A Sense and Respond (SaR) system endows a Business Intelligence system with the intelligence needed to react timely to exogenous as well as endogenous events. To this end, a SaR system needs to know the Key Performance Indicators (KPIs) that must be maximized as well as their relative weights. While the first information can be easily obtained through interviews, the second one is quite hard to get. This motivates the investigation of methods and tools to address this problem.In such a context, the main contributions of this paper are the following. First, we show how KPIs can be effectively defined using linear constraints. Second, we show how the problem of computing the actions that the SaR system proposes to the manager can be formulated as a Mixed Integer Linear Programming (MILP) problem. Third, we show how KPI weights can be computed from previous managing decisions by solving a suitable MILP problem. Fourth, we provide experimental results showing the effectiveness of the proposed approach.

A Constraint Optimization-Based Sense and Response System for Interactive Business Performance Management

Melatti, I;
2021-01-01

Abstract

A Sense and Respond (SaR) system endows a Business Intelligence system with the intelligence needed to react timely to exogenous as well as endogenous events. To this end, a SaR system needs to know the Key Performance Indicators (KPIs) that must be maximized as well as their relative weights. While the first information can be easily obtained through interviews, the second one is quite hard to get. This motivates the investigation of methods and tools to address this problem.In such a context, the main contributions of this paper are the following. First, we show how KPIs can be effectively defined using linear constraints. Second, we show how the problem of computing the actions that the SaR system proposes to the manager can be formulated as a Mixed Integer Linear Programming (MILP) problem. Third, we show how KPI weights can be computed from previous managing decisions by solving a suitable MILP problem. Fourth, we provide experimental results showing the effectiveness of the proposed approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/222082
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