Performance Evaluation Of Manufacturing Systems Research Articles

Performance evaluation of serial-parallel manufacturing systems based on the impact of heterogeneous feedstocks on machine degradation

Heterogeneity of feedstock quality can bring disturbances to machine degradation and product quality. Therefore, machine reliability modeling and machining quality analysis considering the flow of heterogeneous feedstocks are very essential in the performance evaluation of manufacturing systems. This paper presents a new mixture degradation model to evaluate the reliability of manufacturing machines that accounts for the flow and impact of heterogeneous feedstocks in serial-parallel manufacturing systems. Specifically, this mixture model leverages two Weibull distributions to describe machine degradation processes under the conditions of high-quality and low-quality feedstocks. Then, the flow of low-quality feedstocks is modeled by the non-homogeneous Poisson process, so that an interacting chain of quality and reliability is formulated for the serial-parallel manufacturing system. Based on the proposed model, an evaluation framework for the performance of serial-parallel manufacturing systems is provided. Finally, simulation experiments are implemented to analyze the operation status and quality loss of the system. The results showed the effectiveness of the proposed method in performance modeling of serial-parallel manufacturing systems with mixture machine degradation. The proposed approach shows strong potentials for general applications in the performance analysis of complex-structure manufacturing systems.

Standardizing environmental performance evaluation of manufacturing systems through ISO 20140

Abstract To evaluate the environmental performance of manufacturing systems, practitioners often deploy ad-hoc methods or implement one-off solutions that are difficult to replicate in similar scenarios. Such situations can drive-up costs and cause significant delays. Hence, there is a strong need for standard methods that help describe and replicate environmental performance evaluation (EPE) processes. In response, ISO 20140 provides guidelines for converting and aggregating environmental performance evaluation data. This paper provides an overview of ISO 20140, and showcases the usefulness of ISO 20140 by describing and analyzing the usage view of the EPE process. The analysis is conducted with an evaluation experiment about the machining of a mill-tern part, in which energy consumption of a variety of process modes such as active machining, tools change, rapid traverse, and pausing, are evaluated. The experiment is jointly conducted by National Institute of Standards and Technology in the United States and the National Institute of Advanced Industrial Science and Technology in Japan.

Performance analysis of unreliable manufacturing systems with uncertain reliability parameters estimated from production data

ABSTRACTSystem engineering methods for the performance evaluation of manufacturing systems require the estimation of input parameters, which is either based on real data or experts’ knowledge. In both cases, the input parameters are subjected to uncertainty. However, most of the systems engineering methods, which are based on analytical models assume that machine reliability parameters such as Mean Time to Failure and Mean Time to Repairs are precisely known. In order to overcome this limitation, this paper proposes an approach for the performance evaluation of unreliable manufacturing systems that considers uncertain machine reliability estimates. The method enables to calculate the distribution of the output performance, given the distribution of the input parameters’ uncertainty. The evaluation procedure is based on the combined use of Bayesian estimation, probability density function discretization and existing decomposition-based techniques for analyzing transfer lines composed of unreliable machines and capacitated buffers. Experimental results obtained by using the method show that neglecting uncertainty in the input parameter estimates generates significant errors in the output performance measure estimation, thus making the subsequent system operation and reconfiguration decisions sub-performing. Finally, a real case study is presented to demonstrate the potential benefits of the proposed method for real industrial applications.

Performance Analysis of Unreliable Manufacturing Systems with Uncertain Parameter Estimates

Traditional systems engineering methods for the performance evaluation of manufacturing systems assume that machine reliability parameters (Mean Time to Failure and Mean Time to Repair) are precisely known. However, in practical situations, these parameters are either estimated from real life data or based on experts’ knowledge. In both cases, they are subject to uncertainty. This paper proposes for the first time an approach for the performance evaluation of unreliable manufacturing systems that considers uncertain machine parameter estimates. The proposed method is based on the combined use of Bayesian estimation, probability density function discretization and existing decomposition-based techniques for analyzing manufacturing lines composed of unreliable machines and capacitated buffers. Numerical results show that neglecting uncertainty in the input parameter estimates generates consistent errors in the output performance measure estimates, thus making the consequent system design and operation decisions sub-performing. An industrial case is proposed to show the benefits of this method in real production settings.

Lean performance evaluation of manufacturing systems: A dynamic and innovative approach

Lean manufacturing has become an important avenue for both academics and practitioners in recent times. Many organizations around the world have attempted to implement it but the lack of a clear understanding of lean performance and its measurement will contribute to the failure of lean practices. There are many papers, articles, and reports that address lean techniques and tools, but few studies are found to focus systematically on lean performance evaluation. In order to fill the current gap, this paper presents an innovative approach to measure the lean performance of manufacturing systems by using fuzzy membership functions. The model is flexible, dynamic and easy to use. It enables a systematic measurement of lean performance by producing a final integrated unit-less score.

Modular modelling and performance evaluation of manufacturing systems using Max-Plus Algebra

In this paper, an algorithm, known as the Max Plus Algebra (MPA)–algorithm is presented for analytical model building and performance evaluation of manufacturing systems. The manufacturing system is divided into three types of logical modules, namely, a serial production system, an Assembly System (AS) and a Disassembly System (DS). The three logical modules are modelled using place–Timed Event Graph (p–TEG). Max–plus equations are derived from the p–TEG model using the MPA–algorithm. An illustrative cyclic manufacturing system consisting of all three logical modules is taken to explain the modelling methodology. The flow time of the system is evaluated using Karp's theorem.

Performance evaluation of manufacturing systems based on dependability management indicators—case study: chemical industry

Dependability is a key decision factor in today’s global business environment affecting product cost and process. Dependability reflects user confidence in fitness for use by attaining satisfaction in product performance capability, delivery, service availability upon demand, and minimizing cost. The main objective of this study is to develop an integrated approach for evaluation of manufacturing systems based on dependability indicators for conducting a better dependability management system (DMS) through integration of the principal component analysis (PCA) and the data envelopment analysis (DEA). To achieve the objective of this study, an industrial sector—chemicals and chemical products in Iran—is selected as the case of this study in accordance with the International Standard for Industrial Classification of all economic activities (ISIC). Firstly, we define dependability indicators, for both inputs and outputs, based on IEC 60300. Due to the extra amount of indicators, we utilize a hierarchical structure to cluster the indicators for an easier analysis. Secondly, for reducing the number of some variables, we apply pair-wise comparison to assign weights and to unify the related sub-criteria to one main criterion. Finally, an integrated DEA–PCA approach is employed to assess the most and the least dependable units and to find critical indicators in macro and micro levels in order to make policy for implementation of DMS in manufacturing systems.

A queueing network model for semiconductor manufacturing

We develop an open queueing network model for rapid performance analysis of semiconductor manufacturing facilities. While the use of queueing models for performance evaluation of manufacturing systems is not new, our approach differs from others in the detailed ways in which we model the different tool groups found in semiconductor wafer fabrication, as well as the way in which we characterize the effect of rework and scrap on wafer lot sizes. As an application of the model, we describe a method for performing tool planning for semiconductor lines. The method is based on a marginal allocation procedure which uses performance estimates from the queueing network model to determine the number of tools needed to achieve a target cycle time, with the objective being to minimize overall equipment cost.

Performance evaluation of manufacturing systems: a spreadsheet model

In this paper, we describe the spreadsheet modeling of manufacturing systems which are represented by multi-server and multi-product open networks. The spreadsheet software is characterized by easy data manipulation, on-screen numerical and visual feedback, fast calculation (what-if analysis), and its availability. So far, most spreadsheet models of this type have been used to solve static and deterministic problems in manufacturing systems, ignoring most of the uncertainties. We propose here that a spreadsheet can be implemented for capturing not only static features but also stochastic behaviour of manufacturing systems. The experiments show that the results from the spreadsheet model are reasonably close to those from the other existing approaches. The proposed spreadsheet model could be applied by an engineer to the modeling of manufacturing systems, especially for a first-cut design stage, utilizing his/her own spreadsheet software.

Practice of Petri Nets in Manufacturing

Introduction. 1: Introducing Petri nets M. Silva. Nets and net systems. On modelling features. On net systems interpretations. Approaching concurrency qualitative problems. Qualitative analysis of net system models. Some net subclasses and their analysis. Concluding remarks. 3: Principles of system modelling J.M. Proth. Manufacturing system modelling: basic concepts. Transportation system between two points on a shop-floor. Storage facilities. Operation modelling. Tool systems. Material-handling systems. Conclusion. 3: Synthesis for manufacturing systems integration F. DiCesare, Mu Der Jeng. Introduction to Synthesis. Bottom-up synthesis. Top-down methods. Hybrid synthesis: combining top-down and bottom-up. Summary. 4: Performance evaluation of manufacturing systems J.M. Proth. Introduction. Event graphs: definition and properties. Manufacturing systems working on a cyclic basis. The general case. 5: Petri nets for manufacturing information systems G. Harhalakis, F.B. Vernadat. Introduction. Basics of manufacturing information systems. High-level Petri nets. Organization analysis and conceptual database design. Modeling, analysis and implementation of company policies. Conclusions. Index.