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.

这项关于交叉训练和呼叫/代理分配策略的研究是一个成熟的研究主题，不仅有望科学创新，而且还有望迈出呼叫中心管理实践和绩效的重要一步。 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代理商，并通过改进的实践来帮助组织中心的组织，从而提高盈利能力。 此外，它将提高呼叫中心用户所经历的服务质量，其中几乎包括整个人群。根据数据监护仪的数据，在过去的十年中，呼叫中心已成为一个大约3-400万美国人的大型服务业，每年增长约10％。 呼叫中心的运营管理是一项众所周知的艰巨任务，它已经发展到已经可以根据他们的技能和培训的技术来动态地通向最合适的客户服务代表（CSR）（CSR）（CSR）（CSR）或代理的地步。不仅仅是便利和利润受到威胁。 911，警察，救护车和射击等关键紧急服务取决于呼叫中心，并尝试了交叉训练呼叫中心代理商来处理多种呼叫类型。 为了响应这些紧迫的需求，该项目开发了创新的方法来制定有效的策略，以确定哪些代理商可以交叉培训以完成多项任务，以及如何最佳地分配给他们的呼叫。 首席研究人员已经与工业呼叫中心经理和软件解决方案提供商进行了互动，以最大程度地发挥这项工作的影响，这将为呼叫中心环境构建详细的，概念上的分类方案，以确定关键特征的特征，以选择交叉培训策略。 它将创建和分析一系列数学模型，以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，并洞悉从成本/收益的角度以及系统的响应性能确定其功效的因素。该分析将使用包括排队理论，马尔可夫决策过程，离散事件系统理论和仿真的工具。 这项研究的预期结果是：（1）管理洞察力大大加深对哪些系统将从交叉训练和适当实施策略中受益的理解； （2）CSR（客户服务代表）的交叉培训策略，这些策略在广泛的呼叫中心中有效； （3）用于分析和设计敏捷工作系统的有用分析模型； （4）排队技术基础的扩展包括包括广泛的系统，其中服务器根据其技能集以新的和复杂的方式运行。 实施后，结果将通过提高职业发展和生活质量以及通过改进的管理惯例（小型，中，中和大型呼叫中心）的服务质量，代理商以及公司（小型，中和大型呼叫中心）来影响呼叫中心的用户。