Performance Improvement of Flexible Manufacturing System: A Case Study

Transcription

1 Performance Improvement of Flexible Manufacturing System: A Case Study Abid Ali, Rafi Javed Qureshi & Mirza Jahanzaib Department of Industrial Engineering University of Engineering and Technology Taxila, Pakistan. Abstract Extensive competition in manufacturing have left no space for system inefficiencies and it has evolved a manufacturing environment which seeks reduced manufacturing lead times, increased quality standards, vast product variety and competitive cost. The trend towards globalization requires these manufacturing environments to be designed such that it can cater challenges of market place to survive and grow. To cope with these challenges the technologies support with automation and flexibility. The objectives of improvement of manufacturing environment are the basis of emergence of flexible manufacturing systems (FMSs). In this paper a case study of small and medium enterprise (SME) is presented with contribution of some performance improvement methods for flexible manufacturing system. Analytical models illustrated in literature are employed to estimate the performance measures like utilization and maximum production rate. Development of an improved design for existing flexible manufacturing system of SME is also part of this study. In addition to simulation of manufacturing system, various performance parameters are compared and evaluated for existing and improved FMS. Keywords Flexible Manufacturing System, Utilization, Production Rate, Performance Measures 1. Introduction Intense competition in manufacturing environment offers new strains to the manufacturing systems, such as increasing diversity, delivery on time with emphasize on conventional standards of quality and competitive cost. Therefore, in global scenario, the focus is on a developing manufacturing system that can meet all the required conditions within due dates at a reasonable cost. The introduction of flexible manufacturing system (FMS) allows manufacturing industry, to improve their performance, together with the flexibility to make individual product at medium volume. A flexible Manufacturing system (FMS) can be defined as a computer-controlled configuration of semi-dependent jobs and material handling systems designed to efficiently produce different mixes of product with low to medium volume. It blends high flexibility with high productivity and low work-in-process. The delicacy of the FMS is that it is taken from the ideas both from the current loading and batch workshop manufacturing system and is designed to imitate the flexibility of job shops, while maintaining the effectiveness of its own. To meet demands with minimum marksman simultaneously is should be objective a manufacturing system such as FMS. A general FMS is able to process a variety of products in small and medium batches simultaneously. The Flexibility of a flexible manufacturing system (FMS) enabled it as the most appropriate manufacturing systems in the current manufacturing circumstances. With the aim of producing combining flexibility and productivity, the design of the Flexible Manufacturing System (FMS) is the subject of huge investment. Deterministic Models are based on discrete event simulation, can be used to design the production Systems such as FMS. The distinctive design and size of the hardware requirements for an FMS require attention and care at strategic level. The layout and design of the system is to be designed by keeping the targeted production in mind ensuring that FMS will fulfill the fluctuating demands. The design decisions must be based on the FMS to justify the performance improvements. This paper presents the evaluation of existing flexible manufacturing system with the objective of improvements in the performance of system by bringing modifications into the system. 2. Literature Review The concept of providing variety of products with flexible manufacturing processes in large quantities at the lowest possible cost incurred in the late 1980s.The flexible manufacturing, after decades of > RJSITM: Volume: 02, Number: 06, April-2013 Page 7

2 research, seems to be an alternative to other competitors and fragmented. Competition in the manufacturing sector in the next ten years would be focused on flexibility and quick response to market changes. Manufacturing giants have discovered that the production in large quantities for the mass market is no longer the way to stay in business (Fulkerson, Bill 1997). The flexible manufacturing system covers a wide range of automated production system, it basically consists of CNC machines, material handling system, and a control mechanism (Sidhartha, Cem 2004). Manufacturing flexibility is the ability to produce system adapt to uncertain environments and it can a competition concerns (Correa 1994, Fine CH 1985), but gains flexibility has cost associated with it (Sethi AK, Sethi SP 1990) and must be estimated ( Pellegrino R, 2010). Different types of flexibility such as product mix flexibility process flexibility and volume flexibility respectively as capability, multiple products can be defined without much setup costs manufacture, which develops different processes and routing and the possibility of having different output levels (Browne J et al produce 1984, Fontes D 2008). Two main factors which are drivers of the necessity of manufacturing flexibility are identified as Environmental uncertainty and Variability of products and processes (Correa 1994). In the first case, flexibility confronts unexpected situations both from inside the system and outside the system whereas in second case, flexibility is supposed to offer variety of products in order to keep up manufacturing processes. Changes within the manufacturing system are considered inconveniencies like machine failure, variability in work time and unavailability of raw material (Buzacott 1989) Machine flexibility and routine flexibility are more specific categories of flexible manufacturing (Peter Kostal, Karol Velisek 2011). Machine flexibility deals in manufacturing of different products with a given machine and routine flexibility addresses the execution of the same operation by a range of machines. This study will stick with these categories of manufacturing flexibility. The need of flexibility is emerged from factors such as uncertainty of demand, shorter product and technology life cycles, shorter delivery times, increased product variety and increased customization (Toni, Tonchia 1998). The flexibility offered by FMS has limits such as its development is based on fixed amount of information and absence of learning process (Schonberger 1986). Thus, to address highly uncertain demand and manufacturing of very wide variety of products, more responsive system is required so from here shift from flexible to agile manufacturing takes place. 3. The Case SME The understudy enterprise is situated in the capital territory of Pakistan. It is leading manufacturer of CNG equipment electrical control devices from last fifteen years. Domestic and industrial appliances are included in the broad list of Tesla Technologies. Electric element, Electric thermostat, heater safety devices, hoses, gas geysers, Hydraulic compressed natural gas compressor and LPG dispensers are among the products of the understudy enterprise. The products of the enterprise are spread over the markets of thirty cities of Pakistan and from last eight years the enterprise has entered into the global market and expanding its exports at fast pace. The exported products of the enterprise are certified by the British Electro-Technical Approvals Board and British Gas. Various ISO certifications are also part of the credentials of the Case enterprise (Tesla Technologies). The Electric Element shortly known as EE is one of the products of the enterprise whose manufacturing and assembly process is focused in this study. The process of making electric element starts from tube cutting. The tubes are cut in various lengths as per demand by the customers. A semi automatic machine is employed to cut the hollow tubes. The lot of cut tubes is transferred to the head closing lathe where one side of the hollow tube is closed. The > RJSITM: Volume: 02, Number: 06, April-2013 Page 8

3 tubes with closed head are then transferred to the next station where gas welding is done on closed head to avoid leakage. The welded tubes are collected and tested one by one with the help of compressed air and water reservoir in order to test leakage. These tubes are transferred to the Mgo (Magnesium oxide) filling station. In parallel to the tube cutting station the spring making station is working. The springs are made using CNC machine. Spring holding pins are inserted in both sides of the spring and then welded by spot welding machine. The whole lot of springs is transferred to Mgo filling station where the springs are inserted in tubes. After inserting the springs in tubes individually, Mgo is filled in tubes. Mgo filled tubes are sent to the testing station where weight and continuity of each element is tested. The elements with less than standard weight are refilled with Mgo manually. After these test the elements are transferred to the reducing machine where diameter of the element is reduced gradually. The reduced element is now sent to marking station, where elements are marked for upcoming annealing process. The marked elements are annealed at very high temperature for few seconds, after annealing the elements are sent to bending machine where they are bent according to desired shape. The elements after bending are sent to the dryer for drying. The elements after drying process are sent to base assembly station. The elements are attached with bases and tube in which thermostat is to be inserted is also attached with base. From now, the semi finished, elements are sent to brazing station where each element is brazed individually. The brazing is followed by a crucial pressure test for leakage testing. The failed elements are brazed again and the elements which pass the test are transferred to the epoxy filling station where epoxy is filled in every individual element. Epoxy filled elements are sent to first micro line test where electric parameters are checked thoroughly. The elements are now transferred for wire holder attachments. At this station the wire holder is attached to the main element. Cleaning is done at the station and the elements are sent to next station where thermostats are inserted in one tube of elements. After thermostat insertion the elements are sent to last micro line test where elements are tested in detail. The failed elements are placed for drying and rechecked after a delay. The passed elements are sent to packaging station where they are packed and stored in finished goods store. It is to be mentioned that each type of product follows the same processing sequence. The case SME is producing four different types of electric elements. The SME is cycle of continuous growth and frequently goes for expansions. Currently the SME is facing the problems of unmeet demands due to imbalance of resources and very less utilization. Analysis of existing system leads to various improvement strategies. The production process and processing times along with the product mix is presented in Table 1. Table 1 Part mix and corresponding processing times Sr No. Workstations Part Mix EE1 EE2 EE3 EE Time (sec) 1 Cutting Tube Closing Welding Leakage Testing Spring Making Filling Weight Testing Continuity Testing Reducing Marking > RJSITM: Volume: 02, Number: 06, April-2013 Page 9

4 11 Annealing Bending Demoisturizing Continuity Testing Fitting Brazing Pressure Testing Epoxy Filling Microiline Testing Holder Attachment Cleaning Thermostat Assembly MFT Packing Performance Analysis of Manufacturing System Modeling of Existing System Various models are demonstrated in literature to probe into a manufacturing system. Physical models, simulation model and analytical models are most extensively used for study and development of manufacturing systems. Manufacturing systems are so big arrangements that they cannot be physically modeled and experimented. So, physical models are not fit for manufacturing system investigation and improvement. Simulation modeling is partially used in this study. Simulation study involves uncertainties and requires statistical verification at the end. Analytical models offer most certain results and used in this study as primary source of system analysis and improvement. To carry out modeling of manufacturing system various assumptions are need to be established. The study is deterministic Only static parameters are evaluated System has inbuilt bottlenecks Frequency of operation is one (unity) The study is carried out at process level i.e. operational level. Various operational parameters investigated are presented in following lines. 3.2 Operational Parameters s Workloads in Existing Configuration To estimate different performance measure of system the workloads on each workstation is required to be calculated. The workload on each station is precisely defined as the average time spent on any station by the product or item. Further, the workload will help to define bottleneck station(s) of the system. Equation 1 In above equation = Average workload for station (Minutes) = Processing time for operation in process plan at station (Minutes) = operation frequency in operation part at station = Part mix for part j The average workload estimated for different workstations of existing system is presented in table 2 > RJSITM: Volume: 02, Number: 06, April-2013 Page 10

5 Table 2 Workload estimation Sr No. Workstations Average Workload (Sec) Average Workload (Min) 1 Cutting Tube Closing Welding Leakage Testing Spring Making Filling Weight Testing Continuity Testing Reducing Marking Annealing Bending Demoisturising Continuity Testing Fitting Brazing Pressure Testing Epoxy Filling Microline Testing Holder Attachment Cleaning Thermostat Assembly MFT Packaging Bottleneck Estimation of Existing System The bottleneck station of under study system can be found by estimating the following ratio. Equation 2 The workstation which has largest workload to number of servers ratio is a bottleneck station. The microline testing station concluded as bottleneck station. Workload to server ratios of all workstation is summarized is presented in table 3. Table 3 Bottleneck estimation Sr No. Workstations Number of Servers Bottleneck (min) 1 Tube Cutting Tube Closing Welding Leakage Testing Spring Making Filling Weight Testing Continuity Testing Reducing Marking Annealing > RJSITM: Volume: 02, Number: 06, April-2013 Page 11

6 12 Bending Demoisturizing Continuity Testing Fitting Brazing Pressure Testing Epoxy Filling Microiline Testing Holder Attachment Cleaning Thermostat Assembly MFT Packaging Performance Measures Production Rate of Existing System The maximum production rate is controlled by the station which is concluded as a bottleneck station. To have maximum production rate of the parts the ratio of number of bottleneck station severs to the workload of bottleneck station is required to be calculated. This ratio will define the maximum production rate of the existing system. Equation 3 By implementation of above formula the maximum production rate for existing system is Pc. /hr Pc. /day (10 Hours a day). The individual production rate of any manufacturing station can be estimated by applying equation 4. Utilization of Each Workstation Equation 4 The amount of time a specific workstation is working and not in idle condition is defined as mean utilization. Utilization of each workstation can be calculated by using equation 5. The bottleneck station has always 100 % utilization. Equation 5 Table 4 presents the utilization of each workstation of the existing manufacturing system. Sr No. Workstations Table 4 Average utilization of each workstation Bottle Neck Utilization (%) ( ) 1 Tube Cutting Tube Closing Welding Leakage Testing Spring Making Filling Weight Testing > RJSITM: Volume: 02, Number: 06, April-2013 Page 12

7 8 Continuity Testing Reducing Marking Annealing Bending Demoisturizing Continuity Testing Fitting Brazing Pressure Testing Epoxy Filling Microiline Testing Holder Attachment Cleaning Thermostat Assembly MFT Packaging Overall Utilization of Existing Manufacturing System Equation 6 By using equation 6 the overall utilization of system can be calculated. The utilization of existing system is 36 %. 3.5 Proposed Flexible Manufacturing System Sizing the System The investigated manufacturing system after analysis of various performance parameters is found to be very in efficient. The system is unable to respond to fluctuating demands due to inherent imbalance. The resources are distributed inefficiently with improper allocation of workloads. The servers are added in proposed manufacturing setup to balance the work load and to remove the bottlenecks. In the course of balancing the system total eleven servers are added in order to modify the manufacturing system. Along with the additional marking station is removed as it is merged with the annealing station. Production Rate of Proposed Manufacturing System The maximum production rate is controlled by the station which is concluded as a bottleneck station. Mgo filling station is bottleneck station for proposed manufacturing system. To have maximum production rate of the parts the ratio of number of bottleneck station severs to the workload of bottleneck station is required to be calculated. This ratio will define the maximum production rate of the existing system that can be calculated by equation Pc/Day is the throughput of the existing system. Utilization of Each Workstation of Proposed manufacturing System The amount of time a specific workstation is working and not in idle condition is defined as mean utilization. Utilization of each workstation can be calculated by using equation 5. The bottleneck > RJSITM: Volume: 02, Number: 06, April-2013 Page 13

8 station has always 100 % utilization. Equation 5 is employed to calculate the utilization of each workstation. The table 5 presents the workloads and utilization of each workstation. Overall utilization of proposed system is improved from 36% to 76%. It is to be worth noting that further utilization could also be improved but a serious constraint was there. The mgo filling station (new bottleneck) is such a huge set up that addition of that station could only be made if the whole manufacturing facility is to be restructured or at least the ongoing process be halted. SI.No Workstations Table 5 Utilization of proposed system Number of Servers Average Work (Proposed System) load (Min) Utilization (%) 1 Tube Cutting Tube Closing Welding Leakage Testing Spring Making Filling Weight Testing Continuity Testing Reducing Annealing Bending Demoisturizing Continuity Testing Fitting Brazing Pressure Testing Epoxy Filling Microiline Testing Holder Attachment 20 Cleaning Thermostat Assembly 22 MFT Packaging > RJSITM: Volume: 02, Number: 06, April-2013 Page 14

9 Figure 1 SIMIO built Proposed Model Snapshot 4. Results and Discussion The study was done to investigate the loopholes of flexible manufacturing system by means of analytical models. The manufacturing system of understudy SME was found to be much underutilized with production rate of 1072 pc/day (10 hours a day). Except few station all others work stations were being utilized around or fewer than 50 %. By probing into the deficiencies of the FMs the restructuring and redesigning of the system is done by adding few resources to the system. Very small increase in the servers added very large amount in form of resource utilization. The comparison of number of servers increased is presented in figure 2. Figure 2 Proposed Vs Existing FMS Servers > RJSITM: Volume: 02, Number: 06, April-2013 Page 15

10 Figure 3 presents the utilization of proposed FMS against the utilization of resources of existing FMS. More than half of the stations are now brought above 50 % utilization which was previously below 50% utilization. Microline station was bottleneck station in existing configuration and was halting the production rate at 107 pc/ hour. By redesigning and improving the system the new bottleneck station is mgo filling station. The production rate of proposed FMS is 3169 pc /day 316 pc / hour. The table 6 gives the detail. Figure 3 Proposed Vs Existing FMS Utilization Table 6 FMSs Summary (Proposed Vs Existing) Sr. No Performance Parameters Existing FMS Proposed FMS 1 No of Servers Overall Utilization 36 % 76% 3 Production Rate / hour 107 pc 316 pc 5. Conclusion The study was to analyze the existing system and to improve performance of the system by reconfiguration of flexible manufacturing system. Various techniques such as analytical modeling and simulation modeling were used to achieve the objectives. First, various performance and operational parameters were estimated then new FMS has been proposed with the optimum number of servers. The findings explored that microline station was being utilized at 100 %. These results indicated that the microline testing station is a bottleneck station. Since this station is crucial for the processing all subtypes, it is proposed that the bottleneck is to be moved from this station to some other less important in the proposed FMS. Utilization was found another core issue in the understudy system. It is concluded that the resources of the system were underutilized severely. 11 servers were added into proposed configuration that is about 36 % percent increment was made. The utilization was increased from 35% to 76% that is about 110% increase in utilization was made by merely 36 % increase in number of servers. As a result of modification and sizing the FMS the throughput of the system was increased from 107 pc per hour to 316 pc per hour. For future study in the SME the layout and material handling system can also be investigated with a hope of further improvement in the system performance. > RJSITM: Volume: 02, Number: 06, April-2013 Page 16

11 References Fulkerson, Bill (1997) A Response to Dynamic Change in the Market Place Journal of Decision Support Systems. Sidhartha R. Dasa, Cem Canelb 2004 An algorithm for scheduling batches of parts in a multi-cell flexible manufacturing system. Correa HL Linking flexibility, uncertainty and variability in manufacturing systems. Avebury. Fine CH, Hax 1985 A. Manufacturing strategy: a methodology and an illustration. Interfaces. Sethi AK, Sethi SP Flexibility in manufacturing: a survey. The International Journal of Flexible Manufacturing Systems. Pellegrino R Evaluating the expansion flexibility of flexible manufacturing systems in uncertain environments. International Journal of Engineering Management and Economics. Browne J, Dubois D, Rathmill K, Sethi SP, Stecke KE Classification of flexible manufacturing systems. The FMS Magazine. Fontes D. Fixed 2008 versus flexible production systems: a real options analysis. European Journal of Operational Research. Correa HL Linking flexibility, uncertainty and variability in manufacturing systems. Avebury. Fine CH, Hax 1985 A. Manufacturing strategy: a methodology and an illustration. Interfaces. Sethi AK, Sethi SP Flexibility in manufacturing: a survey. The International Journal of Flexible Manufacturing Systems. Pellegrino R Evaluating the expansion flexibility of flexible manufacturing systems in uncertain environments. International Journal of Engineering Management and Economics. Browne J, Dubois D, Rathmill K, Sethi SP, Stecke KE Classification of flexible manufacturing systems. The FMS Magazine. Fontes D. Fixed 2008 versus flexible production systems: a real options analysis. European Journal of Operational Research. De Toni & S. Tonchia1998: Manufacturing flexibility: A literature review International Journal of Production Research. Slack, N., and Correa, H 1992 The flexibility of push and pull. International Journal of Operations and Production Management. Gupta, D., and Buzacott, J. A., 1989, A Framework for Understanding Fexibility of Manufacturing Systems Journal of Manufacturing Systems Peter Kostal, Karol Velisek 2011 Flexible Manufacturing System. Schonberger, R. J.World 1986 Class Manufacturing The Principles of Simplicity Applied > RJSITM: Volume: 02, Number: 06, April-2013 Page 17