Case-based reasoning for complexity management in Industry 4.0

2020 ◽  
Vol 31 (5) ◽  
pp. 999-1021
Author(s):  
Peter Schott ◽  
Matthias Lederer ◽  
Isabella Eigner ◽  
Freimut Bodendorf
Keyword(s):  
Knowledge Transfer ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Production Optimization ◽  
Case Based Reasoning ◽  
Manufacturing Companies ◽  
Content Type ◽  
Complexity Management ◽  
Managing Complexity ◽  
Case Based

PurposeIncreasingly, dynamic market environments lead to growing complexity in manufacturing and pose a severe threat for the competitiveness of manufacturing companies. Systematic guidance to manage this complexity, especially in the context of Industry 4.0 and the therewith rising trends such as digitalization and data-driven production optimization, is lacking. To address this deficit a case-based reasoning (CBR) system for providing knowledge about managing complexity in Industry 4.0 is presented.Design/methodology/approachFirst, the explicit knowledge representation for managing complexity in IT-based manufacturing is introduced. Second, the CBR process step to retrieve knowledge from an artificially composed case base with in total 70 cases of data-based complexity management in the context of Industry 4.0 is set out. Third, knowledge transfer alongside several maturity levels of information technology capabilities of manufacturing systems for reuse in new problem scenarios is introduced.FindingsThe paper comprises the conceptual approach for designing a CBR system to support data-based complexity management in manufacturing systems. Furthermore, the appropriateness of the CBR system to provide applicable knowledge for reducing and managing complexity in corporate practice is shown.Research limitations/implicationsThe presented research results are evaluated in the course of an embedded single case study and may therefore lack generalizability. Future research to test and enhance the appropriateness of the developed CBR system will strengthen the research contribution.Originality/valueThe paper provides a novel approach to systematically support knowledge transfer for data-based complexity management by transferring the well-known and established methodology of CBR to the rising application domain of manufacturing systems in the context of Industry 4.0.

An agent-based supplier selection framework

2015 ◽  
Vol 8 (2/3) ◽  
pp. 180-205 ◽  
Author(s):  
Alireza Jahani ◽  
Masrah Azrifah Azmi Murad ◽  
Md. Nasir bin Sulaiman ◽  
Mohd. Hasan Selamat
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Information Overload ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Common Framework ◽  
Multi Agent ◽  
Case Based

Purpose – The purpose of this paper is to propose an approach that integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning. Unsatisfied customers, information overload and high uncertainty are the main challenges that are faced by today’s supply chains. In addition, a few existing agent-based approaches are tied to real-world supply chain functions like supplier selection. These approaches are static and do not adequately take the qualitative and quantitative factors into consideration. Therefore, an agent-based framework is needed to address these issues. Design/methodology/approach – The proposed approach integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning, as a common framework. These perspectives were rarely used together as a common framework in previous studies. Furthermore, an exploratory case study in an office furniture company is undertaken to illustrate the value of the framework. Findings – The proposed agent-based framework evaluates supply offers based on customers’ preferences, recommends alternative products in the case of stock-out and provides a collaborative environment among agents who represent different supply chain entities. The proposed fuzzy case-based reasoning (F-CBR) approach reduces the information overload by organizing them into the relevant cases that causes less overall search between cases. In addition, its fuzzy aspect addresses the high uncertainty of supply chains, especially when there are different customers’ orders with different preferences. Research limitations/implications – The present study does not include the functions of inventory management and negotiation between agents. Furthermore, only the case description and case retrieval phases of the case-based reasoning approach are investigated, and the remaining phases like case retaining, case reusing and case revising are not included in the scope of this paper. Originality/value – This framework balances the interests of different supply chain structural elements where each of them is represented by a specific agent for better collaboration, decision-making and problem-solving in a multi-agent environment. In addition, the supplier selection and order gathering mechanisms are developed based on customers’ orders.

Contextual case-based reasoning applied to a mobile device

2017 ◽  
Vol 13 (3) ◽  
pp. 282-299 ◽  
Author(s):  
Djamel Guessoum ◽  
Moeiz Miraoui ◽  
Chakib Tadj
Keyword(s):  
Mobile Device ◽  
Inertial Sensors ◽  
Contextual Information ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Two Stage ◽  
Minimal Processing ◽  
Current Case ◽  
Content Type ◽  
Case Based

Purpose This paper aims to apply a contextual case-based reasoning (CBR) to a mobile device. The CBR method was chosen because it does not require training, demands minimal processing resources and easily integrates with the dynamic and uncertain nature of pervasive computing. Based on a mobile user’s location and activity, which can be determined through the device’s inertial sensors and GPS capabilities, it is possible to select and offer appropriate services to this user. Design/methodology/approach The proposed approach comprises two stages. The first stage uses simple semantic similarity measures to retrieve the case from the case base that best matches the current case. In the second stage, the obtained selection of services is then filtered based on current contextual information. Findings This two-stage method adds a higher level of relevance to the services proposed to the user; yet, it is easy to implement on a mobile device. Originality/value A two-stage CBR using light processing methods and generating context aware services is discussed. Ontological location modeling adds reasoning flexibility and knowledge sharing capabilities.

Selection of lean manufacturing systems using the PROMETHEE

2008 ◽  
Vol 3 (1) ◽  
pp. 40-70 ◽  
Author(s):  
G. Anand ◽  
Rambabu Kodali
Keyword(s):  
Decision Making ◽  
Lean Manufacturing ◽  
Manufacturing Systems ◽  
Strategic Decision ◽  
Computer Integrated Manufacturing ◽  
Preference Ranking ◽  
Manufacturing Strategy ◽  
Manufacturing Companies ◽  
Content Type

PurposeIn recent years, many manufacturing companies are attempting to implement lean manufacturing systems (LMS) as an effective manufacturing strategy to survive in a highly competitive market. Such a process of selecting a suitable manufacturing system is highly complex and strategic in nature. The paper aims to how companies make a strategic decision of selecting LMS as part of their manufacturing strategy, and on what basis such strategic decisions are made by the managers.Design/methodology/approachA case study of a small‐ and medium‐sized enterprise is presented, in which the managers are contemplating on implementing either computer integrated manufacturing systems (CIMS) or LMS. To supplement the decision‐making process, a multi‐criteria decision making (MCDM) model, namely, the preference ranking organisation method for enrichment evaluations (PROMETHEE) is used to analyse how it will impact the stakeholders of the organisation, and the benefits gained.FindingsAn extensive analysis of PROMETHEE model revealed that LMS was the best for the given circumstances of the case.Research limitations/implicationsThe same problem can be extended by incorporating the constraints (such as financial, technical, social) of the organisation by utilising an extended version of PROMETHEE called the PROMETHEE V. Since, a single case study approach has been utilised, the findings cannot be generalized for any other industry.Practical limitations/implicationsThe methodology of PROMETHEE and its algorithm has been demonstrated in a detailed way and it is believed that it will be useful for managers to apply such MCDM tools to supplement their decision‐making efforts.Originality/valueAccording to the authors’ knowledge there is no paper in the literature, which discusses the application of PROMETHEE in making a strategic decision of implementing LMS as a part of an organisation's manufacturing strategy.

Integrating fuzzy case-based reasoning, parametric and feature-based cost estimation methods for machining process

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fentahun Moges Kasie ◽  
Glen Bright
Keyword(s):  
Cost Estimation ◽  
Intelligent System ◽  
Machining Process ◽  
Estimation Methods ◽  
Case Based Reasoning ◽  
Cost Estimates ◽  
Content Type ◽  
Estimation Techniques ◽  
Feature Based ◽  
Case Based

Purpose This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers. Design/methodology/approach The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. Findings The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques. Research limitations/implications The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations. Originality/value Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

scholarly journals Implementing Industry 4.0: assessing the current state

2020 ◽  
Vol 31 (5) ◽  
pp. 825-836
Author(s):  
Roland Ortt ◽  
Claire Stolwijk ◽  
Matthijs Punter
Keyword(s):  
Industry 4.0 ◽  
Large Scale ◽  
The State ◽  
Successful Implementation ◽  
Manufacturing Companies ◽  
Special Issue ◽  
Measurement Instruments ◽  
Content Type ◽  
Research Questions ◽  
Implementation Status

PurposeThe purpose of this paper is to introduce, summarize and combine the results of 11 articles in a special issue on the implementation of Industry 4.0. Industry 4.0 emerged as a phenomenon about a decade ago. That is why, it is interesting now to explore the implementation of the concept. In doing so, four research questions are addressed: (1) What is Industry 4.0? (2) How to implement Industry 4.0? (3) How to assess the implementation status of Industry 4.0? (4) What is the current implementation status of Industry 4.0?Design/methodology/approachSubgroups of articles are formed, around one or more research questions involving the implementation of Industry 4.0. The articles are carefully analyzed to provide comprehensive answers.FindingsBy comparing definitions systematically, the authors show important aspects for defining Industry 4.0. The articles in the special issue explore several cases of manufacturing companies that implemented Industry 4.0. In addition, systematic approaches to aid implementation are described: an approach to combine case-study results to solve new implementation problems, approaches to assess readiness or maturity of companies regarding Industry 4.0 and surveys showing the status of implementation in larger samples of companies as well as showing relationships between company characteristics and type of implementation. Small and large firms differ considerably in their process of implementing Industry 4.0, for example.Research limitations/implicationsThis special issue discusses implementation of Industry 4.0. The issue is limited to 11 articles, each of which with its own strengths and limitations.Practical implicationsThe practical relevance of the issue is that it focuses on the implementation of Industry 4.0. Cases showing successful implementation, measurement instruments to assess degree of implementation and advice how to build a database with cases together with large-scale studies on the state of implementation do provide a wealth of information with a large managerial relevance.Originality/valueThe paper introduces an original take on Industry 4.0 by focusing on implementation. The special issue contains both literature reviews, articles describing case studies of implementation, articles developing systematic measurement instruments to assess degree of implementation and some articles reporting large-scale studies on the state of implementation of Industry 4.0 and thereby combine several perspectives on implementation of Industry 4.0.

A case-based reasoning system for fault detection and isolation: a case study on complex gearboxes

2019 ◽  
Vol 25 (2) ◽  
pp. 213-235 ◽  
Author(s):  
Soumava Boral ◽  
Sanjay Kumar Chaturvedi ◽  
V.N.A. Naikan
Keyword(s):  
Decision Making ◽  
Fault Detection ◽  
Large Scale ◽  
Fault Detection And Isolation ◽  
Decision Making Process ◽  
Case Based Reasoning ◽  
Content Type ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Case Based

Purpose Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar. Design/methodology/approach CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI). Findings The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers. Originality/value The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

A business process and portfolio management approach for Industry 4.0 transformation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sophie Richard ◽  
Robert Pellerin ◽  
Jocelyn Bellemare ◽  
Nathalie Perrier
Keyword(s):  
Business Process ◽  
Portfolio Management ◽  
Research Methodology ◽  
Industry 4.0 ◽  
Process Analysis ◽  
Management Approach ◽  
Project Portfolio ◽  
Manufacturing Companies ◽  
Project Portfolio Management ◽  
Content Type

PurposeThe purpose of this paper is to address the difficulties faced by manufacturing enterprises by providing a project portfolio management approach supporting the selection and prioritization of various Industry 4.0 projects where business process analysis is used to ensure the strategic alignment and value of the project portfolio.Design/methodology/approachThe design research methodology, a mixed applied research methodology, was used to develop and test the proposed approach.FindingsDespite the growing interest of the scientific and industrial communities in I4.0, it seems that there is no method by which manufacturing companies can select a large number of improvement projects. Moreover, studies tend to focus on the evaluation and implementation of a single technology, while the transformation of an intelligent plant requires the consolidation and coordination of many initiatives to achieve a global objective.Originality/valueThe proposed project portfolio management model offers support to enterprises during their digital transformation and improves their processes by integrating technology levers through consistent and achievable selection of I4.0 initiatives while meeting strategic goals and objectives.

A developed case-based reasoning system for machine tool selection

2017 ◽  
Vol 24 (5) ◽  
pp. 1364-1385 ◽  
Author(s):  
Shankar Chakraborty ◽  
Soumava Boral
Keyword(s):  
Machine Tool ◽  
Expert Knowledge ◽  
Fine Tuning ◽  
Selection Problem ◽  
Case Based Reasoning ◽  
Tool Selection ◽  
Diverse Range ◽  
Content Type ◽  
Machine Tool Selection ◽  
Case Based

Purpose Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues. Design/methodology/approach Case-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem. Findings A software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose. Originality/value The developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.

scholarly journals Being lean: how to shape digital transformation in the manufacturing sector

2021 ◽  
Vol 32 (9) ◽  
pp. 239-259
Author(s):  
Matteo Rossini ◽  
Fabiana Dafne Cifone ◽  
Bassel Kassem ◽  
Federica Costa ◽  
Alberto Portioli-Staudacher
Keyword(s):  
Empirical Evidence ◽  
Significant Role ◽  
Industry 4.0 ◽  
Continuous Process ◽  
Radical Change ◽  
Digital Transformation ◽  
Manufacturing Companies ◽  
Content Type ◽  
Transformation Pattern

PurposeIndustry 4.0 and Lean Production are a successful match in terms of performance improvement. While we understand the combined potential, there is still poor understanding of how companies should embrace digital transformation to make it successful and sustainable, and the role that lean plays in it. In this paper, we investigate how manufacturing companies embark upon digital transformation and how being lean might affect it.Design/methodology/approachWe conducted multiple case studies with 19 manufacturing companies. We identified two clusters of companies according to their Lean maturity, and we assessed digital transformation patterns by analyzing insights coming both from cases and from the literature. Integrating cross-case analysis results, we developed a framework that shows two different digital transformation patterns according to companies’ commitment to Lean.FindingsOur findings first and foremost show the significant role of lean in driving digital transformation. We identify two patterns, namely Sustaining digital transformation pattern, characterized by the pervasive role of lean culture with small and horizontal digital changes, involvement of people and willingness to maintain continuous process improvement, and Disruptive digital transformation pattern, characterized by few and large digital steps that imply a disruptive and radical change in the company system.Practical implicationsEmpirical evidence supports the relevance of the proposed model and its practical usefulness. It can be used to design digital transformation, prepare properly the introduction of Industry 4.0 through a lean approach, and plan the future desired state, identifying the Industry 4.0 technologies that should be implemented.Originality/valueIt is widely recognized that the relationship between Industry 4.0 and lean is significant and positive, yet little evidence was presented to back that. We aim at bringing this debate forward by providing initial empirical evidence of the significant role that lean has on digital transformation, showing how lean drives the digital transformation pattern of companies.

Combining Case Based Reasoning and Shape Matching Based on Clearance Analyzes to Support the Reuse of Components

2012 ◽  
Author(s):  
Joel Johansson
Keyword(s):  
Process Design ◽  
Lead Time ◽  
Shape Matching ◽  
Product Development Process ◽  
Prototype System ◽  
Case Based Reasoning ◽  
Manufacturing Companies ◽  
Key Factor ◽  
Product Specifications ◽  
Case Based

For manufacturing companies it is important to develop and produce products that meet requirements from customers and investors. One key factor in meeting these requirements is the efficiency of the product development process. Design automation is a powerful tool to increase efficiency in that process resulting in shortened lead-time, improved product performance, and ultimately decreased cost. Further, automation is beneficial as it increases the ability to adapt products to new product specifications, which is critical to some categories of products. In this paper the retrieval and evaluation processes of the Case Based Reasoning (CBR) method are extended to include shape matching. This enhanced CBR method supports the reuse of existing components when introducing new variants of variant-rich products. The matching method is based on clearance analyzes and is performed during the retrieval of cases and supports the evaluation of suggestions. The method is described along with a prototype-system where the process of selecting components for roof racks for cars is targeted for automation. One specific component of the roof rack is targeted, namely a rubber pad used in the interface between the car roof and the rack.

Sign in / Sign up

Export Citation Format

Share Document