Modelling production ramp-up of engineering products

Ball, Peter D.; Roberts, Simon; Natalicchio, Angelo; Scorzafave, C.

doi:10.1177/09544054jem2071

Cited by 27 publications

(26 citation statements)

References 46 publications

(79 reference statements)

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“…Furthermore, the speed of the ramp-up strongly affects the time-to-volume of a product and subsequently the financial success of the new product and the company (Haller, Peikert & Thoma, 2003). All those issues can be managed by integrating NPD and SC processes by SCCC activities (Lakemond, Echtelt & Wynstra, 2001;Ball, Roberts, Natalicchio & Scorzafave, 2011;Elstner & Krause, 2014;Surbier, Alpan & Blanco, 2014;Caniato & Größler, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Supply capability creation process: Key milestone criteria and activities

Verrollot

Tolonen

Härkönen

et al. 2017

JIEM

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Abstract:Purpose: The article focuses on supply capability creation (SCC) within the new product development (NPD). The purpose is to establish an SCC process describing the main SCC activities and milestone criteria in preparing the supply process for new products Design/methodology/approach: The article analyses the earlier research, carries out current state analysis of six case companies regarding the SCC areas and proposes an SCC process. Findings:The developed SCC process aims at preparing the operational supply capability for a developed new product based on the preferred, qualified and contracted suppliers and materials along the NPD process, and ultimately at the product ramp-up.Originality/value: This paper introduces an SCC process that has not been presented earlier in the literature, highlighting the important role of the SCC for successful product ramp-ups.

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Section: Literature Reviewmentioning

confidence: 99%

Supply capability creation process: Key milestone criteria and activities

Verrollot

Tolonen

Härkönen

et al. 2017

JIEM

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“…In such a single ramp-up curve, learning effects may be considered when determining the amount of the stepwise increases (see, e.g. Abernathy and Downloaded by [University of Lethbridge] at 12:42 16 June 2016 Baloff 1973;Terwiesch and Xu 2004;Pruett and Thomas 2008;Ball et al 2011;Levitt, List, and Syverson 2013;Hansen and Grunow 2015). However, existing models assume a single production ramp-up at a single plant, and decisions are made from a tactical planning perspective.…”

Section: Decisions Of Strategic Ramp-up Planning and Related Literaturementioning

confidence: 97%

“…Figure 2 Levinthal and Purohit (1989), Clark and Fujimoto (1991), Billington, Lee, and Tang (1998), Lim and Tang (2006), Koca, Souza, and Druehl (2010), Li, Graves, and Rosenfield (2010) No consideration of production network and production capacities 3 Shape of production capacity over time Tactical Abernathy and Baloff (1973), , Terwiesch and Xu (2004), Pruett and Thomas (2008), Ball et al (2011), Levitt, List, and Syverson (2013), Hansen and Grunow (2015) No consideration of production network, tactical planning perspective 4 Production volume Tactical Cohen, Eliasberg, and Ho (1996), Terwiesch, Bohn, and Chea (2001) No consideration of production network, tactical planning perspective 5…”

Section: Planning Problem and Decision Model 31 Problem Description mentioning

confidence: 98%

Strategic ramp-up planning in automotive production networks

Becker

Stolletz

Stäblein

2016

International Journal of Production Research

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In many industries, the frequency of production ramp-ups has increased due to shorter product life cycles and increased product variety. Production often takes place at globally dispersed facilities for customers in multiple markets, for example, in the automotive industry. Therefore, ramp-up planning must be carried out for a network. The planning problem at hand is to simultaneously optimise the allocation of products of a common product platform to plants, the timing of ramp-ups and ramp-downs, ramp-up duration and shape of capacity and production and transportation volumes with respect to the net present value of the profit. We develop a hierarchical planning framework and place strategic ramp-up planning therein. We provide a mixed integer programming model for this planning problem. Following the example of a German car manufacturer in the premium segment, we conduct a numerical analysis to obtain insights into optimal ramp-up and ramp-down decisions. The results show that it could be beneficial to use both steep and flat ramp-up curves. The impact of flexibility in the choice of ramp-up slots and the number of ramp-up curves provided on the objective value is substantial. The strategic ramp-up planning model outperforms sequential planning approaches.

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“…These factors have been included in ramp-up models, which aim to measure their effect and impact [2], [4], [12]. It is also a common view that knowledge is not static information but comes from process change and experimentation [6].…”

Section: B Learning During Ramp-upmentioning

confidence: 99%

An MDP Model-Based Reinforcement Learning Approach for Production Station Ramp-Up Optimization: Q-Learning Analysis

Doltsinis

Ferreira

Lohse

2014

IEEE Trans. Syst. Man Cybern, Syst.

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Ramp-up is a significant bottleneck for the introduction of new or adapted manufacturing systems. The effort and time required to ramp-up a system is largely dependent on the effectiveness of the human decision making process to select the most promising sequence of actions to improve the system to the required level of performance. Although existing work has identified significant factors influencing the effectiveness of rampup, little has been done to support the decision making during the process. This paper approaches ramp-up as a sequential adjustment and tuning process that aims to get a manufacturing system to a desirable performance in the fastest possible time. Production stations and machines are the key resources in a manufacturing system. They are often functionally decoupled and can be treated in the first instance as independent rampup problems. Hence, this paper focuses on developing a Markov decision process (MDP) model to formalize ramp-up of production stations and enable their formal analysis. The aim is to capture the cause-and-effect relationships between an operator's adaptation or adjustment of a station and the station's response to improve the effectiveness of the process. Reinforcement learning has been identified as a promising approach to learn from rampup experience and discover more successful decision-making policies. Batch learning in particular can perform well with little data. This paper investigates the application of a Q-batch learning algorithm combined with an MDP model of the ramp-up process. The approach has been applied to a highly automated production station where several ramp-up processes are carried out. The convergence of the Q-learning algorithm has been analyzed along with the variation of its parameters. Finally, the learned policy has been applied and compared against previous ramp-up cases.

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Modelling production ramp-up of engineering products

Cited by 27 publications

References 46 publications

Supply capability creation process: Key milestone criteria and activities

Supply capability creation process: Key milestone criteria and activities

Strategic ramp-up planning in automotive production networks

An MDP Model-Based Reinforcement Learning Approach for Production Station Ramp-Up Optimization: Q-Learning Analysis

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