This research focused on using design of experiments (DoE) and simulation modeling to optimize the facility layout in a server assembly line, while introducing a ...
Proceedings of the 2008 Industrial Engineering Research Conference J. Fowler and S. Mason, eds.
Using Design of Experiments and Simulation Modeling to Study the Facility Layout for a Server Assembly Process Sreekanth Ramakrishnan, Pei-Fang Tsai, Krishnaswami Srihari Watson Institute for System Excellence Department of Systems Science and Industrial Engineering Binghamton University, Binghamton, NY 13902 Christopher Foltz Advanced Manufacturing Sciences, IBM Integrated Supply Chain 2455 South Road, Poughkeepsie, NY 12601, USA Abstract This research focused on using design of experiments (DoE) and simulation modeling to optimize the facility layout in a server assembly line, while introducing a new product. The critical factors that impact the performance metrics of cycle time, work-in-process and throughput were identified by using DoE principles. Subsequently, simulation modeling was used to evaluate the various what-if scenarios for determining the optimal physical location of the key operations in the server fabrication process. The factors identified by DoE, such as distances between operations, batch sizes, frequency of transactions, transportation types and resource utilizations were studied closely for the feasibility of the re-layout scenarios. The performance measures and results from the simulation models were then used to develop the layout for the server assembly process.
Keywords Fabrication-fulfillment model, simulation modeling, facility layout
1. Basic Premise Today’s manufacturers, facing intensifying competition and steady pressure for higher levels of customer service, are compelled to continuously improve their supply chain management. Most of these manufacturers use production control philosophy that combines build-to-plan with make-to-order operations, commonly referred to as the fabrication/fulfillment process. The fabrication stage is a build-to-plan process, where components are procured, tested and partially assembled, and then kept in stock prior to the final assembly into end-products. The fulfillment stage is a make-to-order process, which the final assembly starts only after the customer order is received [2]. The fabrication process, along with the fulfillment process, is employed to be responsive to customer orders with lower inventory holding costs. It is observed that this process strategy, combined with the uncertainty in customer orders, poses a tremendous challenge for inventory management, production planning in the manufacturing floor, and even for the physical layout of operations [2, 3]. The introduction of new products is another challenge in the fabrication/ fulfillment environment since a fine balance needs to be reached between the demands of the existing products and the readiness for the new product. Analyzing the impact of introducing new products and concurrently supporting the additional process steps could be a sophisticated exercise. This research focused on using design of experiments (DoE) and simulation modeling techniques to determine the facility layout, which can optimize the process flow in the server assembly process during new product introduction. The decision of physical location of one key operation, the Node Build operation, in a server manufacturing process was further considered in this study. Two potential locations were evaluated for the Node Build operation based on the performance measures of the fabrication process. This was accomplished through discrete event simulation models. Subsequently, a business case was developed and recommendations were made. This paper is organized as follows. Section 2 presents the problem statement and the objectives of this study. The methodology used for this
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Ramakrishnan, Tsai, Srihari and Foltz study, the process of building the simulation model and the what-if analysis are discussed in Section 3. Section 4 presents the conclusions of this study.
2. Problem Statement and Review of Relevant Literature The objective of this study was to design an efficient and effective facility layout for the server assembly area, while introducing a new product to its product mix. The ultimate goal was to minimize the flow time, while concurrently ensuring efficient usage of resources and reducing overall costs (transportation and construction). The process parameters being studied include the location of operations, batch sizes, transport modes, work-in-process, and dispatching rules. The existing physical facility layout is shown in Figure 1. It should be noted here that modifications to the layout to support the new products were required to be made without adversely impacting the existing processes for the current product base. This constraint presented an additional challenge to the facility layout team. Building A Front-end of Line Existing Node Build Room
Building B Available Clean Room Warehouse
Server Assembly Area Warehouse and Crib
Fabrication Test (Ambient)
Fabrication Test (Thermal)
Frame Build
Fulfillment Area
Rework Area
Line Side Stock
Figure 1 – Existing layout with process flows As shown in Figure 1, the existing node build operation is performed in Building A, which is further away from the server assembly area. Node build is a key operation in the server assembly process, wherein the printed circuit board is assembled with multi-chip modules and inter-connectors. For the new product being introduced, a controlled environment room was required to perform the node build operations. Neither Building A nor the server assembly area had a controlled environment available. However, a controlled environment area was available in Building B, adjacent to Building A. Hence, the question under consideration was to determine the most appropriate location for the controlled environment to support the node build operation for the new product series. Numerous factors and their interactions have to be studied prior to making any decisions. For this purpose, designed experiments and simulation modeling were employed. Simulation is used as a tool for system analysis and can be applied to various problem domains [1]. The responses from the experiments were analyzed to measure the main effects and the interactions between the factors. This analysis helps identify the significant factors affecting the flow time of the server assembly process. Based on the results from these experiments, facilities can be designed efficiently and effectively. Typically, simulation tools have been used for business analysis at a strategic level, at high levels of abstraction. Using simulation to analyze and provide decisions using highly granular data has not been widely practiced in the industry. The need to create realistic models for complex manufacturing systems using simulation software is an extremely challenging task. Unlike most other existing methodologies discussed in the literature, the approach described in this paper accomplished these challenging requirements vis-à-vis providing highly granular modules that could assist with decision making pertaining to facility re-layout [2-4]. The existing approaches focus primarily only on adjacency-based or frequency-based facility layout. Hence, these models are deterministic, without accounting for the inherent randomness in the process, coupled with frequent product and process changes.
3. Research Methodology In this section, the process of using Design of Experiments (DoE) and building simulation models for the server assembly and fabrication process at the server manufacturing area is presented. The methodology followed in this research is listed below:
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Study the server assembly area and understand the process flow through the use of process mapping. Review data that is available from the information systems (shop floor control systems), conduct time studies at various stages to validate the data from the information systems and, obtain additional data for developing the simulation model. Design experiments to identify critical factors impacting the physical layout. Develop the baseline simulation model, which would accurately reflect the existing process at the server assembly area. Use the historical data to verify and validate the baseline model. Conduct what-if analysis, study the performance measures in the alternative scenarios, and subsequently compare them with the baseline model. Run sensitivity analysis, document suggestions, and build a business case, using the outputs from the simulation model and other relevant data.
3.1 Process Mapping The server assembly fabrication process involves assembling a wide range of components, such as nodes, power supplies, cabling and memory. Process mapping would help identify the various operations in the process under study. In addition, it would also help identify potential bottlenecks in the process. The server assembly process starts with a Printed Circuit Board (PCB) from the front end of the line. The process of building and assembling the board is done in the board assembly area. Once the boards are completely assembled, they are sent to the Node Build area, where Multi Chip Modules (MCM) are attached to each board, based on the product design. The assembly of the board and the MCM is called a ‘Node’ and hence, the operation is called Node Build. These nodes are then assembled with heat sinks and pass through an In-Circuit Test (ICT), before they are cleaned and transported to the fabrication area. Once the nodes arrive at the server assembly area, they are moved to the ‘untested’ queue in the warehouse, to begin the fabrication process. In the fabrication process, testing of nodes and other commodities is conducted, based on a fabrication schedule. The untested nodes, along with the other commodities, undergo testing, both in ambient and thermal testing conditions. Once the testing is complete, these commodities (memory cards, nodes and multiplexers) are de-configured and stored in the tested stock warehouse, available to fulfill customer orders. The defective nodes can be repaired in the ‘rework area’ when the damages are minor. However, for major repairs (such as board damage), the defective node has to return to the front-end of the line. This is an important criterion to be addressed when the decision regarding the location of the Node Build operation is made. 3.2 Data Collection The data for the simulation model was collected from all four quarters of 2007, from the shop floor control system. Using the data that was collected, the statistical distributions for the arrival rate and frequency of the boards and the modules into the node room were developed. Extensive data mining was conducted to obtain the processing times at each stage of the process flow. The distributions for the processing times at each stage of the process were validated through interviewing operators and conducting time studies. The distributions for the transportation times and the process times were also developed. The same approach was used for the server assembly process. The arrival rates of the nodes and other commodities were also obtained and their characteristics studied closely. However, for the new product being introduced, there was no historical data available for processing times, product yields and other key metrics. In order to overcome this challenge, data was obtained based on prototyping and identifying similar processes in the existing products and the new product. Moreover, unit hour and yield prediction models were used to estimate processing times and product yields respectively. 3.3 Determining the Key Factors In this phase of the research, the goal was to determine the key factors that impact the efficiency of the facility layout in a server fabrication process. Based on process mapping and data collection activities, the following factors were identified. Through designed experiments, these factors and their interactions were studied closely prior to building the simulation model. The factors included: 1. Layout types with different proximity of operations and frequency of interactions between operations; 2. Batch sizes; 3. Transportation modes; 4. Utilization rates of machines, which were based on yields and process times; 5. Dispatching rules; and
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Shared tools and shared resources.
Each factor had two levels and a full factorial design was used as the experimental matrix. Figure 2 shows the major effect plot and the normal probability plot of the effects when cycle time was used as the response variable. The experiment was conducted for different product types and it was observed that the factors that impacted the cycle time the most were (i) layout types, (ii) batch sizes, (iii) transport modes, and (iv) shared tools and resources. In addition, the other factors that were not studied in the experiment included the availability of parts, type of facility floor and environment (controlled or normal), and product scrap and rework. Since spreadsheet-based models can not account for these complex interactions effectively, simulation modeling is an excellent candidate to model this process. Layout
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Figure 2: Major effects plot and normal probability plots for the effects 3.4 Model Development, Verification and Validation The baseline simulation model was developed using Arena® 10.0 based on the information obtained from the process mapping activities and time study. As mentioned in the previous section, during the process mapping phase, it was observed that one of the key factors that cause an increase in the cycle time was the shortage of parts required to configure a node, such as memory cards and multiplexers. The simulation model was developed in order to account for these shortages. Another important consideration in the model was to demonstrate the ‘reuse’ of the tested parts to help alleviate the shortages of these parts. In other words, the tested commodities (called ‘golden parts’) could be re-used to test the untested commodities, in the event of any shortage of parts. Validation is to ensure the accuracy of the computer model with respect to the intended application of the model. However, the validation of a simulation model does not guarantee the credibility and acceptability of simulation results [1]. This baseline model was verified and validated against a different set of historical data. The flow of entities was monitored to ensure that the logic of the model was error free. Validation ensured that the outputs from the simulation model were consistent with the real-world scenario. The simulation results were obtained after running 25 replications, and each replication had 82 days with a warm up period of 8 days. Then, statistical tests, including the hypothesis test and paired t-tests, were conducted [6]. The results comparing the throughput are summarized in Table 1. It was concluded that the simulation model was statistically identical to the historical data, within a confidence level of 95%. Once the baseline model was validated, the model was updated with the data for the new products (demands, processing times and product yields). The performance measures were collected and recorded for subsequent analysis. Table 1: Baseline model performance measures
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Ramakrishnan, Tsai, Srihari and Foltz 3.4. What-if Analysis Two alternative scenarios were further considered to determine the optimal location for placing the Node Build operation for the new product. The decisions were made based on metrics such as cycle time, Work-In-Process (WIP) and the cost of re-layout. These two scenarios are described below.
Scenario 1 – Node Build room in the server assembly area. This scenario recommends constructing a new controlled environment for the Node Build operation in the server assembly area. The advantage of this scenario is the reduction in transport times for the boards that required rework at the front-end of the line. However, in this case, the boards would have to travel a longer distance to the server assembly area since the area is located farther away from the current node build room. Scenario 2 – Use an existing controlled environment area in Building B for the Node Build operation. This scenario suggests moving the Node Build operation to the controlled environment available in an adjacent area. Although this scenario has no cost for developing the controlled environment, it incurs additional travel time for boards (and nodes) between the Node Build area and the server assembly line. This is also true for nodes that require rework and need to travel between these two areas. The updated baseline simulation model was modified to compare these two scenarios. For both scenarios, simulation runs were made for the different levels of the factors identified through the designed experiments (e.g. yields, demand sets and dispatching rules). The data for the new product was used in the what-if analysis. Based on the results as shown in Table 2, it was observed that Scenario 1 would reduce the cycle time of the node built process by approximately 4%, while reducing the WIP of the nodes by 3.5%. Implementing Scenario 2 was observed to increase the cycle time of the nodes by 24%, while increasing its WIP by 16%. Table 2: Summary of what-if analysis Performance Measure
Baseline Model
Scenario 1
Scenario 2
Throughput (Nodes)
1698
1701
0.2%
1612
-5.1%
Throughput (Boards)
1507
1489
-1.2%
1499
-0.5%
Cycle Time (Nodes) - Days
10.87
10.41
-4.2%
13.49
24.1%
Cycle Time (Boards) - Days
4.22
4.47
5.9%
4.33
2.6%
WIP (Nodes)
227
219
-3.5%
264
16.3%
WIP (Boards)
77
81
5.2%
80
3.9%
The business case was developed by including costs associated with each scenario. The cost factors considered included the inventory carrying costs, the capital cost of construction, and the space occupancy costs for both the scenarios. It was observed that the costs associated with scenario 1 were approximately 28% of the costs incurred by scenario 2. From the business case, it was concluded that Scenario 1 provides a better solution than Scenario 2. Hence, the most appropriate location for the Node Build operation would be in the server assembly area. Additionally, other proximity benefits were also gained. Figure 3 shows the layout that was recommended by this study.
4. Conclusions Facility layout poses challenging problems in both the design of new facilities and the redesign of existing ones. This exercise involves determining the location of machines, workstations, minimizing transportation costs and reducing the circles of motion. Most of the facility layout problems focus on reducing the distances traveled between operations alone, ignoring other factors, such as shared resources and the frequency of transactions between operations. This may result in sub-optimal facility layouts, resulting in process inefficiencies. In this research, the benefits of using simulation modeling and designed experiments to study the server assembly process layout are demonstrated. The critical factors that impact the effectiveness of the facility layout were determined through designed experiments. Through simulation modeling, the most appropriate location for the Node Build operation was determined while catering to the requirements (both process and physical layout) of the new product and concurrently supporting the existing products and processes. Some of the unique features of this research effort are listed below:
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Ramakrishnan, Tsai, Srihari and Foltz Building A Front-end of Line
Building B Available Clean Room
Node Build Area with Rework
Fabrication Test (Ambient)
Warehouse & Shipping
Server Assembly Area
Fulfillment Area Fabrication Test (Thermal)
Frame Build Area
Figure 3: Recommended layout with a streamlined process flow 1.
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Simulation modeling can account for the randomness and complex interdependencies of the various processes involved in server manufacturing. In this research, although the focus was on identifying the location for the Node Build operation, the impact of any changes made to the end-to-end server manufacturing process can be quantified. Additionally, factors such as the non-availability of parts along with shared tooling, resources and transportation modes can be studied closely. By integrating designed experiments with simulation modeling, any additional factors that impact the efficiency of the facility layout can be studied and subsequently updated to the simulation model. Building credible models in the absence of actual data has always been a challenge for simulation modelers dealing with new products. While introducing new products, there exists no historical data from which statistical distributions could be built. This research, on the other hand, identifies similar processes (in the existing products and the new products) and uses statistical distributions from existing processes. Additionally, this research also uses yield models to predict the yields for the new products. Using the integrated DoE and simulation modeling approach, different factors could be studied by toggling them at different levels to perform sensitivity analysis. This provides the decision makers with increased confidence in the performance measures from the simulation models. For example, in this study, different values of yields in the two key processes (node build and fabrication test) were studied and the performance measures were monitored.
As of 2008, Scenario 1 had been implemented in the facility. Based on the post-implementation data analysis, a 3.4% reduction in cycle time (4% predicted by the simulation model) was realized. This shows the accuracy of the simulation model and the effectiveness of the proposed approach. Additionally, during the analysis of the simulation model, certain inefficiencies in the process that were identified, such as part shortages and sub-optimal scheduling of test cells, have also been addressed. The approach presented in this study could be used for both re-layout analysis and for designing new layouts.
References 1. 2. 3. 4. 5. 6.
Law, A. Kelton, D.W., “Simulation Modeling and Analysis”, 3rd Edition, McGraw-Hill, 2000. Cao, H., Cheng, F., Buckley, S., “A Simulation-based Tool for Inventory Analysis in a Server Manufacturing Environment” Proceedings of the 2003 Winter Simulation Conference, New Orleans, LA, 2003, pp. 1313-1318. Smith, S.F., Cao, H., Xi, H., “A Reinforcement Learning Approach to Production Planning in a Fabrication/Fulfillment Manufacturing Process”, Proceedings of the 2003 Winter Simulation Conference, New Orleans, LA, 2003, pp. 1417-1423. Ramakrishnan, S., Tsai, P., Srihari, K., Foltz, C., Boldrin, W., “An Alternate Configuration Management System for Parts Configuration in a Server Manufacturing Environment”, Proceedings of the IIE Annual Conference and Exposition, Nashville, TN, 2007, pp. 1-6. Kelton, D., Sadowski, R., Sadowski, D.A., “Simulation with Arena”, McGraw- Hill, 2007. Montgomery, D. C., “The Design and Analysis of Experiments”, 7th Ed. New York: John Wiley and Sons, 2006.
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