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.

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.

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.

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.

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.

Fuzzy preference programming formulation in data envelopment analysis for university department evaluation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dyanne Brendalyn Mirasol-Cavero ◽  
Lanndon Ocampo
Keyword(s):  
Data Envelopment Analysis ◽  
Real Life ◽  
The Philippines ◽  
Data Envelopment ◽  
Data Accessibility ◽  
Content Type ◽  
Proposed Model ◽  
University Department ◽  
Fuzzy Dea ◽  
Fuzzy Preference

Purpose University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the efficiency of academic departments in data envelopment analysis (DEA) deals with crisp data, which may be, often, imprecise, vague, missing or predicted. Current literature offers various approaches to addressing these uncertainties by introducing fuzzy set theory within the basic DEA framework. However, current fuzzy DEA approaches fail to handle missing data, particularly in output values, which are prevalent in real-life evaluation. Thus, this study aims to augment these limitations by offering a fuzzy DEA variation. Design/methodology/approach This paper proposes a more flexible approach by introducing the fuzzy preference programming – DEA (FPP-DEA), where the outputs are expressed as fuzzy numbers and the inputs are conveyed in their actual crisp values. A case study in one of the top higher education institutions in the Philippines was conducted to elucidate the proposed FPP-DEA with fuzzy outputs. Findings Due to its high discriminating power, the proposed model is more constricted in reporting the efficiency scores such that there are lesser reported efficient departments. Although the proposed model can still calculate efficiency no matter how much missing and unavailable, and uncertain data, more comprehensive data accessibility would return an accurate and precise efficiency score. Originality/value This study offers a fuzzy DEA formulation via FPP, which can handle missing, unavailable and imprecise data for output values.

scholarly journals Assessment of mixed network processes with shared inputs and undesirable factors

2020 ◽  
Vol 30 (1) ◽  
Author(s):  
Maryam Nematizadeh ◽  
Alireza Amirteimoori ◽  
Sohrab Kordrostami ◽  
Mohsen Vaez-Ghasemi
Keyword(s):  
Data Envelopment Analysis ◽  
Real World ◽  
Network Data ◽  
Data Envelopment ◽  
Parallel Section ◽  
Mixed Network ◽  
Weak Disposability ◽  
Proposed Model ◽  
Shared Inputs

In the real world, there are processes whose structures are like a parallel-series mixed network. Network data envelopment analysis (NDEA) is one of the appropriate methods for assessing the performance of processes with these structures. In the paper, mixed processes with two parallel and series components are considered, in which the first component or parallel section consists of the shared in-puts, and the second component or series section consists of undesirable factors. By considering the weak disposability assumption for undesirable factors, a DEA approach as based on network slack-based measure (NSBM) is introduced to evaluate the performance of processes with mixed structures. The proposed model is illustrated with a real case study. Then, the model is developed to discriminate efficient units.

scholarly journals An Integrated Analytic Hierarchy Process—Slack Based Measure-Data Envelopment Analysis Model for Evaluating the Efficiency of Logistics Service Providers Considering Undesirable Performance Criteria

2019 ◽  
Vol 11 (8) ◽  
pp. 2330 ◽  
Author(s):  
Patricija Bajec ◽  
Danijela Tuljak-Suban
Keyword(s):  
Data Envelopment Analysis ◽  
Service Providers ◽  
Performance Criteria ◽  
Data Envelopment ◽  
Analytic Hierarchy ◽  
Dea Model ◽  
Logistics Service ◽  
Logistics Service Providers ◽  
Proposed Model ◽  
Hierarchy Process

Sustainable concerns are reputed to be of the utmost priority among governments. Consequently, they have become more and more of a concern among supply chain partners. Logistics service providers (LPs), as significant contributors to supply chain success but also one of the greatest generator of emissions, play a significant role in reducing the negative environmental impact. Thus, the performance evaluations of LPs should necessarily involve such a measure which, firstly, represents a balance between all three pillars of sustainability and, secondly, consider the desirable and undesirable performance criteria. This paper proposes an integrated analytic hierarchy process (AHP) and slack-based measure (SBM) data envelopment analysis (DEA) model, based on the assumption of a variable return to scale (VRS). An AHP pairwise comparison enables selecting the most influential input/output variables. Output-oriented SBM DEA provides simultaneously evaluation of both the undesirable and desirable outputs. The proposed model was tested on a numerical example of 18 LPs. The comparison of output Charnes, Cooper and Rhodes (CCR) and SBM DEA models resulted in a higher number of inefficient LPs when the SBM DEA model was applied. Moreover, efficiency scores of inefficient LPs were lower in SBM DEA model. The proposed model is fair to those LPs that are environmentally friendly.

Sign in / Sign up

Export Citation Format

Share Document