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Model to Predict Schedule Variance in Software Application Development Projects

机译:预测软件应用程序开发项目中进度差异的模型

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Metamorphic changes in today’s business are inevitable due to advancement in technology. For a business to either flourish or perish technology plays an important role. In Digital era Information Technology plays a pivotal role in complex business transformations. IT organizations have its own challenges of delivering right the first time, delivering on time and on budget, sustaining profit margin and customer satisfaction. A research, with at most focus on software development project schedule and on-time delivery was administered. The primary data was dissected and analyzed using suitable statistical techniques. Objectives : (1) To develop a predictive model which measures schedule variance on the basis of multiple independent variables (2) To describe the skills required for project managers to effectively handle on time delivery of project using factor analysis. (3) To measure the strength of association between the schedule variance and demographic variables of a project manager and project. Methods: Data from 70 different projects and additional responses from the project managers managing those projects are collected using mail survey techniques. Findings : The output of multiple linear regression (MLR) technique helped to predict change in software application project schedule with a degree of accuracy of R-Value, the coefficient of correlation 90.1%. The research analysis indicates that the relationship between the dependent variable, which is project schedule variance and independent variables, of the developed model is good. The model will fit the real time project environment. Using multivariate factor analysis, sixteen key skills needed for a project manager for efficient on-time delivery has been identified. These are highly loaded into five different factors. Chi-square goodness of fit which is a non-parametric test helped to identify the strength of association between schedule variance and demographic variables. Conclusion : The analysis has resulted in a predictive model with eleven independent variables (excluding constant) impacting schedule variance. The five different competencies are the factor labels which are estimate and re-estimate, managing risks, people management, requirement management and time management and control over runs required for project managers, to have a control on the project schedule. It is also inferred that there is no significant association between the project manager’s demographic variables and ability to complete the project within the agreed time in statement of work (SOW)
机译:由于技术的进步,当今业务中的变态变化是不可避免的。对于一家企业来说,繁荣或灭亡的技术起着重要的作用。在数字时代,信息技术在复杂的业务转型中起着举足轻重的作用。 IT组织在首次交付时,按时按预算交付,维持利润率和客户满意度方面面临着自身的挑战。进行了一项研究,该研究最多关注软件开发项目的进度和按时交付。使用适当的统计技术对主要数据进行解剖和分析。目标:(1)建立一个预测模型,该模型基于多个独立变量来测量进度差异。(2)描述项目经理使用因子分析有效处理项目按时交付所需的技能。 (3)衡量项目经理和项目进度偏差与人口统计变量之间的关联强度。方法:使用邮件调查技术收集来自70个不同项目的数据以及管理这些项目的项目经理的其他回复。结果:多元线性回归(MLR)技术的输出有助于以R值的准确性(相关系数为90.1%)来预测软件应用项目进度中的变化。研究分析表明,开发模型的因变量,即项目进度差异和自变量之间的关系是良好的。该模型将适合实时项目环境。使用多元因素分析,已经确定了项目经理高效按时交付所需的十六项关键技能。这些被高度加载为五个不同的因素。卡方拟合优度是一种非参数检验,有助于确定日程差异与人口统计变量之间的关联强度。结论:分析得出了一个预测模型,其中有11个独立变量(不包括常数)影响进度差异。五个不同的能力是要素标签,它们是估计和重新估计,风险管理,人员管理,需求管理和时间管理以及对项目经理所需的运行进行控制,以控制项目进度。还可以推断,项目经理的人口统计学变量与在工作陈述中约定的时间内完成项目的能力之间没有显着关联。

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