Mark P. Van Oyen

Benefits of Skill Chaining in Serial Production Lines with Cross-Trained Workers

To gain insight into the potential logistical benefits of worker cross-training and agile workforce policies, we study simple models of serial production systems with flexible servers operating under a constant work-in-process (CONWIP) release policy. Two important and interrelated issues are: (a) how to decide which skill(s) are strategically most desirable for workers to gain, and (b) how to coordinate these workers to respond dynamically to congestion. We address these by considering two cross-training strategies: a straightforward capacity-balancing approach, which we call cherry picking (CP), and an innovative overlapping zone strategy that we call skill chaining . Our comparison shows that skill-chaining strategies have the potential to be robust and efficient methods for implementing workforce agility in serial production lines.

Agile Workforce Evaluation: A Framework for Cross-training and Coordination *

This paper outlines approaches for assessing and classifying manufacturing and service operations in terms of their suitability for use of cross-trained (flexible) workers. We refer to our overall framework as agile workforce evaluation. The primary contributions of this paper are: (i) a strategic assessment framework that structures the key mechanisms by which cross-training can support organizational strategy; (ii) a tactical framework that identifies key factors to guide the selection of an architecture and worker coordination policy for implementing workforce agility; (iii) a classification of workforce agility architectures; (iv) a survey of a broad range of archetypical classes of worker coordination policies; (v) a survey of the literature with an operational perspective on workforce agility; and (vi) identification of opportunities for research and development of architectures for specific production environments.

Performance Opportunity for Workforce Agility in Collaborative and Noncollaborative Work Systems

To gain insight into the potential logistical benefits of worker cross-training and agile workforce policies, we study simple models of flexible workers in serial production systems. The primary control issue is how to assign workers to jobs/stations over time. Under assumptions of complete worker flexibility and collaborative work, we prove that a simple expedite policy minimizes along each sample path the cycle time (delay) for each job. Therefore, the expedite policy also minimizes work in process and maximizes throughput along every sample path. We also compute the performance improvement opportunity achievable using flexible workers as opposed to the optimal static allocation of workers. This enables us to examine the factors that make workforce agility a potentially attractive strategy. We also consider the intuitive analog of the expedite policy for the noncollaborative work environment, which we call the pick-and-run policy; however, we demonstrate by counterexample that it is not always optimal. Finally, we extend some of our insights from the demand-constrained environment to a capacity-constrained environment operating under a CONstant WIP (CONWIP) protocol.

Patient Streaming as a Mechanism for Improving Responsiveness in Emergency Departments

Crisis-level overcrowding conditions in emergency departments EDs have led hospitals to seek out new patient-flow designs to improve both responsiveness and safety. One approach that has attracted attention and experimentation in the emergency medicine community is a system in which ED beds and care teams are segregated and patients are “streamed” based on predictions of whether they will be discharged or admitted to the hospital. In this paper, we use a combination of analytic and simulation models to determine whether such a streaming policy can improve ED performance, where it is most likely to be effective, and how it should be implemented for maximum performance. Our results suggest that the concept of streaming can indeed improve patient flow, but only in some situations. First, ED resources must be shared across streams rather than physically separated. This leads us to propose a new “virtual-streaming” patient flow design for EDs. Second, this type of streaming is most effective in EDs with 1 a high percentage of admitted patients, 2 longer care times for admitted patients than discharged patients, 3 a high day-to-day variation in the percentage of admitted patients, 4 long patient boarding times e.g., caused by hospital “bed-block”, and 5 high average physician utilization. Finally, to take full advantage of streaming, physicians assigned to admit patients should prioritize upstream new patients, whereas physicians assigned to discharge patients should prioritize downstream old patients.

Call-Center Labor Cross-Training: It's a Small World After All

It is well known that flexibility can be created in manufacturing and service operations by using multipurpose production sources such as cross-trained labor, flexible machines, or flexible factories. We focus on flexible service centers, such as inbound call centers with cross-trained agents, and model them as parallel queueing systems with flexible servers. We propose a new approach to analyzing flexibility arising from the multifunctionality of sources of production. We create a work sharing (WS) network model for which its average shortest path length (APL) metric can predict the more effective of two alternative cross-training structures in terms of customer waiting times. We show that the APL metric of small world network (SWN) theory is one simple deterministic solution approach to the complex stochastic problem of designing effective workforce cross-training structures in call centers.

Complexity-Augmented Triage: A Tool for Improving Patient Safety and Operational Efficiency

Hospital emergency departments (EDs) typically use triage systems that classify and prioritize patients almost exclusively in terms of their need for timely care. Using a combination of analytic and simulation models, we demonstrate that adding an up-front estimate of patient complexity to conventional urgency-based classification can substantially improve both patient safety (by reducing the risk of adverse events) and operational efficiency (by shortening the average length of stay). Moreover, we find that EDs with high resource (physician and/or examination room) utilization, high heterogeneity in the treatment time between simple and complex patients, and a relatively equal number of simple and complex patients benefit most from complexity-augmented triage. Finally, we find that (1) although misclassification of a complex patient as simple is slightly more harmful than vice versa, complexity-augmented triage is relatively robust to misclassification error rates as high as 25p; (2) streaming patients based on complexity information and prioritizing them based on urgency is better than doing the reverse; and (3) separating simple and complex patients via streaming facilitates the application of lean methods that can further amplify the benefit of complexity-augmented triage.

Optimal dynamic assignment of a flexible worker on an open production line with specialists

This paper models and analyzes serial production lines with specialists at each station and a single, cross-trained floating worker who can work at any station. We formulate Markov decision process models of K-station production lines in which (1) workers do not collaborate on the same job, and (2) two workers can work at the same task/workstation on different jobs at the same time. Our model includes holding costs, set-up costs, and set-up times at each station. We rigorously compute finite state regions of an optimal policy that are valid with an infinite state space, as well as an optimal average cost and the worker utilizations. We also perform a numerical study for lines with two and three station. Computations and bounds insightfully expose the performance opportunity gained through capacity balancing and variability buffering. � 2004 Published by Elsevier B.V.

Factors affecting opportunity of worksharing as a dynamic line balancing mechanism

We consider the problem of optimal worksharing between two adjacent workers each of whom processes a fixed task in addition to their shared task(s). We use a Markov Decision Process (MDP) model to compute optimal policies and provide a benchmark for evaluating threshold policy heuristics. Our approach differs from previous studies of dynamic line balancing in that we focus on system architecture factors that affect the performance improvement opportunity possible through worksharing relative to a traditional static worker allocations, as well as practical heuristics for worksharing. We find that three such factors are significant whether we use an optimal or a heuristic control policy: ability to preempt the shared task, granularity of the shared task and overall variability of the task times. Understanding the role of these factors in a given production environment provides a means for determining where and how worksharing can have significant logistical benefits.

Stochastic scheduling of parallel queues with set-up costs

We consider the problem of allocating a single server to a system of queues with Poisson arrivals. Each queue represents a class of jobs and possesses a holding cost rate, general service distribution, and a set-up cost. The objective is to minimize the expected cost due to the waiting of jobs and the switching of the server. A set-up cost is required to effect an instantaneous switch from one queue to another. We partially characterize an optimal policy and provide a simple heuristic scheduling policy. The heuristics performance is evaluated in the cases of two and three queues by comparison with a numerically obtained optimal policy. Simulation results are provided to demonstrate the effectiveness of our heuristic over a wide range of problem instances with four queues.

OPTIMALITY OF INDEX POLICIES FOR STOCHASTIC SCHEDULING WITH SWITCHING PENALTIES

We investigate the impact of switching penalties on the nature of optimal scheduling policies for systems of parallel queues without arrivals. We study two types of switching penalties incurred when switching between queues: lump sum costs and time delays. Under the assumption that the service periods of jobs in a given queue possess the same distribution, we derive an index rule that defines an optimal policy. For switching penalties that depend on the particular nodes involved in a switch, we show that although an index rule is not optimal in general, there is an exhaustive service policy that is optimal.