Service Level Analysis method - SLAM

Motta, Gianmario; Pignatelli, Giovanni; Barroero, Thiago; Longo, Antonella

doi:10.1109/iccsit.2010.5563930

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…Other related studies [8] discuss developing intuitive models but use them for simulation to develop new processes rather than methods to validate the same with real SS. More studies exist [13,19,12], however we did not find them to be proposing a method to validate the relationships of KPIs.…”

Section: Related Workmentioning

confidence: 95%

A Method for Assessing Influence Relationships among KPIs of Service Systems

Shrinivasan

Dasgupta

Desai

et al. 2012

Service-Oriented Computing

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The operational performance of service systems is commonly measured with key performance indicators (KPIs), e.g., time-to-resolve, SLA compliance, and workload balance. The assumption is that healthy KPIs lead to healthy business outcomes such as customer satisfaction, cost savings, and service quality. Although the domain experts have an intuitive understanding of the causal relationships among the KPIs, the degree of influence a cause KPI has on the effect KPI is difficult to estimate based on intuition. Also, the intuitive understanding could be wrong. Further, we show how the causal relationships are intricate with aspects such as the rate of influence and conditionality in measurements. As a result, although crucial, it is nontrivial to estimate the degree of influence. Without the degree of influence, prediction of business outcomes and decisions based on them tend to be ad hoc. This paper presents a novel method for validating the intuitive direction and the polarity of a causal relationship provided by domain experts. Further, the method also estimates the degree of influence based on the measure of Pearson's correlation. Using the degree of influence and least squares regression, the method predicts values of effect KPIs. The method is evaluated by applying it on 10 widely used KPIs from 29 real-life service systems. We find that the method validates 8 of the 15 intuitive relationships and estimates the degree of influence for each of the validated relationships. Further, based on the degrees of influence and the regression model learned from the 29 service systems, the method could estimate the values of the effect KPIs with an average root-mean-squared error (RMSE) of 1.2%, in 9 additional service systems.

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Section: Related Workmentioning

confidence: 95%

A Method for Assessing Influence Relationships among KPIs of Service Systems

Shrinivasan

Dasgupta

Desai

et al. 2012

Service-Oriented Computing

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“…Process based performance analysis has been addressed before( [7][15], [5]), but not in the particular flavor of service delivery outcomes. Lag selection using regression for high dimensional data is addressed in [10].…”

Section: Related Workmentioning

confidence: 99%

Optimal Strategy for Proactive Service Delivery Management Using Inter-KPI Influence Relationships

Dasgupta

Shrinivasan

Nayak

et al. 2013

Service-Oriented Computing

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Service interactions now account for major source of revenue and employment in many modern economies, and yet service operations management remains extremely complex. To lower risks, every Service Delivery (SD) environment needs to define its own key performance indicators (KPIs) to evaluate the present state of operations and its business outcomes. Due to the over-use of performance measurement systems, a large number of KPIs have been defined, but their influence on each other is unknown. It is thus important to adopt datadriven approaches to demystify the service delivery KPIs interrelationships and establish the critical ones that have a stronger influence on the business outcomes. Given a set of operational KPIs and SD outcomes, we focus on the problem of (a) extracting interrelationships and impact delays among KPIs and outcomes, and building a regression-based KPI influence model to estimate the SD outcomes as functions of KPIs. (b) Based on the model we propose a schedule of action plans to transform the current service delivery system state. (c) We also build a visualization tool that enables validation of extracted KPIs influence model, and perform what-if analysis.

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