Behavioral Analysis of Service Delivery Models
IBM1
02 Dec 2013-pp 652-666
TL;DR: Results show while Collaborative models are beneficial for highest priority work, Integrated models works best for volume-intensive work, through up-skilling the population with additional skills, and return-on-investment is highest when people have at most two skills.
Abstract: Enterprises and IT service providers are increasingly challenged with the goal of improving quality of service while reducing cost of delivery. Effective distribution of complex customer workloads among delivery teams served by diverse personnel under strict service agreements is a serious management challenge. Challenges become more pronounced when organizations adopt ad-hoc measures to reduce operational costs and mandate unscientific transformations. This paper simulates different delivery models in face of complex customer workload, stringent service contracts, and evolving skills, with the goal of scientifically deriving design principles of delivery organizations. Results show while Collaborative models are beneficial for highest priority work, Integrated models works best for volume-intensive work, through up-skilling the population with additional skills. In repetitive work environments where expertise can be gained, these training costs are compensated with higher throughput. This return-on-investment is highest when people have at most two skills. Decoupled models work well for simple workloads and relaxed service contracts.
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07 Sep 2014
TL;DR: The finding, on the basis of the data, is that experts are more suitable for complex or high priority work with strict service levels than novices, which demonstrates that data driven techniques is useful for making more accurate staffing decisions by understanding worker efficiency derived from the analysis of operational data.
Abstract: Knowledge intensive business services such as IT Services, rely on the expertise of the knowledge workers for performing the activities involved in the delivery of services. The activities performed could range from performing simple, repetitive tasks to resolving more complex situations. The expertise of the task force can also vary from novices who cost less to advanced skill workers and experts who are more expensive. Staffing of service systems relies largely on the assumptions underlying the operational productivity of the workers. Research independently points to the impact of factors such as complexity of work and expertise of the worker on worker productivity. In this paper, we examine the impact of complexity of work, priority or importance of work and expertise of the worker together, on the operational productivity of the worker. For our empirical analysis, we use the data from real-life engagement in the IT service management domain. Our finding, on the basis of the data indicates, not surprisingly, that experts are more suitable for complex or high priority work with strict service levels. In the same setting, when experts are given simpler tasks of lower priority, they tend to not perform better than their less experienced counterparts. The operational productivity measure of experts and novices is further used as an input to a discrete event simulation based optimization framework that model real-life service system to arrive at an optimal staffing. Our work demonstrates that data driven techniques, similar to the one presented here is useful for making more accurate staffing decisions by understanding worker efficiency derived from the analysis of operational data.
9 citations
Cites background from "Behavioral Analysis of Service Deli..."
...These theories can be applied to service delivery principles as well [8]....
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01 Jan 2018
TL;DR: This dissertation investigates the variation in resource efficiencies with varying case attributes, using a log of past execution histories as the evidence base, also demonstrating how data-driven techniques can serve as the basis for methodological guidelines for effective dispatching and staffing policies required to meet the contractual service levels (quality) of the service system and the business process.
Abstract: Business process provisioning involves the allocation of resources (people, technology, or information) to process tasks in order to optimally realize the goals of the process. Resource allocation or task allocation refers to matching the right resource(s) to a task. The allocation of resources to process tasks can have a significant impact on the performance (such as cost, time) of those tasks, and hence of the overall process. While the problem of optimal process provisioning is hard, process execution logs or event logs contain rich information about the task, resource and process outcome. Past resource allocation decisions, when correlated with process execution histories annotated with quality of service (or performance) measures, can be a rich source of knowledge about the best resource allocation decisions. This dissertation offers a number of different approaches to support data-driven business process provisioning. In complex and knowledge intensive processes and services, human process participants (resources) often play a critical role. Process execution data from a range of sources suggest that human workers with the same organizational role and capabilities can have heterogeneous efficiencies based on their operational context. This dissertation investigates the variation in resource efficiencies with varying case attributes (or process instance attributes), using a log of past execution histories as the evidence base, also demonstrating how data-driven techniques can serve as the basis for methodological guidelines for effective dispatching and staffing policies required to meet the contractual service levels (quality) of the service system and the business process. This evidence bases also suggests that the optimality of resource allocation decisions is not determined by the process instance alone, but also by the context in which these instances are executed. Current approaches on resource allocation have not considered process context, case attributes and resource efficiency together. In this dissertation, a context model that considers resource behaviors is defined to support process provisioning. A range of approaches are proposed to support different dispatching scenarios such as pull-based dispatching and push-based dispatching. These methods use the process context, resource context as well as the functional goals and Quality of Service (QoS) requirements of past process executions to derive
3 citations
Cites background from "Behavioral Analysis of Service Deli..."
...[74], [75] Enhancement Team allocation 7 3 7 3 3 (predictive model for team identifcation) [78],[77] Enhancement Team organization 7 3 7 3 3 (Simulation based analysis ) [20], [21], [22] Enhancement Process performance patterns 7 3 3 3 7...
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...Similar study on team organization compares the navigation of a service request (SR) or a work item through various teams [78]....
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16 Nov 2015
TL;DR: The role that data (specifically service execution histories) can play in identifying optimal policies for allocating service tasks to service workers is explored and use of data is demonstrated to generate critical insights on resource behavior and efficiencies that can further aid in improving task assignment to resources.
Abstract: Service organizations increasingly depend on the operational efficiency of human resources for effective service delivery. Hence, designing work assignment policies that improve efficiency of resources is important. This paper explores the role that data (specifically service execution histories) can play in identifying optimal policies for allocating service tasks to service workers. Using data from the telecommunications domain, we investigate the impact of assigning similar and distinct tasks within the temporal frames of a day, across days and a week. We find that similar work, when done within a day, significantly improves the efficiency of workers. However, workers working on distinct tasks across days also have higher efficiency. We build a simulation model of the service system under study, to gain insights into the dispatch policy considering similarity and variety of tasks assigned. Our work demonstrates use of data to generate critical insights on resource behavior and efficiencies, that can further aid in improving task assignment to resources.
1 citations
01 Jan 2015
TL;DR: In this article, the authors use data from process execution logs to identify resource allocations that have resulted in an expected service quality, to guide future resource allocations, and build a learning model using Support Vector Machine (SVM) that predicts the quality of service for specific allocation of tasks to workers.
Abstract: Effective and efficient delivery of services requires tasks to be allocated to appropriate and available set of resources. Much of the research in task allocation, model a system of tasks and resources and determine which tasks should be executed by which resources. These techniques when applied to service systems with human resources, model parameters that can be explicitly identified, such as worker efficiency, worker capability based on skills and expertise, authority derived from organizational positions and so on. However, in real-life workers have complex behaviors with varying efficiencies that are either unknown or are increasingly complex to model. Hence, resource allocation models that equate human performance to device or machine performance could provide inaccurate results. In this paper we use data from process execution logs to identify resource allocations that have resulted in an expected service quality, to guide future resource allocations. We evaluate data for a service system with 40 human workers for a period of 8 months. We build a learning model using Support Vector Machine (SVM), that predicts the quality of service for specific allocation of tasks to workers. The SVM based classifier is able to predict service quality with 80 % accuracy. Further, a latent discriminant classifier, uses the number of tasks pending in a worker’s queue as a key predictor, to predict the likelihood of allocating a new incoming request to the worker. A simulation model that incorporates the dispatching policy based on worker’s pending tasks shows an improved service quality and utilization of service workers.
1 citations
IBM1
TL;DR: In this paper, the authors discuss different dimensions one can organize service delivery by and recommend patterns based on customer profiles, business functions technologies, geographies and operational characteristics, with the large variations in the technical and domain skills required to address customer requirements.
Abstract: The organizations in the business of IT service delivery have conventionally adopted the team structure of dedicated customer teams to deliver services. A dedicated team is assigned to address all requirements that are specific to the customer. However, this way of organizing service delivery leads to inefficiencies in using expertise and available resources across teams in a flexible manner. In contrast the shared services model became very popular in the last decade, but soon faced challenges of losing customer focus. This gives rise to the question of what is the best way of grouping shared resources across customers. Especially, with the large variations in the technical and domain skills required to address customer requirements, what should be the service delivery model for diverse customer profile? This chapter looks at different dimensions one can organize delivery by and recommends patterns based on customer profiles, business functions technologies, geographies and operational characteristics.
1 citations
References
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IBM1
TL;DR: A science of service systems could provide theory and practice around service innovation in the service sector.
Abstract: The service sector accounts for most of the world's economic activity, but it's the least-studied part of the economy. A service system comprises people and technologies that adaptively compute and adjust to a system's changing value of knowledge. A science of service systems could provide theory and practice around service innovation
1,282 citations
TL;DR: Notably, task and team familiarity are more substitutive than complementary in their joint effects on team performance: Task familiarity improves team performance more strongly when team familiarity is weak and vice versa.
Abstract: While prior research has found that familiarity is beneficial to team performance, it is not clear whether different kinds of familiarity are more or less beneficial when the work has different types of complexity. In this paper, we theorize how task and team familiarity interact with task and team coordination complexity to influence team performance. We posit that task familiarity is more beneficial with more complex tasks (i.e., tasks that are larger or with more complex structures) and that team familiarity is more beneficial when team coordination is more difficult (i.e., for larger or geographically dispersed teams). Finally, we propose that the effects of task familiarity and team familiarity on team performance are complementary. Based on a field study of geographically distributed software teams, two of our hypotheses are disconfirmed: Our results show that the beneficial effects of task familiarity decline when tasks are more structurally complex and are independent of task size. Conversely, the hypotheses for team familiarity are confirmed as the benefit of team familiarity for team performance is enhanced when team coordination is more challenging---i.e., when teams are larger or geographically dispersed. Finally, surprisingly, we find that task and team familiarity are more substitutive than complementary in their joint effects on team performance: Task familiarity improves team performance more strongly when team familiarity is weak and vice versa. Our study contributes by revealing how different types of familiarity can enhance team performance in a real-world setting where the task and its coordination can be highly complex.
415 citations
TL;DR: Three interrelated frameworks are presented as a first attempt to define the fundamentals of service systems, which can be applied together to describe, analyze, and study how service systems are created, how they operate, and how they evolve through a combination of planned and unplanned change.
Abstract: Service systems produce all services of significance and scope, yet the concept of a service system is not well articulated in the service literature. This paper presents three interrelated frameworks as a first attempt to define the fundamentals of service systems. These frameworks identify basic building blocks and organize important attributes and change processes that apply across all service systems. Although relevant regardless of whether a service system uses information technology, the frameworks are also potentially useful in visualizing the realities of moving toward automated service architectures. This paper uses two examples, one largely manual and one highly automated, to illustrate the potential usefulness of the three frameworks, which can be applied together to describe, analyze, and study how service systems are created, how they operate, and how they evolve through a combination of planned and unplanned change.
355 citations
TL;DR: An iterative cutting-plane algorithm on an integer program for minimizing the staffing costs of a multiskill call center subject to service-level requirements that are estimated by simulation is studied.
Abstract: We study an iterative cutting-plane algorithm on an integer program for minimizing the staffing costs of a multiskill call center subject to service-level requirements that are estimated by simulation. We solve a sample average version of the problem, where the service levels are expressed as functions of the staffing for a fixed sequence of random numbers driving the simulation. An optimal solution of this sample problem is also an optimal solution to the original problem when the sample size is large enough. Several difficulties are encountered when solving the sample problem, especially for large problem instances, and we propose practical heuristics to deal with these difficulties. We report numerical experiments with examples of different sizes. The largest example corresponds to a real-life call center with 65 types of calls and 89 types of agents (skill groups).
195 citations
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121 citations