A decision model for it supplier selection

En los últimos años la externalización de TI ha ganado mucha importancia en el mercado y, por ejemplo, el mercado externalización de servicios de TI sigue creciendo cada año. Ahora más que nunca, las organizaciones son cada vez más los compradores de las capacidades necesarias mediante la obtención de productos y servicios de los proveedores, desarrollando cada vez menos estas capacidades dentro de la empresa. La selección de proveedores de TI es un problema de decisión complejo. Los gerentes que enfrentan una decisión sobre la selección de proveedores de TI tienen dificultades en la elaboración de lo que hay que pensar, además en sus discursos. También de acuerdo con un estudio del SEI (Software Engineering Institute) [40], del 20 al 25 por ciento de los grandes proyectos de adquisición de TI fracasan en dos años y el 50 por ciento fracasan dentro de cinco años. La mala gestión, la mala definición de requisitos, la falta de evaluaciones exhaustivas, que pueden ser utilizadas para llegar a los mejores candidatos para la contratación externa, la selección de proveedores y los procesos de contratación inadecuados, la insuficiencia de procedimientos de selección tecnológicos, y los cambios de requisitos no controlados son factores que contribuyen al fracaso del proyecto. La mayoría de los fracasos podrían evitarse si el cliente aprendiese a comprender los problemas de decisión, hacer un mejor análisis de decisiones, y el buen juicio. El objetivo principal de este trabajo es el desarrollo de un modelo de decisión para la selección de proveedores de TI que tratará de reducir la cantidad de fracasos observados en las relaciones entre el cliente y el proveedor. La mayor parte de estos fracasos son causados por una mala selección, por parte del cliente, del proveedor. Además de estos problemas mostrados anteriormente, la motivación para crear este trabajo es la inexistencia de cualquier modelo de decisión basado en un multi modelo (mezcla de modelos adquisición y métodos de decisión) para el problema de la selección de proveedores de TI. En el caso de estudio, nueve empresas españolas fueron analizadas de acuerdo con el modelo de decisión para la selección de proveedores de TI desarrollado en este trabajo. Dos softwares se utilizaron en este estudio de caso: Expert Choice, y D-Sight. ABSTRACT In the past few years IT outsourcing has gained a lot of importance in the market and, for example, the IT services outsourcing market is still growing every year. Now more than ever, organizations are increasingly becoming acquirers of needed capabilities by obtaining products and services from suppliers and developing less and less of these capabilities in-house. IT supplier selection is a complex and opaque decision problem. Managers facing a decision about IT supplier selection have difficulty in framing what needs to be thought about further in their discourses. Also according to a study from SEI (Software Engineering Institute) [40], 20 to 25 percent of large information technology (IT) acquisition projects fail within two years and 50 percent fail within five years. Mismanagement, poor requirements definition, lack of comprehensive evaluations, which can be used to come up with the best candidates for outsourcing, inadequate supplier selection and contracting processes, insufficient technology selection procedures, and uncontrolled requirements changes are factors that contribute to project failure. The majority of project failures could be avoided if the acquirer learns how to understand the decision problems, make better decision analysis, and good judgment. The main objective of this work is the development of a decision model for IT supplier selection that will try to decrease the amount of failures seen in the relationships between the client-supplier. Most of these failures are caused by a not well selection of the supplier. Besides these problems showed above, the motivation to create this work is the inexistence of any decision model based on multi model (mixture of acquisition models and decision methods) for the problem of IT supplier selection. In the case study, nine different Spanish companies were analyzed based on the IT supplier selection decision model developed in this work. Two software products were used in this case study, Expert Choice and D-Sight.

Outsourcing and acquisition models comparison related to IT supplier selection decision analysis

This paper presents a comparison of acquisition models related to decision analysis of IT supplier selection. The main standards are: Capability Maturity Model Integration for Acquisition (CMMI-ACQ), ISO / IEC 12207 Information Technology / Software Life Cycle Processes, IEEE 1062 Recommended Practice for Software Acquisition, the IT Infrastructure Library (ITIL) and the Project Management Body of Knowledge (PMBOK) guide. The objective of this paper is to compare the previous models to find the advantages and disadvantages of them for the future development of a decision model for IT supplier selection.

An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

Systematic Approach for the selection of monitoring technologies in CO2 geological storage projects. Application of multicriteria decision making

Geologic storage of carbon dioxide (CO2) has been proposed as a viable means for reducing anthropogenic CO2 emissions. Once injection begins, a program for measurement, monitoring, and verification (MMV) of CO2 distribution is required in order to: a) research key features, effects and processes needed for risk assessment; b) manage the injection process; c) delineate and identify leakage risk and surface escape; d) provide early warnings of failure near the reservoir; and f) verify storage for accounting and crediting. The selection of the methodology of monitoring (characterization of site and control and verification in the post-injection phase) is influenced by economic and technological variables. Multiple Criteria Decision Making (MCDM) refers to a methodology developed for making decisions in the presence of multiple criteria. MCDM as a discipline has only a relatively short history of 40 years, and it has been closely related to advancements on computer technology. Evaluation methods and multicriteria decisions include the selection of a set of feasible alternatives, the simultaneous optimization of several objective functions, and a decision-making process and evaluation procedures that must be rational and consistent. The application of a mathematical model of decision-making will help to find the best solution, establishing the mechanisms to facilitate the management of information generated by number of disciplines of knowledge. Those problems in which decision alternatives are finite are called Discrete Multicriteria Decision problems. Such problems are most common in reality and this case scenario will be applied in solving the problem of site selection for storing CO2. Discrete MCDM is used to assess and decide on issues that by nature or design support a finite number of alternative solutions. Recently, Multicriteria Decision Analysis has been applied to hierarchy policy incentives for CCS, to assess the role of CCS, and to select potential areas which could be suitable to store. For those reasons, MCDM have been considered in the monitoring phase of CO2 storage, in order to select suitable technologies which could be techno-economical viable. In this paper, we identify techniques of gas measurements in subsurface which are currently applying in the phase of characterization (pre-injection); MCDM will help decision-makers to hierarchy the most suitable technique which fit the purpose to monitor the specific physic-chemical parameter.

A management model for closed-loop supply chains of reusable articles

This PhD dissertation is framed in the emergent fields of Reverse Logistics and ClosedLoop Supply Chain (CLSC) management. This subarea of supply chain management has gained researchers and practitioners' attention over the last 15 years to become a fully recognized subdiscipline of the Operations Management field. More specifically, among all the activities that are included within the CLSC area, the focus of this dissertation is centered in direct reuse aspects. The main contribution of this dissertation to current knowledge is twofold. First, a framework for the so-called reuse CLSC is developed. This conceptual model is grounded in a set of six case studies conducted by the author in real industrial settings. The model has also been contrasted with existing literature and with academic and professional experts on the topic as well. The framework encompasses four building blocks. In the first block, a typology for reusable articles is put forward, distinguishing between Returnable Transport Items (RTI), Reusable Packaging Materials (RPM), and Reusable Products (RP). In the second block, the common characteristics that render reuse CLSC difficult to manage from a logistical standpoint are identified, namely: fleet shrinkage, significant investment and limited visibility. In the third block, the main problems arising in the management of reuse CLSC are analyzed, such as: (1) define fleet size dimension, (2) control cycle time and promote articles rotation, (3) control return rate and prevent shrinkage, (4) define purchase policies for new articles, (5) plan and control reconditioning activities, and (6) balance inventory between depots. Finally, in the fourth block some solutions to those issues are developed. Firstly, problems (2) and (3) are addressed through the comparative analysis of alternative strategies for controlling cycle time and return rate. Secondly, a methodology for calculating the required fleet size is elaborated (problem (1)). This methodology is valid for different configurations of the physical flows in the reuse CLSC. Likewise, some directions are pointed out for further development of a similar method for defining purchase policies for new articles (problem (4)). The second main contribution of this dissertation is embedded in the solutions part (block 4) of the conceptual framework and comprises a two-level decision problem integrating two mixed integer linear programming (MILP) models that have been formulated and solved to optimality using AIMMS as modeling language, CPLEX as solver and Excel spreadsheet for data introduction and output presentation. The results obtained are analyzed in order to measure in a client-supplier system the economic impact of two alternative control strategies (recovery policies) in the context of reuse. In addition, the models support decision-making regarding the selection of the appropriate recovery policy against the characteristics of demand pattern and the structure of the relevant costs in the system. The triangulation of methods used in this thesis has enabled to address the same research topic with different approaches and thus, the robustness of the results obtained is strengthened.

Metagenomic Anlaysis of microsymbiont selection by the legume plant host

Rhizobium leguminosarum bv.viciae is able to establish nitrogen-fixing symbioses with legumes of the genera Pisum, Lens, Lathyrus and Vicia. Classic studies using trap plants (Laguerre et al., Young et al.) provided evidence that different plant hosts are able to select different rhizobial genotypes among those available in a given soil. However, these studies were necessarily limited by the paucity of relevant biodiversity markers. We have now reappraised this problem with the help of genomic tools. A well-characterized agricultural soil (INRA Bretennieres) was used as source of rhizobia. Plants of Pisum sativum, Lens culinaris, Vicia sativa and V. faba were used as traps. Isolates from 100 nodules were pooled, and DNA from each pool was sequenced (BGI-Hong Kong; Illumina Hiseq 2000, 500 bp PE libraries, 100 bp reads, 12 Mreads). Reads were quality filtered (FastQC, Trimmomatic), mapped against reference R. leguminosarum genomes (Bowtie2, Samtools), and visualized (IGV). An important fraction of the filtered reads were not recruited by reference genomes, suggesting that plant isolates contain genes that are not present in the reference genomes. For this study, we focused on three conserved genomic regions: 16S-23S rDNA, atpD and nodDABC, and a Single Nucleotide Polymorphism (SNP) analysis was carried out with meta / multigenomes from each plant. Although the level of polymorphism varied (lowest in the rRNA region), polymorphic sites could be identified that define the specific soil population vs. reference genomes. More importantly, a plant-specific SNP distribution was observed. This could be confirmed with many other regions extracted from the reference genomes (data not shown). Our results confirm at the genomic level previous observations regarding plant selection of specific genotypes. We expect that further, ongoing comparative studies on differential meta / multigenomic sequences will identify specific gene components of the plant-selected genotypes