Cross‐Training with Imperfect Training Schemes

Cross‐training workers is one of the most efficient ways of achieving flexibility in manufacturing and service systems for increasing responsiveness to demand variability. However, it is generally the case that cross‐trained employees are not as productive on a specific task as employees who were originally trained for that task. Also, the productivity of the cross‐trained workers depends on when they are cross‐trained. In this work, we consider a two‐stage model to analyze the effects of variations in productivity levels on cross‐training policies. We define a new metric called achievable capacity and show that it plays a key role in determining the structure of the problem. If cross‐training can be done in a consistent manner, the achievable capacity is not affected when the training is done, which implies that the cross‐training decisions are independent of the opportunity cost of lost demand and are based on a trade‐off between cross‐training costs at different times. When the productivities of workers trained at different times differ, there is a three‐way trade‐off between cross‐training costs at different times and the opportunity cost of lost demand due to lost achievable capacity. We analyze the effects of variability and show that if the productivity levels of workers trained at different times are consistent, the decision maker is inclined to defer the cross‐training decisions as the variability of demand or productivity levels increases. However, when the productivities of workers trained at different times differ, an increase in the variability may make investing more in cross‐training earlier more preferable.     

On‐Call Overtime for Service Workforce Scheduling when Demand Is Uncertain

This article builds on prior research to develop shift scheduling models that include on‐call overtime for service environments where demand is uncertain. The research is motivated by recent developments in nurse scheduling, such as laws prohibiting mandatory overtime and the popularity of self‐scheduling systems. For single‐period scenarios, models are developed, solution methods are described, and results are explored for a variety of environments. Results show that the use of on‐call overtime can reduce costs slightly, with the amount of savings dependent on characteristics of the scheduling environment. The factor that most significantly affects cost savings is the cost of outside agency workers relative to overtime workers. In addition to lowering costs, on‐call overtime greatly reduces reliance on outside agency workers, which can have important practical implications in terms of quality of service and workforce morale. Results based on single‐period models motivate multiperiod formulations for single‐ and multidepartment scenarios, and solution methods are outlined for those cases. The possibility of using multiperiod models within a rolling horizon framework with forecast updating is discussed. This goes along with an extension of the traditional workforce management hierarchy that separates overtime and regular‐time scheduling, as seen in practice with self‐scheduling and shift‐bidding systems.     

Schedule Recovery: Unplanned Absences in Service Operations*

The U.S. service sector loses 2.3% of all scheduled labor hours to unplanned absences, but in some industries, the total cost of unplanned absences approaches 20% of payroll expense. The principal reasons for unscheduled absences (personal illness and family issues) are unlikely to abate anytime soon. Despite this, most labor scheduling systems continue to assume perfect attendance. This oversight masks an important but rarely addressed issue in services management: how to recover from short‐notice, short‐term reductions in planned capacity. In this article, we model optimal responses to unplanned employee absences in multi‐server queueing systems that provide discrete, pay‐per‐use services for impatient customers. Our goal is to assess the performance of alternate absence recovery strategies under various staffing and scheduling regimes. We accomplish this by first developing optimal labor schedules for hypothetical service environments with unreliable workers. We then simulate unplanned employee absences, apply an absence recovery model, and compute system profits. Our absence recovery model utilizes recovery strategies such as holdover overtime, call‐ins, and temporary workers. We find that holdover overtime is an effective absence recovery strategy provided sufficient reserve capacity (maximum allowable work hours minus scheduled hours) exists. Otherwise, less precise and more costly absence recovery methods such as call‐ins and temporary help service workers may be needed. We also find that choices for initial staffing and scheduling policies, such as planned overtime and absence anticipation, significantly influence the likelihood of successful absence recovery. To predict the effectiveness of absence recovery policies under alternate staffing/scheduling strategies and operating environments, we propose an index based on initial capacity reserves.     

SCHEDULING FLEXIBILITY IN THE SERVICE SECTOR: A POSTAL CASE STUDY

This is a case study of workforce scheduling in the U.S. postal system. We use it to analyze the benefits of scheduling flexibility at postal distribution systems, which can come from several sources. We focus on the additional flexibility deriving from increasing the proportion of part-time employees, as well as from increasing the cross-training of part-time employees. These two dimensions of scheduling flexibility are decision points of particular interest to the postal management. We used a large-scale simulation model of the dynamic functioning of the postal distribution system in conjunction with a staff-scheduling model to obtain insights into policy issues of interest. Our results show that the operating environment at a distribution station affects cost and customer service performance, and that gains from greater scheduling flexibility are situationally dependent. The benefit of cross-training part-time workers is modest over the range of levels considered realistic for distribution stations. Our overall recommendation is for postal managers to increase the proportion of part-time employees, allowing more efficient matching of resources with the varying demands for mail delivery on different routes. For the range of conditions we modeled, cost reductions from more part-time workers average over six percent when capacity utilization is low. Customer service improvements are even larger, and range between 20 and 43% when capacity utilization is high. Improvements at the upper end of the range are achieved when demand variability is also at its highest.     

Shop Floor Control When Tacit Worker Knowledge is Important

We investigate a shop where the workers and supervisors have tacit knowledge of how to operate efficiently and where efficiency is important to providing capacity to meet demand. This tacit knowledge includes setup dependencies between products as well as which worker or machine is best suited for a particular product. We discuss a real‐world shop where this is the case. Management expects workers and supervisors to use their knowledge to schedule efficiently by monitoring their performance based on standards. The question that we explore is how management should control for due date performance in light of the discretion given to the workers and supervisors to sequence jobs on the basis of efficiency. We explore management control of due date performance through the use of order review and release (ORR) and management expediting. We find that although ORR is quite effective at reducing work‐in‐process (WIP) inventories, it may foster very late deliveries in a shop such as this. In fact, under such conditions, deftly executed expediting with no ORR at all can be far more effective at supporting all deliveries. Even improving ORR into a hybrid by actively updating path efficiencies (observed from supervisor/worker scheduling) did not support a change to this conclusion. Conversely, when conditions are created where tacit knowledge plays a reduced role or utilization is decreased, ORR delivers in a timely manner. The interaction between utilization, WIP levels, and worker knowledge all help dictate the appropriate control methodology.     

Reexamining the Nurse Scheduling Problem: Staffing Ratios and Nursing Shortages*

Legislators at the state and national levels are addressing renewed concerns over the adequacy of hospital nurse staffing to provide quality care and ensure patient safety. At the same time, the well‐known nursing shortage remains an ongoing problem. To address these issues, we reexamine the nurse scheduling problem and consider how recent health care legislation impacts nursing workforce management decisions. Specifically, we develop a scheduling model and perform computational experiments to evaluate how mandatory nurse‐to‐patient ratios and other policies impact schedule cost and schedule desirability (from the nurses' perspective). Our primary findings include the following: (i) nurse wage costs can be highly nonlinear with respect to changes in mandatory nurse‐to‐patient ratios of the type being considered by legislators; (ii) the number of undesirable shifts can be substantially reduced without incurring additional wage cost; (iii) more desirable scheduling policies, such as assigning fewer weekends to each nurse, have only a small impact on wage cost; and (iv) complex policy statements involving both single‐period and multiperiod service levels can sometimes be relaxed while still obtaining good schedules that satisfy the nurse‐to‐patient ratio requirements. The findings in this article suggest that new directions for future nurse scheduling models, as it is likely that nurse‐to‐patient ratios and nursing shortages will remain a challenge for health care organizations for some time.     

Real-time schedule adjustment decisions: a case study

Service capacity management has been extensively studied and successfully applied in many industries, with an emphasis on tour/shift scheduling and assignment decisions. However, few studies have addressed real-time work schedule adjustment decisions made necessary by demand uncertainty and/or labor supply disruption. This case study deals with the real-time work schedule adjustment decision and investigates the association of managerial experience, workforce mix (full- and part-time staff), and information accuracy with managers’ adjustment decisions. We designed an experiment that involved practicing service managers from a McDonald's franchise who provided their adjustment decisions for a given set of test scenarios. Their decisions were analyzed and evaluated via a controlled experiment. Using profitability as the primary performance measure, the study identifies the following outcomes: First, senior managers of a store with a higher proportion of part-time staff made more adjustments and attained slightly higher profitability than junior managers, when all employees accepted adjusted schedules. Second, managers of a store with a higher proportion of part-time staff were able to make slightly higher profits, particularly when capacity shortages occurred. And third, to achieve the majority of the benefits from schedule adjustments, it is sufficient to search for information that correctly identifies the direction of demand changes, rather than identifying the exact magnitude of the changes.     

Home healthcare integrated staffing and scheduling

Workforce planning for home healthcare represents an important and challenging task involving complex factors associated with labor regulations, caregivers’ preferences, and demand uncertainties. This task is done manually by most home care agencies, resulting in long planning times and suboptimal decisions that usually fail to meet the health needs of the population, to minimize operating costs, and to retain current caregivers. Motivated by these challenges, we present a two-stage stochastic programming model for employee staffing and scheduling in home healthcare. In this model, first-stage decisions correspond to the staffing and scheduling of caregivers in geographic districts. Second-stage decisions are related to the temporary reallocation of caregivers to neighboring districts, to contact caregivers to work on a day-off, and to allow under- and over-covering of demand. The proposed model is tested on real-world instances, where we evaluate the impact on costs, caregiver utilization, and service level by using different recourse actions. Results show that when compared with a deterministic model, the two-stage stochastic model leads to significant cost savings as staff dimensioning and scheduling decisions are more robust to accommodate changes in demand. Moreover, these results suggest that flexibility in terms of use of recourse actions is highly valuable as it helps to further improve costs, service level, and caregiver utilization.     

Sequential and Simultaneous Decision Making for Optimizing Health Care Resource Flexibilities

Health care administrators commonly employ two types of resource flexibilities (demand upgrades and staffing flexibility) to efficiently coordinate two critical internal resources, nursing staff and beds, and an external resource (contract nurses) to satisfy stochastic patient demand. Under demand upgrades, when beds are unavailable for patients in a less acute unit, patients are upgraded to a more acute unit if space is available in that unit. Under staffing flexibility, nurses cross‐trained to work in more than one unit are used in addition to dedicated and contract nurses. Resource decisions (beds and staffing) can be made at a single point in time (simultaneous decision making) or at different points in time (sequential decision making). In this article, we address the following questions: for each flexibility configuration, under sequential and simultaneous decision making, what is the optimal resource level required to meet stochastic demand at minimum cost? Is one type of flexibility (e.g., demand upgrades) better than the other type of flexibility (e.g., staffing flexibility)? We use two‐stage stochastic programming to find optimal resource levels for two nonhomogeneous hospital units that face stochastic demand following a continuous, general distribution. We conduct a full‐factorial numerical experiment and find that the benefit of using staffing flexibility on average is greater than the benefit of using demand upgrades. However, the two types of flexibilities have a positive interaction effect and they complement each other. The type of flexibility and decision timing has an independent effect on system performance (capacity and staffing costs). The benefits of cross‐training can be largely realized even if beds and staffing levels have been determined prior to the establishment of a cross‐training initiative.     

Optimal Workforce Mix in Service Systems with Two Types of Customers

We consider a service system with two types of customers. In such an environment, the servers can either be specialists (or dedicated) who serve a specific customer type, or generalists (or flexible) who serve either type of customers. Cross‐trained workers are more flexible and help reduce system delay, but also contribute to increased service costs and reduced service efficiency. Our objective is to provide insights into the choice of an optimal workforce mix of flexible and dedicated servers. We assume Poisson arrivals and exponential service times, and use matrix‐analytic methods to investigate the impact of various system parameters such as the number of servers, server utilization, and server efficiency on the choice of server mix. We develop guidelines for managers that would help them to decide whether they should be either at one of the extremes, i.e., total flexibility or total specialization, or some combination. If it is the latter, we offer an analytical tool to optimize the server mix.     

The Employment–Hours Trade‐off: Theory and an Application to the Portuguese Case

Abstract. Using traditional production theory, it is possible to estimate production functions in which hours per worker and number of workers hired are treated as endogenous and chosen by the firm, priced respectively by the variable hourly wage and the so‐called quasi‐fixed unit cost. The available data suggested the use of Cobb–Douglas or CES specifications or the use of two‐stage separable technologies, with a second‐level Cobb–Douglas being possible. Quasi‐fixed costs were associated with legal social security payments, which were also conditioned by the data. The estimates for the Portuguese metal products and engineering industries — using cross‐section evidence for 1987 — showed that disaggregation of the labor input is empirically justifiable, and total man‐hours employed and the numbers of workers hired are complements in production. Hours per worker respond negatively to the variable hourly wage and positively to the quasi‐fixed unit cost. Nevertheless, in our framework, this would be so by construction. Total labor costs increase with both the hourly wage and the quasi‐fixed unit costs.     

Optimizing the Staffing and Routing of Small‐Size Hierarchical Call Centers

Multiple‐skill call centers propagate rapidly with the development of telecommunications. An abundance of literature has already been published on call centers. Here, we want to focus on centers that would typically occur in business‐to‐business environments; these are call centers that handle many types of calls but where the arrival rate for each type is low. To find an optimal configuration, the integrality of the decision variables is a much more important issue than for larger call centers. The present paper proposes an approach that uses elements of combinatorial optimization to find optimal configurations. We develop an approximation method for the evaluation of the service performance. Next, we search for the minimum‐cost configuration subject to service‐level constraints using a branch‐and‐bound algorithm. What is at stake is to find the right balance between gains resulting from the economies of scale of pooling and the higher cost or cross‐trained agents. The article shows that in most cases this method significantly decreases the staffing cost compared with configurations with only cross‐trained or dedicated operators.     

Focusing on Customer Time in Field Service: A Normative Approach

Although customer convenience should be rightfully considered a central element in field services, the customer experience suggests that service enterprises rarely take the customer's preferred time into account in making operational and scheduling decisions. In this paper we present the results of our exploratory research into two interrelated topics: the explicit inclusion of customer time in nonemergency field service delivery decisions and the analysis of trade‐off between the customer's convenience and field service provider's cost. Based on prior research in service quality we identify and illustrate two time‐based performance metrics that are particularly appropriate for assessing service quality in nonemergency field services: performance and conformance quality. To determine vehicle routes, we develop a hybrid heuristic derived from the existing and proven heuristic methods. A numerical example closely patterned after real‐life data is generated and used within a computational experiment to investigate alternate policies for promise time windows. Our experiment shows that over a reasonable range of customer cost parameters the policy of shorter promise time windows reduces the combined total cost incurred by the provider and the customers and should be considered a preferred policy by the field service provider. Managerial implications of this result are discussed.     

Supply Performance in Multi‐Echelon Inventory Systems with Intermediate Product Demand: A Perspective on Allocation

In this article, we study the performance of multi‐echelon inventory systems with intermediate, external product demand in one or more upper echelons. This type of problem is of general interest in inventory theory and of particular importance in supply chain systems with both end‐product demand and spare parts (subassemblies) demand. The multi‐echelon inventory system considered here is a combination of assembly and serial stages with direct demand from more than one node. The aspect of multiple sources of demands leads to interesting inventory allocation problems. The demand and capacity at each node are considered stochastic in nature. A fixed supply and manufacturing lead time is used between the stages. We develop mathematical models for these multi‐echelon systems, which describe the inventory dynamics and allow simulation of the system. A simulation‐based inventory optimization approach is developed to search for the best base‐stock levels for these systems. The gradient estimation technique of perturbation analysis is used to derive sample‐path estimators. We consider four allocation schemes: lexicographic with priority to intermediate demand, lexiographic with priority to downstream demand, predetermined proportional allocation, and proportional allocation. Based on the numerical results we find that no single allocation policy is appropriate under all conditions. Depending on the combinations of variability and utilization we identify conditions under which use of certain allocation polices across the supply chain result in lower costs. Further, we determine how selection of an inappropriate allocation policy in the presence of scarce on‐hand inventory could result in downstream nodes facing acute shortages. Consequently we provide insight on why good allocation policies work well under differing sets of operating conditions.     

Identifying Demand and Supply of Part‐time Jobs Using Personnel Data. An Application to Italy

The percentage of part‐time workers in Italy is very low compared with most European countries. In this paper we try to contribute to an explanation. We use data on the employees of a large Italian company operating in the service sector, and apply a particular econometric framework that allows identification of potential demand and supply. We find that demand and supply are potentially very large on average, but they are difficult to match at the individual worker/job level. The firm might observe a relevant employee’s characteristics that are positively correlated with the employee’s propensity to a part‐time job but are negatively correlated with the profitability of that employee on that job. The firm might also use the revealed willingness to switch to a part‐time job as a sign that the employee is likely to be unprofitable for the company.     

Managing Retail Budget Allocation between Store Labor and Marketing Activities

The performance of a retail store depends on its ability to attract customer traffic, match labor with incoming traffic, and convert the incoming traffic into sales. Retailers make significant investments in marketing activities (such as advertising) to bring customers into their stores and in‐store labor to convert that traffic into sales. Thus, a common trade‐off that retail store managers face concerns the allocation of a store's limited budget between advertising and labor to enhance store‐level sales. To explore that trade‐off, we develop a centralized model to allocate limited store budget between store labor and advertising with the objective of maximizing store sales. We find that a store's inherent potential to drive traffic plays an important role, among other factors, in the relative allocation between advertising and store labor. We also find that as advertising instruments become more effective in bringing traffic to stores, managers should not always capitalize this effectiveness by increasing their existing allocations to advertising. In addition, we discuss a decentralized setting where budget allocation decisions cannot be enforced by a store manager and present a simple mechanism that can achieve the centralized solution. In an extension, we address the budget allocation problem in the presence of marketing efforts to shift store traffic from peak to off peak hours and show that our initial findings are robust. Further, we illustrate how the solution from the budget allocation model can be used to facilitate store level sales force planning/scheduling decisions. Based on the results of our model, we present several insights that can help managers in budget allocation and sales force planning.     

The Effect of Integrated Scheduling and Capacity Policies on Clinical Efficiency

In outpatient healthcare clinics, capacity, patient flow, and scheduling are rarely managed in an integrated fashion, so a question of interest is whether clinic performance can be improved if the policies that guide these decisions are set jointly. Despite the potential importance of this issue, we find surprisingly few studies that look at how the allocation of capacity, paired with various appointment scheduling policies and different patient flow configurations, affects patient flow and clinical efficiency. In this paper, we develop an empirically based discrete‐event simulation to examine the interactions between patient appointment policies and capacity allocation policies (i.e., the number of available examination rooms) and how they jointly affect various performance measures, such as resource utilization and patient waiting time. Findings suggest that scheduling lower‐variance, shorter appointments earlier in the clinic (and, conversely, higher‐variance, longer appointments later) results in less overall patient waiting without reducing physician utilization or increasing clinic duration. Additionally, exam rooms exhibited classic bottleneck behavior: there was no effect on physician utilization by adding exam rooms beyond a certain threshold, but too few exam rooms were devastating to clinic throughput. Some significant interactions between these variables were observed, but were not influential to the level of managerial concern. Clinicians' intuition about managing capacity in healthcare settings may differ substantially from best policies.     

Intelligent Procedures for Intra‐Day Updating of Call Center Agent Schedules

For nearly all call centers, agent schedules are typically created several days or weeks before the time that agents report to work. After schedules are created, call center resource managers receive additional information that can affect forecasted workload and resource availability. In particular, there is significant evidence, both among practitioners and in the research literature, suggesting that actual call arrival volumes early in a scheduling period (typically an individual day or week) can provide valuable information about the call arrival pattern later in the same scheduling period. In this paper, we develop a flexible and powerful heuristic framework for managers to make intra‐day resource adjustment decisions that take into account updated call forecasts, updated agent requirements, existing agent schedules, agents' schedule flexibility, and associated incremental labor costs. We demonstrate the value of this methodology in managing the trade‐off between labor costs and service levels to best meet variable rates of demand for service, using data from an actual call center.     

Workforce Age and Innovation: A Literature Survey

The relationship between age and creative performance has been found to follow a hump‐shaped profile in the arts and sciences, and in great technological achievement. Accordingly, accelerating workforce aging raises concern about whether future capacity to innovate is endangered. This paper provides a literature survey and critical discussion of existing studies exploring the effects of age on innovative performance, both at the individual and the firm level. However, as individual‐level evidence and some of the firm‐level studies are of purely cross‐sectional nature, the existing results have to be interpreted with caution owing to the presence of selectivity biases and unobserved heterogeneity. Studies at the aggregate level of firms address some of these issues. In particular, the scarce longitudinal evidence reveals that it is very likely that older workers fare much better in innovation than previous cross‐sectional evidence suggests. Moreover, up to now, the literature survey does not find conclusive evidence that a youth‐centred human resource strategy (always) fosters innovation. Apart from integrating the existing empirical evidence on different levels of aggregation, a strong focus of this paper is on methodological challenges in the empirical study of workforce age and innovation and it aims to offer a sound conceptual and methodological basis for further studies in this field of management research.     

Real‐Time Work Schedule Adjustment Decisions: An Investigation and Evaluation

Service managers often find that available worker capacity does not match with actual demand during a given day. They then must attempt to modify the planned work schedule to improve service and increase profitability. This study, which defines such a setting as the real‐time work schedule adjustment decision, pr oposes mathematical formulations of the real‐time adjustment and develops efficient heuristic approaches for this decision. The study evaluates the relative effectiveness of these heuristics versus experienced service managers, investigates the effect of the degree of schedule adjustment on profitability, and assesses the effect of demand forecast update errors on the performance of the schedule adjustment efforts. First, the results indicate that the computer based heuristics achieve higher profit improvement than experienced managers. Second, there is a trade‐off between schedule stability and profitability so that more extensive schedule revisions (efficiency first heuristics) generally result in higher profitability. However, the incremental return on schedule changes is diminishing. Third, we find that active adjustments of work schedules are beneficial as long as the direction of demand change is accurately identified.