| 1. |
- Gandhi, Kanika, et al.
(författare)
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A Fuzzy Multi-Criteria Optimization Model for Allocating SKU and Suppliers in SC System
- 2016
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Ingår i: Retail Marketing in India. - : Emerald Group Publishing (India). - 9781786354105 ; , s. 117-129
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Bokkapitel (refereegranskat)abstract
- Supply chain stakeholders are increasingly paying attention to the optimal design of their supply chains because of several reasons like increasing production cost, reducing product life cycles, shrinking resources, and environmental sustainability. There has been greater emphasis on environmental concerns whilst designing the supply chains because of emerging government legislation in this domain and pressure from society.As a result, supply chain partners need to analyse their operations more critically. This study proposes a strategic decision-making model considering the operational costs caused by coordination and optimization of the sustainable supply chain design to satisfy the demand at retailers. In the study, an integrated supplier selection, procurement,inventory control and transportation model is discussed that helps in evaluating the suppliers, determining optimum quantity to procure, choosing transportation vehicle type along with managing environmental issues, obtaining optimal stock keeping units(SKU) and safety stock for each product category to fulfil a specified service level for retailers at minimum cost for the next planning horizon. The model demonstrates that how demand at retailer drives the full supply chain coordination and selection of distribution centre. The model has been validated through a case study.
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| 2. |
- Gandhi, Kanika, et al.
(författare)
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Machine maintenance decision support system : A systematic literature review
- 2018
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Ingår i: Advances in Manufacturing Technology XXXII. - Amsterdam : IOS Press. - 9781614999010 - 9781614999027 ; , s. 349-354
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Konferensbidrag (refereegranskat)abstract
- Growing competition market situations have emerged the requirement of the real-time data, understanding data behaviour, and maintenance actions in the manufacturing system. The future decision-making process in manufacturing needs to be more flexible to adapt to various methods for maintenance decision support systems (MDSS). This paper classifies various application areas of MDSS through a systemic literature review. Specifically, it identifies the relationship between the machine maintenance areas and the processes in which it integrates different tools and techniques to develop MDSS. The accumulated information helps in analyzing trends and shortcomings to concentrate the efforts for future research work. The reviewed papers are selected based on the contents, application tool assessments and clustered by their application areas. Furthermore, it proposes a structure outlined based on the functional knowledge as well as the information flow design during the development of MDSS, along with the relationship among application areas.
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| 3. |
- Gandhi, Kanika, et al.
(författare)
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Three Echelon Supply Chain Design with Supplier Evaluation
- 2014
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Ingår i: Proceedings of the Third International Conference on Soft Computing for Problem Solving. - Berlin : Springer-Verlag GmbH. - 9788132217671 - 9788132217688 ; , s. 867-881
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Konferensbidrag (refereegranskat)abstract
- Effective supply chain management (SCM), which facilitates companies to react to changing demand by swiftly communicating those needs to their supplier, is at the root of successful manufacturing. Optimizing a supply chain (SC) performance is a key factor for success in long term SC relationships. Much information like price, delivery time percentage and acceptance percentage are discussed in the process. A factor as imprecise demand is added in the same process that fuzzifies coordination between buyer and supplier. The paper considers nondeterministic conditions in the environment of business, coordination in procurement and distribution in a supplier selection problem that was proposed and a fuzzy model with two objectives was defined. The proposed model is a “fuzzy bi-objective mixed integer nonlinear” problem. A “fuzzy solution and fuzzy goal programming method” is used to convert the model into crisp form and solved using differential evolution
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| 4. |
- Gandhi, Kanika, et al.
(författare)
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Towards data mining based decision support in manufacturing maintenance
- 2018
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271 .- 2212-8271. ; 72, s. 261-265
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Tidskriftsartikel (refereegranskat)abstract
- The current work presents a decision support system architecture for evaluating the features representing the health status to predict maintenance actions and remaning useful life of component. The evaluation is possible through pattern analysis of past and current measurements of the focused research components. Data mining visualization tools help in creating the most suitable patterns and learning insights from them. Estimations like features split values or measurement frequency of the component is achieved through classification methods in data mining. This paper presents how the quantitative results generated from data mining can be used to support decision making of domain experts.
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| 5. |
- Hane Hagström, Malin, 1971, et al.
(författare)
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Evaluating the effectiveness of machine acquisitions and design by the impact on maintenance cost – a case study
- 2020
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Ingår i: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963. ; 53:3, s. 25-30
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Konferensbidrag (refereegranskat)abstract
- Industry 4.0 and circular economy are paradigm shifts for the industry. More and more machines will be used and the capability to maintain the machines becomes vital. The maintainability of a machine is to a large extent set already in the design phase; the goal of this article is to use a case study to investigate the quality of the machine design from a maintenance perspective. The results show that maintenance cost is gradually increasing in the initial part of the machine life cycle, that the new machines have higher maintenance costs than the machines approaching end of life, and that design weakness is a significant contributor to the maintenance cost. To understand more clearly why, further research in knowledge management, complementary qualitative interviews and smart maintenance is suggested.
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| 6. |
- Ito, Adriana, 1985, et al.
(författare)
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Improved root cause analysis supporting resilient production systems
- 2022
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Ingår i: Journal of Manufacturing Systems. - : Elsevier BV. - 0278-6125. ; 64, s. 468-478
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Forskningsöversikt (refereegranskat)abstract
- Manufacturing companies struggle to be efficient and effective when conducting root cause analyses of production disturbances; a fact which hinders them from creating and developing resilient production systems. This article aims to describe the challenges and enablers identified in current research relating to the different phases of root cause analysis. A systematic literature review was conducted, in which a total of 14 challenges and 17 enablers are identified and described. These correlate to the different phases of root cause analysis. Examples of challenges are “need for expertise”, “employee bias”, “poor data quality” and “lack of data integration”, among others. Examples of enablers are “visualisation tools”, “collaborative platforms”, “thesaurus” and “machine learning techniques”. Based on these findings, the authors also propose potential areas for further research and then design inputs for new solutions to improve root cause analysis. This article provides a theoretical contribution in that it describes the challenges and enablers of root cause analysis and their correlation to the creation of resilient production systems. The article also provides practical contributions, with an overview of current research to support practitioners in gaining insights into potential solutions to be implemented and further developed, with the aim of improving root cause analysis in production systems.
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| 7. |
- Nupur, Reena, et al.
(författare)
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Six Sigma Implementation in Cutting Process of Apparel Industry
- 2017
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Ingår i: Quality, IT and Business Operations. - Singapore : Springer. - 9789811055775 - 9789811055768 ; , s. 279-295
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Bokkapitel (refereegranskat)abstract
- The present competitive market is focusing on industrial efforts in producing high-quality products with the lowest possible cost. In every real-life system, there are a number of factors that cause disturbance in the process performance and their output. Process improvements through minimizing or removing such factors provide advantages such as reduced wastage or re-machining and improved market share. To help in accomplishing these objectives, various quality improvement philosophies have been put forward in recent years that can maximize the quality characteristics to ensure the enhancement of product and process. Six Sigma is an emerging data-driven approach that uses methodologies and tools that lead to improved quality levels and fact-based decision-making. This paper presents the application of the Six Sigma methodology to reduce defects in a cutting process of a garment manufacturing company in India, which is concluded through an action plan for improving product quality level. The define–measure–analyze–improve–control (DMAIC) approach has been followed here to solve the underlying problem of reducing defects and improving sigma level through continuous improvement process. The process helps in establishing specific inspection methods adapted for defect type which causes maximum rejection and to prevent their appearance in product.
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| 8. |
- Schmidt, Bernard, 1981-, et al.
(författare)
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Context preparation for predictive analytics – a case from manufacturing industry
- 2017
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Ingår i: Journal of Quality in Maintenance Engineering. - : Emerald Publishing Limited. - 1355-2511 .- 1758-7832. ; 23:3, s. 341-354
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Tidskriftsartikel (refereegranskat)abstract
- PurposeThe purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed.Design/methodology/approachThis paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified.FindingsMultiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.Originality/valueThis paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.
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| 9. |
- Schmidt, Bernard, 1981-, et al.
(författare)
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Diagnosis of machine tools : assessment based on double ball-bar measurements from a population of similar machines
- 2018
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271 .- 2212-8271. ; 72, s. 1327-1332, s. 1327-1332
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Tidskriftsartikel (refereegranskat)abstract
- The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automaticselection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population ofsimilar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System,Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performedwith use of Monte Carlo simulation based on data from real manufacturing setup.
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| 10. |
- Schmidt, Bernard, 1981-, et al.
(författare)
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Integration of events and offline measurement data from a population of similar entities for condition monitoring
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, an approach for integration of data from different sources and from a population of similar monitored entities is presented with evaluation procedure based on multiple machine learning methods that allows selection of a proper combination of methods for data integration and feature selection. It is exemplified on the real-world case from manufacturing industry with application to double ball-bar measurement from a population of machine tools. Historical data from the period of four years from a population of 29 similar multitask machine tools are analysed. Several feature selection methods are evaluated. Finally, simple economic evaluation is presented with application to proposed condition based approach. With assumed parameters, potential improvement in long term of 6 times reduced amount of unplanned stops and 40% reduced cost has been indicated with respect to optimal time based replacement policy.
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