A framework to predict energy related key performance indicators of manufacturing systems at early design phase

Assad, Fadi; Alkan, Buğra; Chinnathai, Malarvizhi Kaniappan; Ahmad, Mus'ab H.; Rushforth, Emma; Harrison, Robert

doi:10.1016/j.procir.2019.03.026

Cited by 17 publications

(9 citation statements)

References 17 publications

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“…The use of KPIs emerges as a tool to evaluate and quantify the performance of the processes, regarding a set of previously established objectives [25]. The use of these metrics has been embraced in several areas such as education, agriculture [26], financial service [27], transport [28], industry [29], healthcare [30], supply chain [31], risk management [32], quality control [33], maintenance [34], and construction [35], among others.…”

Section: Performance Evaluation and Performance Measuresmentioning

confidence: 99%

Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry

2021

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In the last few decades, the fast technological development has caused high competitiveness among companies, encouraging a pursuit for strategies that allow them to gain competitive advantage, such as the monitoring of performance by using key performance indicators (KPIs). However, its selection process is complex since there are several KPIs available to evaluate performance and different relationships between them. To overcome this challenge, the use of a multiple criteria decision-making model (MCDM) was proposed, namely the analytic network process (ANP) through which a reduced number of them are prioritized. To identify which KPIs are suitable for the press cast and die manufacturing industry, a literature review was made, and 58 unique KPIs were identified. Thus, to validate the proposed methodology, a case study was carried out in an automotive press molding industry. With the implementation of the proposed ANP model it was possible to identify 9 KPIs that ensure the correct molding process monitoring, while being aligned with the Balanced Scorecard criteria. The results show that the proposed model is suitable for selecting KPIs for the molding industry.

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Section: Performance Evaluation and Performance Measuresmentioning

confidence: 99%

Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry

2021

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“…The frequency at which KPIs are monitored and assessed is vital, and mainly depends on the nature of the manufacturing operation. In general, KPIs are tracked in real-time, however, they can be displayed periodically or on-demand (Assad et al, 2019).…”

Section: Manufacturing Kpismentioning

confidence: 99%

Assessing operational complexity of manufacturing systems based on algorithmic complexity of key performance indicator time-series

Alkan

Bullock

2020

Journal of the Operational Research Society

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This article presents an approach to the assessment of operational manufacturing systems complexity based on the irregularities hidden in manufacturing key performance indicator time-series by employing three complementary algorithmic complexity measures: Kolmogorov complexity, Kolmogorov complexity spectrum's highest value and overall Kolmogorov complexity. A series of computer simulations derived from discrete manufacturing systems are used to investigate the measures' potentiality. The results showed that the presented measures can be used in quantitatively identifying operational system complexity, thereby supporting operational shop-floor decision-making activities.

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“…As energy is not visible to the human eye, which complicates the impairment of energy efficiency. Therefore, the availability and visualization of energy data and information of energy assets, for example, in the form of sustainable key performance indicators (KPIs) or flows increase awareness about demands and support its efficiency [1,15,18,17,10,12]. Nevertheless, energy demands in real applications are influenced by complex settings, such as the effects of different process parameters, workflows and quality requirements.…”

Section: Elements and Factors In Current Research To Apply Vr For Inc...mentioning

confidence: 99%

“…Besides, energy demands in [15] were assumed to be constant, whereas the energy losses of, i.e., a cooling system, were neglected. Furthermore, the EE-KPIs defined in [1,3] do not account for the fluctuation of the process times and/or cycle times. Both process time and cycle time influence the energy consumption of a particular line at the production phase.…”

Section: Elements and Factors In Current Research To Apply Vr For Inc...mentioning

confidence: 99%

Application of Virtual Reality Technologies for Achieving Energy Efficient Manufacturing: Literature Analysis and Findings

Nabati¹,

Nieto²,

Decker³

et al. 2020

IFIP Advances in Information and Communication Technology

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Improvement in current manufacturing settings for enabling energy efficiency is a challenge for many manufacturers. Although virtual reality has been so far applied in manufacturing for training, visualization and product development, the use of this technology in manufacturing for increasing energy efficiency has been less addressed. This paper investigates the potential of virtual reality for a better analysis of energy demands in manufacturing. By envisioning and illustrating energy flows and consumption, virtual reality can support energy efficiency. The paper provides a systematic review of the literature. The findings are analysed from the perspective of research gaps in making virtual-based technologies to enable energy-efficient manufacturing. Particularly, the elements and factors (opportunities) and methods that can be transmitted from current research to energy-efficient manufacturing, are identified and discussed.

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A framework to predict energy related key performance indicators of manufacturing systems at early design phase

Cited by 17 publications

References 17 publications

Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry

Key Performance Indicators Selection through an Analytic Network Process Model for Tooling and Die Industry

Assessing operational complexity of manufacturing systems based on algorithmic complexity of key performance indicator time-series

Application of Virtual Reality Technologies for Achieving Energy Efficient Manufacturing: Literature Analysis and Findings

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