Review and Comparison of Performance Measurement Systems

Stříteská, Michaela; Špičková, Markéta

doi:10.5171/2012.114900

Cited by 68 publications

(67 citation statements)

References 25 publications

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“…First, despite the BSC being based on the stakeholder theory (Hubbard, 2009), the generic BSC does not reflect the interests of all stakeholders (Striteska & Spickova, 2012). This deficiency needs to be addressed, due to the existence of many stakeholders (e.g.…”

Section: Emergence Of the Need To Use The Bsc-anp Combination In Logimentioning

confidence: 99%

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

Küçükaltan

Irani

Aktaş

2016

Computers in Human Behavior

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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.

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Section: Emergence Of the Need To Use The Bsc-anp Combination In Logimentioning

confidence: 99%

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

Küçükaltan

Irani

Aktaş

2016

Computers in Human Behavior

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“…This model constructed based on the BSC but focus more on the stakeholder. EFQM has no focus or priorities in the models which more suitable for benchmarking (Striteska, M. and Spickova, M. 2012). Theory of Constraint (TOC) is far from being the complete performance system, hence this model is least popular performance measurement.…”

mentioning

confidence: 99%

Review on Performance Measurement Systems

Sorooshian¹,

Aziz²,

Ahmad³

et al. 2015

MJSS

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“…SMART's strength is in its direct intention of integrating corporate objectives with operational performance, as well as its focus on managing strategic objectives. However, as Striteska et al [53] have pointed out, SMART's weakness in comparison with BSC and the measurement matrix is that it does not provide a mechanism for specifying KPIs. …”

Section: Strategic Measurement and Reporting Technique -Smartmentioning

confidence: 99%

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

Kibira

Morris

Kumaraguru

2016

J. RES. NATL. INST. STAN.

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The emerging concept of smart manufacturing systems is defined in part by the introduction of new technologies that are promoting rapid and widespread information flow within the manufacturing system and surrounding its control. These systems can deliver unprecedented awareness, agility, productivity, and resilience within the production process by exploiting the ever-increasing availability of real-time manufacturing data. Optimized collection and analysis of this voluminous data to guide decision-making is, however, a complex and dynamic process. To establish and maintain confidence that smart manufacturing systems function as intended, performance assurance measures will be vital. The activities for performance assurance span manufacturing system design, operation, performance assessment, evaluation, analysis, decision making, and control. Changes may be needed for traditional approaches in these activities to address smart manufacturing systems. This paper reviews the current methods and tools used for establishing and maintaining required system performance. It then identifies trends in data and information systems, integration, performance measurement, analysis, and performance improvement that will be vital for assured performance of smart manufacturing systems. Finally, we analyze how those trends apply to the methods studied and propose future research for assessing and improving manufacturing performance in the uncertain, multi-objective operating environment.

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Review and Comparison of Performance Measurement Systems

Cited by 68 publications

References 25 publications

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

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

Review on Performance Measurement Systems

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

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