Sheng-Hung Chang

Heuristic PAC model for hybrid MTO and MTS production environment

Abstract Two distinct types of semiconductor plants in Taiwan are integrated device manufacturing (IDM) plants and foundry plants. Most IDM plants are make-to-stock (MTS) operations, focusing on throughput and machine utilization. However, foundry plants are make-to-order (MTO) operations, focusing on due date and cycle time. Besides the challenge of different process technology, the mode of hybrid operation (a combination of MTO and MTS operations) is also a formidable task for these plants. This study develops a heuristic production activity control model to achieve the two different criteria in a hybrid wafer production environment.

Enhancement of Theory of Constraints replenishment using a novel generic buffer management procedure

Managing a distribution system requires the right inventory in the right place at the right time. A Theory of Constraints replenishment solution is presented to aggregate inventory buffers at the central warehouse in plant and change the mode of operation from push to pull. The solution is powerful, but the optimal amount of buffer remains undetermined. In Theory of Constraints, choosing a specific buffer size is not crucial if the buffer is accurately monitored in a timely manner. Accordingly, Theory of Constraints is offered a buffer management approach for monitoring the buffer. Such buffer management is feasible and effective but is insufficiently rigorous. This paper elucidates a generic buffer management procedure, based on the concept of Theory of Constraints buffer management that rigorously defines a method of monitoring to size and adjust the buffer. An example demonstrates the feasibility of the proposed generic buffer management procedure.

The construction of production performance prediction system for semiconductor manufacturing with artificial neural networks

The major performance measurements for wafer fabrication system comprise WIP level, throughput and cycle time. These measurements are influenced by various factors, including machine breakdown, operator absence, poor dispatching rules, emergency order and material shortage. Generally, production managers use the WIP level profile of each stage to identify an abnormal situation, and then make corrective actions. However, such a measurement is reactive, not proactive. Proactive actions must effectively predict the future performance, analyze the abnormal situation, and then generate corrective actions to prevent performance from degrading. This work systematically constructs artificial neural network models to predict production performances for a semiconductor manufacturing factory. An application for a local DRAM wafer fabrication has demonstrated the accuracy of neural network models in predicting production performances.

Due-date performance improvement using TOC's aggregated time buffer method at a wafer fabrication factory

Due-date performance is one of the most important production indexes for success utilized by wafer fabrication factories. Traditionally, the industry sets a specific due-date tightness level and a dispatching rule based on the total processing time, the production capacity, pre-defined order release criteria and historical data, to ensure deliveries are made on-time. However, such policies typically do not solve the due-date performance problem at wafer fabrication factories, since the processes are highly complex. This investigation explores the due-date performance problem using the concept of the aggregated time buffer in critical chain project management (CCPM), which was developed by Dr. Goldratt. A simulation model was constructed and the performance of the proposed method is evaluated based on four dispatching rules at a wafer fabrication factory. The findings reveal that applying aggregated time buffer control system improved the overall due-date control, in terms of on-time delivery rate, average tardiness, and variances in average tardiness and lateness.

Model to determine the backup capacity of a wafer foundry

Foundry companies are very concerned about fully utilizing equipment and increasing productivity because of the rapid depreciation and large investments made in equipment. Factory output is limited by its bottleneck machine. If a discrepancy exists between the planned and actual product mix, a new machine will become the bottleneck and will have a strong impact on the capacity. All famous foundry companies, such as TSMC, UMC, and others, implement backup solutions to such problems. However, the level of protective capacity at the non-bottleneck workstations strongly affects production performance. Therefore, the backup quantity should be well managed. Accordingly, a backup capacity determination model is presented in two parts: the Backup Capacity Determination Model (BCDM) and the Performance Evaluation Model (PEM). The concept of protective capacity is applied to determine the backup capacity in the BCDM. The determination of protective capacity is based on the statistical fluctuation of factors that include machine downtime and unreasonable queuing time in front of the non-bottleneck workstations. The PEM applies queuing theory and Littles Law to evaluate the effects of the backup capacity exceeding the backup capacity on throughput, work in process and the cycle time of the backup factory. A backup capacity determination model provides important information, so that managers have access to a sufficient and reliable reference when confronted with a problem that involves backup capacity.

Manufacturing bill-of-material planning

The bill-of-material BOM in the machine tool industry takes two different forms in design and manufacturing functions: Engineering BOM E BOM , which is used by the design engineer to represent designed product structure; and manufacturing BOM M BOM , which is used by MRPII system for MRP explosion. The designer constructs the E BOM after the product has been designed. Next, the E BOM is transformed into the M BOM by considering assembly sequence and constraints. Constructing a M BOM simply involves compressing the E BOM into a three-level M BOM. Planning of a M BOM still depends primarily on the experience input of a manufacturing engineer and is performed manually. This trial and error and time consuming approach creates an inconsistent method for planning the M BOM. Therefore, in this study, a three-stage M BOM planning method is developed. Stage one plans the initial M BOM, stage two improves the M BOM and stage three tunes the M BOM. Concepts and algorithms of each stage are highlighted in this study....

Model to determine a general X-factor contribution and apply to cycle time improvement for wafer fabrication

Shortening cycle time and maximising output are the major concerns of highly competitive industry. In this paper, an Adjusted X-Factor Contribution (AXFC) measurement is developed, which considers batching process, un-batching process and machine failure. A general model is established to determine the X-factor contribution for all types of machines. In this model, GI/G/m queuing theory is applied to estimate the aggregated cycle time. The machine downtime variability, lot arrival variability, batching and un-batching processing are considered. Finally, the effects of system performances by improving the workstations with high utilisation and high AXFC are explored. The results showed that the cycle time and cycle time variability of products could be affected by the relative locations of high utilisation and high AXFC workstations. Furthermore, the results also revealed that reducing failure frequency of high AXFC workstation will perform as good as high utilisation workstation on cycle time improving.

Model to Evaluate Production Performance of Twin-Fab under Capacity Support

Semiconductor manufacturing is a capital-intensive and high-tech industry. In order to reduce installation cost and increase production flexibility, twin-fab concept has been established over the past decade, which means two neighboring fabs can be connected to each other by automatic transportation system (AMHS). The capacity backup can be performed between twin fabs to increase whole performance. In this work, a performance evaluation model is proposed to estimate the whole production performance of twin fabs under backup policy. Two situations of capacity shortage are discussed, temporary and permanent capacity shortage. The queuing theory and Little’s Law are used in this model. Besides, the expected value is applied for the estimation of the transportation time under backup activities. Based on the evaluation model, managers can obtain an appropriate estimation of performance under capacity backup in twin-fab environment, which will help to get the reliable information for decision making.

TOC Portfolio Selection Model for NPD projects of the biotechnological industry

Portfolio management can provide optimal investment strategies and the best allocation of scarce resources that will allow a company to make the largest profits. In Taiwan, most individual enterprises in the biotechnology industry are small and most of their limited resources are put into R&D. Therefore, picking out an effective choice of companies to include in an investment portfolio is an important issue for long-term business development. This study presents the five-step focusing procedure of the Theory of Constraints, also known as the TOC Portfolio Selection Model. This model allows a company to concentrate on the optimal allocation of constrained resources and to realize the maximum expected profits. A set of selection methods based on the key resource is established. A case study profiling the bio-chip industry is used here as an example to illustrate the TOC Portfolio Selection Model. The results indicate that the proposed method is superior to more traditional ones.

Limited number of open projects to shorten NPD schedule effectively

Many companies will open many projects simultaneously due to market trends which results in crowding out effect because of limited resources. R&D engineers become overloaded and scheduling of products develop delay resulting in timing misses and losing lead in the market. The company in this case study (Company A), often opens up many projects simultaneously in order to response market needs quickly. The engineers are overloading and, of course, the schedule delay. In order to identify problems, Company A began using Dr. Goldratts Thinking Processes (TP) during new product development (NPD). During the analysis phase, it identifies “the Quantity of Kick-off project” as core conflict in developing procedure. Then, the new rules and procedures are proposed while opening, suspending, stopping, and closing project. Finally, Future Reality Tree ensures that the proposed rules and procedures is an available solution and receives approval for practical running by executives. One year in practical running shows that the Project Completion Rate increased by 22% and index of Project Duration Rate was reduced at least 68%.