Abstract
Purpose The purpose of this paper is to explore the applicability of the failure mode and effects analysis (FMEA) as an effective tool for decreasing failure risk in the early phase of the new product development (NPD), which adds to existing literature on the application of FMEA in NPD. Design/methodology/approach Through the application of action research (AR) methodology, it was possible to develop a case study examining the use of FMEA to decrease NPD risk in an early phase of NPD execution. Findings The importance and immediate gains of identifying NPD failures support FMEA's usefulness for NPD risk decrease. Moreover, its user-friendliness, timeliness and cost advantages facilitate the introduction of FMEA in the early phase of NPD execution. Originality/value FMEA is a well-known method used in manufacturing companies to identify and correct failures in products, processes and systems. This article explores the lack of practice-oriented evidence on the use of FMEA in the early phase of NPD execution and provides support to its applicability and effectiveness.

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Abstract

Abstract
Co-branding in the hospitality luxury sector is still understudied in the literature. This study aims at tackling this gap through the analysis of a case of a co-branding strategy between a vinous-concept luxury hotel in Portugal and premium wine brands of domestic producers. Fourteen in-depth interviews with managers of the luxury hotel and wine brand partners supported the exploratory research. This chapter represents a case of qualitative data application to an underestimated topic in the literature from the managers' point of view. The study offers evidence on the benefits for both parties, reasons for adopting co-branding, and partners' selection attributes. The improvement of brand image emerges as one of the main advantages of co-branding with a luxury hotel. Based on the literature review and the interviews with managers, the study proposes a set of hypotheses to be tested in future research. This chapter provides interesting cues for academics and practitioners. © 2021, IGI Global.

Abstract

Abstract
Priority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study.