基于模糊评判的银行大客户的识别与评价

本文构建了银行客户等级的评价指标体系,采用层次分析法和模糊综合评价方法确定各因素的权重,对客户进行综合评判。并且结合对银行黄金客户群体的综合评判来调整银行的经营策略。     

基于模糊评判的银行大客户的识别与评价

本文构建了银行客户等级的评价指标体系,采用层次分析法和模糊综合评价方法确定各因素的权重,对客户进行综合评判.并且结合对银行黄金客户群体的综合评判来调整银行的经营策略.     

基于AHP模糊综合评价餐饮行业满意度研究

本文以某饭店作为具体的调研目标,首先对客户评价因素进行分析,建立层次模型,然后用层次分析法对模型进行定量分析,运用模糊综合评价最后得出客户等级综合评定结果。     

集装箱码头客户满意度及发展对策研究

分析集装箱码头运作机理,从不同视角剖析客户需求,并建立具有内部依赖性的集装箱码头客户满意度指标体系。运用网络层次分析法和模糊综合评判方法进行指标权重确定和综合评价,判定服务主体综合服务水平。最后,运用该方法对厦门港集装箱码头进行实例分析,发现制约厦门港发展关键点,并根据分析结果提出解决对策。     

移动商务环境下商业银行移动支付接纳客户信用评价

论文首先通过文献综述和专家咨询方法建立了商业银行移动支付接纳客户信用多维多层的评价指标体系。其次,利用网络层次分析法和模糊综合评价理论构建商业银行移动商务接纳客户信用评价模型,强调了信用评价指标间的相互影响;最后,利用案例银行移动支付接纳客户进行实证研究,研究结果表明本文建立的组合模型具有一定的可行性。     

知识型员工绩效评价研究

本文基于层次分析法和模糊评价理论,利用层次分析法确定各评价指标的权重,然后利用模糊理论对各级指标进行了综合评价,为知识型员工绩效综合评价提供了科学依据。     

基于模糊层次分析法的高校财务管理评价探析——以甘肃中医药大学为例

本文首先介绍了高校财务管理的现状和模糊层次分析法,并结合实际建立了甘肃中医药大学财务管理评价指标体系,同时构建了甘肃中医药大学财务管理的模糊判断矩阵;其次通过计算和模糊综合评价,确定了基于模糊层次分析法的甘肃中医药大学财务管理的各级指标权重;依据权重计算,最后阐析了基于模糊层次分析法的甘肃中医药大学财务管理综合评价的结果。     

C2C交易快递服务满意度研究

服务满意度在不同的行业与企业有着不同的定义，其评价标准也难以确定。文章针对C2C客户和C2C网站对快递服务满意度评价问题，采用三角模糊数对定性指标进行处理，采用熵评价的方法将信息质量反映到组合权中，最终结合层次分析法确定组合权重，从而改进了传统层次分析法确定权重的方法。     

基于模糊层次分析法的高校财务风险识别研究

模糊层次分析法是在模糊数学的综合评判法的基础上,在权重集的构建上运用基于层次分析法的思想引入的模糊一致性矩阵。模糊层次分析法是一种定性与定量相结合、综合化程度较高的评价方法。本文试图运用模糊层次分析法对高校的财务风险进行识别。     

汽车零部件配送中心配送绩效的模糊综合评价

探讨了汽车零部件配送中心配送活动的绩效评价指标体系和评价方法。从质量、成本、交付、安全、效率、客户满意度六个方面构建了评价指标体系、确定各指标的权重采用层次分析法,对指标体系的评价采用模糊综合评价模型,将评价的结果转化为一个数值,使评价结果更加准确。     

用模糊层次分析法确定QFD中消费者要求权重

质量功能配置(QualityFunctionDeployment,QFD),是能在产品开发及生产的各个阶段,将顾客要求转化为合理的技术要求的整体概念。在实施QFD的过程中,确定消费者的要求权重是一个关键而不可缺少的步骤。本文将模糊一致矩阵引入层次分析中,通过建立模糊一致矩阵来确定QFD中消费者要求权重。由于模糊一致矩阵更符合人们在决策过程中的心理特性,因此,它能提高确定消费者的要求权重的准确性。最后,用一个实例阐述了这种方法在QFD中确定消费者的要求权重中的应用。     

基于电子商务的顾客满意度评价研究

针对电子商务环境下顾客需求的新特点,分析了顾客满意度评价项目的设计要求,在此基础上提出了基于二级权重的顾客满意度评价方法,然后对一简单实例进行了分析。该方法可以有效分析出在电子商务环境下顾客的各种需求。     

Applying fuzzy quality function deployment to identify service management requirements for customer quality needs

In this paper a fuzzy quality function deployment to identify service management requirements for customer quality needs is developed. First, customer quality needs with importance degree have been investigated followed by development of service management requirements for satisfying the observed customer quality needs. Furthermore, construct a fuzzy relation matrix to link service management requirements and customer quality needs based on cross-functional expertise. The aggregated fuzzy assessments of various service management requirements are ranked to determine their importance priority. Finally, an empirical study for identifying the service management requirements for customer quality is used to demonstrate the proposed approach.     

A fuzzy group decision-making approach in quality function deployment

Quality function deployment (QFD) is comprised of two major group decision-making processes. One is to collect the customer requirements from a group of customers, whereas the other is to determine the relationship between customer requirements and technical measures by a cross-functional team. Generally, different and/or even subjective opinions are quite often in a group decision-making process due to the limitations of experience and impreciseness. Obviously, the importance of each customer requirement and the relationship between customer requirements and technical measures are determined by a group of people with imprecision and vagueness. Under such circumstances, a fuzzy group decision-making approach can be applied in QFD to deal with a group decision-making process when the information is imprecise and fuzzy. Moreover, an example is provided as well as the computational steps to show this fuzzy group decision-making approach can be effectively used in QFD to make decisions with imprecision and vagueness.     

浅谈客户关系价值评价指标体系

随着世界经济的快速发展,市场竞争越发激烈,由此对企业的发展提出了更高的要求,企业发展的关键在于客户,而客户的关键在于其价值,从而评价客户关系价值指标体系就越来越重要。本文对指标体系的影响因素进行分析,通过一个简单的数学模型,并采用一个移动通讯业的实证进行论证,表明了客户关系价值评价指标体系的应用性,以期加强客户关系管理,更好的实现企业利润的最大化。     

基于BP神经网络的第三方物流企业客户满意度评价研究

客户对第三方物流业务的需求是物流企业生存发展的源泉。因此,对客户满意度评价的测度应作为物流企业发展战略的重要组成部分。文中在构建第三方物流企业客户满意度评价指标体系的基础上,采用了BP神经网络算法模型来对企业的客户满意度进行评价,经过对BP神经网络模型进行训练和对模型的泛化推广能力进行检验,证明BP神经网络模型可以对第三方物流企业客户满意度进行可行有效的评价。     

Customer-centered measurement of service operations: a B2B case study

This study examines how the customer can be utilized in performance measurement and evaluation of complex engineered service operations in the business-to-business environment. The framework put forth by Ukko and Pekkola for a customer-centered approach to performance measurement in service operations is used, focusing on the special nature of service processes and the customer’s perspective. The results of this case study show that in non-interactive customer-related processes, performance evaluation can be a viable alternative to performance measurement. In addition, enabling the customer to participate in the evaluation and measurement of service operations will generate not only new measures but also new services.     

Applying a hidden Markov chain model in quality function deployment to analyze dynamic customer requirements

This study applies a hidden Markov chain model in quality function deployment to analyze dynamic customer requirements from probabilities viewpoints. In reality, the needed probabilities can be computed based upon the experts’ opinions for economic conditions as well as the customers’ surveys by asking customers’ preferences under different economic conditions. Each customer requirement can be analyzed as time goes by. In addition, the changes for each technical measure can be closely examined from time to time. More importantly, when new customers’ surveys are conducted and available as well as the new economic conditions analyzed by experts have been updated, both customer requirements and technical measures can be adjusted in a timely basis to reflect and fulfill the dynamic customer requirements. As a result, this proposed approach provides a decision maker to analyze and satisfy both past and present customer needs early on such that a better strategy can be made based upon the most updated customers’ surveys and economic conditions.     

A heuristic algorithm for capacity sensitive requirements planning

Available lot sizing rules for use in MRP (Material Requirements Planning) systems ignore capacity limitations at various work centers when sizing future orders. Planned order releases are instead determined by the tradeoff only between the item's set up and inventory holding costs. This limitation can cause unanticipated overloads and underloads at the various work centers, along with higher inventories, poorer customer service, and excessive overtime.This research explores one way to make MRP systems more sensitive to capacity limitations at the time of each regeneration run. A relatively simple heuristic algorithm is designed for this purpose. The procedure is applied to those planned order releases that standard MRP logic identifies as mature for release. The lot sizes for a small percentage of these items are increased or decreased so as to have the greatest impact in smoothing capacity requirements at the various work centers in the system. This algorithm for better integrating material requirements plans and capacity requirements plans is tested with a large scale simulator in a variety of manufacturing environments. This simulator has subsequently undergone extensive tests, including its successful validation with actual data at a large plant of major corporations.Simulation results show that the algorithm's modest extension to MRP logic significantly helps overall performance, particularly with customer service. For a wide range of test environments, past due orders were reduced by more than 30% when the algorithm was used. Inventory levels and capacity problems also improved. Not surprisingly, the algorithm helps the most (compared to not using it at all as an MRP enhancement) in environments in which short-term bottlenecks are most severe. Large lot sizes and tight shop capacities are characteristic of these environments. The algorithm works the best when forecast errors are not excessive and the master schedule is not too “nervous.”This proposed procedure is but one step toward making MRP more capacity sensitive. The widely heralded concept of “closed-loop” MRP means that inventory analysts must change or “fix up” parts of the computer generated material requirements plan. What has been missing is a tool for identifying the unrealistic parts of the plan. Our algorithm helps formalize this identification process and singles out a few planned order releases each week. This information comes to the analyst's attention as part of the usual action notices. These pointers to capacity problems go well beyond capacity requirements planning (CRP) and would be impossible without computer assistance.Our study produced two other findings. First, short-term bottlenecks occur even when the master production schedule is leveled. The culprits are the lot sizing choices for items at lower levels in the bills of material. “Rough-cut” capacity planning, such as resource requirements planning, therefore is not a sufficient tool for leveling capacity requirements. It must be supplemented by a way to smooth bottlenecks otherwise caused by shop orders for intermediate items. Second, the disruptive effect of large lot sizes is apparent, both in terms of higher inventories and worse customer service. Large lot sizes not only inflate inventories, but paradoxically hurt customer service because they create more capacity bottlenecks. The only reason why management should prefer large lot sizes is if set-up times are substantial and cannot be efficiently reduced. This finding is very much in step with the current interest in just-in-time (JIT) systems.     

考虑多模糊时间窗医药物流配送路径优化

以医药物流客户满意度最大和总成本最低为目标,建立了考虑多模糊时间窗的车辆路径问题优化模型。为满足低碳物流的要求,在路径规划中引入碳排放成本,为防止类似大型医疗器械运输超载问题引入超载惩罚项,引入多模糊时间窗来评价客户在可以忍受送达时间段和期待送达时间段的满意度。以单个配送中心3辆配送车辆25个客户为算例,运用遗传算法对模型求解,求解得到的最优车辆配送路径提高了满意度并降低了总成本。该模型亦适用于满足相应约束条件的类似行业的供应链优化。