获取农机具联合企业生产力的模式:案例研究

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-06-26 DOI:10.1108/jm2-11-2023-0279
Balakrishnan Anand, Saleeshya P.G., Thenarasu M., Naren Karthikeyan S.
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引用次数: 0

摘要

目的 本文介绍了一项案例研究的结果,该研究旨在重振一家面临长期亏损的农业设备制造联合企业。本文的目的是通过识别和消除制造流程中的浪费,提高生产率和盈利能力。本研究采用精益原则和工具来实现这一目标。设计/方法/途径本研究首先制作了一份调查问卷,向该联合企业发放,以收集见解。调查问卷的答复是确定需要立即关注的关键领域的基础。为了应对在没有客户数据的情况下进行需求预测的挑战,引入了需求预测模型。价值流图(VSM)用于识别和强调流程中的低效和浪费。研究结果通过帕累托图表进一步分析,以确定减少浪费工作的优先次序。基于这些见解,研究提出了替代制造方法和消除浪费策略。研究结果该研究通过 VSM 发现了联合企业制造流程中的流程断裂,并突出了浪费领域。帕累托图表分析揭示了需要立即干预的最主要浪费领域。本研究通过应用精益原则和工具来解决一家农业设备制造联合企业所面临的独特挑战,为该领域做出了贡献。需求预测模型的整合和多阶段精益框架的开发为在这种情况下提高生产率和盈利能力提供了创新方法。
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A model to access the productivity of an agricultural implements consortium: a case study

Purpose

This work presents the results of a case study aimed at revitalizing an agricultural equipment manufacturing consortium facing prolonged losses. The purpose of this paper is to enhance productivity and profitability by identifying and eliminating waste within the manufacturing processes. The study uses lean principles and tools to achieve this objective.

Design/methodology/approach

The study begins with the creation of a questionnaire, administered to the consortium to gather insights. The questionnaire responses serve as a foundation for pinpointing critical areas in need of immediate attention. To tackle the challenge of demand forecasting without customer data, a demand forecasting model is introduced. Value stream mapping (VSM) is used to identify and highlight process inefficiencies and waste. The findings are further analyzed using a Pareto chart to prioritize waste reduction efforts. Based on these insights, the study proposes alternative manufacturing methods and waste elimination strategies. A multiphase lean framework is developed as a step-by-step roadmap for implementing lean manufacturing.

Findings

The study identifies a broken process flow within the consortium’s manufacturing processes and highlights areas of waste through VSM. The Pareto chart analysis reveals the most significant waste areas requiring immediate intervention. Recommendations for process improvements and waste reduction strategies are provided to the consortium.

Originality/value

This study contributes to the field by applying lean principles and tools to address the unique challenges faced by an agricultural equipment manufacturing consortium. The integration of a demand forecasting model and the development of a multiphase lean framework offer innovative approaches to enhancing productivity and profitability in this context.

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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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