Production quality improvement during manufacturing systems ramp-up

Colledani, Marcello; Tolio, Tullio; Yemane, A.

doi:10.1016/j.cirpj.2018.07.001

Cited by 33 publications

(28 citation statements)

References 51 publications

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“…During production ramp up, product quality and system disturbances are significant and common issues. Available quality methods focus on high volume production and long term system performance, which lose their effectiveness during system ramp up (Colledani et al, 2018). On implementing a new production process, whether for a new product, new production technology or both, system disturbances are highly likely to lead to unpredictable behaviour (Basse, Schmitt, Gartzen, & Schmitt, 2014).…”

Section: Ramp Upmentioning

confidence: 99%

“…Manufacturing transition, when moving from producing one product to another or between variants, is a traditional problem facing many manufacturers. The existing planning and control structures focus on mature steady state environments for high volume and long term performance (Colledani, Tolio, & Yemane, 2018); rather than the volatile transitional state, where there are many unforeseen disruptions during ramp up (Surbier, Alpan, & Blanco, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Anarchic manufacturing: Distributed control for product transition

Andrew

Frantzén

Snider

et al. 2020

Journal of Manufacturing Systems

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Manufacturers are poorly equipped to manage product transition scenarios, when moving from one product to another. Most tools consider a mature system, yet during transition and ramp up disturbances and inefficiency are common. The traditional methods, using centralised planning and control structures are too rigid and often resort to simple dispatch heuristics in this highly dynamic environment. Distributed systems have been proposed to leverage their self-organising and flexible traits to manage highly volatile and complex scenarios.Anarchic manufacturing, a free market based distributed planning and control system, delegates decision-making authority and autonomy to system elements at the lowest level; this system has previously been shown to manage job and flowshop style problems. The system has been adapted to use a dynamic batching mechanism, where jobs cooperate to benefit from economies of scale. The batch enables a direct economic viability assessment within the free market architecture, whether an individual machine should changeover production to another product type. This profitability assessment considers the overall system state and an agent's individual circumstance, which in turn reduces system myopia. Four experiments, including a real-world automotive case study, evaluate the anarchic manufacturing system against two centralised systems, using three different ramp-up curves. Although not always best performing against centralised systems, the anarchic manufacturing system is shown to manage transition scenarios effectively, displaying self-organising and flexible characteristics. The hierarchical system was shown to be impeded by its simplifying structure, as a result of structural rigidity.

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Section: Ramp Upmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anarchic manufacturing: Distributed control for product transition

Andrew

Frantzén

Snider

et al. 2020

Journal of Manufacturing Systems

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“…In a continuously changing context, the ramp-up of a manufacturing system should be as short as possible. Ramp-up represents a challenge for manufacturing companies because they have to deal with disruptions coming from various and unknown sources [7]. Indeed, after a change, the manufacturing system is not well-known and therefore optimization is done with respect to partial information rather than complete knowledge or sufficient data [34].…”

Section: Ramp-up Managementmentioning

confidence: 99%

The Paradigm of Pit - Stop Manufacturing

Tolio

Magnanini

2019

Proceedings of the 4th International Conference on the Industry 4.0 Model for Advanced Manufacturing

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The context in which manufacturing companies are operating is more and more dynamic. Technological and digital innovations are continuously pushing manufacturing systems to change and adapt to new conditions. Therefore, traditional planning strategies tend to be inadequate because both the context and short-term targets are continuously changing. Indeed, one of the goals of manufacturing companies is to keep manufacturing systems efficiently running, and reduce and control the impact of disruptive events , that may originate from different sources, not always known or well defined. In order to do so, manufacturing systems should be kept relatively close to the current optimal condition, while, at the same time, taking into account information about future possible events, which may require new optimal conditions. In fact, the reaction time to the change must be short, in order to remain competitive in the market. In addition companies to be competitive should lead the introduction of changes therefore they have to be both reactive and proactive. From this analysis, the new paradigm of 'pit-stop manufacturing' is introduced, in which the overall goal is to dynamically keep the manufacturing system close to an improvement trajectory, instead of statically optimizing the system. It is shown how the 'pit-stop manufacturing' deals with various aspects of current manufacturing systems, therefore providing novel research questions and challenges.

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“…To better appreciate the significance of ramp-up for the manufacturing process, it is essential to understand the characteristics and difficulties experienced throughout that phase. The duration of ramp-up is not straightforward to specify, but it can be understood as the period from once the system is first put together and prototype production has started until the specified performance targets are achieved [8]. Key performance indicators for ramp-up are usually production quality [9], [10] and yield [1].…”

Section: Related Workmentioning

confidence: 99%

Understanding Human Decision-making during Production Ramp-up using Natural Language Processing

Zimmer

Al-Yacoub

Ferreira

et al. 2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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Ramping up a manufacturing system from being just assembled to full-volume production capacity is a time consuming and error-prone task. The full behaviour of a system is difficult to predict in advance and disruptions that need to be resolved until the required performance targets are reached occur often. Information about the experienced faults and issues might be recorded, but usually, no record of decisions concerning necessary physical and process adjustments are kept. Having these data could help to uncover significant insights into the ramp-up process that could reduce the effort needed to bring the system to its mandatory state. This paper proposes Natural Language Processing (NLP) to interpret human operator comments collected during ramp-up. Recurring patterns in their feedback could be used to gain a deeper understanding of the cause and effect relationship between the system state and the corrective action that an operator applied. A manual dispensing experiment was conducted where human assessments in form of unstructured freeform text were gathered. These data have been used as an input for initial NLP analysis and preliminary results using the NLTK library are presented. Outcomes show first insights into the topics participants considered and lead to valuable knowledge to learn from this experience for the future.

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Production quality improvement during manufacturing systems ramp-up

Cited by 33 publications

References 51 publications

Anarchic manufacturing: Distributed control for product transition

Anarchic manufacturing: Distributed control for product transition

The Paradigm of Pit - Stop Manufacturing

Understanding Human Decision-making during Production Ramp-up using Natural Language Processing

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