Manufacturing systems complexity: An assessment of manufacturing performance indicators unpredictability

Efthymiou, Konstantinos; Pagoropoulos, Aris; Papakostas, Nikolaos; Mourtzis, Dimitris; Chryssolouris, George

doi:10.1016/j.cirpj.2014.07.003

Cited by 53 publications

(32 citation statements)

References 47 publications

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“…The fourth symptom in this class is the degree of uncertainty associated to the predictability of manufacturing operations and system KPIs. This symptom is a consequence of dynamic complexity occurring due to the factors such as: incompleteness of information, disturbances, and uncertainties inherent to the manufacturing environment, and captured by analysing the prediction efficiency of manufacturing processes Butala, 2011, 2012) and by analysing unpredictability of manufacturing system KPIs (Chryssolouris et al, 2013;Efthymiou et al, 2014;Mourtzis et al, 2013). The last symptom in this class is the existence of manufacturing flow turbulence arising due to the interactions among system performance, lead time, process structure and manufacturing system configurations.…”

Section: Symptoms Observed Via Operational Uncertaintiesmentioning

confidence: 99%

Complexity in manufacturing systems and its measures: a literature review

Alkan

Vera

Ahmad

et al. 2018

EJIE

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Complexity in manufacturing systems still remains a challenge and leads to operational issues and increased production cost. In this paper, drivers of complexity and typical symptoms of complex manufacturing systems are identified. A comprehensive review of studies published within the last two decades to assess manufacturing system complexity are presented. The key contributions of this review are: 1) a classification of complexity assessment methods based on perceived complexity symptoms; 2) a comprehensive review of assessment methods with cross-evaluation to identify appropriate use based on available data; 3) recommendations for the wider academic and industrial community, based on research trends identified in the literature, as to how complexity assessment should be addressed in the future. It is concluded that the assessment of complexity is necessary so that it can be controlled effectively, however the industry suffers from a lack of practical tools to support in this endeavour. [

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Section: Symptoms Observed Via Operational Uncertaintiesmentioning

confidence: 99%

Complexity in manufacturing systems and its measures: a literature review

Alkan

Vera

Ahmad

et al. 2018

EJIE

View full text Add to dashboard Cite

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“…Performance indicators for manufacturing systems are unpredictable. In [23], the authors identified a set of four manufacturing performance indicators: cost, time, quality and flexibility. The authors proposed a method to analyze the complexity of a manufacturing system, considering the unpredictable nature of the performance indicators.…”

Section: Improvement Actions and Impact On The Manufacturing Plantmentioning

confidence: 99%

Evaluation of improvement actions impact on manufacturing operational performance

Barbosa

Azevedo

2015

2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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Due to the current demands from the market, technological improvements and action of competitors, manufacturing companies are pushed to compete in shorter product development cycles. This poses a great challenge, as the conventional product development cycle is shortened. Hence, companies are forced to introduce some improvement action plans and adopt certain manufacturing operational strategies to remain competitive and achieve a good market share. Due to the importance of this topic, in this paper we revise the current manufacturing improvement plans in the literature, to establish the basis of our future research work. The findings of this work point towards the need to develop a system that integrates the evaluation of the manufacturing improvement plans in the future overall performance of the manufacturing plant and aid in the process of decision-making.

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“…Manufacturing has been benefiting from the advantages of choosing globalization, at the same time, manufacturing industries have been facing a dynamic environment with increased complexity of production (Efthymiou et al 2014;ElMaraghy et al 2012). In the era of customization , uncertainties such as changes from market fluctuations and customer demands, as well as variations of product designs and processing technology force companies to become more reliable and responsive against various risks.…”

Section: Introductionmentioning

confidence: 99%

Reliability evaluation and component importance measure for manufacturing systems based on failure losses

Zhang

et al. 2015

J Intell Manuf

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Little work has been done to assess the reliability of a vital system like the manufacturing system. In this article, a novel and effective system reliability evaluation method in terms of failure losses has been proposed for manufacturing systems of job shop type, and then the failure losses based component importance measure (CIM) is used for importance analysis of equipment. The former indicates the present system reliability situation and the latter points the way to reliability improvement efforts. In this scheme, the problem is described and modeled by a dynamic directed network. Consider that the actual processing time of machines is to contribute to failure occurrence, it is used to calculate the failure times and failure losses. The obtained total failure times and failure losses of the system are applied to evaluate its reliability. Techniques to estimate two kinds of failure losses based CIMs are presented. They offer guidelines to realize system reliability growth cost-effectively. A case study of a real job shop is provided as an example to demonstrate the validity of the proposed methods. Comparison to other commonly used methods shows the efficiency of the proposed methods. CNCComputer numerical control DNC Distributed numerical control ERP Enterprise resource planning FT Failure times MES Manufacturing execution system MPT Machine processing time MTBF Mean time between failure MTTF Mean time to failure MTTR Mean time to repair OEE Overall equipment effectiveness RFID Radio frequency identification WDN Weighted and directed network WIP Work-in-process t, T Sequence number of time intervals (number of all intervals is T ) i, I t Sequence number of products items (number of all items is I t ) j, J t Sequence number of processes during t (J t is the maximum value) k, K Sequence number of equipment (number of all equipment is K ) G t WDN during t M k Equipment k r k Failure rate of M k F T tk Failure times of M k during t T total Total time V ti Volume size of item i during t B ti j Processing time of item i for the jth process during t x ti jk Be equal to 1 if M k is used for machining the jth process of item i during t, 0 otherwise MT B F k MTBF of M k MT T R k MTTR of M k C k Average maintenance cost for every repair process of M k 123 J Intell Manuf P L tk Production losses for M k during time period t C f k

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Manufacturing systems complexity: An assessment of manufacturing performance indicators unpredictability

Cited by 53 publications

References 47 publications

Complexity in manufacturing systems and its measures: a literature review

Complexity in manufacturing systems and its measures: a literature review

Evaluation of improvement actions impact on manufacturing operational performance

Reliability evaluation and component importance measure for manufacturing systems based on failure losses

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