In the context of globalisation, the companies are urged to innovate the products and the services they provide to their customers, in order to keep their competitive edge. New product introduction in an already existing production environment generates numerous perturbations to handle in the supply chain and operations management during the production ramp-up. The purpose of this article is to present a thorough state of the art on the production ramp-up. Different overviews of the research body are given: comparing definitions, characteristics and problems of the ramp-up phase, classifying research papers according to their industrial context and organising them in different classifications (by keywords, focus extent …). Interesting issues that could be addressed in future research about the production ramp-up phase are identified.

A comparative study on production ramp-up: state-of-the-art and new challenges

Production ramp-up is a critical step in the life cycle of a new product, and efficiently managing ramp-ups is a key to business success and market leadership. To support the planning of ramp-ups in practice, researchers have developed decision support models in the past that help to solve problems that arise during the ramp-up phase, such as lot sizing, the assignment of workers to workplaces or the determination of the capacity of the production equipment. Decision support models for production ramp-up typically consider the specific characteristics of this phase, such as uncertainty, growth in demand, worker learning or imperfect production processes. The aim of this paper is to provide a comprehensive overview of decision support models for production ramp-up and to identify areas where more research is needed. First, the paper develops a conceptual framework of production ramp-up by categorising typical planning problems and process characteristics of the ramp-up phase. Secondly, a systematic literat...

Decision support models for production ramp-up: a systematic literature review

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 ramp-up, 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 ramp-up 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 ramp-up 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.

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

Process monitoring of full mass production phase of multistage manufacturing processes (MMPs) has been successfully implemented in many applications; however, monitoring of ramp-up phase of MMPs is often more difficult to conduct due to the limited information to establish valid process control parameters (such as mean and variance). This paper focuses on the estimation of the process control parameters used for monitoring scheme design of ramp-up phase of MMPs. An engineering model of variation propagation of an MMP is developed and reconstructed to a linear model, establishing a relationship between the error sources and the variation of product characteristics. Based on the developed linear model, a two-step Bayesian method is proposed to estimate the process control parameters. The performance of the proposed Bayesian method is validated with simulation data and real-world data, and the results demonstrate that the proposed method can effectively estimate process parameters during ramp-up phase of MMP.

Engineering model-based Bayesian monitoring of ramp-up phase of multistage manufacturing process

An effective fuzzy collaborative forecasting approach for predicting the job cycle time in wafer fabrication

Aggressive capacity ramp rate of a semiconductor wafer fabrication is vital for the commercial success of the enterprise. Basic requirements are short and stable production cycle times to timely qualify equipment and to provide acceptable yield. Therefore, in the ramp-up environment which is characterized by high variability and uncertainty, an adequate methodology is required to properly manage the conflict of short cycle times and fast throughput increase. This paper presents a methodology to manage cycle time by closely monitoring and limiting the work in process (WIP), by means of the so-called “WIP caps”. Used consequently, this methodology allows the ramp rate to accelerate as soon as the factory performance enables this while keeping cycle times under control.

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M Haller

Papers

Cycle time management during production ramp-up