Tracking customer portfolio composition: A factor analysis approach

In this paper, we address the changing composition of a customer portfolio taking into account actions undertaken by the company to adapt its service offer to market conditions and/or technological innovations. We present a specific methodology to identify clusters of customers in different periods and then compare them over time. The classification process takes into account both qualitative and quantitative aspects of the consumption levels of the services or products offered by the company. The possibility of period‐to‐period variation in the customer portfolio and the service or product offer is also considered, in order to achieve a more realistic scenario. The core of the proposed methodology is related to the family of exploratory factorial and cluster techniques. The customers are classified by using a bicriterial clustering methodology based on ‘tandem’ analysis (multiple factor analysis+cluster analysis of the main factors). The bicriterial approach allows for a compromise between customers' consumption levels (a quantitative criterion) and their consumption/non‐consumption pattern (a qualitative criterion). The evolution of the customer portfolio composition is explored through multiple correspondence analysis. This technique allows visual comparison of the position of different clusters against time and the identification of key changes in customer consumption behavior. The methodology is tested on realistic customer portfolio scenarios for a major telecommunication company. We simulate various scenarios to show the strengths of our proposal. Copyright © 2009 John Wiley & Sons, Ltd.     

Hospital preparedness during epidemics using simulation: the case of COVID-19

Central European Journal of Operations Research - This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially...     

Combining optimization with simulation to obtain credible models for intensive care units

In this paper we develop a simulation model to study bed occupancy levels in an Intensive Care Unit (ICU). The main contributions of this study are: (1) A proposal for generalized regression models to fully capture the high variability of patients’ length of stay; (2) Proof that a simulation model that does not incorporate the management decisions by clinical staff cannot be considered valid; (3) The development of a mathematical model to represent these management decisions, and (4) A proposal for a method combining optimization with simulation to estimate the model parameters. This provides a valid simulation model that includes the physician management of an ICU. Validation is accomplished by comparing distribution patterns in daily bed occupancy records against simulated bed occupancy data. The methodology is tested using data provided by the Hospital of Navarre in Spain.     

Multivariate weighted renewal functions

Let (X,Y),(X₁,Y₁),(X₂,Y₂),… denote independent positive random vectors with common distribution function F(x,y)=P(X?x,Y?y) with F(x,y)<1 for all x,y. Based on the X_i and the Y_j we construct the sum sequences and respectively. For a double sequence of weighting constants {b(n,m)} we associate a weighted renewal function G(x,y) defined as . The function G(x,y) can be expressed in terms of well-known renewal quantities. The main goal of this paper is to study asymptotic properties of G(x,y). In the one-dimensional case such results have been obtained among others by Omey and Teugels [Weighted renewal functions: a hierarchical approach, Adv. in Appl. Probab. 34 (2002) 394-415.] and Alsmeyer [Some relations between harmonic renewal measures and certain first passage times, Statist. Probab. Letters 12 (1991) 19-27; On generalized renewal measures and certain first passage times, Ann. Probab. 20 (1992) 1229-1247]. Here we prove a multivariate version of the elementary renewal theorem and moreover we obtain a rate of convergence result in this elementary renewal theorem. We close the paper with an application and some concluding remarks. For convenience we prove and formulate the results in the two-dimensional case only.