1,721,029 research outputs found
The use of quality function deployment in hazard analysis and risk evaluation
No full textOccupational health and safety (OHS) still represent a relevant problem at a global level despite the ever-increasing effort made in the last years at the normative level. Recent research on the factors influencing this phenomenon has shown the difficulties in bringing to light accidents’ causalities and in the definition of the related measures for safety management. Actually, the risk assessment should take into account the mutual influences among the different risks and the related potential effects, while performing risk assessment in a sequential manner cannot always consider these interactions properly. To deal with such an issue, the use of the Quality Function Deployment (QFD) method has been proposed in several studies to perform hazard analysis and risk assessment more thoroughly. Nevertheless, although the above-mentioned studies provide remarkable research insights concerning the use of QFD in such a context, a comparative analysis bringing to light the effectiveness of the different approaches is missing. To reduce this gap the current study aims at investigating the recent research proposing QFD as a tool for hazard analysis and risk evaluation. The outcome of the study has shown that while all the approaches rely on the cause-effect mechanism inherent to the functioning of the House of Quality (HoQ), different goals and results can be achieved depending on the analysis standpoint (i.e. a top-down or a bottom-up approach) and the supportive tools used, such as analytic network process (ANP), or fuzzy logic. Accordingly, the study output can contribute practically to augmenting knowledge on the use of decision-making tools based on QFD in safety management
A meta-model-driven approach to support digitization evolution with maturity models
No full textDigitization makes knowledge and information a centerpiece since these connect all dimensions affected by innovation, i.e., people, processes, organization, business, and technology. Maturity Models (MMs) support managers in this evolution. The paper provides a theoretical formalization of MMs to give a practical knowledge contribution to increasing the intensiveness of information in enterprises, through digitization evolution. The paper reviews the main MMs, and presents their state of the art. Then, it defines a backbone structure common to MMs to abstract and describe their features by a meta-model. The new meta-model-driven approach guides companies in the selection of MMs, and in designing a new MM, where needed. The meta-model formalizes the two-levels inputs, the process, and the output to align the company's motivations with the MM features, resulting in the definition of an appropriate MM for organizations. A qualitative exploratory case study shows the approach and its results, providing guidelines for future actions
The occupational health and safety risks of ongoing digital transformation. A knowledge management software powered literature review
No full textThe fast technical-organizational transformation undergoing, promoted by Industry 4.0 as well as other similar initiatives, partly sped up due to the Covid-19 pandemic, is radically changing actors, modes, and environments of human work. Although part of these innovations is directed at occupational health and safety (OHS), some scholars raise reasonable doubts, arguing that the same innovations even if they solve some problems, could create new ones. The 4th industrial revolution is likely introducing entirely new categories of worker risks. This review explores the evidence base that supports the latter hypothesis. Besides, it proposes an innovative and potentially useful combination of methods and computer applications. By applying the Prisma methodology, tagging one-by-one activity, and hyperlinks, the paper proposes a knowledge graph explorable in terms of semantic, logical and chronological links as well as argumentative. In the final phase, the paper synthesizes in a meta-annotation ten recurring themes and clusters that emerged in this bottom-up process of knowledge elicitation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
EOQ inventory model for perishable products under uncertainty
Full text linkPerishable products require accurate inventory control models as their effect on operations management can be critical. This assumption is particularly relevant in highly uncertain and dynamic markets, as for the ones generated by the pandemic era. This paper presents an inventory control model for perishable items with a demand rate variable over time, and dependent on the inventory rate. The model also considers the potential for backlogging and lost sales. Imperfect product quality is included, and deterioration is modelled as a time-dependent variable. The framework envisages the possibility to define variables affected by uncertainty in terms of probability distribution functions, which are then jointly managed via a Monte Carlo simulation. This paper is intended to provide an analytical formulation to deal with uncertainty and time-dependent inventory functions to be used for a variety of perishable products. The formulation is designed to support decision-making for the identification of the optimal order quantity. A numerical example exemplifies the outcomes of the paper and provides a cost-based sensitivity analysis to understand the role of main parameters
Yet Another Warehouse KPI’s Collection
Full text linkWarehouses are strategic systems for all supply chains since their performances impact operations and efficiency of all direct and indirect stakeholders. Therefore, monitoring warehouses' performances constantly and real-time is getting so important, both to guarantee an effective warehouse management and to detect in advance anomalous and potentially destructive trends. The current literature about warehousing Key Performance Indicators (KPI) appears to lack an extensive collection. Classification logics are often partial or based on specific contexts. At the same time, the amount and typology of data collected on the warehouse often hinder a consistent performance monitoring. This paper aims to fill such gap and guide organizations in identifying the relevant information to gather for warehouse performance monitoring. Firstly, a scoping literature review was conducted to provide an extensive list of warehouse KPIs. Then, the collected results set the groundwork for a dynamic and interactive database called YAWKC. This tool is designed as a knowledge graph allowing for non-linear exploration of data and for continuous enrichment by experts’ contribution, representing the starting point for further knowledge generation in an explorable, dynamic and potentially ever-growing way
Functional resonance in industrial operations: A case study in a manufacturing plant
Full text linkThe increasing complexity of large-scale industrial processes demands for innovative approaches to manage risk and safety. Considering the tendency to automate activities, the role of humans changed over years, generating different types of tight, even symbiotic, inter-relationships. In these coupled interacting scenarios, industrial processes have to be studied focusing jointly on technical and human elements of work, following thus a socio-technical perspective. This paper deals with an application of the Functional Resonance Analysis Method (FRAM) to analyze socio-technical safety-related issues in manufacturing. A detailed case study related to forging operations clarifies the outcomes of the proposed method, supporting the identification of mitigating actions to reduce risks and increase system's resilience
A systemic approach for stochastic reliability management in human–machine systems
No full textIn today's complex engineered systems, comprising a multitude of interacting components, preserving system performance is of utmost importance. The challenge often lies in effectively prioritizing components with the highest potential to compromise system reliability, mainly when human interaction with technical artefacts is not negligible. This study proposes a systemic methodology for pragmatic reliability management within human–machine systems. The proposed approach combines a rule-based adaptation of the well-established Failure Mode, Effects, and Criticality Analysis (FMECA) with a probabilistic Fault Tree Analysis (FTA). Furthermore, the technical considerations are seamlessly integrated into a human-centric analysis, utilizing the Standardized Plant Analysis Risk – Human Reliability Analysis (SPAR-H). The proposed decision-support methodology is instantiated through Monte Carlo simulations to account for stochastic phenomena and uncertain operating conditions. The effectiveness and practicality of the proposed approach are elucidated through a case study involving a high-reliability system, specifically a high-mobility multi-wheeled vehicle. This study demonstrates the step-by-step application of the proposed approach and its implications in challenging operating scenarios, reaffirming its potential to enhance reliability management within human–machine systems
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