Discrete-event simulation of process control in low volume high value industries

This paper presents a new method of process control for set-up dominant processes. This new method known as Set-up Process Algorithm (SUPA) was compared with existing industrial practices and statistical techniques in the literature. To test the method’s robustness, a generic discrete-event simulation model was built. This model was used to test four different statistical approaches to process control. It was concluded that SUPA offers a method of process control for set-up dominant processes, which is easier to apply than classically derived SPC approaches, by using simple rules and a traffic light system based on design specification. Simulation analysis shows that SUPA: is more sensitive, at detecting an incapable process as it will monitor more units when a process is less capable; is more sensitive than PRE-Control at detecting mean shifts in a process. SUPA is also a nonparametric methodology and therefore robust against processes with non-Gaussian distributions

Simulation of High Precision Process Control for Set-up Dominant Processes

The main focus of this paper is to use discrete-event simulation models, to test the robustness of two process control methods against processes with different statistical distributions. The two methods under scrutiny are the Small-Batch Full-size image (3 K) & R chart and the Set-Up Process Algorithm (SUPA). These have been developed for ‘setup dominant processes’, were the major source of product variation is detected between batches. Minimizing this type of variation is critical to ensure spare parts produced at a later date will fit in operating assemblies, maintaining a Through-life Engineering Service. This paper shows their suitability to industry

Concise process improvement definition with case studies

Purpose – The purpose of this paper is to examine the efficiency and objectivity of current Six Sigma practices when at the measure/analyse phase of the DMAIC quality improvement cycle. Design/methodology/approach – A new method, named process variation diagnostic tool (PROVADT), demonstrates how tools from other quality disciplines can be used within the Six Sigma framework to strengthen the overall approach by means of improved objectivity and efficient selection of samples. Findings – From a structured sample of 20 products, PROVADT was able to apply a Gage R&R and provisional process capability study fulfilling the pre-requisites of the measure and early analyse phases of the DMAIC quality improvement cycle. From the same sample, Shainin multi-vari and isoplot studies were conducted in order to further the analysis without the need of additional samples. Practical implications – The method was tested in three different industrial situations. In all cases PROVADT’s effectiveness was shown at driving forward a quality initiative with a relatively small number of samples. Particularly in the third case, it lead to the resolution of a long standing complex quality problem without the need for active experimentation on the process. Originality/value – This work demonstrates the need to provide industry with new statistical tools which are practical and give users efficient insight into potential causes of a process problem. PROVADT makes use of data needed by quality standards and Six Sigma initiatives to fulfil their requirements but structures data collection in a novel way to gain more information

Autonomic Road Transport Support Systems

The work on Autonomic Road Transport Support (ARTS) presented here aims at meeting the challenge of engineering autonomic behavior in Intelligent Transportation Systems (ITS) by fusing research from the disciplines of traffic engineering and autonomic computing. Ideas and techniques from leading edge artificial intelligence research have been adapted for ITS over the last years. Examples include adaptive control embedded in real time traffic control systems, heuristic algorithms (e.g. in SAT-NAV systems), image processing and computer vision (e.g. in automated surveillance interpretation). Autonomic computing which is inspired from the biological example of the body’s autonomic nervous system is a more recent development. It allows for a more efficient management of heterogeneous distributed computing systems. In the area of computing, autonomic systems are endowed with a number of properties that are generally referred to as self-X properties, including self-configuration, self-healing, self-optimization, self-protection and more generally self-management. Some isolated examples of autonomic properties such as self-adaptation have found their way into ITS technology and have already proved beneficial. This edited volume provides a comprehensive introduction to Autonomic Road Transport Support (ARTS) and describes the development of ARTS systems. It starts out with the visions, opportunities and challenges, then presents the foundations of ARTS and the platforms and methods used and it closes with experiences from real-world applications and prototypes of emerging applications. This makes it suitable for researchers and practitioners in the fields of autonomic computing, traffic and transport management and engineering, AI, and software engineering. Graduate students will benefit from state-of-the-art description, the study of novel methods and the case studies provided

Control of set-up dominant multivariate manufacturing processes

A practical control chart is introduce, called multivariate Set-Up Process Algorithm (m-SUPA), which can be used to signal when a process is statistically off-target. This control chart uses a traffic light system to provide simple information to an operator about how close a measured part is to its global target. The chart works with a simple rule set resulting in process adjustments at a calculated point, rather than relying on rule-of-thumb methods. A final consideration is calculating the size of process adjustment, when one control adjustment has multiple effects on different design features. Simple feedback controllers are suggested for calculating process adjustments, providing consistency to an action taken. Simulation results suggest that m-SUPA with adjustments based on this kind of controllers is able to steer the process to a desired performance region

Autonomic Systems Design for ITS Applications

This paper discusses a systems design approach inspired from the autonomic nervous system for ITS applications. This is done not with reference to the employed computing system, but to the requirements of traffic engineering applications. It is argued that the design and development of autonomic traffic management systems must identify the control loop that needs to be endowed with autonomic properties and subsequently use this framework for defining a desired set of self-* properties. A macroscopic network modelling application is considered for showing how autonomic systems design can be used for defining and obtaining self-* properties, with particular emphasis given in self-optimisation