Mixing It Up Operational Impact of Hospitalist Workload

Hospitalists are physicians that specialize in the care of hospital inpatients, a job that until recently belonged to primary care physicians. We develop an operational model of hospitalist-patient interaction with rounding and responding service modes, optimizing hospitalist case-mix and workload to achieve the maximal reduction in patient length of stay (LOS). We show that hospitalists are effective at reducing LOS for patients with complex conditions, matching clinician intuition. However, we show that the optimal hospitalist case-mix also includes "simple" patients with few interventions and short LOS, as they can effectively reduce discharge delays. This actionable insight is particularly salient for small community hospitals with simple, short-stay patients, where hospitalists may be undervalued due to the prevailing belief that they are primarily effective for complex patients. We conduct a comparative case study of a small community hospital and a large academic hospital, drawing a stark contrast between the two in terms of ideal workload and patient coverage. Despite the fact that the academic hospital treats higher complexity patients, hospitalists at the community hospital should actually have a lower workload than hospitalists at the academic hospital due to shorter stays in the community hospital. Both hospitals are understaffed but for different reasons: the academic hospital needs to staff more hospitalists to reduce the current workload of its hospitalists, whereas the community hospital needs to staff more hospitalists to expand its hospitalist coverage to more patients. We estimate that these hospitals can save $1.5 million annually by implementing the optimal staffing policie

Call to Duty Just-in-Time Scheduling in a Restaurant Chain

Just-in-time scheduling has become ubiquitous in the service industries. While effective in reducing staffing level, hence labor cost, the potential impact of just-in-time scheduling on workers' productivity and the firm's revenue is not well understood. Using a dataset of 1,444,044 transactions from 25 stores of a full-service casual dining restaurant chain in the US, we study how just-in-time scheduling impacts worker productivity. We consider two types of just-in-time schedules: (1) short-notice schedules that are assigned to servers shortly prior to the day of service (mostly two days in our data), and (2) real-time schedules that are assigned to servers on the day of service. We show that short-notice schedules do not harm server productivity overall but real-time schedules do, by 4.4%. Our analysis indicates this may be because servers reduce their up-selling and cross-selling efforts when working on real-time schedules. We then propose an analytical scheduling model that accounts for both the value of staffing flexibility created through just-in-time scheduling and its impact on server productivity to inform the firm how to use just-in-time scheduling to improve profitability. Through a case study, we demonstrate that with the 4.4% productivity loss during the real-time schedules, the managers should shift from the heavy use of real-time scheduling toward scheduling more servers with longer advance notice. Such a shift not only provides more predictable work schedules for the workers, but can also improve restaurants' expected profit by up to 1%, a significant number for the low-margin restaurant industr

Developing a resilient supply chain through supplier flexibility and reliability assessment

In this study, we examine the optimal allocation of demand across a set of suppliers in a supply chain that is exposed to supply risk and environmental risk. A two-stage mixed-integer programming model is used to develop a flexible sourcing strategy under disruptions. Our model integrates supplier selection and demand allocation with transportation channel selection and provides contingency plans to mitigate the negative impacts of disruptions and minimise total network costs. Finally, a numerical example is presented to illustrate the model and provide insights. The findings suggest that developing contingency plans using flexibility in suppliers' production capacity is an effective strategy for firms to mitigate the severity of disruptions. We also show that flexibility and reliability of the suppliers and regions play a significant role in determining contingency plans for during disruption. Findings generally show that highly flexible suppliers receive less allocation, and their flexible capacity is reserved for disruptions. For firms that do not incorporate risk management into supplier selection and allocation, the recommendation is to source from fewer, more reliable suppliers with less risk of disruption. Our findings also emphasise that the type of disruption has important implications for supplier selection and demand allocation. This study highlights the supply chain risk management strategy of regionalising as a means for minimising the impact of environmental disruptions

An assessment of supply chain disruption mitigation strategies

We assess the effectiveness of incorporating three types of redundancy practices (pre-positioning inventory, backup suppliers, and protected suppliers) into a firm's supply chain that is exposed to ttwo types of risk: supply risk and environmental risk. Supply risk disrupts an individual supplier, while environmental risk makes a number of suppliers in a given region unavailable. An additional factor is supplier interdependence, where a disruption in one supplier may also disrupt other active suppliers. Utilizing the concept of a decision tree to capture different disruption scenarios, we develop a two-stage mixed-integer programming (two-stage MIP) model as a General Model to address the problem of supplier selection and order allocation under supplier dependencies and risk of disruptions. In the General Model, multi-sourcing is the only supplier strategy that the firm implements. Then we develop three separate extensions of the General Model, one for each of the three redundancy practices, and evaluate the expected supply chain cost of each extended model. We quantitatively show how adding redundancy to the supply chain in different forms, along with contingency plans, can help firms mitigate the impact of supply chain disruptions. The findings suggest that all three strategies reduce costs and risks compared to the General Model. An analysis of reliability, risks, dependence, and costs is conducted on each strategy to provide insights into supplier selection, demand allocation, and capability development in a supply chain under supply chain risks. Finally, we show that regionalizing a supply chain is an effective way to mitigate the negative impacts of environmental disruptions on the supply chain

A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research

The purpose of this paper is to investigate the research development in supply chain resilience. We undertake a literature survey to review the literature on enterprise and supply chain resilience. We aim to provide a platform for researchers and practitioners trying to identify the existing state of the work, gaps in current research, and future directions on the topic. For this purpose, we employed two methods to collect publications in supply chain resilience. First, we selected keywords and searched the relevant databases and journals. Next, we tracked the references of those papers collected in the first method to look for other publications published in conference proceedings and book chapters. As a result, a sample of 100 papers was collected, studied, and analyzed. We summarize our findings in several areas including enterprise and supply chain resilience definitions, supply chain resilience principles, and supply chain resilience strategies. Based on the assessment, we develop a framework for the principles of supply chain resilience that can be used as a basis for understanding supply chain resilience. (C) 2015 Elsevier B.V. All rights reserved

The impact of flexibility and redundancy on improving supply chain resilience to disruptions

This paper examines the relative impact on supply chain responsiveness of adding flexibility and redundancy. We seek to investigate the effectiveness of flexibility and redundancy in terms of minimising expected supply chain cost and maximizing expected service delivery when a supply chain is exposed to supplier and environmental disruptions. Compared to a supply chain that uses neither strategy and is exposed to supplier and environmental disruptions, both practices reduce expected lost sales, reduce expected total cost, and increase the expected service level. Our results show that the backup-suppliers practice is more effective than the flexible-suppliers practice, as measured by cost reduction and service-level improvement. In addition, we show that a hybrid practice created by adding some level of flexibility to a backup-suppliers practice is more desirable in terms of lowering pre-disruption investment, increasing the responsiveness of the supply chain, and lowering the expected total cost. Moreover, the result of the simulation shows that on average, the proposed stochastic model could decrease the company's total cost by 5.51%. It also shows that planning and investing in proactive demand management could decrease the company's total cost by 7.87%. Our findings provide insights into the contingencies that support implementation of each practice

Call to Duty: Just-in-Time Scheduling in a Restaurant Chain

Just-in-time scheduling has become ubiquitous in the service industries. Although effective in reducing staffing level, hence labor cost, the potential impact of just-in-time scheduling on workers’ productivity and the firm’s revenue is not well understood. Using a data set of 1,444,044 transactions from 25 stores of a full-service casual dining restaurant chain in the United States, we study how just-in-time scheduling impacts worker productivity. We consider two types of just-in-time schedules: (1) short-notice schedules that are assigned to servers shortly before the day of service (mostly two days in our data) and (2) real-time schedules that are assigned to servers on the day of service. We show that short-notice schedules do not harm server productivity overall, but real-time schedules do by 4.4%. Our analysis indicates this may be because servers reduce their up-selling and cross-selling efforts when working on real-time schedules. We then propose an analytical scheduling model that accounts for both the value of staffing flexibility created through just-in-time scheduling and its impact on server productivity to inform the firm how to use just-in-time scheduling to improve profitability. Through a case study, we demonstrate that with the 4.4% productivity loss during the real-time schedules, the managers should shift from the heavy use of real-time scheduling toward scheduling more servers with longer advance notice. Such a shift not only provides more predictable work schedules for the workers but can also improve restaurants’ expected profit by up to 1%, a significant number for the low-margin restaurant industry. This paper was accepted by Vishal Gaur, operations management

Mixing It Up: Operational Impact of Hospitalist Caseload and Case-Mix

Hospitalists are medical doctors that specialize in the care of hospitalized patients, a role that until recently belonged to primary care physicians. We develop an operational model of hospitalist-patient interactions with rounding and responding service modes, optimizing hospitalist caseload and case-mix to achieve the maximal reduction in patient length of stay (LOS). We show that hospitalists are effective at reducing LOS for patients with complex conditions, corroborating intuitive reasoning. However, the optimal hospitalist case-mix also includes "simple" patients with few interventions and short LOS, as they can effectively reduce discharge delays. This actionable insight is particularly salient for small community hospitals with simple, short-stay patients, where hospitalists may be undervalued due to the prevailing belief that they are primarily effective for complex patients. We conduct a comparative case study of a small community hospital and a large academic hospital, drawing a stark contrast between the two in terms of ideal caseload and patient coverage. Despite the fact that the academic hospital treats higher complexity patients, hospitalists at the community hospital should actually have a lower caseload than hospitalists at the academic hospital due to shorter stays in the community hospital. We find that both hospitals are understaffed but for different reasons: the academic hospital needs to staff more hospitalists to reduce the current caseload of its hospitalists, whereas the community hospital needs to staff more hospitalists to expand its hospitalist coverage to more patients. We estimate that these hospitals can save on average $1.5 million annually by implementing the optimal staffing policies