Manufacturing Systems Complexity Review: Challenges and Outlook

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

doi:10.1016/j.procir.2012.07.110

Cited by 82 publications

(38 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is evidently shown in Figure 2 where these three components (product quality, on-time delivery and minimum cost) have the higher mean score compared to the others component measured. Manufacturing industries in pursuit of cost and time reduction without decreasing product quality [17,[19][20]. Improved product quality and on-time delivery result in improved performance [21].…”

Section: Measures Of Current Manufacturing Performance (Cmp)mentioning

confidence: 99%

Managing Manufacturing Complexity Drivers on Performance – An Initial Study

2018

JESR

View full text Add to dashboard Cite

Manufacturing systems, in pursuit of cost, time and flexibility optimisation are becoming more and more complex, exhibiting a dynamic and nonlinear behaviour. Unpredictability is a distinct characteristic of such behaviour and effects production planning significantly. Complexity continues to be a challenge in manufacturing systems, resulting in ever-inflating costs, operational issues and increased lead times to product realisation. This challenge must be met with appropriate decision-making by manufacturing companies to secure competitive advantage without compromising sustainability. Assessing complexity realises the reduction and management of complexity sources which contribute to lowering associated engineering costs and time, improves productivity and increases profitability. Therefore, this study was undertaken to investigate the priority level and current achievement of manufacturing performance in Malaysia's manufacturing industry and the complexity drivers on manufacturing productivity performance. The results showed that Malaysia's manufacturing industry prioritised product quality and they managed achieved a good on-time delivery performance. However, for other manufacturing performance, there was a difference where the current achievement of manufacturing performances in Malaysia's manufacturing industry is slightly lower than the priority given to them. The strong correlation of significant value for priority status was observed between efficient production levelling (finished goods) and finish product management while the strong correlation of significant value for current achievement was minimised the number of workstation and factory transportation system. This indicates that complexity drivers have an impact towards manufacturing performance. Consequently, it is necessary to identify complexity drivers to achieve well manufacturing performance.

show abstract

Section: Measures Of Current Manufacturing Performance (Cmp)mentioning

confidence: 99%

Managing Manufacturing Complexity Drivers on Performance – An Initial Study

2018

JESR

View full text Add to dashboard Cite

show abstract

“…These papers bring manifold topics and approaches, and show also more or less connections with inventory control theory, in general. Another field of applications is focused on system complexity analysis, where there is engineering design and manufacturing chains, see [18][19][20][21]. In general, the complexity of a system increases with the increasing level of disorder and uncertainty of its states, as presented in [22].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Operational Complexity of Supplier-Customer Systems Measured by Entropy—Case Studies

Lukáš

Hofman

2016

Entropy

View full text Add to dashboard Cite

This paper discusses a unified entropy-based approach for the quantitative measurement of operational complexity of company supplier-customer relations. Classical Shannon entropy is utilized. Beside this quantification tool, we also explore the relations between Shannon entropy and (c,d)-entropy in more details. An analytic description of so called iso-quant curves is given, too. We present five case studies, albeit in an anonymous setting, describing various details of general procedures for measuring the operational complexity of supplier-customer systems. In general, we assume a problem-oriented database exists, which contains detailed records of all product forecasts, orders and deliveries both in quantity and time, scheduled and realized, too. Data processing detects important flow variations both in volumes and times, e.g., order-forecast, delivery-order, and actual production-scheduled one. The unifying quantity used for entropy computation is the time gap between actual delivery time and order issue time, which is nothing else but a lead time in inventory control models. After data consistency checks, histograms and empirical distribution functions are constructed. Finally, the entropy, information-theoretic measure of supplier-customer operational complexity, is calculated. Basic steps of the algorithm are mentioned briefly, too. Results of supplier-customer system analysis from selected Czech small and medium-sized enterprises (SMEs) are presented in various computational and managerial decision making details. An enterprise is ranked as SME one, if it has at most 250 employees and its turnover does not exceed 50 million USD per year, or its balance sheet total does not exceed 43 million USD per year, alternatively.

show abstract

“…This kind of inter-level relations may give the impression that the division of the energy efficiency study into these levels is not very useful. It is true, though, that an attempt for the simultaneous optimization of all the parameters involved, at all levels, presents very high complexity [32,34,35]. Even if such an approach is allowed by the current computational systems, the classification of the energy efficiency study into the four aforementioned levels significantly reduces the difficulty of the problem.…”

Section: Example 2 (Machine-production Line Inter-level Interaction)mentioning

confidence: 99%

On a generalized approach to manufacturing energy efficiency

Fysikopoulos

Pastras

Alexopoulos

et al. 2014

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Energy efficiency has become a very significant factor, requiring its inclusion in the manufacturing decision-making attributes. This paper proposes a generalized approach to manufacturing energy efficiency. The basic element of the approach is the division of energy efficiency definition and study into four manufacturing levels, namely process, machine, production line, and factory. Process-level definitions are provided for the majority of manufacturing processes. A machine-level study indicates and solves difficulties, generated by the workpiece geometry, and points out the interaction with the process level through factors, such as the process time. Moreover, machine tool peripherals are responsible for a significant portion of the consumed energy, and classification based on the dependence of their consumption on process variables is required. Studies made on the production line and factory levels show that energy efficiency, at these levels, is heavily dependent on production planning and scheduling and can be improved through the appropriate utilization of machines, with the inclusion of shutdown and eco-modes. Finally, a case study is presented, showing that many of the difficulties towards the optimization of energy efficiency can be dealt with successfully, using the proposed generalized approach.

show abstract

Manufacturing Systems Complexity Review: Challenges and Outlook

Cited by 82 publications

References 40 publications

Managing Manufacturing Complexity Drivers on Performance – An Initial Study

Managing Manufacturing Complexity Drivers on Performance – An Initial Study

Operational Complexity of Supplier-Customer Systems Measured by Entropy—Case Studies

On a generalized approach to manufacturing energy efficiency

Contact Info

Product

Resources

About