Collaborative Research: Robust Strategies for Cross-Training Call Center Agents - Taxonomy, Models, and Analysis

协作研究：交叉培训呼叫中心座席的稳健策略 - 分类、模型和分析

• 批准号: 0099803
• 负责人: Seyed M. R. Iravani
• 金额: $ 18.94万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-06-01 至 2005-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0099803&HistoricalAwards=false
• 关键词: Collaborative; Research; Robust; Strategies; Cross

This research on strategies for cross-training and call/agent assignment is a ripe research topic that promises not only scientific innovation, but also a significant step forward in call center managerial practice and performance. This research has the potential to impact call center agents through increased career development and quality of life and help organizations with call centers through improved practices that lead to improved profitability. Moreover, it will increase the quality of service experienced by the users of call centers, which includes nearly the entire population. Within the last decade, call centers have become a large service industry employing roughly 3-4 million Americans, growing at about 10% annually, according to Data Monitor. The operational management of call centers, which is a notoriously difficult task, has developed to the point where the technology is already available to dynamically route incoming calls to the most suitable customer service representative (CSR), or agent, based upon their skills and training. Much more than convenience and profit are at stake. Critical emergency services such as 911, police, ambulance, and fire dispatching depend upon call centers and have experimented with cross-training call center agents to handle multiple call types. In response to these pressing needs, this project develops innovative approaches to setting effective strategies for determining which agents to cross-train for more than one task as well as how to best assign calls to them. The principal investigators have interacted with industrial call center managers and software solution providers to maximize the impact of this workThis research will construct a detailed, conceptual classification scheme for call center environments that identifies key characteristics germane to the selection of a cross training strategy. It will create and analyze a series of mathematical models that predict the performance of various cross-training patterns utilizing skills-based call routing and provide insight into the factors that determine their efficacy from a cost/benefit perspective as well as the system's response performance. The analysis will use tools that include queuing theory, Markov decision processes, discrete event systems theory, and simulation. The anticipated results of this research are: (1) managerial insights that greatly deepen the understanding of which systems will benefit from cross-training and a suitable strategy for implementation; (2) CSR (Customer Service Representative) cross-training strategies that are robustly effective across a wide range of call centers; (3) useful analytical models for the analysis and design of agile work systems; and (4) extensions of the queuing technology base to include broad classes of systems where servers operate in new and complex ways based on their skill sets. Upon implementation, the results will impact users of call centers with increased quality of service, agents through increased career development and quality of life, and firms (small, medium, and large call centers) through improved management practices.

交叉培训和呼叫/座席分配策略的研究是一个成熟的研究课题，不仅有望实现科学创新，而且在呼叫中心管理实践和绩效方面迈出了重要的一步。这项研究有可能通过提高职业发展和生活质量来影响呼叫中心座席，并通过改进实践来帮助呼叫中心组织提高盈利能力。此外，它将提高呼叫中心用户体验到的服务质量，这几乎包括了所有的人口。Data Monitor的数据显示，在过去10年里，呼叫中心已经成为一个庞大的服务行业，雇佣了大约300万至400万美国人，年增长率约为10%。呼叫中心的运营管理是一项众所周知的困难任务，但它已经发展到可以根据技能和培训动态地将传入呼叫路由到最合适的客户服务代表（CSR）或代理的技术。利害攸关的不仅仅是便利和利润。911、警察、救护车和消防调度等关键紧急服务依赖于呼叫中心，并且已经尝试对呼叫中心代理进行交叉培训，以处理多种呼叫类型。为了应对这些迫切的需求，该项目开发了创新的方法来制定有效的策略，以确定哪些座席需要交叉培训以完成多个任务，以及如何最好地分配呼叫给他们。主要研究人员与工业呼叫中心管理人员和软件解决方案提供商进行了互动，以最大限度地发挥这项工作的影响。这项研究将为呼叫中心环境构建一个详细的概念分类方案，确定与交叉培训策略选择相关的关键特征。它将创建和分析一系列数学模型，预测利用基于技能的呼叫路由的各种交叉训练模式的性能，并从成本/效益的角度以及系统的响应性能来洞察决定其有效性的因素。分析将使用的工具包括排队论、马尔可夫决策过程、离散事件系统理论和仿真。本研究的预期结果是：(1)管理见解，大大加深了对哪些系统将受益于交叉培训和适当实施策略的理解；(2) CSR（客户服务代表）交叉培训策略，该策略在广泛的呼叫中心中非常有效；(3)为敏捷工作系统的分析和设计提供了有用的分析模型；(4)队列技术基础的扩展，以包括广泛的系统类别，其中服务器根据其技能集以新的和复杂的方式操作。在实施后，结果将通过提高服务质量来影响呼叫中心的用户，通过提高职业发展和生活质量来影响座席，通过改进管理实践来影响公司（小型，中型和大型呼叫中心）。