On The Applicability of Data Envelopment Analysis for Multiple Attriliute Decision Making in the Context of Information Systems Appraisals

This article elaborates on the applicability of basic and extended data envelopment analysis (DEA) models for various information system (IS) decision use-cases including illustrative examples from an enterprise resource planning (ERP) software investment appraisal. The usage of data envelopment analysis models and their extensions for IS decisions remains limited. This omission seems critical in particular for two reasons. First, organizational studies have shown that in practice business management fails to appreciate the portfolio of investment appraisal techniques available. Second, DEA based methodologies, especially new extensions, promise valuable insights to support the complex IS decision problem. The results indicate DEA applicability in a number of use-cases, e.g. for structural analysis of system alternatives or validation of ranking outcomes

Intelligent cost-effective winter road maintenance by predicting road surface temperature using machine learning techniques

Since Winter Road Maintenance (WRM) is an important activity in Nordic countries, accurate intelligent cost-effective WRM can create precise advance plans for developing decision support systems to improve traffic safety on the roads, while reducing cost and negative environmental impacts. Lack of comprehensive knowledge and inaccurate WRM information would lead to a certain loss of WRM budget, safety reduction, and irreparable environmental damage. This study proposes an intelligent methodology that uses data envelopment analysis and machine learning techniques. In the proposed methodology, WRM efficiency is calculated by data envelopment analysis for different decision-making units (roads), and inefficient units need to be considered for further assessments. Therefore, road surface temperature is predicted by means of machine learning methods, in order to achieve efficient and effective WRM on the roads during winter in cold regions. In total, four different methods have been used to predict road surface temperature on an inefficient road. One of these is linear regression, which is a classical statistical regression technique (ordinary least square regression); the other three methods are machine-learning techniques, including support vector regression, multilayer perceptron artificial neural network, and random forest regression. Graphical and numerical results indicate that support vector regression is the most accurate method

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

The Potential of Data Envelopment Analysis (DEA) in Labor Adjustment Policy

[Excerpt] Through labor-management policy, the management is responsible for establishing mutually beneficial relations between workers and employers. Establishing proper relations from the perspective of the worker refers to protecting workers’ rights by guaranteeing their quality of life. Areas of labor-management policy set by management include those that appropriate wages and labor conditions; stipulate and settle collective relations between management and workers; and establish industrial safety and workers’ compensation. Management also sets employment policies related to labor welfare, vocational training, employment security, maintenance of employment agencies, and other policies that push forward human resource development. When setting these policies, management seeks to find a solution that is mutually beneficial for workers and employers. However, in the case of downsizing, there are not sufficient guidelines specific or objective enough for both workers and employers to agree on. Therefore, it is difficult to resolve differences when it comes to labor settlements. The following case study hypothetically illustrates ways in which the management could resolve labor settlements that are jointly accepted by workers and employers

Value Efficiency Analysis of Academic Research [Updated 19 August 1998]

We propose a systematic approach to analyzing academic research performance at universities and research institutes. The analysis of research performance based on a set of (abstract) criteria which are relevant from the decision maker's point of view. The scales for these criteria are defined by means of concrete indicators. All indicators, are, however, not necessarily quantitative. Qualitative information is quantified using appropriate analytical tools. Once the criteria and indicators have been agreed upon and quantified, data on the research units is collected and a Value Efficiency Analysis is performed. The efficiency of research units is defined in the spirit of Data Envelopment Analysis (DEA), complemented with a decision makers (Rector in the European university system) preference information. This information is obtained by asking the decision maker to locate a point on the efficient frontier having the most preferred input and output values. Our approach and the accompanying Decision Support System enables a university to allocate resources more efficiently for its research units. Using data from the Helsinki School of Economics, we describe how our approach can be used

A Quantitative Model for CRM Performance Evaluations

This paper identifies important performance measurements for the operation of CRM units in the call center industry, and then uses Data Envelopment Analysis (DEA) to compare the performances of a selected set of call centers. The results provide several managerial insights that will assist CRM managers in effective decision making, especially in the areas of increasing efficiencies and improving customer service. Overall, this paper contributes towards managing resources and processes that would aid in the acquisition and support of information technology within a firm

Application of data envelopment analysis to measure technical efficiency on a sample of Irish dairy farms

peer-reviewedThe aim of this study was to determine the levels of technical efficiency on a sample of Irish dairy farms utilizing Data Envelopment Analysis (DEA) and to identify key management and production factors that differ between producers indentified as efficient and inefficient. DEA was used in this study to generate technical efficiency scores under assumptions of both constant returns to scale (CRS) and variable returns to scale (VRS). The average technical efficiency score was 0.785 under CRS and 0.833 under VRS. Key production characteristics of efficient and inefficient producers were compared using an analysis of variance. More technically efficient producers used less input per unit of output, had higher production per cow and per hectare and had a longer grazing season, a higher milk quality standard, were more likely to have participated in milk recording and had greater land quality compared to the inefficient producers

Assessing efficiency differences in a common Agriculture Decision Support System - A comparative analysis between Greek and Italian durum wheat farms -

This study assesses inputs use efficiency of durum wheat farmers, subscribed under a common Agricultural Decision Support System (ADSS), especially designed by Barilla and HORTA for this cultivation. Data Envelopment Analysis was the main analysis used to highlight differences in the implementation stage of ADSS’s suggestions, between 4 agricultural firms (2 Italian and 2 Greek) (N= 563 farmers). By incorporating economic (variable costs) and environmental factors (Carbon, Water and Environmental footprints), performance differences between farms both on regional and national level arose. Lastly, closer monitoring for clarifying the reasoning of the obtained differences in the implementation stage is proposed