Staffing and equipment decisions for services: An experimental analysis
研究服务企业如何同时考虑工作量波动、员工能力和设备约束来制定人员配置计划，通过银行数据仿真发现增加人员灵活性可降低设备投资，而按峰值配置设备会大幅增加运营成本。
Abstract Many service‐oriented firms face demand/work load variations that occur both within and between days, and seasonally during the year. These service firms tend to develop staffing plans based on peak periods, resulting in substantial idle time during low demand periods. To counter this, some firms hire part‐time employees to work peak periods to improve productivity. In this way, the cost per customer/transaction (unit) can be reduced. However, the staffing decision is more complex than just determining the number and timing of the work force. The development of good staffing plans must consider the impact of available work stations like telephone consoles and data entry terminals. To use effectively more personnel during peak periods, the firm must have sufficient work stations to assign this work force. Effective staff scheduling must consider simultaneously the work load variations, employ capabilities, and equipment requirements. This article presents a model that incorporates these factors into the decision‐making process and examines their interaction. The model analysis illustrates how uncertainty in work load forecasts and staffing flexibility influence expected cost performance and equipment requirements. A set of computer simulation experiments are conducted using operating data from the proof and encoding departments for Ohio National Bank and Chemical Bank. The results suggest that increased staffing flexibility reduces the needed equipment investment, since part time staff can be scheduled more conveniently to meet varying work loads. Also, basing capital equipment decisions on peak work load levels can substantially increase total operating costs. With these costs explicitly estimated, managers can weigh them against desired service level goals to determine the appropriate balance.