Organizational Models for Service Delivery

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.

Using Visual Analytics to Diagnose Productivity and Quality Issues on IT Service Pools

This chapter describes and validates the applicability of a visual analytics tool, named Workload Profile Analytics (WPA), in the context of incident management (IM) inside a large IT services provider. The WPA tool implements a method to evaluate the performance and quality of IM systems that uses temporal information from incident records (tickets). The method is based on a graphical representation, called Workload Profile Chart (WPC), which consists of a log-log chart obtained by computing and plotting the spreading of ticket data. Then, using the chart, a visual inspection is done to examine the concentration of tickets over the various areas of interest of the WPC and a diagnosis of potential issues is suggested. To illustrate how powerful WPA can be, two real world scenarios are depicted and used to validate the use of WPA in a large service delivery organization. Finally, some discussions about further improvements on the tool and research directions are done.

A Mashup-Based Approach to Optimize Human Performance in IT Service Management

Modern IT service provider organizations are under a continuous pressure to increase their competitiveness. Ways to reduce costs while improving performance of services are a key focus area for companies in the IT industry. However, despite all the solutions that have been proposed, modeling and optimizing human-centered processes remains a challenging task. The human operator may be influenced by multiple factors and execute the process in a different way each time, thus introducing a significant variability in the final process outcome. Therefore, the goal of this chapter is to introduce the concept of mashups as an effective approach to improve performance in terms of productivity and reliability of human-centered IT Service Management (ITSM) activities. In particular, this chapter aims to define management solutions required to deliver and demonstrate improvements in performance of human-centered ITSM processes. The introduced management solutions are examined through a real case study, related to the Request Fulfillment process.

Optimization of Service Development Strategy in a Global Environment

A well-managed project utilizing global resources helps companies create distinct competitive and operational advantages over centrally managed counterparts. Such advantages come from increased profitability through outsourcing. In this chapter, we present a technique used to assess risk in globally distributed industrial software projects – with a specific focus on custom application development projects. Our objective is to show how to maximize the benefits of globalization and how effective management and resource choices can avoid potential risks by more deeply understanding the effects of external global factors. Specifically, we provide mathematical models for the following global factors: 1) time zone differences, 2) language difference, 3) working time alignment, and 4) turnover rates among skilled workers in expanding economies. We also consider the utility functions of the developer and client and demonstrate that by allowing adjustments to the time to deliver the project one can achieve better profitability with global resources.

A Service-Oriented Algebra for Optimizing the Management of Service Requests

The Work-as-a-Service (WaaS) paradigm models work engagements as compositions of service requests, with the intention of separating the work coordination and enactment. In this chapter we present a definition of the WaaS conceptual meta-model a WaaS protocol, based on algebraic ideas from the area of business artifacts, that enables work decomposition, delegation, control, and enactment. The meta-model supports management and coordination during planning and enactment of work. The essential part is a coordination lifecycle, consisting of loosely coupled milestones, domain-specific information attributes, and sets of abstract observable activities to be performed. The presented service-oriented algebra consists of operations for coordination lifecycles when encapsulated service requests are torn, merged, paused, and resumed. The meta-model and protocol presented in this chapter are independent from the specific coordination enactment model which may employ centralized coordination, fully distributed coordination, or other models of coordination between requestors and providers under various optimization objectives.

Optimization Model for IT Change Management

Today's enterprises face the challenge of managing large, complex information technology (IT) systems consisting of software applications, servers, network routers, and other type of resources. Change management, and especially change scheduling, is known to be one of the most challenging problems in managing IT operations. Change scheduling must comply with strict availability Service Level Agreements (SLAs) and tackle complex IT environments. Various challenges arise from complex IT component dependencies, unpredictable durations of work processes, and large volume of concurrent changes and affected IT components. In this chapter, we address the problem of optimal IT change scheduling. We illustrate how the optimization problem can be formulated and solved using a mixed integer programming technique so that IT change management practitioners can efficiently schedule large change volumes and determine schedules that are robust to uncertainty in durations and costs with lower risks of SLA violations.

Improving Application Management Services through Ticket Data Clustering

In the area of Application Management Services (AMS), good resource planning and effective cross-skill training are critical to success. Meeting these objectives would require systematic and repeatable approaches for determining the best way of forming resource pools and identifying who to train for what skills under a constrained budget. This chapter presents a methodology that aims to achieve above goals based on an optimal clustering of service request data (aka. ticket data). Specifically, tickets that require similar problem-solving skills are first clustered using a statistical clustering technique into groups, which are then used to assist resource pooling and cross-skill training plan generation. Preliminary results have shown that an average 40% of resource saving can be achieved in our simulation scenario, while maintaining the same Service-Level Agreements (SLA) with the customer. Encouraging feedback on the cross-skill training recommendation has also been received from several real AMS customers.

Modeling and Optimization of Complex Service Delivery Systems

The services delivery business is highly dynamic and highly competitive, with thin profit margins. Strict service quality targets coupled with highly variable service request arrival patterns and ever increasing cost containment targets make it challenging for a service delivery provider to deliver consistent quality and remain profitable. In this chapter we present a modeling and optimization framework that provides a solution to this challenge. A discrete event simulation model is used as a proxy for the real world service delivery environment, containing the interaction among dynamic customer workload and strict service level constraints. Afterwards, an optimization model provides recommended staffing levels while considering the skills required to respond to different types of service requests, and the shift schedules that the service agents must follow. We demonstrate the applicability of the modeling and optimization framework in a large IT services delivery environment.

Capacity Planning and Management of IT Incident Management Services based on Queuing Models

Incident management or resolution services for the information technology (IT) infrastructure and software of large enterprises are labor-intensive operations. Because incidents have to be resolved in timely manner, performance targets are often set regarding the time to respond and the time to resolve for incident tickets. To meet these targets, adequate staffing is critical. At the same time, utilization rate of the staff must also be taken into account, because extra cost is often associated with an underutilized workforce. The management of IT incident resolution services always faces the question: how to properly staff a given operation, especially when the volume of service requests is expected to rise. Queuing models can be used to help address such questions. This chapter reviews the basic concepts in queuing models and discusses some practical issues in the application of queuing models to the capacity planning and management of IT incident resolution services.

Optimizing Cloud Storage Management Services

Storage services are an essential part of an organization's IT infrastructure services and contribute a significant part of total IT costs. For this reason, various service management techniques are applied to optimize a service's storage resource usage while still addressing requirements related to performance, high availability, or disaster recovery. While storage virtualization has been the basis for many storage service management optimizations, the relatively stable environments of enterprise IT enabled all management activity to proceed in the context of change processes on specialized storage controllers. Completely virtualized environments require frequent topological changes but also enable optimized resource usage across shared resource pools. This enables lower resource and service management costs if the right storage service management architecture is deployed. This chapter focuses on cloud service management from a storage perspective, providing a set of proven methods and services to optimize storage resource usage and the management architecture that enables them.