The Impact of Agile Methodologies and Cost Management Success Factors: An Empirical Study

تعد إدارة تكلفة البرامج ميزة هامة لإدارة المشاريع. على هذا النحو ، يجب أن تستخدم في مشروع أو خط عمل. تعد إدارة تكلفةالبرامج جزءًا لا يتجزأ من إخفاقات تطوير البرامج ، والتي بدورها تتسبب في فشل البرنامج. وبالتالي، من الضروري أن يطور المهنيون فيمجال تطوير البرمجيات مهاراتهم في إدارة التكاليف لتقديم مشاريع برمجية ناجحة. الهدف من هذه الدراسة هو دراسة تأثير عوامل النجاح فيإدارة التكاليف مع عوامل إدارة المشروع وثلاث منهجيات ذكية منهجيات البرمجة المتطرفة - (XP) و Scrum و Kanban التي تستخدم فيصناعة البرمجيات الباكستانية. ولتحديد النتائج، طبق الباحثون منهجًا كميًا من خلال مسح موسع ل 52 شركة لتطوير البرمجيات الذكية فيباكستان. تم إجراء التقنيات الإحصائية، مثل ارتباط بيرسون والانحراف المعياري والمعياري لفحص النتائج. بعد هذا التحليل، وجدنا أن إدارةالتكاليف لها تأثير إيجابي على عوامل إدارة المشاريع الأخرى، وهي الجدول الزمني، والمجال، والمخاطر، والموارد، والجودة. علاوة علىذلك، فقد تقرر أن أداء كانبان (Kanaban) بشكل عام أفضل من سكروم (Scrum) وإكس بي (XP) في سياق عوامل إدارة المشروعSoftware cost management is a significant feature of project management. As such, it needs to be employed in a project or line of work. Software cost management is integral to software development failures, which, in turn, cause software failure. Thus, it is imperative that software development professionals develop their cost management skills to deliver successful software projects. The aim of this study is to examine the impact of cost management success factors with project management factors and three agile methodologies – Extreme Programming (XP), Scrum and Kanban methodologies which are used in the Pakistani software industry. To determine the results, the researchers applied quantitative approach through an extensive survey on 52 agile software development companies in Pakistan. Statistical techniques, such as Pearson’s correlation and mean and standard deviation were performed to examine the results. Following this analysis, we found that cost management has a positive effect on other project management factors, which are schedule, scope, risk, resources, and quality. Furthermore, it is determined that, in general, Kanban performed better than both, Scrum and XP in the context of project management factors

The impact of agile methodologies and cost management success factors: an empirical study

Software cost management is a significant feature of project management. As such, it needs to be employed in a project or line of work. Software cost management is integral to software development failures, which, in turn, cause software failure. Thus, it is imperative that software development professionals develop their cost management skills to deliver successful software projects. The aim of this study is to examine the impact of cost management success factors with project management factors and three agile methodologies – Extreme Programming (XP), Scrum and Kanban methodologies which are used in the Pakistani software industry. To determine the results, the researchers applied quantitative approach through an extensive survey on 52 agile software development companies in Pakistan. Statistical techniques, such as Pearson’s correlation and mean and standard deviation were performed to examine the results. Following this analysis, we found that cost management has a positive effect on other project management factors, which are schedule, scope, risk, resources, and quality. Furthermore, it is determined that, in general, Kanban performed better than both, Scrum and XP in the context of project management factors

Factors of Successful Management of Information Systems Development Projects

The tradition on IS research has established the so called iron triangle , the three dimensions that characterize the project management success (PMS) if it is delivered on time, within the budget and according to specifications. However, less attention has been given to the continuum characterized by deviations from the baseline from each of these three dimensions. This paper draws on the definition of the PMS continuum and analyzes four potential factors that may influence PMS: team, project manager, project, and portfolio. We develop hypotheses and test them in a hierarchical linear regression using a sample of 899 IS projects of a leading bank, collected between January, 2014 and December, 2015. Besides proposing and discussing a new continuous PMS indicator, we identify factors that influence IS PMS positively (project size, duration, postponement, and project manager formal power) and negatively (team size and team allocation dispersion). The results suggest guidance of team members’ allocation

Optimization of workers quantity using mathematical model

Production and maintenance processes are inherent in the life cycle of every product. Despite great efforts to automate these processes, a great deal of human resources are still required, which represent a significant part of the financial costs. Each process is composed of sub-tasks that require certain specifics in terms of the number of staff, their expertise, qualifications and experience. It is assumed that the staff are divided according to specifics into different groups with differing wages. Workers' wages are reflected in the final financial cost of the product, its life cycle and its return. Reducing labour costs in a production or maintenance process can be achieved by reducing the total number of staff deployed in the process and by appropriately composing groups of workers. Reducing labour costs leads to increased competitiveness in the market. The main tools of competitiveness are price, speed and range of services offered. This paper examines a strategy that uses price as the main tool for competitiveness in the market. One way to reduce the final price of the product for the customer is to optimise the costs of human resources. This can be achieved through appropriate planning of staff shifts. The specifics of the deployment of staff in a production or maintenance process depend on the requirements of the process sub-tasks. This means that each group of workers can only handle a certain group of tasks according to their qualifications. A Binary Programming Problem with Linear Bonds will be used to plan the deployment of staff, aiming to minimize the number of workers needed in a production or maintenance process within a predefined timeframe.Web of Science20236345633

Portfolio Management: The Holistic Data Lifecycle

21 pagesMachine learning provides many benefits to Portfolio Managers in analysing data and has the potential to provide much more. A concern with the approach to Machine Learning in Portfolio Management is that is caught between two domains: finance and information systems. In reality, to ensure its success, having these two separate and distinct domains are problematic. What is required is a holistic view, facilitating discussions, with data being the unifying concept and the one that is key to success. The data value map is a lens that allows all involved, in the use or adoption of Machine Learning in Portfolio Management, to form a shared understanding of the lifecycle of the data involved. Rather than being seen as a financial concept or a technical concept, this view of the data lifecycle provides a platform for all involved to determine what is required, and to identify and deal with any potential pitfalls along the way. A holistic view, and shared understanding, are required for the success of Machine Learning in Portfolio Management. Research on the intersection between Machine Learning and Portfolio Management is currently lacking. A focus on the different parts of the data lifecycle provides an opportunity for further research

Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization

[EN] The paper aims to identify the individuals who influence the knowledge sharing processes from an internal social network and to forecast the future knowledge flows that may cross it. Exploratory research is employed, and a four-phase methodology is developed which combines a social network analysis with structural modeling. This is applied to the internal enterprise social network used by a British insurance company. The main results emphasize the most influential groups, their relationships, future knowledge flows, and the connection between the network's heterogeneity and structure, and employees' future knowledge sharing intention. These findings have both theoretical and practical implications. The theory is extended by proving that a social network analysis can be used as a tool for evaluating and predicting future knowledge flows. At the same time, a solution is offered to decision-makers so they will be able to: (i) identify the potential knowledge loss; (ii) determine leaders; (iii) establish who is going to act as a knowledge diffuser, by sharing what they know with their coworkers, and who is going to act as a knowledge repository, by focusing on acquiring increasingly more knowledge; (iv) identify the elements that influence employees' future knowledge sharing intention. (C) 2016 Elsevier Inc. All rights reserved."The research reported in this paper is supported by the European Commission for the project "Engaging in Knowledge Networking via an interactive 3D social Supplier Network (KNOWNET)" (FP7-PEOPLE-2013-IAPP 324408)".Leon, R.; Rodríguez Rodríguez, R.; Gómez-Gasquet, P.; Mula, J. (2017). Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization. Technological Forecasting and Social Change. 114:103-118. https://doi.org/10.1016/j.techfore.2016.07.032S10311811

Enterprise, project and workforce selection models for industry 4.0.

Abstract Enterprise, project, and workforce selection models for Industry 4.0. Rupinder Kaur The German federal government first coined industry 4.0 in 2011. Industry 4.0 involves the use of advanced technologies such as cyber-physical system, internet of things, cloud computing, and cognitive computing with the aim to revolutionize the current manufacturing practices. Automation and exchange of big data and key characteristics of Industry 4.0. Due to its numerous benefits, industries are readily investing in Industry 4.0, but this implementation is an uphill struggle. In this thesis, we address three key problems related to Industry 4.0 implementation namely Enterprise selection, Project selection and Workforce selection. The first problem involves identification of enterprises suitable for Industry 4.0 implementation. The second problem involves prioritization and selection of Industry 4.0 projects for the chosen digital enterprises. The third and last problem involves workforce selection and assignment for execution of the identified Industry 4.0 projects. Multicriteria solution approaches based on TOPSIS and Genetic Algorithms are proposed to address these problems. Industry experts are involved to prioritize the criteria used for enterprise, project and workforce selection. Numerical applications are provided. The proposed work is innovative and can be useful to manufacturing and service organizations interested in implementing Industry 4.0 projects for performance improvement

Exploring A Project Management Dilemma: A Case Study Examining the Shortage of Qualified Project Personnel in a Four-Year Research University

Qualified individuals are an essential resource of project teams. A primary goal of the project manager is to ensure appropriate team members are hired to meet project objectives and stakeholder expectations. However, this is not always the case since a shortage of qualified project personnel seldom occurs, and prevents the project from being completed successfully. The project personnel shortage problem is prevalent in a four-year research university as inaccessibility to qualified individuals threatens the project deadline and delivery to sponsors. Research suggested these qualified individuals are recruited to provide scientific and technical expertise to academic projects. They possess knowledge and capabilities utilized in three dimensions of research, fundamental, applied and experimental scientific. Some results of their effort in these dimensions have produced theories, methods, algorithms, technology, equipment, instrument, mechanism, and systems. This study explored the reasons for the shortage of qualified project personnel and the actions of project managers relating to the shortage. The study utilized a qualitative methodology, along with a case study design to explore the problem, as experienced by project managers in a four-year research university in the Southeast. Findings revealed problems in the areas of compensation, leadership involvement, pipeline issues, project funding, and the need for adequate project planning. The implications of the findings were also examined, followed by recommendations to project stakeholders. As Christ cautioned his followers that before building a tower one must first consider the cost; likewise, project managers must carefully plan before execution of project resources, specifically qualified project personnel which are needed to achieve successful project completion

Beneficios de la polifuncionalidad laboral en la industria retail: enfoque “k-chaining” con incertidumbre en la demanda

La polifuncionalidad es una estrategia de flexibilidad laboral en la que las compañías educan a un conjunto de empleados para que éstos puedan desempeñar efectivamente un conjunto de tipos de tareas. Además, cuando los planes de polifuncionalidad son estructurados a través de políticas k-chaining es posible obtener la máxima flexibilidad para responder ante una demanda de personal incierta. A nivel estratégico, esta investigación diseña el plan de capacitación para una mano de obra polifuncional, tal que ante altos niveles de variabilidad en la demanda se pueda alcanzar los mayores ahorros en los costos de sobredotación y subdotación de personal y, a su vez, satisfacer los niveles de demanda requeridos. Se desarrollaron modelos de programación entera-mixta con demanda estocástica para resolver un problema de asignación de personal polifuncional bajo una política k-chaining con k≥2. Estos modelos fueron evaluados para manos de obra homogénea y heterogénea. Para un caso de estudio en la industria retail, los resultados mostraron que para altos niveles de variabilidad en la demanda una política k-chaining con k≥2 es más costo-efectiva que una política estricta con k=2 para alcanzar los máximos beneficios de la polifuncionalidad. Además, que los requerimientos de polifuncionalidad aumentan a medida que la incertidumbre de la demanda es mayor. También se observó que la consideración del fenómeno de aprendizaje y olvido permite obtener niveles de polifuncionalidad más ajustados a la operación real de una tienda de retail.MaestríaMagister en Ingeniería Industria