Using flow simulation as a decision tool for improvements in sawmill productivity

We developed a sawmill fl ow simulation model to identify production bottlenecks and determine where productivity improvements could be made. Sawmills often invest in a new machine center and then find out that the processing bottleneck just moves somewhere else. Our approach was specifically designed to investigate the effects of such changes on the entire system. We determined that the trimmer was the system bottleneck when both the small log and large log lines were running concomitantly. Under base case conditions, the model predicted an average board output of 13,147 boards. An increase in the processing capability of the trimmer resulted in a shift of the bottleneck from the small log line to the large log line (at the edger). This bottleneck shift was further investigated and, by allowing the simulation model to manipulate machine settings for the trimmer and edger, it was able to maximize the modeled average board output to 17,996 boards per shift (when edger set up times were not considered) and 16,708 boards per shift (with edger setup times included). These findings were presented to the sawmill management and subsequently implemented as specific improvements at the trimmer machine center, which in turn resulted in an actual increase of 10% in their sawmill’s lumber volume output

APPLICATION OF MODELING AND SIMULATION IN A MANUFACTURING SYSTEM

The aim ofthis project is to develop a simulation model of an air conditioners manufacturing system with a discrete event simulation tool. The model would be utilized as a decision support system for the investigation of improving the process by implementing several options like cost cutting and simplifying operation. This report discusses steps in the development of a simulation model for a manufacturing system using the DES tool, ARENA. A modeling procedure for the development of manufacturing simulation model is presented. The current manufacturing system model is developed to ascertain its limitations and problems to achievethe production target. The steps include data gathering, model building, verification and validation. Several experiments were conducted to recognize parameters useful in the interpretation of the simulation data like the warm up period, run length and number of repetition. The results show that the manufacturing system was improved by 40% by speeding up parts delivery to the system, whilst the waiting time andqueue at each station can be improved by proper line balancing. The findings demonstrates the ability if the approach to provide potential solution to the decision maker

MODELING, SIMULATION AND ANALYSIS OF AN AUTOMOTIVE MANUFACTURING SYSTEM USING ARENA SOFTWARE

The objective of this project is to develop a model, simulate and analysis a manufacturing system using ARENA The scope of study is focusing on an automotive manufacturer, specifically on the automotive part component stamping line. The aim is to provide the best method to improve the workstation process efficiency and to ascertain its limitations and problems to achieve production target. The procedures include data gathering, model building, simulation, verification, and validation and performance analysis. To improve understanding about ARENA, a case study is carried out to make a simple simulation model. Then the model is simulated using the actual stamping productions data gathered which include the production index daily, process specification, parameters, production schedule and machine breakdown. The output of the simulation is generated in a form of report. The report is organized into sections which summarized across all replications. The results show that the percentage error of ARENA model is less than 5% as targeted. This model would be used as a decision support system for the investigation of improving the process by implementing several decisions like line balancing and simplifYing operation. "What-if" analysis is applied to give a review on the decision is presented. The findings confirm the qualitative behaviour of the manufacturing system in response to the different decision options

An object-oriented simulation system for softwood sawmills

S3 (Softwood Sawmill Simulator) is a sawmill simulation system for modeling the operations of Pacific Northwest softwood lumber mills. S3 consists of three main parts. The first part is the framework for construction of the sawmill layout. The second part focuses on individual machine centers, their process and down times, and their interconnections. The third part consists of databases for raw material and final products. S3 inputs process logic from external data files. All parts are integrated in an object-oriented framework. The system was developed using the object-oriented environment, Actor. All data input and output are through database files in dBASE IV format. S3 can model a sawmill represented by the machine center and connection types defined in S3. The size of the model is controlled by the Actor programming environment. The construction of a sawmill model is demonstrated

MODELING, SIMULATION AND ANALYSIS OF AN AUTOMOTIVE MANUFACTURING SYSTEM USING ARENA SOFTWARE

The objective of this project is to develop a model, simulate and analysis a manufacturing system using ARENA The scope of study is focusing on an automotive manufacturer, specifically on the automotive part component stamping line. The aim is to provide the best method to improve the workstation process efficiency and to ascertain its limitations and problems to achieve production target. The procedures include data gathering, model building, simulation, verification, and validation and performance analysis. To improve understanding about ARENA, a case study is carried out to make a simple simulation model. Then the model is simulated using the actual stamping productions data gathered which include the production index daily, process specification, parameters, production schedule and machine breakdown. The output of the simulation is generated in a form of report. The report is organized into sections which summarized across all replications. The results show that the percentage error of ARENA model is less than 5% as targeted. This model would be used as a decision support system for the investigation of improving the process by implementing several decisions like line balancing and simplifYing operation. "What-if" analysis is applied to give a review on the decision is presented. The findings confirm the qualitative behaviour of the manufacturing system in response to the different decision options