Industrial and Manufacturing Designs (eBook)

Quantitative and Qualitative Analysis

Rohit Raja, Rakesh D. Raut, Anoop Kumar Sahu, Atul Kumar Sahu, Nitin Kumar Sahu (Herausgeber)

eBook Download: EPUB

2024 | 1. Auflage
432 Seiten
Wiley (Verlag)
978-1-394-21264-4 (ISBN)

Infos

This book promotes the idea of creative thinking to those working on qualitative and quantitative analysis for reinforcing engineering designs relating to industrial and manufacturing domains.

The book focuses on the development of the theoretical foundations and discusses the utility of integration of critical components and systems engineering for effective systems design. The book presents research directions and can be used as a tool to understand in-depth possible ways related to the management of industrial and manufacturing activities, processes, actions, benchmarks, and so on. The new ideas, theories, and methods related to qualitative and quantitative research will improve manufacturing knowledge linked to the manufacturing process.

Readers will find that the book:

• Explains qualitative and quantitative research for exploiting system characteristics and attaining system efficiency, as well as what decision-making tools are available for attaining sustainability in industrial fields;
• Discusses the utility of qualitative and quantitative models and analytical frameworks for enduring sustainability and the exploration of new ideas, critical theories, and methods related to qualitative and quantitative research;
• Disseminates industrial and manufacturing knowledge and allied boundaries, including case studies and sustainable ways to retain excellence in industrial and manufacturing domains based on qualitative and quantitative analysis;
• Presents critical aspects related to lean manufacturing and lean management tools, and demonstrates pure research and practical solutions to manufacturing problems;
• Offers theoretical content and demonstrations of manufacturing applications, as well as critical thinking and methodological support for optimizing resources and consumption;
• Reviews the development of supply chain network designs based on qualitative and quantitative aspects.

Atul Kumar Sahu, PhD, is an assistant professor in the Department of Industrial and Production Engineering, Guru Ghasidas Central University, Bilaspur, India. He obtained a PhD from the National Institute of Technology, Raipur, India. His research focuses on multi-criteria decision-making and supply chain performance measurement. Dr. Atul Kumar Sahu has worked under the dimensions of decision modeling in industrial and manufacturing practices to attain sustainability and has more than 50 papers published in international journals.

Rakesh D. Raut, PhD, is an associate professor in the Department of Operations and Supply Chain Management, National Institute of Industrial Engineering, Mumbai, India. He obtained a PhD from the National Institute of Industrial Engineering. His research is dedicated to studying partner selection, collaborative network organizations, managing supplier relations, etc. He has published 100+ articles by authors in industrial and manufacturing practices.

Rohit Raja, PhD, is an associate professor in the Department of Information Technology, Guru Ghasidas Central University, Bilaspur, India. He obtained a PhD in computer science and engineering from Dr. CVRAMAN University in 2016. His research focuses on face recognition and identification, digital image processing, signal processing, etc. Raja has published 11 patents and 85 research papers in various national and international journals.

Anoop Kumar Sahu, PhD, is an assistant professor in the Department of Mechanical Engineering, Guru Ghasidas Central University, Bilaspur, India. He studied in the realm of logistics and supply chains for his PhD in 2015. He has 60 published research documents. He actively works within the architecture of decision-making domain to address industrial processes.

Nitin Kumar Sahu, PhD, is an assistant professor in the Department of Industrial and Production Engineering, Guru Ghasidas Central University, Bilaspur, India. His areas of interest include optimization, manufacturing, production engineering, and industrial engineering. He has published more than 50 papers in international journals.

Textauszug

1
Demonstrating the Role of Qualitative and Quantitative Information in Industrial and Manufacturing Designs

Nitin Kumar Sahu1*, Sanju Kumar Nishad1, Atul Kumar Sahu1 and Anoop Kumar Sahu2

1Department of Industrial and Production Engineering, Guru Ghasidas Central Vishwavidyalaya, Bilaspur, India

2Department of Mechanical Engineering, Guru Ghasidas Central Vishwavidyalaya, Bilaspur, India

Abstract

In the present chapter, the role of qualitative and quantitative (Q&Q) information for evaluating industrial and manufacturing designs is presented, where a group of application cases to reveal the importance of Q&Q analysis is demonstrated. Here, application cases related with the selection of solar panels, evaluation of automatic/robotic welding system, selection of smart alloys, identification of logistic service provider, evaluation of machine tool, and election of industrial robot are presented to represent the utility and importance of aforesaid information in evaluation. Various cases under aforesaid aspects are presented to report the importance of Q&Q information. The chapter will help readers in understanding the worth and values of Q&Q information in analysis. The chapter describes the developed multi-criteria decision-making (MCDM) methods that can be used for reinforcing industrial and manufacturing practices based on the utilization of Q&Q information. The present chapter will assist in creating a learning atmosphere and developing capabilities in effectively evaluating decisions that involve multiple criteria and factors. Additionally, the chapters will assist learners in prioritizing and ranking different alternatives based on their alignment with specific criteria and objectives. The understanding of Q&Q information to quantify and incorporate subjective preferences into decision-making and the ability to analyze complex data related to multiple criteria for making better decisions with a large amount of information can be attained through the present chapter. The chapter will help in understanding how Q&Q criteria values impact the final decision outcomes and can help in enhancing your ability to make robust choices. Familiarization with different MCDM methods to choose the most suitable approach for different decision contexts can be attained through this chapter. The chapter will help in handling decision complexity related toward analyzing and addressing complex decision challenges, especially in scenarios where there are conflicting objectives or numerous alternatives. Moreover, the chapter will assist in modeling a structured framework for strategic planning to enable learners to align your decisions with long-term objectives based on Q&Q information.

Keywords: Qualitative and quantitative, decision-making, industrial and manufacturing designs, multi-criteria decision-making (MCDM), evaluation

1.1 Introduction

In the present scenario, manufacturers around the world are moving toward manufacturing their product in sustainable manner. Moreover, sustainable practices are highly demandable to be inculcated in the manufacturing activities for streamlining manufacturing operations. In addition, various regulatory norms, regulation policies, acts, etc., are imposed on manufacturing organizations to produce their products via proper utilization of resources and minimal waste generation for respecting environmental structure. Manufacturing is the wide expression and is typically used to illustrate machining of materials to attain desired workpiece. It comprises various activities like metal forming, customized operations, cutting, grinding, unconventional machining, foundry practices, and sheet metal working. Nowadays, modern manufacturing facilities are incorporating computer software platforms in manufacturing information systems to produce desired shapes with ease and effectiveness. The same are incorporating to facilitate the manufacturing managers and judgment makers to appraise and assess the conditions of the production process and assist in understanding “how the production system can be optimized and enhanced”. Today, there is a need for manufacturing information systems with real-time data and feedback support with miscellaneous information related with the process, machines, inputs, personnel, and many more. Similarly, it will help in monitoring the tendency of faults, operational accuracy, precision, fruitful utility of production time, worker time, right repairs, and quality concerns, as well as in increasing safety and in decreasing business threats throughout the operation, which demands the exploitation of Q&Q factors.

The available data with the manufacturing systems are needed to evaluate for observing effectiveness, organizing inventory, and goods; taking action against consumer demands; developing financial execution; and delivering essential and judicious information to banks, associates, sponsors, and key stakeholders. Thus, it is needed to capture more data from the manufacturing system characteristics that can be supported by qualitative and quantitative (Q&Q) measures. The more data will be collected, the more accuracy will be attained by the system. It is found that categorization of the data can be done by considering two aspects, i.e., primary data or secondary data based on the source of collection. Primary data are directly gathered from the main source, i.e., experiments, monitoring, inspection, surveys, and questionnaires. Secondary data are the data that were gathered by other indirect sources, i.e., government organization records, research foundation reports, previous investigations, manuscripts, journals, newspapers, reporters, publications, data records, and catalogs. Moreover, one can say that data are the spirit and essence to monitor and evaluate the system, and it only provides information when it is in the structured form. The Q&Q data play a significant role in evaluating and understanding the behavior of any systems. The authentic, accurate, and trustworthy data help in revealing and enhancing the performance of your intrusion and support the decision-making (DM) process.

It is ascertained that the utilization of the combination of Q&Q data can improve the evaluation and DM process by assuring that the restrictions of one type of data are influenced by the potency of another data. Qualitative data collection incorporates intangible factors like knowledge, judgment, inspiration, behaviors, and explanations of activity, occasion, or a particular significant situation. Hence, in further expressions, one can say that a qualitative approach uses public chronicle as well as their knowledge and outlooks to evaluate transformation; is more open, informal, and unstructured; and supplies more freedom toward the data collection. The frequent qualitative data gathering means and equipments are unrestricted surveys, unrestricted interviews, society interviews/gathering, centred assembly discussions, case learning, examination/surveillance, techno science, visual procedures, literature, document review, spoken history, etc. The quantitative methodology is more prearranged, uncomplicated, clearcut, and always prescribed and employed to derive responses for multiple locations assessment that engage huge group of respondents. Typical quantitative data collection approaches comprise experiments, collecting appropriate facts from management information systems, administering surveys with closed-ended queries, as well as monitoring and recording clear described events. It is easier to collect and analyze quantitative data, and there are fewer chances of biasness in the interpretation of results. Results of the quantitative data are mathematical, goal-oriented, decisive, and, to the point, easier to conclude and helpful for building comparative analysis. The frequent quantitative data gathering approaches are structured closed-ended interview, investigations and opinion polls, investigational research, correlation study, comparative analysis, statistical data examination, laboratory testing, etc.

The process of data collection for Q&Q methods has its own advantages. Moreover, modern industrial and manufacturing practices are complex as well as intricate and cannot be disentangled through one method alone. Therefore, integration of Q&Q methods is too important to enable researchers to gain a more holistic understanding of the intervention. Mixed methods introduce precision in the monitoring and assessment. Accordingly, it is advocated to develop Q&Q methods toward focusing on the deficiencies and restrictions of each method in order to provide more logical, consistent, and constructive conclusions that augment the overall confidence in the soundness of the assessment outcomes. Accordingly, in the present chapter, the role of Q&Q information for evaluating industrial and manufacturing designs is presented, where a group of application cases to reveal the importance of Q&Q is presented. Various cases are presented to report the importance of Q&Q information. The chapter will help readers in understanding the worth and values of Q&Q information in analysis.

1.2 Literature Review

Today, it is needed to explore new opportunities in industrial and manufacturing designs for crucially satisfying the dynamic needs of world, which requires mental and physical abilities to make vital DM. The same is needed to explore different solutions for handling the manufacturing situation and to receive elevated outcomes. A dignified decision in manufacturing is needed to...

Produkt-Details

• Erscheint lt. Verlag: 8.7.2024
• Sprache: englisch
• Themenwelt: Technik ► Bauwesen
• Themenwelt: Technik ► Maschinenbau
• ISBN-10: 1-394-21264-X / 139421264X
• ISBN-13: 978-1-394-21264-4 / 9781394212644