A Theory of Information Quality and its Implementation in Systems Engineering

Grenn, Michael; Sarkani, Shahram; Mazzuchi, Thomas A.

doi:10.1109/jsyst.2013.2290737

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…For example, reference [39] and [40] use the standard Cronbach alpha to assess the information quality based on data from the questionnaire. Information entropy is an important concept of information theory, reference [42] takes information entropy as a tool to evaluate the information quality. Besides, the mean designates method and grey incidence clustering model are employed to evaluate the marketing information quality based on the questionnaire [43].…”

Section: Dimensionsmentioning

confidence: 99%

Supply Chain Logistics Information Quality Evaluation From Just-in-Time Perspective

et al. 2020

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In this paper, we seek to evaluate supply chain logistics information quality (SCLIQ) from the Just-In-Time (JIT) perspective. First, based on the analysis of SCLIQ, it is proposed that the SCLIQ evaluation should be combined with JIT philosophy. Second, based on SPC and information entropy method, an evaluation method of SCLIQ is presented. The statistical process control method (SPC) is adopted to evaluate the information quality of quantity and time respectively, the information entropy method is employed to determine their weight and the comprehensive evaluation results, and these results are analyzed according to SPC. Finally, a numerical example is used to demonstrate the feasibility of the proposed method. The major contribution of this paper is the combination of SQLIQ and JIT philosophy, while an objective and comprehensive evaluation method of SCLIQ from the JIT perspective is developed. The results are useful for evaluating SCLIQ and determining the best direction of improvement activities. INDEX TERMS supply chain; logistics; information quality; Just-In-Time; statistical process control method (SPC); information entropy method

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Section: Dimensionsmentioning

confidence: 99%

Supply Chain Logistics Information Quality Evaluation From Just-in-Time Perspective

et al. 2020

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“…The sequential transition of this theoretical case is highly unlikely to occur in practice, however it provides insight into the expected H R behavior over time as R increase in quality and decrease in uncertainty as they approach the desired end state. More information on the theory is available in Grenn, Sarkani, and Mazzuchi [].…”

Section: The Requirements Entropy Frameworkmentioning

confidence: 99%

The Requirements Entropy Framework in Systems Engineering

Grenn

Sarkani

Mazzuchi

2014

Systems Engineering

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This paper introduces a requirements entropy framework (REF) for measuring requirements trends and estimating engineering effort during system development. The REF treats the requirements engineering process as an open system in which the total number of requirements transition from initial states of high requirements entropy, disorder and uncertainty toward the desired end state of min as increase in quality. The cumulative requirements quality reflects the meaning of the requirements information in the context of the SE problem. The distribution of among discrete quality levels is determined by the number of quality attributes accumulated by at any given time in the process. The number of possibilities reflects the uncertainty of the requirements information relative to min . The is measured or estimated using , and by extending principles of information theory and statistical mechanics to the requirements engineering process. The requirements information increases as and uncertainty decrease, and Δ is the additional information necessary to achieve the desired state from the perspective of the receiver. The may increase, decrease or remain steady depending on the degree to which additions, deletions and revisions impact the distribution of among the quality levels. Current requirements volatility metrics generally treat additions, deletions and revisions the same and simply measure the quantity of these changes over time. The REF measures the quantity of requirements changes over time, distinguishes between their positive and negative effects in terms of , , and Δ , and forecasts when a specified desired state of requirements quality will be reached, enabling more accurate assessment of the status and progress of the engineering effort. Results from random variable simulations suggest the REF is an improved leading indicator of requirements trends that can be readily combined with current methods. The additional engineering effort Δ needed to transition from their current state to the desired state can also be estimated. Simulation results are compared with measured engineering effort data for Department of Defense programs, and the results suggest the REF is a promising new method for estimating engineering effort for a wide range of system development programs. C⃝ 2014 Wiley Periodicals, Inc. Syst Eng 17: 462-478, 2014

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Design and Control System of Sluice Gate With Web-Based Information

Desnanjaya

Nugraha²

2021

2021 International Conference on Smart-Green Technology in Electrical and Information Systems (ICSGTEIS)

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A Theory of Information Quality and its Implementation in Systems Engineering

Cited by 5 publications

References 22 publications

Supply Chain Logistics Information Quality Evaluation From Just-in-Time Perspective

Supply Chain Logistics Information Quality Evaluation From Just-in-Time Perspective

The Requirements Entropy Framework in Systems Engineering

Design and Control System of Sluice Gate With Web-Based Information

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