Empirical study of the effect of stochastic variability on the performance of human-dependent flexible flow lines

Manufacturing systems have developed both physically and technologically, allowing production of innovative new products in a shorter lead time, to meet the 21st century market demand. Flexible flow lines for instance use flexible entities to generate multiple product variants using the same routing. However, the variability within the flow line is asynchronous and stochastic, causing disruptions to the throughput rate. Current autonomous variability control approaches decentralise the autonomous decision allowing quick response in a dynamic environment. However, they have limitations, e.g., uncertainty that the decision is globally optimal and applicability to limited decisions. This research presents a novel formula-based autonomous control method centered on an empirical study of the effect of stochastic variability on the performance of flexible human-dependent serial flow lines. At the process level, normal distribution was used and generic nonlinear terms were then derived to represent the asynchronous variability at the flow line level. These terms were shortlisted based on their impact on the throughput rate and used to develop the formula using data mining techniques. The developed standalone formulas for the throughput rate of synchronous and asynchronous human-dependent flow lines gave steady and accurate results, higher than closest rivals, across a wide range of test data sets. Validation with continuous data from a real-world case study gave a mean absolute percentage error of 5%. The formula-based autonomous control method quantifies the impact of changes in decision variables, e.g., routing, arrival rate, etc., on the global delivery performance target, i.e., throughput, and recommends the optimal decisions independent of the performance measures of the current state. This approach gives robust decisions using pre-identified relationships and targets a wider range of decision variables. The performance of the developed autonomous control method was successfully validated for process, routing and product decisions using a standard 3x3 flexible flow line model and the real-world case study. The method was able to consistently reach the optimal decisions that improve local and global performance targets, i.e., throughput, queues and utilisation efficiency, for static and dynamic situations. For the case of parallel processing which the formula cannot handle, a hybrid autonomous control method, integrating the formula-based and an existing autonomous control method, i.e., QLE, was developed and validated.InnovateU

Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Flexible flow lines use flexible entities to generate multiple product variants using the same serial routing. Evaluative analytical models for the throughput rate of asynchronous serial flow lines were mainly developed for the Markovian case where processing times, arrival rates, failure rates and setup times follow deterministic, exponential or phase-type distributions. Models for non-Markovian processes are non-standalone and were obtained by extending the exponential case. This limits the suitability of existing models for real-world human-dependent flow lines, which are typically represented by a normal distribution. We exploit data mining and simulation modelling to derive a standalone closed-form formula for the throughput rate of normally distributed asynchronous human-dependent serial flow lines. Our formula gave steady results that are more accurate than those obtained with existing models across a wide range of discrete data sets

Process Control Parameters Evaluation Using Discrete Event Simulation for Business Process Optimization

The quest for manufacturing process improvement and higher levels of customer satisfaction mandates that organizations must be equipped with advanced tools and techniques in order to respond towards ever changing internal and external customer demands by maintaining the optimal process performance, lower cost and higher profit levels. A manufacturing process can be defined as a collection of activities designed to produce a specific output for a particular customer or market. To achieve internal and external objectives, significant process parameters must be identified and evaluated to optimize the process performance. This even becomes more important to deal with fierce competition and ever changing customer demands. This paper illustrates an integrated approach using design of experiments techniques and discrete event simulation (Simul8) to understand and optimize the system dynamic based on operational control parameter evaluation and their boundary conditions. Further, the proposed model is validated using a real world manufacturing process case study to optimize the manufacturing process performance. Discrete event simulation tool is used to mimic the real world scenario, which provides a flexible and powerful way to comprehensively understand the manufacturing process variations and allows controlled 'What-If´ analysis based on design of experiments approach. Finally, this paper discusses the potential applications of the proposed methodology in the cable industry in order to optimize the cable manufacturing process by regulating the operational control parameters such as dealing with various product configurations with different equipment settings, different product flows and work in process (WIP) space limitations

Microwave dielectric measurements of fibrous catalyst using transmission line technique in the frequency range of 1-4 GHz

Materials performance at microwave frequencies has generated a wide interest in recent years for commercial and industrial purposes. However, the interaction between microwave energy and iron compounds is an area where further characterization work is required. The article presents a novel optimized analytical-numerical method for conversion of smoothed transmission scattering (S21) parameters to complex permittivity. The measurement method was validated and subsequently used to characterize the dielectric properties of modified polyacrylonitrile catalyst powder incorporating ligated iron cations. The result is compared to other published iron catalyst materials measured in the same frequency range of 1 to 4 GHz

Web-based Metadata Retrieval Tool for Fine Art and Games Artwork

Developers in art sector are faced with many problems during development of a new product (e.g. game). Resourcing of existing artworks, planning of the product development and commercialisation of their artworks are sources of wastes in the product development processes. Developers usually use multiple software and tools to perform these tasks; however, integration between these tools represents a major issue. In this paper, a web-based product improvement tool is presented. The tool provides developers with one-stop shop for them to resource their artworks, using Metadata, from a centralized web-based server; plan and optimise their production development using Lean principles; advertise and commercialise their artworks to potential customers and other developer

Discrete Event Simulation to Reduce the Effect of Uncertainties on Project Planning

Planning is a vital decision making activity that influences the future of an organization by determining what tasks are to be performed, who required resources are and in what sequence. Organizations often follow a rigorous process to plan and deliver projects optimally based on the given resource and budget constraints. However, uncertainties increase the complexity of the planning process and contribute towards increased cost, delays and resource allocation issues. Therefore, it is important to understand the uncertainties and constraints associated with project activities and their effect on both business processes and organizational goals. Understanding the causal relationships between activities and constraints could allow organizations to operate more effectively and efficiently even in uncertain environments and lead to a more informed decision making process. This paper exemplifies the use of discrete event simulation tool to develop a strategically focused project delivery plan founded on the assessment of uncertainties that could arise during the delivery of the project. Proposed methodology follows a structured and systematic approach in order to identify the factors that can affect the delivery of the project and evaluate solutions that may mitigate or reduce the risk to As Low As Reasonably Practical (ALARP). The main objective is to complement the existing project planning activities rather than replace the existing tools