Purpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR) is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks.\ud\udDesign/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs) and supply chain operations reference (SCOR) in which making decision on uncertain variables will be done by predictive and diagnostic capabilities.\ud\udFindings: After applying the proposed method in one of the biggest automotive companies in Iran, we identified key factors of supply chain performance based on SCOR model through predictive and diagnostic capability of Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’ and its criteria that include costs of labors, warranty, transportation and inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations.\ud\udResearch limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some of the performance metrics.\ud\udPractical implications: Mangers often use simple qualitative metrics for SCRM. However, combining qualitative and quantitative metrics will be more useful. Industries can recognize the important uncertain metrics by predicting supply chain performance and diagnosing possible happenings.\ud\udOriginality/value: This paper proposed a Bayesian method based on SCOR metrics which has the ability to manage supply chain risks and improve supply chain performance. This is the only presented case study for measuring supply chain performance by SCOR metrics.
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机译:目的:除了不确定性和风险的增加之外,组织成本和复杂性的增加还导致管理人员使用风险管理以减少风险承担和偏离目标的情况。 SCRM与供应链绩效密切相关。多年来,研究人员已使用不同的方法来管理供应链风险,但大多数方法是定性或定量的。供应链运作参考(SCOR)是SCP评估的标准模型,其度量标准不确定。本文通过结合SCOR的定性和定量指标,将通过贝叶斯网络来衡量供应链绩效。\ ud \ udDesign / methodology / approach:首先通过识别SCOR模型的不确定指标,然后对其进行量化来进行定性评估,供应链绩效将通过贝叶斯网络(BN)和供应链运营参考(SCOR)进行衡量,其中不确定性变量的决策将通过预测和诊断功能来做出。\ ud \ udFindings:将建议的方法应用于以下方法之一伊朗最大的汽车公司,我们通过贝叶斯网络的预测和诊断能力,基于SCOR模型确定了供应链绩效的关键因素。经过敏感性分析,我们发现“总成本”及其包括人工成本,保修成本,运输成本和库存成本在内的标准对供应链绩效的影响范围最广,影响最大。因此,管理人员在决策时应考虑其重要性。我们可以通过在不同情况下运行模型来简单地做出决策。\ ud \ ud研究的局限性/含意:更精确的模型由许多因素组成,但如果将所有模型都插入贝叶斯模型中,则很难解决,有时甚至不可能解决大型模型。我们通过软件和方法的能力采用了现实世界的特征。另一方面,对于某些性能指标而言,数据较少。\ ud \ ud实践意义:经理经常对SCRM使用简单的定性指标。但是,将定性和定量指标结合起来将更有用。行业可以通过预测供应链绩效并诊断可能发生的事件来识别重要的不确定性指标。\ ud \ ud原始性/价值:本文提出了一种基于SCOR指标的贝叶斯方法,该方法具有管理供应链风险和改善供应链绩效的能力。这是唯一通过SCOR指标衡量供应链绩效的案例研究。
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