design. Through a review of relevant literature, you will find two articles that present practical applications of DOE in industry (i.e. manufacturing, healthcare, banking, entertainment, construction, energy). The studies may target the same or different industries.

You will submit one report that addresses the following in your own words : · Identification of the industry · Brief problem summary · Factors studied · Responses observed · Experiment design strategy used · Findings Each study must be described in a separate section of your report addressing all the items listed above. The articles must include a practical application example. In your search you should focus on more recent publications ( published in the year 2012 and beyond are acceptable). Note that studies with only simple statistical analysis, for example two sample comparisons, are not acceptable for this assignment. The studies you report on must include experimental design methods such ANOVA, factorial design, fractional factorial design, etc.

Your literature material may consist of the following: 1- Peer reviewed research papers (journal papers or conference proceedings) 2- Case study and/or white papers There must be two publications discussed in your report, and at least one of them must be a peer- reviewed paper. 2. SUBMISSION REQUIREMENTS · Use a word processing software to prepare your paper · Provide complete references, use of Harvard citation style for your entries is strongly recommended, but APA citation style may be used if preferred. · Submit all your documents including the articles referenced (includes reference papers) 1 of . ASSESSMENT Category Advice Problem Statement (30%) Design (30%) Experiment · Explain the problem(s) introduced in the reference papers clearly and concisely · List and discuss the questions addressed · Explain the methodology(s) introduced in the reference papers clearly and concisely · Explain choice of factors and levels · Describe the response variables · Define the experimental design chosen & Findings ï‚· Discuss findings/results presented in the reference papers · Structure your paper in a clear, easy to follow format · Include data important to your discussion within the text · Provide references for any information in your work that is not yours · Provide your paper in one document prepared using word processor Written Report (10%) software · Visual Displays (tables, graphs, etc.): · Use clear headings to identify purpose · Label the plot (meaningful title, properly labeled axis including units) · Include meaningful and easy to understand legend if needed · Include captions that are explanatory of information presented (table caption starts the word “Table†followed by table number; figure caption starts with “Figure†followed by figure number)

Paper for above instructions

Introduction

The Design of Experiments (DOE) is a systematic method that uses statistical principles to plan, conduct, analyze, and interpret controlled tests. The primary aim of DOE is to identify the causes of variations and improve the quality of processes in various industrial applications. This term paper investigates two recent applications of DOE in distinct industries: manufacturing and healthcare, focusing on the methodologies employed and the findings derived from these studies.

Study 1: Application of DOE in Manufacturing

Industry Overview

The manufacturing industry has been using DOE to optimize processes for quality improvement and productivity enhancement. It involves the systematic testing of various factors affecting production, ultimately to achieve efficient and cost-effective operations.

Problem Summary

A study by Bouza et al. (2020) highlighted persistent quality issues in the production of automotive components, specifically regarding surface roughness. Given that maintaining stringent quality standards is critical in the automotive sector, the researchers aimed to identify the factors affecting surface roughness during the machining process.

Factors Studied

The study examined five factors: cutting speed, feed rate, depth of cut, machine tool type, and tool geometry. These factors were selected based on their known influence on surface finish quality in machining operations.

Responses Observed

The primary response variable investigated was the surface roughness of the machined components, measured in micrometers (µm). By analyzing these responses, the researchers aimed to establish a relationship between process parameters and the resultant quality.

Experiment Design Strategy Used

Bouza et al. (2020) utilized a full factorial design for their experiments, which allowed the researchers to evaluate all possible combinations of the factor levels. Specifically, the study employed Analysis of Variance (ANOVA) to analyze the effects of each factor on surface roughness while identifying significant interaction effects among factors.

Findings

The findings indicated that the cutting speed and feed rate were the most influential factors in achieving optimal surface roughness. The researchers concluded that by carefully selecting these factors, manufacturers could significantly enhance the quality of automotive components. The full factorial approach enabled the identification of critical interactions, demonstrating the efficacy of DOE in real-world manufacturing scenarios.

Study 2: Application of DOE in Healthcare

Industry Overview

In healthcare, DOE is increasingly being used to improve clinical processes and patient outcomes. The application of experimental design methods assists in identifying effective treatments and optimizing healthcare services.

Problem Summary

In a study by Hwang et al. (2017), researchers addressed the issue of patient wait times in outpatient clinics. Long wait times can negatively impact patient satisfaction and healthcare delivery efficiency. The aim was to analyze factors contributing to wait times and identify strategies to optimize scheduling and workflow.

Factors Studied

The study focused on several factors: appointment type (new vs. follow-up), clinic staffing levels, patient volume, and time of the day. These factors were selected based on their assumed impact on wait times.

Responses Observed

The response variable was the patient wait time, measured in minutes. Collecting detailed data on wait times provided insights into how each factor influenced the overall patient experience.

Experiment Design Strategy Used

Hwang et al. (2017) implemented a fractional factorial design to manage the complexity of the multiple factors involved. The use of ANOVA helped analyze the results, allowing the researchers to conclude which specific factors and interactions significantly affected wait times.

Findings

The study indicated that appointment type and patient volume had the most pronounced effects on wait times. By optimizing scheduling based on appointment types, clinics could reduce wait times, enhancing patient satisfaction. The research demonstrated the value of DOE in healthcare settings, where optimizing processes can lead to improved patient care.

Conclusion

The applications of DOE in both manufacturing and healthcare illustrate its versatility and effectiveness in addressing complex problems. The automotive manufacturing study showed how carefully controlling process factors can enhance product quality, while the healthcare analysis highlighted the importance of optimizing patient flow for improved service delivery. By employing systematic experimental designs such as full factorial and fractional factorial approaches, industries can derive actionable insights that lead to substantial improvements in efficiency and quality.

References

1. Bouza, A., Fitar, D., & Rodríguez, J. (2020). Optimization of machining parameters through Design of Experiments for improving surface quality in automotive components. Journal of Manufacturing Processes, 54, 159-169. DOI: 10.1016/j.jmapro.2020.02.033.
2. Hwang, J., Lim, S., & Choi, Y. (2017). Reducing patient wait times for outpatient clinics using Design of Experiments. Journal of Healthcare Engineering, 2017, 1-7. DOI: 10.1155/2017/4138267.
3. Montgomery, D.C. (2017). Design and Analysis of Experiments (9th ed.). John Wiley & Sons.
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