Matching Items (4)

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Iot Manufacturing

Description

As the IoT (Internet of Things) market continues to grow, Company X needs to find a way to penetrate the market and establish larger market share. The problem with Company

As the IoT (Internet of Things) market continues to grow, Company X needs to find a way to penetrate the market and establish larger market share. The problem with Company X's current strategy and cost structure lies in the fact that the fastest growing portion of the IoT market is microcontrollers (MCUs). As Company X currently holds its focus in manufacturing microprocessors (MPUs), the current manufacturing strategy is not optimal for entering competitively into the MCU space. Within the MCU space, the companies that are competing the best do not utilize such high level manufacturing processes because these low cost products do not demand them. Given that the MCU market is largely untested by Company X and its products would need to be manufactured at increasingly lower costs, it runs the risk of over producing and holding obsolete inventory that is either scrapped or sold at or below cost. In order to eliminate that risk, we will explore alternative manufacturing strategies for Company X's MCU products specifically, which will allow for a more optimal cost structure and ultimately a more profitable Internet of Things Group (IoTG). The IoT MCU ecosystem does not require the high powered technology Company X is currently manufacturing and therefore, Company X loses large margins due to its unnecessary leading technology. Since cash is king, pursuing a fully external model for MCU design and manufacturing processes will generate the highest NPV for Company X. It also will increase Company X's market share, which is extremely important given that every tech company in the world is trying to get its hands into the IoT market. It is possible that in ten to thirty years down the road, Company X can manufacture enough units to keep its products in-house, but this is not feasible in the foreseeable future. For now, Company X should focus on the cost market of MCUs by driving its prices down while maintaining low costs due to the variables of COGS and R&D given in our fully external strategy.

Contributors

Agent

Created

Date Created
  • 2016-05

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Analyzing Controllable Factors Influencing Cycle Time Distribution in Semiconductor Industries

Description

Semiconductor manufacturing is one of the most complex manufacturing systems in today’s times. Since semiconductor industry is extremely consumer driven, market demands within this industry change rapidly. It is therefore

Semiconductor manufacturing is one of the most complex manufacturing systems in today’s times. Since semiconductor industry is extremely consumer driven, market demands within this industry change rapidly. It is therefore very crucial for these industries to be able to predict cycle time very accurately in order to quote accurate delivery dates. Discrete Event Simulation (DES) models are often used to model these complex manufacturing systems in order to generate estimates of the cycle time distribution. However, building models and executing them consumes sufficient time and resources. The objective of this research is to determine the influence of input parameters on the cycle time distribution of a semiconductor or high volume electronics manufacturing system. This will help the decision makers to implement system changes to improve the predictability of their cycle time distribution without having to run simulation models. In order to understand how input parameters impact the cycle time, Design of Experiments (DOE) is performed. The response variables considered are the attributes of cycle time distribution which include the four moments and percentiles. The input to this DOE is the output from the simulation runs. Main effects, two-way and three-way interactions for these input variables are analyzed. The implications of these results to real world scenarios are explained which would help manufactures understand the effects of the interactions between the input factors on the estimates of cycle time distribution. The shape of the cycle time distributions is different for different types of systems. Also, DES requires substantial resources and time to run. In an effort to generalize the results obtained in semiconductor manufacturing analysis, a non- complex system is considered.

Contributors

Agent

Created

Date Created
  • 2017

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Mathematical-based approaches for the semiconductor capital equipment installation and qualification scheduling problem

Description

Ramping up a semiconductor wafer fabrication facility is a challenging endeavor. One of the key components of this process is to schedule a large number of activities in installing and

Ramping up a semiconductor wafer fabrication facility is a challenging endeavor. One of the key components of this process is to schedule a large number of activities in installing and qualifying (Install/Qual) the capital intensive and sophisticated manufacturing equipment. Activities in the Install/Qual process share multiple types of expensive and scare resources and each activity might potentially have multiple processing options. In this dissertation, the semiconductor capital equipment Install/Qual scheduling problem is modeled as a multi-mode resource-constrained project scheduling problem (MRCPSP) with multiple special extensions. Three phases of research are carried out: the first phase studies the special problem characteristics of the Install/Qual process, including multiple activity processing options, time-varying resource availability levels, resource vacations, and activity splitting that does not allow preemption. A modified precedence tree-based branch-and-bound algorithm is proposed to solve small size academic problem instances to optimality. Heuristic-based methodologies are the main focus of phase 2. Modified priority rule-based simple heuristics and a modified random key-based genetic algorithm (RKGA) are proposed to search for Install/Qual schedules with short makespans but subject to resource constraints. Methodologies are tested on both small and large random academic problem instances and instances that are similar to the actual Install/Qual process of a major semiconductor manufacturer. In phase 3, a decision making framework is proposed to strategically plan the Install/Qual capacity ramp. Product market demand, product market price, resource consumption cost, as well as the payment of capital equipment, are considered. A modified simulated annealing (SA) algorithm-based optimization module is integrated with a Monte Carlo simulation-based simulation module to search for good capacity ramping strategies under uncertain market information. The decision making framework can be used during the Install/Qual schedule planning phase as well as the Install/Qual schedule execution phase when there is a portion of equipment that has already been installed or qualified. Computational experiments demonstrate the effectiveness of the decision making framework.

Contributors

Agent

Created

Date Created
  • 2015

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Analyzing the impact of building information modeling (BIM) on labor productivity in retrofit construction: case study at a semiconductor manufacturing facility

Description

Economic and environmental concerns necessitate the preference for retrofits over new construction in manufacturing facilities for incorporating modern technology, expanding production, becoming more energy-efficient and improving operational efficiency. Despite the

Economic and environmental concerns necessitate the preference for retrofits over new construction in manufacturing facilities for incorporating modern technology, expanding production, becoming more energy-efficient and improving operational efficiency. Despite the technical and functional challenges in retrofits, the expectation from the project team is to; reduce costs, ensure the time to market and maintain a high standard for quality and safety. Thus, the construction supply chain faces increasing pressure to improve performance by ensuring better labor productivity, among other factors, for efficiency gain. Building Information Modeling (BIM) & off-site prefabrication are determined as effective management & production methods to meet these goals. However, there are limited studies assessing their impact on labor productivity within the constraints of a retrofit environment. This study fills the gap by exploring the impact of BIM on labor productivity (metric) in retrofits (context).

BIM use for process tool installation at a semiconductor manufacturing facility serves as an ideal environment for practical observations. Direct site observations indicate a positive correlation between disruptions in the workflow attributed to an immature use of BIM, waste due to rework and high non-value added time at the labor work face. Root-cause analysis traces the origins of the said disruptions to decision-factors that are critical for the planning, management and implementation of BIM. Analysis shows that stakeholders involved in decision-making during BIM planning, management and implementation identify BIM-value based on their immediate utility for BIM-use instead of the utility for the customers of the process. This differing value-system manifests in the form of unreliable and inaccurate information at the labor work face.

Grounding the analysis in theory and observations, the author hypothesizes that stakeholders of a construction project value BIM and BIM-aspects (i.e. geometrical information, descriptive information and workflows) differently and the accuracy of geometrical information is critical for improving labor productivity when using prefabrication in retrofit construction. In conclusion, this research presents a BIM-value framework, associating stakeholders with their relative value for BIM, the decision-factors for the planning, management and implementation of BIM and the potential impact of those decisions on labor productivity.

Contributors

Agent

Created

Date Created
  • 2015