非对称信息下再制造产品质量控制决策研究

本文研究了风险中性的单再制造商与单销售商构建的二级闭环供应链模型,基于博弈论和委托代理理论。在不同回收水平下,再制造商都存在单边道德风险,销售商根据影响再制造商质量预防水平的因素来引导再制造商的决策,并制定激励契约来降低再制造商的道德风险,以达到闭环供应链协调的目的。研究表明：回收水平和质量检测水平均影响再制造商的质量预防水平,回收水平同质量预防水平呈正相关,质量检测水平同质量预防水平呈负相关;在不同回收水平下,通过销售商质量检测控制后,闭环供应链收益增加,同时求得最优控制水平和最优契约金额。在最后,通过算例分析验证了契约的有效性。     

供应链库存优化管理模型研究

供应链库存管理是供应链管理的重要组成部分,如何设置和维持一个合理的订货量和库存水平量,以平衡存货不足带来的短缺风险和损失,以及库存过多所增加仓储成本和资金成本,成为一个企业必须回答的问题。本文从供应链整体角度出发,提出了在缺货费用很小以至于可忽略的前提下,适应供应链模式下,新的库存控制优化模型,并以简单实证加以研究。     

模糊综合评价法在工程供应链风险管理中的应用

风险管理是工程项目管理的重要内容,是维持工程供应链稳定运作的重要保证。以工程供应链为研究视角,分析项目全寿命周期管理的四个主要周期阶段与工程供应链的相互关联。再对其供应链各环节中的风险进行识别,通过运用模糊数学的相关理论,对各阶段识别出的六大风险进行模糊综合评价,找出工程供应链风险特征,并提出相应的应对策略。     

基于模糊综合评价的零售企业供应链风险评价

供应链风险评价是对所识别的风险因素采取适当的措施,包括衡量潜在的损失频率和损失程度。文中从采购、库存、配送、销售和售后5个风险视角构建了零售供应链风险评估指标体系,运用模糊综合评价模型对企业供应链风险进行定量评价,在评价过程中采用德尔菲法来确定各指标的权重,并根据评价结果提出改进建议。     

连锁企业物流服务质量的评价

文中从企业管理的角度分析连锁企业物流服务质量问题是连锁企业发展过程中亟需关注的问题,对连锁企业物流的服务质量进行一定的评价,实现优势和劣势的总结与分析,最终提出更具优势的服务体系,为连锁商业企业的发展提供强大的竞争力,为连锁商业企业的发展提供良好的内部环境,提升企业的综合竞争力提供有力支撑。     

碳排放限额约束下的供应链优化模型研究

文中从供应链整体设计框架出发,针对碳排放限额约束建立了以经济和环境为多目标的数学模型,对供应链网络进行了优化并用一个算例验证了模型。该供应链网络优化模型有一定的创新性,能够给企业带来管理启示与参考价值。     

供应链库存管理模式比较研究

供应链库存管理是供应链管理的重要内容之一。本文首先描述了供应链库存管理的特征及与传统库存管理的区别;描述分析了目前流行的供应链库存管理策略,JIT库存管理策略、VMI库存管理策略、JMI库存管理策略、多级库存优化与控制,并描述了各自在库存管理过程的优缺点。     

连锁经营管理专业“四段一体”式人才培养方案的探讨

我国连锁经营近几年发展非常迅速,对连锁经营管理的人才需求量日益剧增,高校为此开设了连锁经营管理专业,但是该专业对人才的培养还是处于摸索阶段。本文通过对连锁经营管理专业现状的分析,提出基于工学交替的四段一体式人才培养方案;从基础理论学习、职业技能训练、理论提升与拓展、毕业顶岗实习为阶段,对以市场需求为导向,采用专业基础课与专业课融于一体、理论与实践融于一体,实践强于理论,滚动式前进、螺旋式提高的分段渐进式教学体系进行分析,并提出了相关的建议。     

Information systems and technology sourcing strategies of e-Retailers for value chain enablement

In the e-Retail industry, a well-designed IT infrastructure is essential in creating a tightly integrated value chain and delivering high quality service. With intense competition for market share and profits, information systems and technology (IST) sourcing decisions are becoming increasingly important to e-Retail firms to support continued growth and market responsiveness. Drawing on the contingency theory, we examine organizational and environmental factors that influence an e-Retailer's IST sourcing strategy of make versus buy in enabling its value chain activities, and we also look at firm-level performance impacts of IST sourcing decisions that involve bundling across value chain activities. We test the proposed model and hypotheses using a panel data set of 307 firms over the period of 2006–2010. The results show that firms that make transformative IT investments tend to source a smaller portion of IST for their e-Retail value chain activities than firms that pursue automate or informate as their strategic role for IT investment. Capabilities are positively associated with IST sourcing. Firms experienced in e-Retail are more likely to build rather than buy their IST. In addition, we find mimicking behavior for IST sourcing among firms in the same merchandizer category. We find that IT strategic role is strongly associated with growth metric, whereas sourcing decisions predominantly impact operational performance measures. There is partial evidence that alignment between IT strategic role and IST sourcing decisions results in better performance effects. Moreover, complementary IST sourcing of synergistic marketing and sales activities positively impacts Web sales and conversion rate, but the sourcing combination of logistics, operations, and sales activities is associated with lower Web sales and conversion rate.     

Efficient learning via simulation: A marginalized resample-move approach

In state–space models, parameter learning is practically difficult and is still an open issue. This paper proposes an efficient simulation-based parameter learning method. First, the approach breaks up the interdependence of the hidden states and the static parameters by marginalizing out the states using a particle filter. Second, it applies a Bayesian resample-move approach to this marginalized system. The methodology is generic and needs little design effort. Different from batch estimation methods, it provides posterior quantities necessary for full sequential inference and recursive model monitoring. The algorithm is implemented both on simulated data in a linear Gaussian model for illustration and comparison and on real data in a Lévy jump stochastic volatility model and a structural credit risk model.