Suchergebnisse

Purpose
Fierce competition in the airline industry compels airline companies to offer various services. Yet, while companies strive to become preferable, customers confront numerous airline selection indicators, and as such causes vagueness in human thinking that needs to be systematically and normatively resolved. Accordingly, the purpose of this paper is initially to establish a strategic decision model that incorporates key selection indicators, among hundreds of criteria, through a systematic approach. Subsequently, it also aims to investigate the relative importance of these indicators for passengers.


Design/methodology/approach
This research first utilises a comprehensive literature review to uncover key indicators used in airline selection. Afterwards, the outcome obtained from the first phase initiated the phase of determining the prioritisation of these key selection indicators, through the analytic hierarchy process (AHP) method, based on passengers' judgments.


Findings
The outcome of structuring a strategic decision model reveals 32 key selection indicators to be mainly considered by passengers and these indicators are grouped under five dimensions in this paper. Then, the prioritisation results given by the AHP indicate that "price-related factors" and "customer satisfaction-related factors", respectively, are more important dimensions for passengers while selecting the best airline company.


Originality/value
The proposed approach provides a novel way to identify and prioritise key airline selection indicators for different passengers, through using the AHP, as a response to the need of adopting a systematic and comprehensive manner with the inclusion of general industry norms. Within this scope, the established model and the prioritisation results can be used as a reference by both airline passengers during their decision-making processes and airline companies which aim for becoming more competitive.

AbstractThis study proposes an ANP‐based evaluation of retail locations. Authors acted as decision analysts and interacted with professionals in Turkish apparel retailing sector for acquiring their knowledge and expertise. As a result, evaluation criteria of the study were identified. These criteria are clustered into five groups: store, other retailers, shopping centre/business district, accessibility, and environmental. The relationship among criteria were assessed and a network was formed. ANP approach was then utilized in order to evaluate possible retail locations for the apparel stores. Besides global preferences of these alternatives, relative importance of the criteria was also revealed. Copyright © 2007 John Wiley & Sons, Ltd.

AbstractAn occupational health and safety management system (OHSMS) is a set of people, resources, and policies interacting in an organized way to reduce damage and losses generated in the workplace. Although there are various OHSMSs in the shipping industry, the International Safety Management (ISM) Code which is managed by the International Maritime Organization is mandatory for all shipping companies. Occupational Health and Safety Assessment Series (OHSAS) 18,001:2007 is a good alternative to strengthen the implementation performance of the ISM Code. An integrated ISM‐OHSAS safety system can potentially construct a more powerful and practical OHSMS by redesigning safety procedures in ships as a workplace. This study aims to build a more comprehensive OHSMS for assessing and improving the safety at sea by coordinating the constituents of the ISM Code and OHSAS. The criteria sets of both systems are analysed using a Decision Making Trial and Evaluation Laboratory approach to reveal the relations between each other. The result of the proposed approach that represents the connections among important safety constituents may be very useful especially for shipping companies for designing their safety management processes.

PurposeBlood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.Design/methodology/approachThis study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.FindingsThe results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.Practical implicationsThis study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.Originality/valueThis study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

PurposeIn the automotive industry, the high process complexity becomes an important issue because of the increased number of product and process variants demanded by the customers. To avoid quality defects in assembly and losses in such a complex manufacturing environment, new predictive support systems are required. This study aims to develop a multiple attribute decision support system (DSS) for the prediction and quantification of the risk of failures on the workstations of a leading Turkish automotive manufacturing company.Design/methodology/approachInitially, the factors affecting the failures in workstations and the attributes to evaluate the factors are identified. Subsequently, the relations among the attributes are specified and priorities of them are calculated. Finally, the risk of failures is calculated and tested in a pilot study and validated with real production data.FindingsTo the best of authors' knowledge, this is a unique study that computes the risk scores on the workstations via DSS. The DSS has various advantages for improvements of the manufacturing quality: the risk of failures can be detectable and comparable, the effect of changes in the design of new workstations can be observed. Stations that have medium or high complexity scores demonstrated strong correlation with failure rates. A sensitivity analysis is conducted to predict the effect of improvement actions on the riskiness of the workstations.Originality/valueHigh level of production complexity becomes a crucial issue for companies that use various production processes. Considering this fact, it is a requirement for companies to observe and monitor the risk factors, especially in the assembly lines to be able to eliminate failures derived from complexity. Accordingly, to measure risk scores of the workstations in the assembly lines, a decision support for companies aids executives to manage the complexity level in a reliable and effective way. In this study, the authors develop such a DSS for TOFAS, a leading Turkish automotive company. The proposed DSS is verified and applied through a pilot study on a specific basic production unit. A sensitivity analysis is also conducted to see the effects of potential improvements on the risk scores. Additionally, the trend of risk scores for the stations can also give valuable information for tracing the changes in the time horizon. The proposed DSS also enables an opportunity for the executives in their decision of design processes of new production lines by allocating limited resources in an appropriate way based on the risk scores of possible workstations. The proposed DSS is the first and unique proactive failure prevention model developed in a Fiat Chrysler Automobiles (FCA) plant across the world. TOFAS executives also plan to introduce and enlarge the usage of the model to other FCA plants. It may also be possible to apply the model to other assembly lines in any sector. Another plan of the executives of TOFAS is developing a software, which manages each parameter, to constitute data to the DSS to run this system more instantly and effectively. Moreover, they can take integration actions of the software with world-class manufacturing problem management system that is currently in use in TOFAS.