Manufacturing cell layout improvement using Simulation …ieomsociety.org/ieom_2016/pdfs/40.pdf · 2016-10-18 · nimized. The manufacturing system throughput improvement is obtained

Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management

Kuala Lumpur, Malaysia, March 8-10, 2016

Manufacturing cell layout improvement using Simulation

Modeling and Group Technology Annamalai Pandian

Mechanical Engineering Department

Saginaw Valley State University

University Center, MI 48710, USA

Ahad Ali

A Leon Linton Department of Mechanical Engineering

Lawrence Technological University

Southfield, MI 48075, USA

Abstract— In this paper, an earnest investigative attempt was made to provide valuable information regarding the use of Simulation

modeling and Group Technology. The Group Technology is used to improve the part process travel distances. The simulation modeling is

used to improve the overall throughput of the manufacturing cell layout. The manufacturing cell layout consists of 11 different machine

cells and 12 different parts and processes with operation times. A typical part goes through the band saw machine, vertical mill, radial drill

press and the NC turret mill. The machine failure times and the failure effects on the system throughput presented in this case study. The

Group Technology ideas are presented here to improve the material handling from cell to cell using matrix manipulation techniques. The

manufacturing cell layout is modeled using discrete event simulation modeling in Arena. The parts process movements are modelled using

transporter routing module in Arena. The forklift speed, loading and unloading times were modeled in this study. The improved layout

minimized part routing distance with 32% improvement. The improved layout simulation model predicted 12% improvement in through

put.

Keywords— Part routing; discrete event simulation; Cell layout; group technology; Burbidge matrix

I. INTRODUCTION

There are numerous articles have been written on simulation modeling. However, the practice of simulation modeling has not reached to the small-scale manufacturers. The large automotive corporations have adapted the modeling technology in the 90’s based on the authors own automotive experience. The simulation modeling is still at the academia stage and it should be promoted like CAD programs that already reached the small-scale manufacturers. In this paper, an earnest attempt was made to provide valuable information regarding the use of Simulation modeling and the Group Technology to improve the manufacturing cell layout minimizing the part travel distances to maximize the overall throughput of the manufacturing system.

In this paper, an investigation is conducted using Group Technology to improve the cell layout combining with Discrete Event Simulation (DES) modeling to improve the cell layout throughput. The cell layout scenarios: original and the improved layout; the distance travelled by the parts through the machines are minimized. The manufacturing system throughput improvement is obtained using simulation modeling.

II. LITERATURE SURVEY

Flynn, B. and Jacobs, R., [1] compared four different layouts, designed to emphasize different features of traditional job shops and group technology shops, and four distributions of demand for end items. The group technology shops exhibited superior performance in terms of average move time and average set-up time. The traditional job shops had superior performance in queue related variables (average queue length, average waiting time, work-in-process inventory, etc.). This was caused by group technology's dedication of machines. The effects of the queue related variables outweighed the effects of average move time and average set-up time. The average flow time was shorter in the traditional job shop than in the group technology shops. Khaledan, S. and Shirouyehzad, H., [2] Utilized ARENA software for simulating both the job-shop and group-technology systems and compared the results. The results showed that the group-technology system performed better than the job-shop system in both productivity and queue factors.

Pandian, A. and Ali, A., [3] investigated the maintenance assumptions for an automotive production line using simulation modeling. They proved assuming assembly line average MTTR/MTBF data to predict the line throughput is wrong for the welding assembly line. Pandian, A. and Ali, A., [4] developed a forecasting model to

103© IEOM Society International



predict an automotive assembly line throughput using ANN modeling. The prediction model data were validated with actual production data. Pandian, A. and Ali, A., [5] research paper provided a systematic development of a forecasting model based on Box-Jenkin’s ARMA methodology to analyze the complex assembly line process performance data. The developed ARMA forecast models predicted the future throughput performance within 99.52 percent accuracy. The predicted data were validated with actual production data. Pandian, A. [6] modeled a complex assembly line production considering robots failure and operator cycle times to prove reducing machine MTBF is more important in the assembly plant than buying capital equipment which then causes more breakdowns. Computer based discrete event simulation (DES) is one of the most commonly used aids for the design of automotive manufacturing systems. However, DES tools represent machines in extensive detail, while only representing workers as simple resources. This presents a problem when modelling systems with manual work content, such as an assembly line.

Baines, T. et al. [7] have shown that it is possible to connect external HPM tools to DES tools such as Witness, in order to introduce models of human variability. The accuracy of the resulting simulation is dependent on the validity and accuracy of the micro-models used, and hence the next stages of the research will focus on developing accurate models of appropriate human factors. A significant shortcoming of traditional simulation languages is the lack of attention paid to the modeling of control structures, i.e., the humans or systems responsible for manufacturing planning and control, their activities and the mutual tuning of their activities Van der zee, D.J. [8] set guidelines for more disciplined model building and to serve as a conceptual basis for extending capabilities of simulation tools. To study the practical relevance of the framework for manufacturing simulation its concepts were implemented in an object-oriented simulation language and applied to a case example.

Wang J., et al. [9] developed a data driven simulation methodology to automatically model a production system and rapidly modify the model corresponding to dynamic requirements and real time information. This methodology provides a ‘‘rapid prototyping’’ capability for production system modeling and enables a quick analyzing and remodeling capability to respond to the fluctuation of demands. The approach is developed and applied to an automotive general assembly plant with an online material handling system. Grassmann, W.K. [10] investigated whether warm-up periods should be used, and how long they should be when estimating an equilibrium expectation. In particular, he demonstrated that for well-selected starting states, no warm-up period should be used if the objective is to minimize the MSE of the time average. In some cases, finding good starting states is costly or inconvenient, and in this case, warm-up periods are often necessary.

Xu Y.T., et al. [11] studied a single machine group-scheduling problem with deterioration effect, where the jobs are already put into groups, before any optimization. They assumed that the actual processing times of jobs are increasing functions of their starting times. The setup times of groups were assumed to be fixed and known. For some special cases of minimizing the make span with ready times of the jobs, they showed that the problem can be solved in polynomial time for the proposed model.

In this paper, an earnest attempt was made to provide valuable information regarding the use of Group Technology to improve the system layout minimizing the material handling time to improve overall throughput of the manufacturing system.

III. MANUFACTURING LAYOUT AND PROCESSES

In this investigation, the manufacturing facility produces 12 basic part types (families). The manufacturing facility is

currently designed as shown below in Fig.1. The machines have been placed on 11 of 16 available concrete pads. Note: These pads provide sufficient structural support and dampening for the machines. The center-to-center distance from one pad to any adjacent pad is 25 feet (see Fig 1. below). Parts are carried from machine to machine via a forklift. It is desirable to minimize travel distances from machine-to-machine (measured by the center-to-center distance between machines, including diagonal paths).




Fig. 1. Original facility layout




The part routings for the 12 parts are shown in Table 1. For example, part 1 goes through operation 10 to cut to length using the

vertical band saw (G) machine. In the next operation 20, the part is milled using vertical milling (H) machine, then moved to the next

operation 30 for drilling using radial drill (I) and finally the part moved to NC turret mill (K) for final slot milling operation 40. Then, the

part #1 is shipped. Similarly, other parts go through all other machines based on their part process routing.

TABLE 1. Parts process routing map

A. Original facility scenario distances

The parts process routing map and the travel distances are shown in Fig 2. The distance travelled by each part is

calculated based on the cell layout. For example, part 1 travels 85 ft. for part completion. Similarly, other parts' travel

distances are calculated. The total distance travelled by 12 parts thru 11 machines is 1293 ft.




Fig 2. Original part process travel distance

B. Original parts/machines matrix

The original part/machine routing matrix is shown below in Fig 3. This matrix is developed denoting machines A thru K as

rows and parts 1 through 12 are denoted as columns 1 thru 12.




Fig 3. Original part/machine matrix

C. Improved parts/machines matrix

For example, part 1 travelled through many empty spaces A through F to get to the first processing machine, vertical

Band saw, G. In addition, the same part travelled through another empty space between machine I and K. The Group

Technology method efficiently route the parts through the machines by combing parts and the machines. The parts and the

machines are grouped together efficiently to minimize the part routing distance using John Burbidge’s Heuristic Matrix

Manipulation Technique [12]. A Macro subroutine was developed to run the matrix manipulation in Excel. The improved

layout for the part/machine is shown in Fig 4. Now the part 1 immediately goes to the first process machine and without

travelling any empty distance completes its process cycle. The machines can be grouped in to Zones to process group of

parts.




Fig 4. Grouped part/machine using Burbidge matrix

The improved manufacturing layout is grouped as three Zones as shown in Fig 5.

Zone1: G H I K machines, process parts 1, 4, 9, 5, 3, 6

Zone2: J A F E machines, process parts 5, 3, 6, 2, 11, 8

Zone3: D B C machines, process parts 2, 11, 8, 7, 10, 12




Fig 5. Improved facility layout

The parts routing distances are re-calculated using the re-arranged new manufacturing layout to verify the total

distance travelled through machines by all parts. The total distance travelled by the parts thru all the machines is 880 ft.

compared to 1293 ft. an improvement of 31.94%.




Fig 6. Improved part routing distances

IV. SIMULATION DEVELOPMENT

For the above two manufacturing layout scenarios, two simulation models were developed using the discrete event simulation model in Arena [13]. The 12 parts, its process times, its routing, machine failure times were incorporated in to the simulation model. A forklift was modeled to transfer the parts from station to station. The part's travel distances were included in the model to accurately represent the two manufacturing layouts. For example for part 1, the total distance travelled is 85 ft. is incorporated in the revised model (ref. Fig 2.) per process map.

A. Original facility scenario simulation

The original manufacturing layout simulation model is shown in Fig 7. The simulation run length was set at 40 hours

simulating one-week production. The original layout simulation model produced 113 parts.




Fig 7. Parts/stations simulation model

The Arena simulation model animation output is shown in Fig. 8.

Fig 8. Original ARENA simulation model animation




B. Improved facility scenario simulation

The part's travel distances are incorporated in the model per improved process layout distances. Other input parameters were unchanged. For example for part 1, the improved distance travelled by the part is 75 ft. (ref. Fig 6.) previously it was 85 ft. an improvement of 10 ft. The improved manufacturing layout model animation is shown below in Fig 9. The improved layout design simulation model produced 128 parts, an improvement of 12% in production throughput.

Fig 9. Improved layout simulation model animation

V. CONCLUSION

This investigative paper demonstrated how to improve the manufacturing layout using the Simulation modeling and the Group Technology. The 12 parts, process times, machine failures times, part travel distances, forklift speed, loading and unloading times were modeled using discrete simulation modeling in Arena to demonstrate the manufacturing layout. The original manufacturing layout simulation model predicted 113 parts for 40-hour simulation run length. The layout was improved using Group Technology method. The improved manufacturing layout simulation model produced 128 parts for the same run length, an improvement of 12% in production throughput. This paper successfully demonstrated that the production throughput could be improved using the simulation modeling and the Group Technology.

ACKNOWLEDGMENT

The authors would like to give special thanks and appreciation to the reviewers for their constructive comments that helped to improve the quality of this paper.

REFERENCES

[1] Flynn, B. and Jacobs, R., "A simulation comparison of group technology with traditional job shop manufacturing", International

Journal of Production Research, Vol. 24, Issue 5, pp 1171-1192, 1986. Published on line Oct 2007.

[2] Khaledan, S. and Shirouyehzad, H., " A Comparison of Job-Shop and Group Technology Using Simulation by ARENA", Journal of Modern Processes in Manufactuirng and Production", Vol. 2, Issue 3, pp 59-66, 2014.

[3] Pandian, A. and Ali, A., “Investigation of Maintenance Assumptions for an Automotive Production line using Simulation Modeling”. Proceedings of the 2015 International Conference on Industrial Engineering and Operations Management, Dubai, UAE, March 3 – 5, 2015. pp 455-461, 2015.

[4] Pandian, A. and Ali, A., "Throughput Forecasting Based on ANN Modeling Using Automotive Robotic Systems Failure Data” Journal of Industrial and Systems Engineering (JISE), Vol.6, No.4, pp 217-239, 2013.




[5] Pandian, A. and Ali, A., "Performance measurement of an Automotive BIW robotic assembly", Measuring Business Excellence, Vol.17 Issue: 1 pp 3-2, 2013.

[6] Pandian, A., “Complex Assembly Line Production Simulation Modeling considering Robots Failure and Operator cycle times”.Summer Simulation Multi-Conference, The Society for Modeling & Simulation International, July 7-10, Toronto, Canada, pp 50-56,2013.

[7] Baines, T., Mason, S., Siebers, P., and Ladbrook, J., “ Humans: the missing link in manufacturing simulation? Simulation ModellingPractice and Theory 12 pp 515–526, 2004.

[8] Van der Zee, D.J., “Modeling decision making and control in manufacturing simulation”, Int. J. Production Economics 100 (1) pp155–167, 2006.

[9] Wang, J., Chang, Q., Xiao, G., Wang, N., Li, S., “A Data driven production modeling and simulation of complex automobile generalassembly plant”, Computers in Industry 62 pp 765–775, 2011.

[10] Grassmann, W.K., “Factors affecting warm-up periods in discrete event simulation. Simulation: Transactions of the Society forModeling and Simulation International, Vol. 90 (1) pp 11–23, 2014.

[11] Xu, Y.T., Zhang, Y., Huang, X., “Single-machine ready times scheduling with group technology and proportional lineardeterioration”, Applied Mathematical Modelling Vol. 38, pp 384–391, 2014.

[12] Burbidge, J.L, “The Introduction of Group Technology”, John Wiley, New York, 1975.

[13] Kelton, D.W.,“Simulation with Arena, Sixth Edition, McGraw Hill, 2010.

BIOGRAPHY

Annamalai Pandian is an assistant professor at Saginaw Valley State University. He earned his B.Eng. & M.Eng. Degree in Mech. Eng.

from University of Madras, Chennai, India, and M.S Degree in Mech. Eng. from Louisiana State University, Baton Rouge, LA, USA and

D. Eng., Degree in Manufacturing Systems from Lawrence Technological University, Southfield, MI, USA. He has wide range of

industrial experience in sheet metal stamping, robotic welding, automation, product design, project management, six sigma and lean

manufacturing methods. He has very good certification knowledge on ISO 9000 standards and procedures. He worked in University of

Wisconsin-Stout for few years before moving to Saginaw Valley State University. Dr. Pandian has worked in Advanced Manufacturing

Engineering division in Chrysler LLC, Auburn Hills, MI, USA for 13+ years. He has wealth of experience in automotive tooling design

and manufacturing. He has taught several design and manufacturing engineering courses including Engineering Mechanics, CAD, Jigs &

Fixtures, Robotics & Machine Vision, Manufacturing Process Eng., and Manufacturing Systems Design and Simulation. Dr. Pandian’s

research interests include Sheet metal forming, 3D printing, Simulation, DOE, Robotics, ARMA and ANN. He is a member of ASQ,

ASEE, IEOM, IIE, and SAE.

Ahad Ali is an Associate Professor and Director of Master of Engineering in Manufacturing Systems and Master of Science in Industrial

Engineering in the A. Leon Linton Department of Mechanical Engineering at the Lawrence Technological University, Michigan, USA. He

earned B.S. in Mechanical Engineering from Khulna University of Engineering and Technology, Bangladesh, Masters in Systems and

Engineering Management from Nanyang Technological University, Singapore and PhD in Industrial Engineering from University of

Wisconsin-Milwaukee. He has published journal and conference papers. Dr Ali has done research projects with Chrysler, Ford, DTE

Energy, Delphi Automotive System, GE Medical Systems, Harley-Davidson Motor Company, International Truck and Engine Corporation

(ITEC), National/Panasonic Electronics, and Rockwell Automation. His research interests include manufacturing, simulation, optimization,

reliability, scheduling, manufacturing, and lean. He is a member of ASEE, IEEE, IEOM, IIE, INFORMS, and SME.


Manufacturing cell layout improvement using Simulation …ieomsociety.org/ieom_2016/pdfs/40.pdf · 2016-10-18 · nimized. The manufacturing system throughput improvement is obtained

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