Estimating the Impact of Understaffing on Sales and Profitability in Retail Stores

In this study, we use hourly data on store traffic, sales, and labor from 41 stores of a large retail chain to identify the extent of understaffing in retail stores and quantify its impact on sales and profitability. Using an empirical model motivated from queueing theory, we calculate the benchmark staffing level for each store, and establish the presence of systematic understaffing during peak hours. We find that all 41 stores in our sample are systematically understaffed during a 3‐hour peak period. Eliminating understaffing in these stores can result in a significant increase in sales and profitability in these stores. Also, we examine the extent to which forecasting errors and scheduling constraints drive understaffing in retail stores and quantify their relative impacts on store profits for the retailer in our study.     

Staffing Call Centers with Uncertain Arrival Rates and Co‐sourcing

In a call center, staffing decisions must be made before the call arrival rate is known with certainty. Once the arrival rate becomes known, the call center may be over‐staffed, in which case staff are being paid to be idle, or under‐staffed, in which case many callers hang‐up in the face of long wait times. Firms that have chosen to keep their call center operations in‐house can mitigate this problem by co‐sourcing; that is, by sometimes outsourcing calls. Then, the required staffing N depends on how the firm chooses which calls to outsource in real time, after the arrival rate realizes and the call center operates as a M/M/N + M queue with an outsourcing option. Our objective is to find a joint policy for staffing and call outsourcing that minimizes the long‐run average cost of this two‐stage stochastic program when there is a linear staffing cost per unit time and linear costs associated with abandonments and outsourcing. We propose a policy that uses a square‐root safety staffing rule, and outsources calls in accordance with a threshold rule that characterizes when the system is “too crowded.” Analytically, we establish that our proposed policy is asymptotically optimal, as the mean arrival rate becomes large, when the level of uncertainty in the arrival rate is of the same order as the inherent system fluctuations in the number of waiting customers for a known arrival rate. Through an extensive numerical study, we establish that our policy is extremely robust. In particular, our policy performs remarkably well over a wide range of parameters, and far beyond where it is proved to be asymptotically optimal.     

结构式团体在临床护士心理压力调解中的应用

目的探讨应用结构式团体在临床护士心理压力调解中的改善效果。方法选择临床护士60例,均为2020年06月~2020年12月间我院在册在岗内科病区护士,依据护士入科先后顺序采取分组,即前期入科护理人员30例采取自我调节方式应用心理压力,观察组为后期入科护理人员30例,采取结构式团体干预模式进行心理压力调节,对比护士不同调节方式下,情绪分值变化及对自我职业的倦怠评分、心理健康状态评分,并采取科室设计的满意度问卷展开评估。结果由自评量表结果所示,观察组护理人员在干预前焦虑、抑郁分值均无明显变化(P>0.05),干预后观察组护理人员焦虑、抑郁分值有所下降,与对照组呈鲜明对比(P<0.05)。职业倦怠量表测评结果所示,观察组护理人员情绪衰竭、工作态度、个人成就感三个维度的分值均低于对照组,差异显著(P<0.05)。由科室自我设计满意度问卷所示,观察组对自我工作满意度较高,与对照组比值差异明显(P<0.05)。两组在SCL-90量表测评下,观察组护理人员各项指标得分低于对照组(P<0.05)。结论结构式团体在临床护士心理压力的调解中的应用效果值得肯定,可改善职业倦怠感,提高工作效率。