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  • 1.
    Gandhi, Kanika
    et al.
    Bhavan’s Usha & Lakshmi Mittal Institute of Management, New Delhi, India.
    Goyal, Kirit
    Gazelle Information Technologies, New Delhi, India.
    Jha, Abhinav
    Gazelle Information Technologies, New Delhi, India.
    A Fuzzy Multi-Criteria Optimization Model for Allocating SKU and Suppliers in SC System2016In: Retail Marketing in India: Trends and Future Insights / [ed] Anshu Gupta, Kartik Dave, Emerald Group Publishing (India) , 2016, p. 117-129Chapter in book (Refereed)
    Abstract [en]

    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.

  • 2.
    Gandhi, Kanika
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Machine maintenance decision support system: A systematic literature review2018In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11 – 13, 2018, Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, p. 349-354Conference paper (Refereed)
    Abstract [en]

    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.

  • 3.
    Gandhi, Kanika
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Towards data mining based decision support in manufacturing maintenance2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 261-265Article in journal (Refereed)
    Abstract [en]

    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.

  • 4.
    Nupur, Reena
    et al.
    Department of Applied Mathematics, School of Vocational Studies and Applied Sciences, Gautam Buddha University, Noida, India.
    Gandhi, Kanika
    Bhavan’s Usha & Lakshmi Mittal Institute of Management, New Delhi, India.
    Solanki, Anjana
    Department of Applied Mathematics, School of Vocational Studies and Applied Sciences, Gautam Buddha University, Noida, India.
    Jha, P. C.
    Department of Operational Research, University of Delhi, New Delhi, India.
    Six Sigma Implementation in Cutting Process of Apparel Industry2017In: Quality, IT and Business Operations: Modeling and Optimization / [ed] P.K. Kapur, Uday Kumar, Ajit Kumar Verma, Springer, 2017, p. 279-295Chapter in book (Refereed)
    Abstract [en]

    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.

  • 5.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Diagnosis of machine tools: assessment based on double ball-bar measurements from a population of similar machines2018In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 72, p. 1327-1332Article in journal (Refereed)
    Abstract [en]

    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.

  • 6.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    School of Engineering Science, Kungliga Tekniska Högskolan, Stockholm, Sweden.
    Galar, Diego
    Department of Civil, Environmental and Natural Resources Engineering, Luleå Tekniska Universitet, Luleå, Sweden.
    Context preparation for predictive analytics – a case from manufacturing industry2017In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 341-354Article in journal (Refereed)
    Abstract [en]

    Purpose

    The 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/approach

    This 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.

    Findings

    Multiple 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/value

    This 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.

  • 7.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Integration of events and offline measurement data from a population of similar entities for condition monitoringIn: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052Article in journal (Refereed)
    Abstract [en]

    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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