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The increasing organizational complexity and competitiveness in the industry are driving interest in project management to ensure successful businesses. Uncertainty in estimating activities' duration is one of the main criticalities of the planning phase, especially in the industrial environment, where most projects, such as the optimization or qualification of new lines, usually require the involvement of resources whose commitment is almost never bound to just one task. In this context, the impact of human behavior if not properly considered can lead to serious damage in terms of time and costs and these hidden risks need to be properly assessed. The present study aims to analyze and quantify the impact of human behaviors (as described by Parkinson's Law, Student Syndrome, and hidden safety) on the project in terms of delays in scheduled time though the use of a Monte Carlo simulation. The objective of the methodology is to provide the project manager with the insight required to consciously address the uncertainties of the scheduling phase in order to ensure the success of the project. In order to illustrate the methodology in a more comprehensive way, a case study regarding a project for the innovation of the production process in a real industrial plant is provided. [ABSTRACT FROM AUTHOR]

Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability. [ABSTRACT FROM AUTHOR]