International Conference on Industrial Engineering (ICIE 2016 ...

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Available online at www.iiec2016.com

International Conference on Industrial Engineering (ICIE 2016) Iran Institute of Industrial Engineering

ICIE (2016) 000–000

Kharazmi University

Determination the percent of bottleneck in stations of Rio vehicle mechanical assembly line in SAIPA automotive corporation Sobhanallahi Mohammad Ali a, Gharaei Abolfazlb,*, Pilbala Mohammadc a

Ph.D., Department of Industrial Engineering, Associate Professor and Faculty Member, Kharazmi University, Tehran, Iran. b Ph.D. Candidate, Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran. c Department of Industrial Engineering, Faculty of Engineering, Firouzkouh Islamic Azad University, Tehran, Iran. * Corresponding author: E-mail address: [email protected]

Abstract Systems of the automobile manufacturing process in order to develop an effective and efficient process to ensure the system throughput. Implementing changes can be a difficult task for any automobile manufacturing process. For this purpose modeling of complex systems such as manufacturing systems is an arduous task. Simulation allows designers to imagine new systems and enabling them to both quantify and observe behavior. In some automobile manufacturing systems, bottleneck station in assembly lines is variable and related to some stations in each cycle, not only one station. In other words, in each of production cycles, every station may be system bottleneck. In such a case, the system bottleneck varies from one production cycle to next production cycle. Mechanical assembly line of Rio vehicle in SAIPA automotive corporation has different bottleneck in each cycle and bottleneck varies from one cycle to next cycle in this assembly line. In this regard, the aim of this paper is determination the percent of bottleneck in stations of mechanical assembly line of Rio vehicle in SAIPA automotive corporation. The best solution for achieving this goal is use of simulation technique. In this regard, we used an effective simulation methodology which is designed by, Michigan University simulation user group, technical committee on simulation methodology in Michigan University. Based on mentioned methodology as 12 steps, we modeled mechanical assembly line of Rio vehicle in SAIPA automotive corporation. We utilized Showflow software as one of the best software in automobile systems simulation for modeling and simulation of mentioned system. Obtained results showed the variability of bottleneck in mechanical assembly line of Rio vehicle in each cycle. Based on mentioned results, in 14% of cases, station 22 (assembly of fuel tank and accessories), in 30% of cases, station 23 (connection of rear axle to body), in 26% of cases, station 24 (connection of engine to body) and in 30% of cases, station 26 (assembly of tires) are bottlenecks of the mechanical assembly line of Rio vehicle and station 25 (connection of exhaust to body) is never a bottleneck for system. Keywords:Simulation, Discrete modeling, State variables, Variability of bottleneck, System logic, Model validity, Simulation methodology, Showflow software.

1. Introduction and Research Literature Manufacturing is defined as the transformation of materials and information into goods for the satisfaction of human needs [1]. In the current highly competitive business environment, the manufacturing industry is facing constant challenges of producing innovative products at shortened time-to-market. The increasing trend towards globalization and decentralization of manufacturing [2] requires real-time information exchanges between the various nodes in a product development life cycle, e.g., design, setup planning, production scheduling, machining, assembly, etc., as well as seamless collaboration among these nodes. Product development processes are becoming increasingly more complex as products become more versatile, intricate and inherently complicated, and as product variations multiply to address to the needs of mass customization [3]. Now a days, systems of the automobile manufacturing process in order to develop an effective and efficient process to ensure the system throughput [4]. Newly emerging composite manufacturing processes, where there exist only limited industrial experience, demonstrate a definite need for process simulations to reduce the time and cost associated with the product and

Author name / IIEC 00 (2016) 000–00 process developments [5]. In this regard, simulation is a very helpful and valuable work tool in manufacturing. It can be used in industrial field allowing the system`s behavior to be learnt and tested. Simulation provides a low cost, secure and fast analysis tool. It also provides benefits, which can be reached with many different system configurations. Hereby, two of the most prominent definitions of simulation in the manufacturing context are presented [6]: “Simulation modelling and analysis is the process of creating and experimenting with a computerized mathematical model of a physical system” [7]. “Simulation is the imitation of the operation of a real-world process or system over time. Simulation involves the generation of an artificial history of the system, and the observation of that artificial history to draw inferences concerning the operating characteristics of the real system which is represented” [8]. It becomes evident from the total number of directly related papers (15,954) from the early 70s till today, that simulation is a continuously evolving field of research with undoubted contribution to the progress of manufacturing systems. Figure 1 shows number of publications related to simulation technology from 1960 to 2014.

Figure1. Number of publications related to simulation technology [6]

The literature review of this research is based on academic peer-reviewed publications that use simulation not only in manufacturing applications but also simulation in general, over a period of 54 years, from 1960 to 2014. As a result, the literature was organized based on keywords enabling the distinction between the relevant and irrelevant topics of academic papers. Choi et al. discusses initial efforts to implement simulation modeling as a visual management and analysis tool at an automotive foundry plant manufacturing engine blocks [9]. Potoradi et al. described how a large number of products are scheduled by a simulation engine to run in parallel on a pool of wire-bond machines to meet weekly demand [10]. Kibira et al. presents a virtual-reality simulation of a design of a production line for a mechanically assembled product [11]. Altiparmak et al. used simulation meta-models to improve the analysis and understanding of the decision-making processes of an asynchronous assembly system to optimize the buffer sizes in the system [12]. Wiendahl et al. used simulation tools in the field of assembly planning and due to the different objectives of the different efforts, the tools are divided into the four-hierarchy classes of an assembly shop, cell, station and component [13]. Gurkan et al. investigated current problems in an order based weaving mill so as to propose a new system for the aforementioned mill [14]. It is generally considered that the contemporary meaning of simulation originated by the work of Comte de Buffon who proposed a Monte Carlo-like method in order to determine the outcome of an experiment consisting of repeatedly tossing a needle onto a ruled sheet of paper. He aimed at calculating the probability of the needle crossing one of the lines. So, it is obvious that although the term “Monte Carlo method” was invented in 1947, at the start of the computer era, stochastic sampling methods were used long before the evolution of computers [15]. About a century later, Gosset used a primitive form of manual simulation to verify his assumption about the exact form of the probability density function for Students t-distribution [16]. Thirty years later, Link constructs the first “blue box” flight trainer and a few years later, the army adopts it in order to facilitate training [17]. In the mid-1940s, simulation makes a significant leap with the contribution of Tochter and Owen develop the General Simulation Program in 1960, which is the first general purpose simulator to simulate an industrial plant that consists of a set of machines, each cycling through states as busy, idle , unavailable and tailed [18]. They also introduce the threephase method for timing executives, published the first textbook in simulation “The Art of Simulation” (1963) and developed the wheel chart or activity-cycle diagram (ACD) (1964). During the period 1960-1961, Gordon introduces the General Purpose Simulation System (GPSS) [16].

Author name / IIEC 00 (2016) 000–00 With use of light, sound motion and even smell to immerse the user in a motorcycle ride, Heilig designed the Sensorama ride, which is considered as a predecessor of Virtual Reality (VR) [19]. Simultaneously, Nygaard and Dahl initiate work on SIMULA and they finally release it in 1963 and Kiviat develops the General Activity Simulation Program (GASP). In 1963, the first version of SIMSCRIPT is presented for non-experts and OPS-3 is developed by MIT [18]. Sutherlend presents manipulation of objects on a computer screen with a pointing device [19]. Although, a significant evolution of simulation is noticed, there are still problems concerning model construction and model analysis which are mentioned and addressed by Conway et al. [16]. General Precision Equipment Corporation and NASA used analogue and digital computers to develop Gemini simulators [17]. Lackner proposes the system theory as a basis for simulation modelling. In 1968, Kiviat introduces the entity/attribute/set concept in SIMSCRIPT II [20]. In the same time, Sutherland constructs head-mounted computer graphics display that also track the position of the users head movements and the Grope project explores real-time force feedback [19]. Two years later, power plant simulators are introduced [17]. In 1972, an explanatory theory of simulation based on systems-theoretic concepts is presented by Zeigler [20]. In 1973, Pritsker and Hurst introduce the capability for combined simulation in GASP IV and Fishman composes the state-of-the-art on random number generation, random variate generation and output analysis with his two classical texts [21]. Clementson extended ECSL (Extended Control and Simulation Language) with the Computer Aided programming System using ACD representation and Mathewson develops several versions of DRAFT to produce different programming language executable representations in 1975. In 1976, Delfosse introduces the capability for combined simulation in SIMSCRIPT II.5 as C-SIMSCRIPT and a year later, user interface is added to it [16]. Moreover, Bryant initiates parallel simulation [20]. In 1978, computer imaging with the introduction of digital image generation is a significant contribution to the advancement of simulation. In the beginning of the 1980s, major breakthroughs take place, military flight simulators, naval and submarine simulators are produced and NASA develops relatively low-cost VR equipment [17]. Nance introduces an object-oriented representational approach in order to join theoretical modelling issues with program-generation techniques and with software engineering concepts. Balci and Sargent contribute to formal verification and validation. Law and Kelton contribute with their first edition which includes advanced methodologies concerning random number generation, random variate generation and output analysis. Furthermore, Schruben develops event graphs in 1983 [16]. While, Visual Interactive Simulation is initiated in 1976 by Hurrion and becomes commercially available in 1979 through SEE-WHY, it is properly described in methodological terms, contrasting the active and passive forms in model development and experimentation, by Bell and O‟Keefe in 1994 [22]. In early 1990s, real-time simulations and interactive graphics become possible due to the increased computer power and commercial VR applications become feasible [19]. In 1990, as well, Cota and Sargent develop a graphical model representation for the process world view, named Control Flow Graphs which are subsequently extended to Hierarchical Control Flow Graphs in order to help the control of representational complexity by Fritz and Sargent in 1995 [20]. In addition, the development of high-resolution graphics focuses on gaming industry surpassing in that way the military industry [17]. In 1997, Knuth describes comprehensively the random number generation techniques and tests for randomness [16]. Nowadays, simulation is accomplished in various industries and with different objectives. For instance, in a research carried out by Sharda et al. [23], in a chemical plant, which produces different types of chemical products with limited life, simulation techniques offered guidance for improving inventory systems. Moreover, they utilized simulation models to perform cost-profit analysis in order to estimate costs of scheduling, programming, changing in demand and operation. In a case study done by Greasley [24], the total space of textile manufacturing facilities was modeled using simulation by defining entity, product attributes about workstations, queue and production process. He achieved applicable quantitative and qualitative results in the industry. Simulation is widely applied in the field of transportation, location and allocation problems, as well. In one case implemented by Hay [25] in England, the location of a superstore in downtowns was simulated to improve the following of the population regarding pollution factors. In another case study in an airport terminal, Suryani et al. [26] employed dynamic simulation to model flow of cargo in order to forecast terminals‟ capacities and put forward applicable and improving suggestions. The simulation models are also used for investigating the flow of reused product in the recycling industry. In the research conducted by Matsumoto [27], at first, the prerequisites of this industry have been recognized and then the parameters and prerequisites have been analyzed via simulation techniques. Shojaie et al. [28] implemented another study for evaluating Non- standard queues in a CNG stations. In that research, the queues of the station was simulated to decrease waiting time of customers. Another application of simulation is to find important setting of industrial machines in some applications of control engineering [29]. The historic evolution of simulation is also depicted in Figure 2 [6].

Author name / IIEC 00 (2016) 000–00

Figure 2. Historical Evolution of Simulation [6]

Many papers are already published in the context of simulation methodology or the process of simulating, successful application of simulation and how to avoid simulation gaps. Successful application of the simulation in fact requires extensive preparations before creation of the simulation computer model. As was stated earlier, in this paper, we utilized an effective simulation methodology which is designed by, Michigan University simulation user group, technical committee on simulation methodology in this University [30]. Also, we used Showflow software as one of the best simulation software in automobile systems simulation for modelling and simulation of Rio vehicle mechanical assembly line with the aim of determination the percent of bottleneck in stations of Rio vehicle mechanical assembly line. The necessity and importance of present study can be seen in the cases which today simulation techniques are conducted in order to gain insight into this kind of complex systems, to achieve the development and testing of new operating or resource policies and new concepts or systems. Also, production capacity simulation of an assembly line is important in order to modify the current status of assembly line. If companies intend to remain competitive in the market, they should have a good understanding of their production systems. Simulation is one of the best techniques for having a proper understanding of the system. The outline of the rest of the paper is as follows. Section 2 sheds light on the problem definition. The research methodology is described in section 3. Finally, conclusion and future research are given in sections 4. 2. Problem definition The initial step involves defining the problem and determining what needs to be solved. So, system analyzer should be taken to determine if simulation is the appropriate tool for the problem under investigation. The problem definition must be clearly specified by its clients. The clients of a project simulation can be internal and/or external to the organization responsible for execution of the study. It is important to involve the highest level of management from the client's organization. Generally, an engineer and an engineering manager/supervisor from the client's organization is assigned to the simulation project team. It is desirable to incorporate the problems of both (all) levels of client management for the study right from the beginning of the study. Due to the inherent nature of the problems of different levels of management, one may decide to have two (or more) separate models to satisfy the problems of all levels of management interested in the study. In most cases it is also desirable to have two levels of management from the simulation group involved with the study. A simulation engineer and a simulation project manager (leader) may be more effective in communicating with the multiple levels of the client organization. The typical problems of a simulation study can be to verify the throughput of a new manufacturing line, identify the bottleneck operations in a system, find the proper buffer capacities to attain certain levels of production, determination the best batch sizes and sequence for a multi-product manufacturing line, etc. It is typical to have multiple problems that may be in conflict in a study. As was stated earlier, in some automobile manufacturing systems, bottleneck station in assembly lines is variable and related to some stations in each cycle, not only one station. In other words, in each of production cycles, every station may be system bottleneck. In such a case, the system bottleneck varies from one production cycle to next

Author name / IIEC 00 (2016) 000–00 production cycle. Mechanical assembly line of Rio vehicle in SAIPA automotive corporation has different bottleneck in each cycle and bottleneck varies from one cycle to next cycle in this assembly line. In this regard, the problem can be defined as “what‟s the percent of bottleneck in stations of mechanical assembly line of Rio vehicle?” As will state later, in many cases, spreadsheet analysis, mathematical programming and optimization approaches such as linear programming and branch and bound techniques or statistical modelling methods such as regression modelling may be suitable ways for problem solving. By taking the defined problem, it can be inferred that none of the above methods can be used for problem solving. Hence, the last way for meeting the mentioned problem is to use the simulation method. In the next section, we will provide simulation methodology of current research which is designed by technical committee on simulation methodology in Michigan University. 3. Research methodology: Michigan University simulation user group 3.1. Step I: determination of research goal Goals of the research can be different or even in contradiction. Moreover, study of a system may have some contradictory goals such as increase in buffer capacity and increase in output capacity. Here, the system which is being studied is “mechanical assembly line of Rio vehicle” and the purpose of this study is “to determine the percent of bottleneck in stations of Rio vehicle mechanical assembly line in SAIPA automotive corporation”. 3.2. Step II: determination of the justification of the simulation In many cases, spreadsheet analysis, mathematical programming and optimization approaches such as linear programming and branch and bound techniques or statistical modelling methods such as regression modelling may be suitable ways for achieving system study goals. By taking the goal of study into account, it can be inferred that none of the above methods can be used. Hence, the last way for meeting the above goal is to use the method of simulation. 3.3. Step III: decision making over the modelling either by discrete, continuous or combined methods In the mechanical assembly line of the Rio vehicle, state variables (variables which change the state of the system) are: system input or injection rate, waiting time in stand or buffer, operations performance times in various stations, line or conveyor downtimes among others. Change of above state variables in various time periods is discrete. Therefore, mechanical assembly line of Rio vehicle is a discrete system and it must be modelled using a simulation tool like discrete modelling systems. 3.4. Step IV: determination of elements which direct the system 



 

Entrance element: this is one of the boundary elements and is located after the last station of Rio body decoration line. It is entrance door of the system for entrance of the Rio body from decoration line to mechanical assembly line. Stand element: after entrance of Rio body to stand and a waiting time, Rio body is loaded using an overhead conveyor and moves with uniform speed among various stations. In each station, assembly of parts on Rio body is carried out. Operator element: by movement of Rio body between various stations, assembly and connection of parts and accessories is carried out in each station using this element. Exit element: this is the second boundary element and is located after the last station of the mechanical assembly line. It is in fact, the Rio body exit from mechanical assembly line to final line.

3.5. Step V: determination of the philosophy of the system performance Philosophy of operations explains the method of system start-up. Issues such as number of shifts per day, preparation time, non-working times of workers, times of production line adjustments, sequence of operations and so on are all philosophies of the system performance. As such, philosophy of mechanical assembly line performance of Rio vehicle is as follows:  Number of shifts per day: one shift  Duration of each shift: 9 hours (by considering workers resting times)  Work hours: 6:30 a.m. to 15:30 p.m.  Non-working times of workers: 9:00 to 9:15, 13:00 to 13:30 and 14:30 to 14:45

Author name / IIEC 00 (2016) 000–00 

Production line adjustment times: adjustment times are not constant and in various time periods, mechanical assembly line of Rio vehicle is adjusted. It means that adjustment times of the line are random and it depends upon the issue that whether production of the system is in accordance with production plan or it is lower than that of plan.

3.6. Step VI: explanation of the system logic (logic of process flow), initiation and end of system elements The nature of some system elements (parts, tools and so on) is inherently dynamic; they move in the system and force other elements to react to signals they send and other elements are not inherently moving and wait for moving entities to react to them and draw or push them in the system. The studied model is moving parts and moving sources, In moving parts models, parts are moving and move between sources and in the model of moving sources, sources pick the parts which need them and transfer them to other sources for complementation. It means that Rio vehicle body moves from one station to other after entering the assembly line by overhead conveyor and assembly, connection of parts and accessories are performed on the vehicle body. In this way, moving elements of the system are: conveyor and operators. In this step, logic of entrance and exit of the moving elements are investigated. Logic of Rio vehicle entrance to mechanical assembly line and exiting from it, can be defined as follows: After exit of the Rio vehicle body from painting room, body is positioned on a skate and is transferred to decoration line. In this line, body is positioned on a rail and by moving along with the rail in decoration line, assembly and connection of parts are performed in this line from one station to another. After completing the body of Rio in decoration line, it enters to mechanical assembly line. In this line, the Rio body is displaced by overhead conveyors between stations #21 and #25 and in station #26 of the mechanical assembly line that is after assembly of tires, Rio body goes out of the assembly system and enters the final assembly line. In this way, after entrance of body from decoration line into assembly line, Rio body is positioned on a stand with the capacity of holding two vehicles. After positioning Rio on stand and waiting for a while, overhead conveyor loads the Rio body positioned on the stand. After loading the body by overhead conveyor, Rio body enters station #22 and in mentioned station, assembly of fuel tank and its accessories are performed by operator 1. After assembly of fuel tank and accessories in station #22, Rio body is transferred to station #23 and in this place, rear axle and accessories are assembled by two operators. After passage of the Rio body from station #23, it enters station #24 and in this place, connection of engine to body is performed by two operators. Then, body enters station #25 and assembly of exhaust and accessories are done by two operators. Then, Rio body enters the last station (station #26) and in this station, assembly of tires is done by two operators. In this case, body exits the assembly line and enters the final assembly line. Logic of entrance and exit of the conveyor to/from the system can be defined so that after entrance of the Rio body from decoration line into mechanical assembly line, body is positioned on a stand with the capacity of holding two vehicles and after waiting on stand for a while, overhead conveyor loads the Rio body and remains in the system till the last station. After passage of body from station #26 which is the last station of mechanical assembly line and entering final assembly line, conveyor discharges the body and in this way, conveyor gets out of the system. Logic of operator entrance and exit can be defined such that after loading the body by conveyor and its entrance to stations of the mechanical assembly line, operators act to perform assembly and connection of parts and accessories. After the operation of assembly in each station, operators remain inactive for a short time until arrival of the next Rio body to the station. To visualize the flow of the process and system logic, two dimensional model of the Rio vehicle mechanical assembly line and in fact, schematic of the current performance of the mentioned system is illustrated in Fig 3. Figure 3 shows two dimensional schematic of the system and current performance of the system in Showflow software.

Figure 3. Two dimensional representation of mechanical assembly line of Rio vehicle in Showflow software


3.7. Step VII: operation of data collection Parameters required for each element are collected as element by element in this step as follows: Input element (in – out)   

Capacity of the entrance of the system: 1 Rio body Entrance rate: 1 body per six minutes from decoration line to mechanical assembly line of the Rio Target element: Stand (buffer) element

According to performed timings, time between two stops of the conveyor, two stops of the line or two repairs of the conveyor are in accordance to Table 1. Table 1. times between two stops of the conveyor, two stops of the line or two repairs of the conveyor

Observation MTBFa (min) MTTRb (min)

T1 30.5 3.5

T2 17.6 3.8

T3 16.7 3.4

T4 21.7 3.7

T5 16.3 3.3

T6 27.4 2.9

Stand (buffer) element  Stand capacity: 2 Rio bodies  Exit policy: First in – first out (FIFO)  Target element: Conveyor  Waiting time on stand: According to performed timings, waiting times on the stand are according to Table 2. Table 2. Waiting times on stand element

Observation Waiting time (min)

T1 6.36

T2 6.63

T3 6.38

T4 6.36

T5 6.38

T6 6.5

Operator element: Assembly time of each station: Assembly time of each station is the sum of assembly times by operators of the station. According to performed timings, assembly time of station operators are according to Table 3. Table 3 Assembly times for operators of each station

Station# Operator No. in station T1

Station 22 9 3.85

Station 23 10 11 2.4 3.9

Station 24 12 13 2.95 4.33

Station 25 14 15 3.93 3.98

Station 26 16 17 2.85 3.53

T2

4.53

2.15

3.08

4.33

4.16

3.93

4.65

3.8

3.71

T3

4.15

3.01

3.75

3.33

3.51

5.51

4.5

3.9

4.93

T4

4.81

3.16

4.05

4.16

3.88

4.93

4.83

4.28

3.58

T5

5.03

3.96

3.75

3.5

3.71

6.25

5

3.93

3.35

T6

5.03

4.41

3.15

3.35

3.66

4.88

5.25

4.43

4.13

Observation (min)

Non-working time of operators: As was stated earlier, by finishing assembly operation of each station, operators of that station will be free until arrival of the next Rio body. After running of the model, this logic will be applied in the model automatically. Therefore, non-working time of operators will not be considered in this model.

a b

Mean time between failures Mean time to repair

Author name / IIEC 00 (2016) 000–00 Conveyor element    

Capacity of conveyor in each station: One Rio body Conveyor speed: 9 m/s Length of conveyor or length of each station: 5.5 m Conveyor stopping time: As expressed previously, by stopping Rio mechanical assembly line, conveyor stops accordingly.

It must be noted that conveyor of mechanical assembly line is an all-round conveyor. However, due to capabilities of the software and for separation of assembly stations from each other, in modelling Rio mechanical assembly line, all-round conveyor is divided into 5 ones. That is, for each station, a conveyor with the capacity of one body and speed as much as 9 m/s and length of 5.5 m is considered.  Number of operators of each station can be noted as follows: Number of operators of each station are 9 persons from whom 1 person works in station #22 (first conveyor), 2 persons work in station #23 (second conveyor), 2 persons work in station #24 (third conveyor), 2 persons work in station #25 (fourth conveyor) and 2 persons work in station #26 (fifth conveyor). Output element (in – out)  Capacity of system exit door: One Rio body  Source element: Fifth conveyor 3.8. Step VIII: analysis of input data In this step, time data collected in previous step (Table 1 to 3) for finding probability distributions are fitted using SPSS software. In this way, probability distributions for model elements are as follows: Input element (in – out)  Input rate distribution: Exponential with exponent 6  Mean time distribution between two stops: Exponential with exponent 21.7  Mean time distribution between two starts (after stop): Exponential with exponent 3.4333 Output of SPSS software corresponding to probability distribution of parameters of the input element is as summarized in Figure 4.

Figure 4. Probability distribution

Stand (buffer) element 

Distribution of waiting time in stand: Normal with mean as much as 6.3450 and standard deviation as much as 0.10913.

Output of SPSS software corresponding to probability distribution of parameter of the buffer element is as summarized in Figure 5.


Figure 5. Probability distribution of buffer element

Operator element              

Distributions of assembly time by operator 9: Normal with mean as much as 4.5667 and standard deviation as much as 0.48521 Distributions of assembly time by operator 10: Normal with mean as much as 3.1817 and standard deviation as much as 0.87406 Distributions of assembly time by operator 11: Normal with mean as much as 3.6133 and standard deviation as much as 0.40233 Distributions of assembly time by operator 12: Normal with mean as much as 3.6033 and standard deviation as much as 0.53185 Distributions of assembly time by operator 13: Normal with mean as much as 3.8750 and standard deviation as much as 0.90306 Distributions of assembly time by operator 14: Normal with mean as much as 4.9050 and standard deviation as much as 0.90306 Distributions of assembly time by operator 15: Normal with mean as much as 4.7017 and standard deviation as much as 0.44052 Distributions of assembly time by operator 16: Normal with mean as much as 3.8650 and standard deviation as much as 0.55342 Distributions of assembly time by operator 17: Normal with mean as much as 3.8717 and standard deviation as much as 0.58084 Distributions of assembly time by station #22: Normal with mean as much as 4.5667 and standard deviation as much as 0.48521 Distributions of assembly time by station #23: Normal with mean as much as 6.795 and standard deviation as much as 0.9622 Distributions of assembly time by station #24: Normal with mean as much as 7.4783 and standard deviation as much as 0.6179 Distributions of assembly time by station #25: Normal with mean as much as 9.6067 and standard deviation as much as 1.0047 Distributions of assembly time by station #26: Normal with mean as much as 7.7367 and standard deviation as much as 0.8022

Output of SPSS software corresponding to probability distribution of parameter of assembly time by operators 9 to 17 is as shown in Figure 6.


Figure 6. Probability distribution of assembly time

3.9. Step IX: Computer modelling of current status of the system In this step, mechanical assembly system of Rio vehicle is modelled in three steps based on previous phases using Showflow software.  First stage: model arrangement and connection of elements In this regard, arrangement of elements in mechanical assembly as well as connections between them showed in Fig. 3 as two dimensional schematic. In this model, elements 1 and 8 are boundary elements of the model (input and output elements), element 2 is the buffer element, elements 3 to 7 are conveyor elements of the model and elements 9 to 17 are operators.  Second stage: model settings In this stage, parameters corresponding to model elements such as elements capacity, relevant probability distributions, service providing policies and so on which were analyzed in previous steps are entered in fields of

Author name / IIEC 00 (2016) 000–00 Model-Elements in ShowFlow software for each element. As stated before, in station #22, element 9, in station #23, elements 10 and 11, in station #24, elements 12 and 13, in station #25, elements 14 and 15 and in station #26, elements 16 and 17 perform assembly and connection. Therefore, in field Aid of window Model-Elements corresponding to elements 3 to 7, following terms is typed respectively,

    

Select 1 from 9 Select 2 from 10, 11 Select 2 from 12, 13 Select 2 from 14, 15 Select 2 from 16, 17

 Third stage: software settings and running of the model Remembering that all times entered in Showflow are in minutes and all distances are in meters, by going through following path, temporal settings of the software will be applied:

Settings>Time Representation By opening View-Animation window and to ensure conformity of the model logic with that of system, speed of animation is set to 1 and animation state is set to Full and running mode is considered two dimensional. Also, by opening Stop Time, Settings Simulate is set to one day. After applying above settings, we run the model to investigate the conformity of the model logic with system logic. 3.10. Step X: validation of the created model through fitting it with real system  Investigation of the conformity of model logic with system logic After running of model, product enters to model by input element and after a waiting in buffer, it enters to station #22 (first conveyor), after assembly operation by element 9 in station 22 and entrance of product to station #23 (second conveyor), assembly and connection of this station was performed by operators 10 and 11 on Rio body. In this case, product enters station #24 (third conveyor) and after assembly of accessories by elements 12 and 13, it goes to station #25 (fourth conveyor). At the end of connection of parts by elements 14 and 15 in this station, product goes to last station (Station #26 or fifth conveyor) and by finalization of the operation of assembly by elements 16 and 17 in this station, product exits from mechanical assembly line of the Rio vehicle. Moreover, upon each stop of the model, entrance door closes and no product enters the model. By investigation of the above logic, it can be easily inferred that model logic completely conforms system logic.

 Model validation aided to statistical hypothesis test Output or production rate of the mechanical assembly line of the Rio vehicle is a discrete random variable in interval [53, 69]. In other words, the mean of production rate in this system is 61 Rio body. So, statistical hypothesis testing criteria is as follows: H0: =61 H1: 61 Unknown parameter is  in the case of is unknown. So, test statistic is T-Student distribution. Now, we had to take random samples from model. Table 4 is obtained after 50 runs of the model and it shows number of Rio body output from mechanical assembly line in one day. Each “Run No” in Table 4 related to one day. Table 4. Daily output of the model

Run No.

1

2

3

4

5

6

7

8

9

10

Output Run No. Output Run No.

61 11 54 21

61 12 54 22

65 13 69 23

62 14 56 24

58 15 63 25

64 16 68 26

59 17 57 27

62 18 64 28

55 19 69 29

67 20 56 30

Output Run No. Output Run No.

56 31 66 41

60 32 62 42

60 33 59 43

68 34 67 44

61 35 56 45

69 36 64 46

53 37 53 47

59 38 55 48

61 39 65 49

63 40 63 50

Output

64

57

58

61

63

57

59

62

66

59


For statistical hypothesis testing, we utilized Minitab software. We entered Table 4 data in Minitab software and we tested the hypothesis at %95 confidence level (α=5%). Figure 7 shows acceptance region of this hypothesis test. As it stands from Figure 7, the null hypothesis is located in the acceptance region. This fact reveals the validity of the created model and its conformity with actual system. So, the created model of mechanical assembly line of Rio vehicle is completely valid and it corresponds to the system.

Figure 7. Statistical hypothesis test in SPSS Software

3.11. Step XI: determination of the warm up time and time of approaching steady state in the model Nature of the most of systems in real world is so that it takes a while for them to achieve a normal performance. It means that it takes a while for the first product which entered the system to go through all stations of the path and approach the last one and exit the system. Hence, last station enters the flow of the process later than the first station. This phenomenon affects previous statistics and leads to difference between obtained statistics and actual ones. In this step, through some controlled runs, warm up time or time to approach steady state is estimated. As such, warm up time of the model is equivalent to the processing of first three products and arrival of the fourth one to the last station. It means that after processing first three products and arrival of the fourth one to the last station, model is loaded and all available elements of the model enter to model. 3.12. Step XII: model running and achieving research goals After running the model, we use “utilization” command to find the efficiency of stations operators. it must be noted that this command is one of the trigger language interpreter (TLI) which is the language of programming in Showflow software and is used for finding the efficiency of operators, machines and so on. This command has single argument and the number of element whose efficiency must be calculated is considered in argument of this command. Taking into account the fact that mechanical assembly line of Rio vehicle is a symmetrical line; that is, all operations which are performed in left side of the operator is performed simultaneously in right side of the same stations by another operator, it is logical that efficiency of two operators are the same in one station. As such, efficiency of operators of mechanical assembly line is calculated as summarized in Table 5 during 50 runs of the model. In Table 5, the bottleneck station of the mechanical assembly line in each run of the model is highlighted and is underlined.

Author name / IIEC 00 (2016) 000–00 Table 5. Efficiency of stations 22 to 26 during Runs 1 to 50

Run No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

Station #22 79.94 61.17 78.15 77.5 82.94 83.52 77.5 83.31 76.52 73.79 68.56 62 76.21 77.29 76 82.96 77.98 74.92 65.92 78.42 86.08 73.04 64.58 75.06 80.13 74.1 79.04 77.69 85.58 74.92 65.87 70.35 76.94 69.75 66.19 78.54 68.5 69.38 83.54 69.81 75.17 84.73 80.98 81.4 78.63 73.63 65.94 66.19 81.37 75.02

Station #23 80.08 61.19 78.17 78.79 82.98 83.58 78.06 83.31 76.56 72.54 68.58 63.29 77.48 77.33 76.06 82.67 78.73 75.69 65.96 78.46 86.54 73.63 63.69 75.13 80.77 74.17 79.13 77.75 85.6 73.67 67.17 70.4 76.98 71.04 64.96 78.71 68.52 69.75 83.58 68.58 73.92 84.77 81.04 81.46 78.67 74.38 66.02 66.25 81.58 75.33

Station #24 80.08 60.71 78.79 78.58 83.04 82.6 78.13 82.12 77.5 72.58 67.38 62.21 76.21 76.06 76.1 82.48 77.5 74.48 67.17 77.23 87.83 74 63.79 75.15 79.52 74.21 79.21 77.79 84.35 72.44 66.5 70.44 75.83 72.33 64.58 78.79 68.54 71.04 82.35 67.35 73.98 83.54 81.1 81.37 77.5 73.63 64.75 66.29 80.31 74.08

Station #25 81.37 62 78.42 77.38 81.81 82.62 76.88 82.17 77.5 71.31 67.42 63.29 75 76.1 74.9 82.52 78.79 74.54 67.17 77.35 86.96 72.75 62.56 73.92 80.08 73.63 78.79 76.54 84.4 72.5 66.54 69.75 75.88 72.08 65.87 77.5 67.29 71.04 82.42 66.73 73.63 82.31 80.08 81.44 78.79 74.92 64.81 65.06 80.33 74.92

Station #26 82.67 63.29 78.75 77.44 82.67 82.67 77.5 82.23 78.29 72.33 67.42 64.58 75.04 76.21 74.92 82.6 79.92 74.54 67.75 77.5 86.98 72.81 62.6 73.98 80.75 74.65 78.85 76.56 84.4 72.58 66.56 70.15 75.94 72.12 66.31 77.52 67.31 72.27 82.44 66.73 74.38 82.35 81.37 81.5 79.31 76.21 64.88 65.87 80.4 76.16

4. Conclusion and Future Research In some automobile manufacturing systems, bottleneck station in assembly lines is different from one production cycle to next cycle and the system bottleneck varies in production cycles. Mechanical assembly line of Rio vehicle in SAIPA automotive corporation has different bottleneck in each cycle. So, the aim of this paper is determination the percent of bottleneck in stations of mechanical assembly line of Rio vehicle in SAIPA automotive corporation. We used simulation technique and an effective simulation methodology which is designed by technical committee on simulation methodology in Michigan University. We did this methodology step by step for modeling of mentioned

Author name / IIEC 00 (2016) 000–00 system in this paper. Also, we utilized Showflow software for modeling, we used SPSS software for data analyzing and we utilized Minitab software for statistical hypothesis testing. After validating the model, we gave required outputs as “efficiency of stations” in this assembly line. According to table 5, it is obvious that bottleneck of the system of mechanical assembly line of Rio vehicle is not unique and is variable. Difference between bottleneck stations confirms this claim. Referring to Table 5, it can be said that stations #23 and #26 have the most percent of bottleneck in stations of Rio vehicle mechanical assembly line and station #22 has lowest percent of bottleneck in stations. Also, station #25 in this assembly line, is never a bottleneck for system. 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