Advanced predictive analytics for employee turnover

员工流动率的高级预测分析

• 批准号: 544453-2019
• 负责人: Leung, CarsonKaiSang
• 金额: $ 0.79万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=674722
• 关键词: Advanced; predictive; analytics; employee; turnover

In the current era of big data, high volumes of a wide variety of valuable data about employees can be easilycollected at a high rate in various industries (e.g., transportation & trucking industry, airline industry, certaintypes of manufacturers & service companies with highly trained/accredited employees). Data sciencesolutions--which apply multidisciplinary techniques such as data mining, machine learning (including deeplearning), and statistical modelling--help discover implicit, previously unknown and potentially usefulinformation and knowledge that is embedded in the big data. Our industrial partner, DecisionWorks ConsultingInc, has reported that the issue of driver turnover is significant for its clients in the transportation & truckingindustry because it costs roughly $5K to train a new driver. Furthermore, bad drivers (e.g., those who are hardon equipment, have poor safety records, or who find driving under anything but good road conditions difficult)are causing problems such as late or mismanaged deliveries, higher than average maintenance costs, etc.Similar issues exist in other industries (e.g., airline industry, manufacturers & service companies with highlytrained/accredited employees) where the cost of turnover on a per-employee basis is high. Having data sciencesolutions to discover knowledge and information about employees therefore helps reveal the characteristics of"good" and "bad" employees, which in turn helps DecisionWorks to enhance its business analytic model for (a)reducing operating costs and (b) enhancing the employees' working environment for its clients. With success inour Engage project in predictive analytics of turnover of a specific type of drivers in transportation & truckingindustry, we plan to further enhance our data science solution for predictive analytics of (a) other types ofdrivers and (b) employees in other industries. Moreover, we also plan to design and develop a visual analyticscomponent for our data science solution so that the discovered knowledge can be represented in a visual formthat can be easily comprehend. The resulting solution is expected to perform effective visual analytics andaccurate predictive analytics to forecast good employees at risk of turnover.

在当前的大数据时代，在各个行业(例如，运输和卡车运输业、航空业、某些类型的制造商和拥有训练有素/经过认证的员工的服务公司)，可以很容易地以高速率收集大量关于员工的各种有价值的数据。数据科学解决方案--应用了数据挖掘、机器学习(包括深度学习)和统计建模等多学科技术--有助于发现隐藏在大数据中的、以前未知的、潜在有用的信息和知识。我们的行业合作伙伴DecisionWorks ConsultingInc.报告称，司机流失率的问题对其运输和卡车行业的客户来说意义重大，因为培训一名新司机的成本约为5000美元。此外，糟糕的司机(例如，那些设备陈旧、安全记录不佳的人，或者他们发现在除良好路况之外的任何情况下驾驶都很困难)正在造成诸如延迟交付或管理不善、高于平均维护成本等问题。类似的问题也存在于其他行业(例如，航空业、制造业和服务公司，拥有高培训/认证员工)，这些行业的人均流动率成本很高。因此，拥有数据科学解决方案来发现员工的知识和信息有助于揭示“好”和“坏”员工的特征，这反过来又有助于DecisionWorks增强其业务分析模型，以(A)降低运营成本和(B)为客户改善员工的工作环境。随着我们的Engage项目在运输和卡车行业特定类型司机的营业额预测分析中取得成功，我们计划进一步增强我们的数据科学解决方案，以用于(A)其他类型的司机和(B)其他行业员工的预测分析。此外，我们还计划为我们的数据科学解决方案设计和开发一个可视化分析组件，以便以易于理解的可视化形式表示所发现的知识。由此产生的解决方案预计将执行有效的视觉分析和准确的预测分析，以预测面临离职风险的优秀员工。