Yes / Market competition is fierce and has been intensified due to globalization, therefore companies have experienced increase pressure to improve cycle and delivery times and achieve a high level of customization. Lean principles have been designed and implemented to respond to market changes. However, these principles are commonly aimed to aid low-mix high-volume (LMHV) manufacturers. This paper aims to develop a systematic approach to implement a lean framework in a high-mix low-volume (HMLV) manufacturing environment. An HMLV manufacturing environment, currently produces a variety of products which differs in terms of shape and size and different sequence of operation and as a much smaller batch size. Additionally, analysis of the manufacturing assembly shows there is a substantial variation in cycle and changeover time from product to product. In this approach, an action based study has been completed. This study uses data collection methods to be applied in order to calculate timings to be used in a Discrete Event Simulation (DES). The simulation uses lean tools to study material movement and facility layout to minimize bottlenecks and eliminate waste from the process. An introduction of lean supermarket is considered within the simulation whereby an operator withdraws products in an specific amount needed by a downstream process before introducing it across the assembly floor and an action plan is created. As a result, a lean manufacturing simulation study has been implemented to evaluate the effects of a supermarket concept within the current manufacturing environment. Initial results show a variation within the cycle and setup times for each of the different products due to the nature of the process. The paper, is limited to applying the approach to a singular production line and a singular manufacturing plant. The practical implications to implement this approach into a manufacturing environment is that there should be a need from the company. This approach should be driven by senior members within the organization as the resistance to change would increase the risk of failure. Another implication of the proposed approach is to ensure the accuracy of the data collected and to introduce a series of briefs within each stage of the approach. This is important as all stakeholders would need to be kept up-to-date with the project. However, the approach is applicable to any organization and can be applied to any sector. This paper, develops a systematic approach to implement a lean framework in a high-mix low-volume (HMLV) manufacturing environment. The approach is validated in an automotive manufacturing organization competing in global markets.

Project management has become a key component for improving organizational performance and is applied in many business areas and industries. Resource-constrained stochastic project networks are quite common. Managing such projects to maximize resource utilization and reduce project duration simultaneously is difficult. Resource loading, assignment rules, and priorities significantly affect project performance, especially in shared-resource, multi-project environments. This thesis provides an approach for using discrete-event simulation to analyze the behavior and performance of project networks that use resource pools. A method to translate project networks into simulation models is developed. The translator is used to convert and evaluate a benchmark test set of resource constrained stochastic project networks. The effect of factors like project network complexity, resource availability and allocation strategies on project performance is analyzed using a completely randomized design with factorial arrangement of the aforementioned factors. The conversion process and results from the analysis are discussed.

Even though simulation technology provides great benefits to industry, it is largely underutilized. One of the biggest barriers to utilizing simulation is the lack of interoperability between simulation models. This is especially true when simulation models that need to interact with each other span an enterprise or supply chain. These models are likely to be distributed and developed in disparate simulation application software. In order to analyze the dynamic behavior of the systems they represent, the models must interoperate. However, currently this interoperability is nearly impossible. The interaction of models also refers to the understanding of them among stakeholders in the different stages of models¡Š lifecycles. The lack of interoperability also makes it difficult to share the knowledge within disparate models. This research first investigates this problem by identifying, defining, and analyzing the types of simulation model interactions. It then identifies and defines possible approaches to allow models to interact. Finally, a framework that adopts the strength of Structured Modeling (SM) and the Object-Oriented (OO) concept is proposed for representing discrete event simulation models. The framework captures the most common simulation elements and will serve as an intermediate language between disparate simulation models. Because of the structured nature of the framework, the resulting model representation is concise and easily understandable. Tools are developed to implement the framework. A Common User Interface (CUI) with software specified controllers is developed for using the proposed framework with various commercial simulation software packages. The CUI is also used to edit simulation models in a neutral environment. A graphical modeling tool is also developed to facilitate conceptual modeling. The resulting graphic can be translated into the common model representation automatically. This not only increases the understanding of models for all stakeholders, but also shifts model interactions to the ¡§formulating¡š stage, which can prevent problems later in the model¡Šs lifecycle. Illustration of the proposed framework and the tools will be given, as well as future work needs.

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Yes / Competition in the manufacturing industry is growing at an accelerated rate due to globalization trend. This global competition urges manufacturing organizations to review and improve their processes in order to enhance and maintain their competitive advantage. One of those initiatives is the implementation of the Six Sigma methodology to analyze and reduce variation hence improving the processes of manufacturing organizations. This paper presents a Discrete Event Simulation methodology to support a Six Sigma approach for manufacturing organizations. Several approaches to implement Six Sigma focus on improving time management and reducing cycle time. However, these efforts may fail in their effective and practical implementation to achieve the desired results. Following the proposed methodology, a Discrete Event Simulation model was built to assist decision makers in understanding the behavior of the current manufacturing process. This approach helps to systematically define, measure and analyze the current state process to test different scenarios to improve performance. The paper is amongst the first to offer a simulation methodology to support a process improvement approach. It applies an action research strategy to develop and validate the proposed modelling methodology in a British manufacturing organization competing in global markets.

The purpose of this research was to quantify the impact of the log variables: length, grade, and scaling diameter, on the cost of producing hardwood lumber, using the activity-based costing technique. The usual technique of calculating hardwood lumber product costs is based upon traditional cost accounting, where manufacturing costs are allocated to the products based upon the volume of each product that is produced. With the traditional cost accounting procedure, the variation in the resources used to process the logs is not taken into consideration. As a result, when the cost to manufacture the products is subtracted from the market value of the products, the resulting profit levels of the products may not be truly representative of the actual resources consumed in manufacturing the product. Using discrete-event simulation, two hardwood sawmills were modeled and a series of experiments were conducted which would not have been feasible to conduct on the mill floors. Results from the simulation experiments illustrated that the activity-based and traditional cost accounting techniques allocated different amounts of manufacturing costs to the products. The largest difference between the two cost accounting techniques was found to be the amount of raw material costs allocated to the products. For one of the sawmills modeled, log grade was identified as having the greatest influence on determining product costs and total manufacturing costs. Results from the model of the second sawmill however demonstrated that log diameter had a greater impact on determining product costs and total manufacturing costs. The commonality of the results from the two simulation models was that the differences in the volume of lumber produced, between the logs that were studied, was a critical component in determining which log parameter had the most effect on changing the dynamics of the sawmill system. To enable hardwood managers a more precise method of allocating raw material costs to the lumber products, a methodology was developed that uses the principles of activity-based costing to allocate raw material costs. The proposed methodology, termed the lumber yield method, uses lumber yield values from logs with similar characteristics to allocate raw material costs to the lumber products. Analysis of the output from the simulation models illustrated that with the lumber yield method, the amount of raw material costs allocated to the products was not significantly different than the amount allocated by the activity-based costing method. The calculated raw material costs of the products were however, found to be significantly different between the lumber yield method and the traditional volume costing method. / Ph. D.

Realistic mobility and cellular traffic modeling is key to various wireless networking applications and have a significant impact on network performance. Planning and design, network resource allocation and performance evaluation in cellular networks require realistic traffic modeling. We propose a Discrete Event Simulation framework, Diamond - (Discrete Event Simulation of Mobility and Spatio-Temporal Spectrum Demand) to model and analyze realistic activity based mobility and spectrum demand patterns. The framework can be used for spatio-temporal estimation of load, in deciding location of a new base station, contingency planning, and estimating the resilience of the existing infrastructure. The novelty of this framework lies in its ability to capture a variety of complex, realistic and dynamically changing events effectively. Our initial results show that the framework can be instrumental in contingency planning and dynamic spectrum allocation. / Master of Science

Advances in computing and communication technologies are blurring the distinction between today's PCs and mobile phones. With expected smart phones sales to skyrocket, lack of awareness regarding securing them, and access to personal and proprietary information, has resulted in the recent surge of mobile malware. In addition to using traditional social-engineering techniques such as email and file-sharing, malware unique to Bluetooth, Short Messaging Service (SMS) and Multimedia Messaging Service (MMS) messages are being used. Large scale simulations of malware on wireless networks have becomes important and studying them under realistic device deployments is important to obtain deep insights into their dynamics and devise ways to control them. In this dissertation, we present EpiNet: an individual-based scalable high-performance oriented modeling environment for simulating the spread of mobile malware over large, dynamic networks. EpiNet can be used to undertake comprehensive studies during both planning and response phase of a malware epidemic in present and future generation wireless networks. Scalability is an important design consideration and the current EpiNet implementation can scale to 3-5 million device networks and case studies show that large factorial designs on million device networks can be executed within a day on 100 node clusters. Beyond compute time, EpiNet has been designed for analysts to easily represent a range of interventions and evaluating their efficacy. The results indicate that Bluetooth malware with very low initial infection size will not result in a major wireless epidemic. The dynamics are dependent on the network structure and, activity-based mobility models or their variations can yield realistic spread dynamics. Early detection of the malware is extremely important in controlling the spread. Non-adaptive response strategies using static graph measures such as degree and betweenness are not effective. Device-based detection mechanisms provide a much better means to control the spread and only effective when detection occurs early on. Automatic signature generation can help in detecting newer strains of the malware and signature distributions through a central server results in better control of the spread. Centralized dissemination of patches are required to reach a large proportion of devices to be effective in slowing the spread. Non-adaptive dynamic graph measures such as vulnerability are found to be more effective. Our studies of SMS and hybrid malware show that SMS-only malware spread slightly faster than Bluetooth-only malware and do not spread to all devices. Hybrid malware spread orders of magnitude faster than either SMS-only or Bluetooth-only malware and can cause significant damage. Bluetooth-only malware spread faster than SMS-only malware in cases where density of devices in the proximity of an infected device is higher. Hybrid malware can be much more damaging than Bluetooth-only or SMS-only malware and we need mechanisms that can prevent such an outbreak. EpiNet provide a means to propose, implement and evaluate the response mechanisms in realistic and safe settings. / Ph. D.