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: 0099821
• 负责人: MARK VAN OYEN
• 金额: $ 18.54万
• 依托单位: Loyola University of Chicago
• 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=0099821&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.

本文对交叉培训和呼叫/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增加职业发展和生活质量来影响呼叫中心代理，并通过改进实践来帮助呼叫中心组织提高盈利能力。 此外，它还将提高呼叫中心用户所体验的服务质量，其中包括几乎所有的人口。在过去的十年里，呼叫中心已经成为一个大型服务行业，雇用了大约300万至400万美国人，每年增长约10%，根据数据监测。 呼叫中心的运营管理是一项非常困难的任务，已经发展到这样一个程度，即技术已经可以根据他们的技能和培训将呼入呼叫动态路由到最合适的客户服务代表（CSR）或代理。利害攸关的不仅仅是便利和利润。关键的紧急服务，如911，警察，救护车和消防调度依赖于呼叫中心，并已试验了交叉训练呼叫中心代理来处理多种呼叫类型。 为了满足这些迫切的需求，该项目开发了创新的方法来制定有效的策略，以确定哪些代理人交叉培训多个任务，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经理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。这项研究将构建一个详细的，概念性的呼叫中心环境分类方案，确定关键特征密切相关的交叉培训策略的选择。 它将创建和分析一系列数学模型，这些模型利用基于技能的呼叫路由来预测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深入了解决定其功效的因素。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模拟。 本研究的预期结果是：（1）管理的见解，大大加深了对哪些系统将受益于交叉培训的理解和适当的实施策略;（2）企业社会责任（客户服务代表）交叉培训策略，这些策略在广泛的呼叫中心中非常有效;（3）用于分析和设计敏捷工作系统的有用分析模型;以及（4）排队技术基础的扩展，以包括其中服务器基于其技能集以新的和复杂的方式操作的大类系统。 实施后，结果将影响呼叫中心的用户，提高服务质量，通过增加职业发展和生活质量的代理商，以及通过改进管理实践的公司（小型，中型和大型呼叫中心）。