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    A decision support model for identification and prioritization of key performance indicators in the logistics industry

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    Irani_Computers_in_Human_Behavior_final.pdf (901.9Kb)
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    Publication date
    2016-12
    Author
    Kucukaltan, B.
    Irani, Zahir
    Aktas, E.
    Keyword
    Logistics performance indicators; Balanced scorecard; ANP; Multi-criteria decision making; Stakeholders; Social media
    Rights
    © 2016 Elsevier B.V. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
    Peer-Reviewed
    Yes
    
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    Abstract
    Performance measurement of logistics companies is based upon various performance indicators. Yet, in the logistics industry, there are several vaguenesses, such as deciding on key indicators and determining interrelationships between performance indicators. In order to resolve these vaguenesses, this paper first presents the stakeholder-informed Balanced Scorecard (BSC) model, by incorporating financial (e.g. cost) and non-financial (e.g. social media) performance indicators, with a comprehensive approach as a response to the major shortcomings of the generic BSC regarding the negligence of different stakeholders. Subsequently, since the indicators are not independent of each other, a robust multi-criteria decision making technique, the Analytic Network Process (ANP) method is implemented to analyze the interrelationships. The integration of these two techniques provides a novel way to evaluate logistics performance indicators from logisticians' perspective. This is a matter that has not been addressed in the logistics industry to date, and as such remains a gap that needs to be investigated. Therefore, the proposed model identifies key performance indicators as well as various stakeholders in the logistics industry, and analyzes the interrelationships among the indicators by using the ANP. Consequently, the results show that educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies.
    URI
    http://hdl.handle.net/10454/8962
    Version
    Accepted manuscript
    Citation
    Kucukaltan B, Irani Z and Aktas E (2016) A decision support model for identification and prioritization of key performance indicators in the logistics industry. Computers in Human Behavior. 65: 346-358.
    Link to publisher’s version
    http://dx.doi.org/10.1016/j.chb.2016.08.045
    Type
    Article
    Collections
    Management and Law Publications

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