A Model Based on Data Envelopment Analysis for the Measurement of Productivity in the Software Factory

2022 ◽  
pp. 792-818
Author(s):  
Pedro Castañeda ◽  
David Mauricio
Keyword(s):  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Data Envelopment ◽  
Model Based ◽  
Software Production ◽  
Software Factory ◽  
Proposed Model ◽  
Efficiency And Effectiveness ◽  
Work Units ◽  
Two Phases

Productivity in software factories is very important because it allows organizations to achieve greater efficiency and effectiveness in their activities. One of the pillars of competitiveness is productivity, and it is related to the effort required to accomplish the assigned tasks. However, there is no standard way to measure it, making it difficult to establish policies and strategies to improve the factory. In this work, a model based on data envelopment analysis is presented to evaluate the relative efficiency of the software factories and their projects, to measure the productivity in the software production component of the software factory through the activities that are carried out in their different work units. The proposed model consists of two phases in which the productivity of the software factory is evaluated and the productivity of the projects it conducts is assessed. Numerical tests on 6 software factories with 160 projects implemented show that the proposed model allows one to assess the software factories and the most efficient projects.

A Model Based on Data Envelopment Analysis for the Measurement of Productivity in the Software Factory

2020 ◽  
Vol 13 (2) ◽  
pp. 1-26 ◽  
Author(s):  
Pedro Castañeda ◽  
David Mauricio
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Numerical Tests ◽  
Model Based ◽  
Software Production ◽  
Software Factory ◽  
Proposed Model ◽  
Efficiency And Effectiveness ◽  
Work Units ◽  
Two Phases

Productivity in software factories is very important because it allows organizations to achieve greater efficiency and effectiveness in their activities. One of the pillars of competitiveness is productivity, and it is related to the effort required to accomplish the assigned tasks. However, there is no standard way to measure it, making it difficult to establish policies and strategies to improve the factory. In this work, a model based on data envelopment analysis is presented to evaluate the relative efficiency of the software factories and their projects, to measure the productivity in the software production component of the software factory through the activities that are carried out in their different work units. The proposed model consists of two phases in which the productivity of the software factory is evaluated and the productivity of the projects it conducts is assessed. Numerical tests on 6 software factories with 160 projects implemented show that the proposed model allows one to assess the software factories and the most efficient projects.

New Factors Affecting Productivity of the Software Factory

2020 ◽  
Vol 13 (1) ◽  
pp. 1-26
Author(s):  
Pedro Castañeda ◽  
David Mauricio
Keyword(s):  
Design Team ◽  
Team Size ◽  
Factors Affecting ◽  
Analysis And Design ◽  
Team Heterogeneity ◽  
Software Production ◽  
Software Factory ◽  
Efficiency And Effectiveness ◽  
Work Units ◽  
Testing Work

Productivity is very important because it allows organizations to achieve greater efficiency and effectiveness in their activities; however, it is affected by numerous factors. While these factors have been identified for over two decades, all of the previous works limited the software factory to the programming work unit and did not analyze other work units that are also relevant. 90% of a software factory's effort is absorbed by the software production component, 85% of which is concentrated in the efforts of the analysis and design, programming, and testing work units. The present work identifies three new factors that influence the software factory, demonstrating that the use of rules and events influences analysis & design, team heterogeneity negatively affects analysis and design and positively affects programming; and the osmotic communication affects programming. An empirical study on software factories in Peru, determined that 95% of the influence came from these factors, which corroborated as well that team size and trust within the team influences in software production.

New Factors Affecting Productivity of the Software Factory

2022 ◽  
pp. 1951-1979
Author(s):  
Pedro Castañeda ◽  
David Mauricio
Keyword(s):  
Empirical Study ◽  
Design Team ◽  
Factors Affecting ◽  
Analysis And Design ◽  
Team Heterogeneity ◽  
Software Production ◽  
Software Factory ◽  
Efficiency And Effectiveness ◽  
Work Units ◽  
Testing Work

Productivity is very important because it allows organizations to achieve greater efficiency and effectiveness in their activities; however, it is affected by numerous factors. While these factors have been identified for over two decades, all of the previous works limited the software factory to the programming work unit and did not analyze other work units that are also relevant. 90% of a software factory's effort is absorbed by the software production component, 85% of which is concentrated in the efforts of the analysis and design, programming, and testing work units. The present work identifies three new factors that influence the software factory, demonstrating that the use of rules and events influences analysis & design, team heterogeneity negatively affects analysis and design and positively affects programming; and the osmotic communication affects programming. An empirical study on software factories in Peru, determined that 95% of the influence came from these factors, which corroborated as well that team size and trust within the team influences in software production.

Benchmarking HBCU Efficiency

2016 ◽  
pp. 95-128
Author(s):  
Jason Coupet
Keyword(s):  
Higher Education ◽  
Data Envelopment Analysis ◽  
Student Retention ◽  
Relative Efficiency ◽  
Vital Role ◽  
Department Of Education ◽  
Data Envelopment ◽  
Education Data ◽  
The Us ◽  
Efficiency And Effectiveness

HBCUs have played a vital role in the US higher education sphere. As initiatives to increase student retention move forward, the reality of funding constraints means that examining efficiency and effectiveness at HBCUs remains a vital part of institutional growth. This chapter presents a two-stage Data Envelopment Analysis (DEA) methodology as a tool to benchmark the relative efficiency of HBCUs. DEA is a quantitative, non-parametric technique used to measure efficiency, and has had a robust history as a benchmarking tool due to its ability to identify top performing organizations as well as less efficient peers. Using Department of Education data, the most efficient and effective HBCUs are identified. Implications for the use of DEA as a benchmarking tool are discussed.

A cross evaluation-based measure of super efficiency in DEA with interval data

Kybernetes ◽  
2016 ◽  
Vol 45 (4) ◽  
pp. 666-679 ◽  
Author(s):  
Qian Yu ◽  
Fujun Hou
Keyword(s):  
Data Envelopment Analysis ◽  
Practical Method ◽  
Interval Data ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Content Type ◽  
Super Efficiency ◽  
Dea Model ◽  
Model Based ◽  
Proposed Model

Purpose – The traditional data envelopment analysis (DEA) model as a non-parametric technique can measure the relative efficiencies of a decision-making units (DMUs) set with exact values of inputs and outputs, but it cannot handle the imprecise data. The purpose of this paper is to establish a super efficiency interval data envelopment analysis (IDEA) model, an IDEA model based on cross-evaluation and a cross evaluation-based measure of super efficiency IDEA model. And the authors apply the proposed approach to data on the 29 public secondary schools in Greece, and further demonstrate the feasibility of the proposed approach. Design/methodology/approach – In this paper, based on the IDEA model, the authors propose an improved version of establishing a super efficiency IDEA model, an IDEA model based on cross-evaluation, and then present a cross evaluation-based measure of super efficiency IDEA model by combining the super efficiency method with cross-evaluation. The proposed model cannot only discriminate the performance of efficient DMUs from inefficient ones, but also can distinguish between the efficient DMUs. By using the proposed approach, the overall performance of all DMUs with interval data can be fully ranked. Findings – A numerical example is presented to illustrate the application of the proposed methodology. The result shows that the proposed approach is an effective and practical method to measure the efficiency of the DMUs with imprecise data. Practical implications – The proposed model can avoid the fact that the original DEA model can only distinguish the performance of efficient DMUs from inefficient ones, but cannot discriminate between the efficient DMUs. Originality/value – This paper introduces the effective method to obtain the complete rank of all DMUs with interval data.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

Measuring Performance of a Three-Stage Network Structure Using Data Envelopment Analysis and Nash Bargaining Game: A Supply Chain Application

2018 ◽  
Vol 17 (05) ◽  
pp. 1429-1467 ◽  
Author(s):  
Mohammad Amirkhan ◽  
Hosein Didehkhani ◽  
Kaveh Khalili-Damghani ◽  
Ashkan Hafezalkotob
Keyword(s):  
Supply Chain ◽  
Data Envelopment Analysis ◽  
Nash Bargaining ◽  
Data Envelopment ◽  
Second Stage ◽  
Proposed Model ◽  
Nash Bargaining Game ◽  
Dea Models ◽  
Overall Efficiency

The issue of efficiency analysis of network and multi-stage systems, as one of the most interesting fields in data envelopment analysis (DEA), has attracted much attention in recent years. A pure serial three-stage (PSTS) process is a specific kind of network in which all the outputs of the first stage are used as the only inputs in the second stage and in addition, all the outputs of the second stage are applied as the only inputs in the third stage. In this paper, a new three-stage DEA model is developed using the concept of three-player Nash bargaining game for PSTS processes. In this model, all of the stages cooperate together to improve the overall efficiency of main decision-making unit (DMU). In contrast to the centralized DEA models, the proposed model of this study provides a unique and fair decomposition of the overall efficiency among all three stages and eliminates probable confusion of centralized models for decomposing the overall efficiency score. Some theoretical aspects of proposed model, including convexity and compactness of feasible region, are discussed. Since the proposed bargaining model is a nonlinear mathematical programming, a heuristic linearization approach is also provided. A numerical example and a real-life case study in supply chain are provided to check the efficacy and applicability of the proposed model. The results of proposed model on both numerical example and real case study are compared with those of existing centralized DEA models in the literature. The comparison reveals the efficacy and suitability of proposed model while the pitfalls of centralized DEA model are also resolved. A comprehensive sensitivity analysis is also conducted on the breakdown point associated with each stage.

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

2020 ◽  
Vol 33 (02) ◽  
pp. 431-445
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Confidence Interval ◽  
Data Envelopment ◽  
Data Set ◽  
Interval Model ◽  
Time Period ◽  
Proposed Model ◽  
The Future ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.

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