Predicting the effects of cycle time variability on the efficiency of electronics assembly mixed-model, zero-buffer flow processing lines.

International Journal of Computer Integrated Manufacturing (Impact Factor: 1.02). 12/2010; 23:1149-1157. DOI: 10.1080/0951192X.2010.500679
Source: DBLP

ABSTRACT The research literature emphasises the need to use flow processing lines to undertake processing and assembly within low demand volume, high product variety electronics manufacturing environments that have significant levels of product, process and demand variability to contend with. Currently, the presence of such high levels of product, process and demand variability prevents the design of efficient flow processing lines by significantly disrupting the synchronisation of materials movement between work stations, resulting in under-utilisation of manufacturing resources, long lead times and poor delivery reliability.In order to ensure efficient flow processing under such conditions, a range of methods has been developed for both reducing levels of variability and for managing the effects of variability. However, ensuring the effective use of each of these methods requires detailed knowledge of the effects this variability has on the resource requirements of individual workstations.The current research is concerned with the development of predictive models that can quantitatively estimate the amounts of blocking and waiting, on individual workstations along a flow line, arising from differences in cycle times between these workstations. Information derived from such models are able to enable more precise and effective use of the methods used to off-set the effects of cycle time variability.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Reliability modelling and verification is indispensable in modern manufacturing, especially for product development risk reduction. Based on the discussion of the deficiencies of traditional reliability modelling methods for process reliability, a novel modelling method is presented herein that draws upon a knowledge network of process scenarios based on the analytic network process (ANP). An integration framework of manufacturing process reliability and product quality is presented together with a product development and reliability verification process. According to the roles of key characteristics (KCs) in manufacturing processes, KCs are organised into four clusters, that is, product KCs, material KCs, operation KCs and equipment KCs, which represent the process knowledge network of manufacturing processes. A mathematical model and algorithm is developed for calculating the reliability requirements of KCs with respect to different manufacturing process scenarios. A case study on valve–sleeve component manufacturing is provided as an application example of the new reliability modelling and verification procedure. This methodology is applied in the valve–sleeve component manufacturing processes to manage and deploy production resources.
    International Journal of Computer Integrated Manufacturing 01/2015; 28(1). DOI:10.1080/0951192X.2013.834462 · 1.02 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Product development (PD) is a broad field of endeavor dealing with the planning, design, creation, and marketing of a new product. This revolutionary research domain has become of paramount importance to beat the competition for multidisciplinary products which are larger in size and have a longer development time. The main focus of this paper is to exploit lean thinking concepts in order to manage, improve and develop the product faster while improving or at least maintaining the level of performance and quality. Lean thinking concepts encompass a board range of tools and methods intended to produce bottom line results however, value stream mapping (VSM) method is used to explore the wastes, inefficiencies, non-valued added steps in a single, definable process out of complete product development process (PDP). This single step is highly complex and occurs once while the PDP lasts for 3–5 years. A case study of gas turbine product has been discussed to illustrate and justify the use of proposed framework. In order to achieve this, the following have been performed: First of all a current state map is developed using the Gemba walk. Furthermore, Subject Matter Experts (SMEs) brainstormed to explore the wastes and their root causes found during the Gemba walk and current state mapping. A future state map is also developed with removing all the wastes/inefficiencies. Besides numerous intangible benefits, it is expected that the VSM framework will help the development teams to reduce the PD lead-time by 50%.
    International Journal of Production Economics 11/2014; DOI:10.1016/j.ijpe.2014.11.002 · 2.08 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Continuous innovation is a key ingredient in maintaining a competitive advantage in the current dynamic and demanding marketplace. It requires an organization to regularly update and create knowledge for the current generation, and reuse it later for the next generation of a product. In this regard, an integrated dynamic knowledge model is targeted to structurally define a practical knowledge creation process in the product development domain. This model primarily consists of three distinct elements; SECI (socialization-externalization-combination-internalization) modes, 'ba,' and knowledge assets. The model involves tacit knowledge and explicit knowledge interplay in 'ba' to generate new knowledge during the four SECI modes and update the knowledge assets. It is believed that lean tools and methods can also promote learning and knowledge creation. Therefore, a set of ten lean tools and methods is proposed in order to support and improve the efficiency of the knowledge creation process. The approach first establishes a framework to create knowledge in the product development environment, and then systematically demonstrates how these ten lean tools and methods conceptually fit into and play a significant role in that process. Following this, each of them is analysed and appropriately positioned in a SECI mode depending on best fit. The merits of each tool/method are discussed from the perspective of selecting the individual mode. The managerial implication is that correct and quick knowledge creation can result in faster development and improved quality of products.
    International Journal of Information Management 12/2014; 35. DOI:10.1016/j.ijinfomgt.2014.12.007 · 2.04 Impact Factor