On an Axiomatic Approach to Manufacturing and Manufacturing Systems

1978 ◽  
Vol 100 (2) ◽  
pp. 127-130 ◽  
Author(s):  
N. P. Suh ◽  
A. C. Bell ◽  
D. C. Gossard
Keyword(s):  
Decision Making ◽  
Quality Control ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Axiomatic Approach ◽  
Entire System ◽  
Materials Selection ◽  
Algorithmic Approach ◽  
Maximum Productivity ◽  
Basic Concepts

A manufacturing system consists of the following processes; design, materials selection and processing, assembly, and quality control. Maximum productivity of a given manufacturing system can be obtained only through optimization of the entire system rather than through optimization of its components individually. A system can be optimized through either a thorough understanding of the entire system and the interactions between its components, i.e., an algorithmic approach; or through the application of axioms to each decision-making step. This paper discusses an axiomatic approach to manufacturing and a methodology for developing axioms. Several hypothetical axioms and some of their corollaries are presented with examples to illustrate the basic concepts.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

DECISION SUPPORT FOR A SUSTAINABLE PRODUCTION IN JOB SHOP MANUFACTURING SYSTEMS

10.6036/9917 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 455-459
Author(s):  
MAHDI NADERI ◽  
ANTONIO FERNÁNDEZ ULLOA ◽  
JOSÉ ENRIQUE ARES GÓMEZ ◽  
GUSTAVO PELÁEZ LOURIDO
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Job Shop ◽  
Sustainable Production ◽  
Ease Of Use ◽  
Manufacturing Companies ◽  
Strategic Decisions ◽  
Production Environments

Despite the growing importance that is being given to the concepts of sustainability in many areas, not only in industry but also in the economy and public opinion in general, until now, most research has focused, practically, on the analysis of the concepts, but has not addressed, in a comprehensive way, its impact in decision making probably due to the complex relations of interdependence between its different aspects. In this context, MAPSAM (Methodology for the Assessment of Sustainability in Manufacturing Processes and Systems) was created to help the decision-making process, allowing a conscious and transparent assessment by administrators and managers at the different levels of the structure of companies and organisations. This article explains its development and application in a "job shop" type manufacturing system with an approach that allows the integration of economic, environmental and social criteria. MAPSAM is based on the use of various techniques and tools to quantify the importance of each aspect of sustainability and it has been applied in other production environments, being implemented in different systems, analysing their ease of use and evaluating their behaviour. The objective is to show how it helps to make operational, tactical and strategic decisions in the management on these type of manufacturing companies and, specifically, in this contribution we want to highlight its versatility and applicability, by validating it in a certain type of layout. With this new application, MAPSAM increases its possibilities as an innovative instrument that allows companies to make conscious and sustainable decisions in order to be more efficient, fair, supportive and respectful of the environment. Keywords: Manufacturing System, Simulation, Decision Support, Sustainable Production, Decision-Making

An application of grey based decision making approach for the selection of manufacturing system

2014 ◽  
Vol 4 (3) ◽  
pp. 447-462 ◽  
Author(s):  
Om Ji Shukla ◽  
Gunjan Soni ◽  
G. Anand
Keyword(s):  
Decision Making ◽  
Lean Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Strategic Decision ◽  
Decision Matrix ◽  
Content Type ◽  
Manufacturing Organization ◽  
Decision Making Model ◽  
Selection Of

Purpose – In the current customer-driven market, the manufacturers have to be highly responsive and flexible to deliver a variety of products. Hence, to meet this dynamic and uncertain market changes, the production system, which enables the manufacturing of such variety of products should be able to meet such diverse, dynamic changes. Hence, selecting a suitable manufacturing system is a key strategic decision for today's manufacturing organization, which needs to survive in these uncertain market conditions. Hence, the purpose of this paper is to present a decision-making model for selecting the best manufacturing system and also discuss the criteria on the basis of which the management can select the same. Design/methodology/approach – A case of small- and medium-sized company is presented, in which the management is deciding to establish a most suitable manufacturing system. To supplement this, a suitable multi-criteria decision-making model (MCDM), the grey approach is used to analyze manufacturing system alternatives based on various decision criteria to arrive a comparative ranking. Findings – An extensive analysis of grey-based decision-making model described grey decision matrix, grey normalized decision matrix, grey weighted normalized decision matrix and grey possibility degrees for three alternatives revealed that lean manufacturing systems was found to be the most suitable manufacturing system among three alternatives for a given case. Research limitations/implications – The same study can be extended by including sub-criteria with main criteria for selection of manufacturing system by utilizing two MCDM techniques such as AHP or ANP with Grey approach. Practical implications – The Grey approach has been discussed in a detailed way and it will be useful for the managers to use this approach as a tool for solving similar type of decision-making problems in their organizations in the future. Originality/value – Although, the problem of selecting a suitable manufacturing system is often addressed both in practice and research, very few reports are available in the literature of Grey-based decision models that demonstrated its application for selecting a suitable manufacturing systems.

An integrated fuzzy-based multi-criteria decision-making approach for the selection of an effective manufacturing system

2018 ◽  
Vol 25 (1) ◽  
pp. 280-296 ◽  
Author(s):  
Ram Prakash ◽  
Sandeep Singhal ◽  
Ashish Agarwal
Keyword(s):  
Decision Making ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Research Work ◽  
Sustainable Growth ◽  
Multi Criteria Decision Making ◽  
Content Type ◽  
Manufacturing Company ◽  
Selection Of

Purpose The research paper presents analysis and prioritization of barriers influencing the improvement in the effectiveness of manufacturing system. The purpose of this paper is to develop an integrated fuzzy-based multi-criteria decision-making (F-MCDM) framework to assist management of the case company in the selection of most effective manufacturing system. The framework helps in prioritizing the manufacturing systems on the basis of their effectiveness affected by the barriers. Design/methodology/approach In this paper, on the basis of experts’ opinion, five barriers have been identified in a brain-storming session. The problem of prioritization of manufacturing system is a multi-criteria decision-making (MCDM) problem and hence is solved by using the F-MCDM approach using dominance matrix. Findings Manufacturing systems’ effectiveness for Indian industries is influenced by barriers. The prioritization of manufacturing systems depends on qualitative factor decision-making criteria. Among the manufacturing systems, leagile manufacturing system is given the highest priority followed by lean manufacturing system, agile manufacturing system, flexible manufacturing system and cellular manufacturing system. Research limitations/implications The selection of an appropriate manufacturing system plays a vital role for sustainable growth of the manufacturing company. In the present work, barriers which influence the effectiveness of manufacturing system have been identified. On the basis of degree of influence of barriers on the effectiveness of the manufacturing system, five alternative manufacturing systems are prioritized. The framework will help the management of the case company to take reasonable decision for the adoption of the appropriate manufacturing system. Practical implications The results of the research work are very useful for the manufacturing companies interested in analyzing the alternative manufacturing systems on the basis of their effectiveness and their sensitivity toward various barriers. The management of Indian manufacturing company will take decision to adopt a manufacturing system whose effectiveness is least sensitive toward barriers. Effectiveness of such manufacturing system will improve with time without having retardation due to barriers. With improved effectiveness of the manufacturing system, the manufacturing company would be able to survive with global competition. The result of the present work is based on the inputs from the case company and may vary for the other manufacturing company. In the present work, only five alternative manufacturing systems and five barriers have been considered. To obtain the better result, MCDM approach with more number of alternative manufacturing systems and barriers might be considered. Originality/value The research work is based on the fuzzy analytic hierarchy process framework and on the case study conducted by the authors. The work carried out is original in nature and based on the real-life case study.

scholarly journals Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

2021 ◽  
Author(s):  
Ilya Kovalenko ◽  
Efe Balta ◽  
Dawn Tilbury ◽  
Kira Barton
Keyword(s):  
Decision Making ◽  
Control Strategy ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Timed Automata ◽  
Shop Floor ◽  
Intelligent Software ◽  
Multi Agent ◽  
Agent Control ◽  
And Performance

Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems.

scholarly journals Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

2021 ◽  
Author(s):  
Ilya Kovalenko ◽  
Efe Balta ◽  
Dawn Tilbury ◽  
Kira Barton
Keyword(s):  
Decision Making ◽  
Control Strategy ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Timed Automata ◽  
Shop Floor ◽  
Intelligent Software ◽  
Multi Agent ◽  
Agent Control ◽  
And Performance

Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems.

Profit-Oriented Decision-Making Model of Manufacturing Systems

2011 ◽  
Vol 201-203 ◽  
pp. 378-382
Author(s):  
Meng Qi Li ◽  
Dong Ying Li
Keyword(s):  
Decision Making ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Objective Evaluation ◽  
Sales Volume ◽  
Evaluation Mode ◽  
Decision Making Model ◽  
Model Environment ◽  
Single Objective ◽  
Evaluation Complexity

Multi-objective evaluation mode is used in manufacturing system all the time, and this would lead to evaluation complexity and subjectivity when objective number increases. Hence, enterprise profit- oriented single-objective is proposed and created. Then three profit structure elements: sales volume, price and cost as well as their influencing factors are analyzed, and "profit-elements(-properties)-scheme" is created. With this model, environment properties scheme is decided to verify its validity and operation simplicity.

Application of Decision Tree as a Data Mining Tool in a Manufacturing System

2011 ◽  
pp. 234-251
Author(s):  
S. A. Oke
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Research Activity ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Classification Prediction ◽  
Effective Decision Making

This work demonstrates the application of decision tree, a data mining tool, in the manufacturing system. Data mining has the capability for classification, prediction, estimation, and pattern recognition by using manufacturing databases. Databases of manufacturing systems contain significant information for decision making, which could be properly revealed with the application of appropriate data mining techniques. Decision trees are employed for identifying valuable information in manufacturing databases. Practically, industrial managers would be able to make better use of manufacturing data at little or no extra investment in data manipulation cost. The work shows that it is valuable for managers to mine data for better and more effective decision making. This work is therefore new in that it is the first time that proper documentation would be made in the direction of the current research activity.

Application of Decision Tree as a Data Mining Tool in a Manufacturing System

2009 ◽  
pp. 940-955
Author(s):  
S. A. Oke
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Research Activity ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Classification Prediction ◽  
Effective Decision Making

This work demonstrates the application of decision tree, a data mining tool, in the manufacturing system. Data mining has the capability for classification, prediction, estimation, and pattern recognition by using manufacturing databases. Databases of manufacturing systems contain significant information for decision making, which could be properly revealed with the application of appropriate data mining techniques. Decision trees are employed for identifying valuable information in manufacturing databases. Practically, industrial managers would be able to make better use of manufacturing data at little or no extra investment in data manipulation cost. The work shows that it is valuable for managers to mine data for better and more effective decision making. This work is therefore new in that it is the first time that proper documentation would be made in the direction of the current research activity.

Application of Decision Tree as a Data mining Tool in a Manufacturing System

2011 ◽  
pp. 117-136
Author(s):  
S.A. Oke
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Research Activity ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Classification Prediction ◽  
Effective Decision Making

This work demonstrates the application of decision tree, a data mining tool, in the manufacturing system. Data mining has the capability for classification, prediction, estimation, and pattern recognition by using manufacturing databases. Databases of manufacturing systems contain significant information for decision making, which could be properly revealed with the application of appropriate data mining techniques. Decision trees are employed for identifying valuable information in manufacturing databases. Practically, industrial managers would be able to make better use of manufacturing data at little or no extra investment in data manipulation cost. The work shows that it is valuable for managers to mine data for better and more effective decision making. This work is therefore new in that it is the first time that proper documentation would be made in the direction of the current research activity.

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