پیش بینی تلاش نرم افزار برنامه ریزی منابع سازمانی با استفاده از مدل استنباطی ماشین بردار پشتیبان تکاملی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|1196||2012||11 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Project Management, Volume 30, Issue 8, November 2012, Pages 967–977
Despite significant advances in procedures that facilitate project management, the continued reliance of software managers on guesswork and subjective judgment causes frequent project time overruns. This study uses an Evolutionary Support Vector Machine Inference Model (ESIM) for efficiently and accurately estimating the person-hour of ERP system development projects. The proposed ESIM is a hybrid intelligence model integrating a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). The SVM mainly provides learning and curve fitting while the fmGA minimizes errors. The analytical results in this study confirm that, compared to artificial neural networks and SVM, the proposed ESIM provides preliminary prediction at early phase of ERP software development effort for the manufacturing firms with superior accuracy, shorter training time and less overfitting. Future research can develop user-friendly expert systems with window or browser interfaces that can be used by planning personnel to flexibly input related variables and to estimate development effort and corresponding project time/cost.
Enterprise resource planning (ERP) enables developers to enhance the global competitiveness and sustainability of their client enterprises by ensuring efficient resource allocation. In practice, ERP software functions and specifications are highly unpredictable at early stages of R&D; thus, initial cost estimation relies mostly on the subjective judgments of experienced software engineers. Although knowledgeable sales managers or estimators may generate accurate cost assessments via a cooperative approach, professionals in small and medium-sized software enterprises are often difficult to train and highly mobile. Thus, the difficulty of retaining experienced personnel with project knowledge results in such problems as loss of project know-how. Despite the significant advances in the procedures that facilitate project management (PMI, 2008), product managers in the software industry still encounter problems requiring guesswork and subjective judgment, which often result in inaccurate estimates. Effort estimation is not functionally related to the basic drivers of ERP system development. Companies can thus lose market share and orders when attempting to attract customers during the early marketing phase. Although human experts can achieve satisfactory outcomes, shortfalls typically result from inefficient information management. Shortcomings in current subjective assessments or analogous methods indicate the urgent need and opportunity for improvement. Unlike traditional manufacturing, most software product development costs are incurred by investment in human resources. As software is a virtual intelligence and customer service–oriented product, software developers must estimate project completion time at early stages. Many studies (Ahmed and Muzaffar, 2009, de Barcelos Tronto et al., 2008, Elish, 2009, Finnie et al., 1997, Huang and Chiu, 2006, Huang et al., 2008, Jørgensen, 2010, Kazemifard et al., 2011, Lopez-Martin, 2011, Mair et al., 2000, Oliveira et al., 2010 and van Koten and Gray, 2006) have proposed cost or effort forecasting methods for software development projects. Nevertheless, the effectiveness and efficiency of approximate inference methods for estimating ERP system development efforts via hybrid intelligence (i.e., combination of multiple artificial intelligence techniques) are rarely addressed.
نتیجه گیری انگلیسی
This study demonstrated an ESIM approach for predicting development time for ERP software projects. The ESIM is a hybrid intelligence technique integrating an SVM with an fmGA. The SVM provides learning and curve fitting by mapping nonlinear input and output, and the fmGA primarily deals with global optimization concurrently while minimizing prediction error. According to the empirical case study, the proposed systematic approach efficiently predicts the development effort of ERP software project at early stages as prediction error is within a satisfactory limit. Through the cross-validation and prediction power testing, ESIM and ANNs show better performance than conventional SVR in ERP project effort prediction. Further, analytical results via random sampling process indicate that the overall MMRE of ESIM is 26.8% for training datasets. For the randomly separated data for testing, the MMRE achieved by ESIM was 27.3%, which is lower than the 36.5% by ANNs (Table 3) with a 25.2% improvement in prediction accuracy. Particularly, the current in-house analogous practice is 35.0% in average indicating an improvement of 22.0% via ESIM over the MMRE of the software provider. Notably, the effort required to complete software development projects also depends on collaboration among multiple engineers, their experience level, and location. The historical project data considered by the ESIM did not include such information and could not be covered in this research. Therefore, to further improve the ESIM performance, future studies are needed to gather comprehensive project data. Software companies typically have high employee turnover. Therefore, they may often have difficulty retaining experience-based and implicit corporate knowledge. Conversely, only incomplete design specifications are available during the early quotation and negotiation stages. Rapidly responding to customer enquiries using little available information is critical to securing orders. Fairly accurate tools that can be used to predict the manpower requirements for developing an ERP system are needed so that engineered-to-order suppliers can maintain competitive advantage in global markets.