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. Proceedings of the ICE-Engineering Sustainability, 164(1), 85-93. El-Gohary, N. M., Osman, H., and El-Diraby, T. E. (2006). Stakeholder management for public private partnerships. International Journal of Project Management, 24(7), 595-604. Etzioni, A. (1964). Modern organizations. (Foundations of modern sociology series). New Jersey: Prentice-Hall. Floricel, S., Michela, J. L., and Piperca, S. (2016). Complexity, uncertainty-reduction strategies, and project performance. International Journal of Project Management . Fowler, M., and Highsmith, J. (2001). The Agile

aims at making project management flexibility explicit by the following actions: 1) recognising the degree of flexibility in practice; 2) finding practitioners’ perspectives regarding flexibility; 3) embedding flexibility into practice; and 4) focusing on improvement of project performance and management of complexity by implementing flexibility. To fulfil these four objectives, four research questions were formulated. 1) What is the status of flexibility in current practice? 2) What are the enablers of flexibility? 3) What are the practitioners’ perspectives

1 Introduction Project performance management and identifying its success factors have been an active area of research over the past few decades. A large body of research has been carried out on the cost, time and quality performances in construction projects, collectively termed as the ‘iron triangle’ by Atkinson (1999) . A detailed literature review has been published by Bassioni et al. (2004) . Many studies have attempted to identify the factors contributing to project management success, group them by project objectives or into macro variables. Researchers

; McKinsey and Company 2010 ; Mani et al. 2017 ; Dixit and Saurabh, 2019 ). Due to the unique nature of the work, planning, timely delivery and reliability have always been a subject to concern. 1.1 Project performance Construction industry is one of the main contributors to the development of any country, which is the most important in creating jobs when it comes to India. However, most of the times, it has seen the downturn because of many internal and external reasons. Nevertheless, the most important of them is time delay and cost overrun that hampers the performance


Each industrial automation project includes tasks that strongly depend on human factors, many of which may belong to the critical path or chain of the project. Multitasking significantly affects human productivity. The reduction in productivity has a direct result of delaying the primary task, which may cause an overall delay to the project with cost and time overruns. A project should be seen with respect to a global environment, such as that of a company, where resources are shared among its portfolio of projects. Although multitasking might have negative results, it is something that cannot be eliminated but can be mitigated by project managers.

This article presents the effects of multitasking on human productivity, especially when the tasks are complex, like programmable logic controller (PLC)/supervisory control and data acquisition (SCADA) software development. Using the analytical hierarchy process (AHP) method, a simple tool is created to be used by project managers, in order to assist them in decision-making. Criteria that influence these decisions are referenced, and their priority vectors are proposed. In addition, some real examples are given.

Project managers face a complex situation when they are asked to decide on the allocation of resources and priorities among different projects. Parameters that are difficult to predict in real situations may have a significant role in the decision-making process.

There are a lot of published works based on AHP applications in different fields, but there is a gap in the field of industrial automation projects and the related project manager’s decision-making. This study focuses on these decision-making processes that determine which tasks should be paused or not for a better allocation of resources, taking into account the global environment of a technical company. The tool can be implemented with changing criteria and priority vectors to adapt to different types of projects. Future research could identify additional criteria and subcriteria with different priority vectors, depending on different project specifications.

This article is the extended version (Part II) of CCC 2017 Procedia Engineering paper.

. Košice: VÚSI. 220 p. [4] Zou, P.X.W., Zhang, G., Wang, J.(2007). Understanding the key risks in construction projects in China. International Journal of Project Management (25), p. 601-614. [5] Barraza, Gabriel A., Back, W. Edward, Mata, Fernando (2004). Probalistic forecasting of project performance using stochastic S curves. Journal of Construction Engineering and Management. 130 (1), p. 25-32. [6] Crandall, K. C., and Woolery, J. C. (1982). Schedule development under stochastic scheduling. J. Constr. Div., Am. Soc. Civ. Eng., 108(2), 321-329. [7] Marshall, Robert


The construction phase of oil and gas projects (OGPs) is a risky process and project managers face numerous challenges during this particular period. A proper risk analysis and management during the construction phase of the OGPs not only will affect the timely and successful operation of the project as a whole, it can also affect occurrence of risks in subsequent phases and overall economic viability of the project. As a result, via using extensive literature review, this study tries to answer the question of what are main risks involved in construction phase of OGPs and which methods are used for identifying them? The outcome of this research would likely be a valuable source for construction professionals to improve project performance while managing existing risks. It is also useful to avoid common problems that befall many project managers and will assist them to have a better understanding of risk management as part of a project plan.


CM is a globally renowned machine, designed to work as a mass production technology for underground coal. Different major coal producers across the globe are using this technology for decades to produce underground coal efficiently. India is also one of the major players globally in the arena of coal production and adopted this cutting edge technology since last decade by implementing at. few of the selective underground coal mining projects. Performance of CM technology is influenced by the geo-mining condition, fleets of other ancillary units and reliability of subsystems while implementation of this system depends largely on the extent of reserve. These aspects generate a scope of large scale research and development in this field. Overall Equipment Effectiveness (OEE) is the parameter to benchmark the equipment performance globally. OEE is the product of equipment availability, performance and product quality. This mining machine based paper focuses on the Overall Equipment Effectiveness (OEE) of the complete CM based operation to identify the vulnerable systems, which helps to design proper preventive maintenance programme. The CM based system is divided into few subsystems, such as; electrical, cutter, gathering arrangement, traction, hydraulic, chassis, feeder breaker, shuttle car, CM conveyor and out-bye conveyor. The downtime data used for this analysis is collected from an underground coal mine situated in the central part of India, belongs to one leading coal producing company of the country. From analysis it was found that, electrical systems and conveyors are among most vulnerable systems and deserves more care during maintenance. On the basis of these results recommendations are made to redesign the Preventive Maintenance Programme, in order to avoid the lower availability as well as lower OEE.


The design–build (DB) project delivery method has been used for several decades in the US construction market. DB contracts are usually awarded on the basis of a multicriteria evaluation, with price as one of the most salient criteria. To ensure the project’s success, an owner usually has to invest enough time and effort during scoping and early design to define a program, scope, and budget, ready for procurement and price generation. However, this process can become a burden for the owner and may lengthen the project development duration. As an alternative to the traditional DB, the progressive design–build (PDB) approach permits the selection of the DB team prior to defining the project program and/or budget. PDB has the advantage of maintaining a single point of accountability and allowing team selection based mainly on qualifications, with a limited consideration of price. Under PDB, the selected team works with the project stakeholders during the early design stage, while helping the owner balance scope and budget. However, the key to the effectiveness of PDB is its provision for the ongoing and complete involvement of the owner in the early design phase. Due to the differences between PDB and the other project delivery methods (e.g., traditional DB), project teams must carefully consider several factors to ensure its successful implementation. The research team conducted a case study of the University of Washington’s pilot PDB project to complete the West Campus Utility Plant (WCUP). This paper carefully explores and summarizes the project’s entire delivery process (e.g., planning, solicitation, design, and construction), its organizational structures, and the project performance outcomes. The lessons learned from the WCUP project will contribute to best practices for future PDB implementation.

Risks and Benefits”, Social Science & Medicine, Vol. 191, pp. 48-56, 2017. [7] Strausz, Roland, “A Theory of Crowdfunding: A Mechanism Design Approach with Demand Uncertainty and Moral Hazard”, American Economic Review, Vol. 107, No. 6, pp. 1430-1476, 2017. [8] Zhao, Liang and Vinig, Tsvi, “Hedonic Value and Crowdfunding Project Performance a Propensity Score Matching-Based Analysis”, Review of Behavioral Finance, Vol. 9, No. 2, pp. 169-186, 2017.