**4. An instance of simulation and analysis of piston production line**

By analyzing the status of an existing production line and design requirements, complete the virtual modeling, layout, simulation analysis and diagnosis of this line, and thus redesign the line to make the full range of logistics flow unblocked.

#### **4.1 Status of production line and design goals**

The line is affiliated to artificial lines, process routing is : finish turning of the spigot rough boring of pin hole finish turning of cylindrical, rough finish and finish turning of the groove of iron hoop finish turning the groove of aluminum hoopsemi-finish boring of pin hole turning of retaining ring groove and outside pin shaftrough turning of combustion chamber fine turning of combustion chamber fine turning of outside round fine turning


Table 4.1. Scheduling of processes on the piston production line

Virtual Design of Piston Production Line 19

the middle part (These stations are reserved for some special processing procedures, but these machines have been moved to special equipment areas at present.). As the machine pitch is too far, and the machine position is not optimized, resulting in a waste of area in the

After the establishment of the virtual physical models, the procedure of definition of the virtual process and control logic is shown in Fig. 4.3—4.7. To realize changes from the virtual physical model to the virtual logical model, because the line does not belong to automated production lines with the central control system, it does not need to create the central control system logic, but it is necessary to define the workers control logic (Labor

Control, as shown in Fig.4.8) to control the following actions of the labors:

2. Rest time and its distribution of workers, as shown in Fig. 4.10-4.11;

Fig. 4.2. Virtual layout and the connection way of process logic of equipment

1. Routes and walking speed of workers, as shown in Fig. 4.9;

line, and increasing walking distance and labor intensity.

**4.3.2 Process parameters** 

Fig. 4.3. Cycle process logic

of roof surface fine boring of pin hole rolling of pin hole boring decompression chamber of pin hole. The parameters of all the processes are shown in Table 4.1.

Because piston species in one production line would be changed 3 to 5 times monthly, for the traditional piston production line, its design method cannot be respond rapidly, and the design of the process time is not balanced and so on, resulting in the serious workpiece products in the line and a serious unbalance labors and machines utilization. Aimed at these problems, ensure the re-design goals: the diameter of piston diameter is between 100 ~ 130mm, monthly production capacity of production line should be not less than 22,000 pieces/ month (two shifts), and cycle should be not more than 47s, the operator should be no more than 9, machine tools should be no more than 16.

## **4.2 Virtual modelling**

According to the real production line, establish the physical models, some typically machines are shown in Fig.4.2 (a-d).

a) Virtual boring machine model BH30 b) Virtual boring machine model BH20

c) Virtual lathe model CAK6250 d) Virtual lathe model CK6146

Fig. 4.1. Physical models of piston line


## **4.3.1 Virtual design parameters**

The layout before optimizing is shown in Fig.4.2, its covers area S= 36772.3 5668.1mm2 = 208.43m2. The layout of the piston machine is very messy and there are many idle stations in

of roof surface fine boring of pin hole rolling of pin hole boring decompression

Because piston species in one production line would be changed 3 to 5 times monthly, for the traditional piston production line, its design method cannot be respond rapidly, and the design of the process time is not balanced and so on, resulting in the serious workpiece products in the line and a serious unbalance labors and machines utilization. Aimed at these problems, ensure the re-design goals: the diameter of piston diameter is between 100 ~ 130mm, monthly production capacity of production line should be not less than 22,000 pieces/ month (two shifts), and cycle should be not more than 47s, the operator should be

According to the real production line, establish the physical models, some typically

a) Virtual boring machine model BH30 b) Virtual boring machine model BH20

c) Virtual lathe model CAK6250 d) Virtual lathe model CK6146

The layout before optimizing is shown in Fig.4.2, its covers area S= 36772.3 5668.1mm2 = 208.43m2. The layout of the piston machine is very messy and there are many idle stations in

chamber of pin hole. The parameters of all the processes are shown in Table 4.1.

no more than 9, machine tools should be no more than 16.

**4.2 Virtual modelling** 

machines are shown in Fig.4.2 (a-d).

Fig. 4.1. Physical models of piston line

**4.3.1 Virtual design parameters** 

**4.3 Virtual design of piston production line** 

the middle part (These stations are reserved for some special processing procedures, but these machines have been moved to special equipment areas at present.). As the machine pitch is too far, and the machine position is not optimized, resulting in a waste of area in the line, and increasing walking distance and labor intensity.

#### **4.3.2 Process parameters**

After the establishment of the virtual physical models, the procedure of definition of the virtual process and control logic is shown in Fig. 4.3—4.7. To realize changes from the virtual physical model to the virtual logical model, because the line does not belong to automated production lines with the central control system, it does not need to create the central control system logic, but it is necessary to define the workers control logic (Labor Control, as shown in Fig.4.8) to control the following actions of the labors:


Fig. 4.2. Virtual layout and the connection way of process logic of equipment

Fig. 4.3. Cycle process logic

Virtual Design of Piston Production Line 21

Fig. 4.7. Machine's failures setup

Fig. 4.8. Labor controller logic


Fig. 4.4. Labor requirements of cycle process

Fig. 4.5. Machine control logic associated with labor

Fig. 4.6. Machine control logic associated with cycle process

Fig. 4.4. Labor requirements of cycle process

Fig. 4.5. Machine control logic associated with labor

Fig. 4.6. Machine control logic associated with cycle process


Fig. 4.7. Machine's failures setup


Fig. 4.8. Labor controller logic

Virtual Design of Piston Production Line 23

The statistical data after simulation includes output of production line, the work piece of buffer area, processing quantities of equipment, machine utilization, utilization of workers'

The number of the work piece in buffers under different simulation time is shown in Table 4.2. It is known that: The work pieces of B1, B3, B5 and B6 are more serious, with which it is shown that the processes after B1, B3, B5 and B6 are the bottleneck processes. However, only adjusting some parameters to improve some of the bottleneck process may trigger new bottleneck processes, so it is necessary to fully investigate and analyze with output of each machine per shift, machine utilization and labor utilization to provide adjustment strategies

Buffer B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 1 hour 14 0 9 0 0 13 1 1 0 1 1 shift 90 0 58 1 97 85 0 0 0 0 1 week 440 0 270 0 469 436 1 1 0 0

According to the relationship between the production and time, the cycle time of the entire production line can be calculated. Fig.4.12 shows that the production cycle tends to be stable gradually with simulation time increasing, and it stabilizes at 70.47s. With this value compared with each process time, it is easy to diagnose the bottleneck process of the line.

Fig. 4.11. Shift break

**4.3.3 Simulation results** 

1. Work piece in the buffers

2. Production and cycle time

working hours and walking paths and so on.

for balancing of the piston production line.

Table 4.2. Accumulation number of parts in buffer

Fig. 4.9. Labor control logic


Fig. 4.10. Daily schedule

Fig. 4.9. Labor control logic

Fig. 4.10. Daily schedule


Fig. 4.11. Shift break

#### **4.3.3 Simulation results**

The statistical data after simulation includes output of production line, the work piece of buffer area, processing quantities of equipment, machine utilization, utilization of workers' working hours and walking paths and so on.

1. Work piece in the buffers

The number of the work piece in buffers under different simulation time is shown in Table 4.2. It is known that: The work pieces of B1, B3, B5 and B6 are more serious, with which it is shown that the processes after B1, B3, B5 and B6 are the bottleneck processes. However, only adjusting some parameters to improve some of the bottleneck process may trigger new bottleneck processes, so it is necessary to fully investigate and analyze with output of each machine per shift, machine utilization and labor utilization to provide adjustment strategies for balancing of the piston production line.



2. Production and cycle time

According to the relationship between the production and time, the cycle time of the entire production line can be calculated. Fig.4.12 shows that the production cycle tends to be stable gradually with simulation time increasing, and it stabilizes at 70.47s. With this value compared with each process time, it is easy to diagnose the bottleneck process of the line.

Virtual Design of Piston Production Line 25

M12 is the highest, up to 94.6%. Considering the output of the all product line, cycle time, and machine utilization, it can be diagnosed that: the finishing turning of the outside round is the most serious bottleneck process. When adjusting and redesigning the production line, the adjustment should be begun from this process. Constrained by the bottleneck process, utilizations of M13 ~ M16 are relatively low. If adjusting those bottleneck processes, the utilizations of M13 ~ M16 can be significantly improved to carry out the purpose of logistics

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 2 3 4 5 6 7 8 9 10 11

Labor utilizations are shown in the labor intensity during the piston manufacturing. The utilization of each labor at different times after the simulation data is shown in Fig. 4.15.

Fig. 4.15 shows: labor utilization of L2 (responsible for finishing turning iron groove process) is the highest, up to 23.9%, followed by labor utilization of L5. But labor utilization of the bottleneck process operated by L7 (responsible for finishing cylindrical) is lower because the percentage of machine processing time is larger. It leads to decrease the

labor number(L1-L11)

at 1 hour simulation at 1 shift simulation at 1 week simulation

equipment number(M1-M16)

balance and increasing output of the whole production line.

Fig. 4.14. Equipment utilization at different simulation time

labor utilization at 1 hour simulation labor utilization at 1 shift simulation labor utilization at 1 week simulation

Fig. 4.15. Labor utilization at different simulation time

5

9

13

labor utilization(%)

4. Utilizations of labor

17

21

25

equipment utilization(%)

Figure 4.13 shows the relationship between process time of each machine and the real cycle time, it is shown that: there was a sudden change of output between the processes operated by worker L1 and L2. The same change occurs between the L3 and L4, L5 and L6, L7 and L6, meanwhile, it is also shown that cycle time is different with processes, the process operated by L1 is the smallest, up to about 35s, which means that the process has a greater redundancy. Suddenly changing point of process time happens in fine /rough turning of combustion chamber operated by L6 and finishing turning of cylindrical operated by L7, those processes are mostly near the entire cycle time. It is referred that those processes may be the most serious bottleneck process in the system.

Fig. 4.12. Relationship between simulation time and cycle time

Fig. 4.13. Relationship between process time and actual cycle time of each unit

3. Equipment utilization

Equipment utilization reflects the load of each machine. Under different simulation times, machine utilization is shown in Fig. 4.14. It is known that: when the line is in a steady state, the machine utilization does not change over time on the whole. But the difference of the utilization on each machine tool is much larger, namely: balance of this production line is very poor. The utilizations of M1, M3, M4, M6, M7, M8, M10, M11 and M12 are much higher, and utilization of finishing turning of cylindrical processes machined by M11 and

Figure 4.13 shows the relationship between process time of each machine and the real cycle time, it is shown that: there was a sudden change of output between the processes operated by worker L1 and L2. The same change occurs between the L3 and L4, L5 and L6, L7 and L6, meanwhile, it is also shown that cycle time is different with processes, the process operated by L1 is the smallest, up to about 35s, which means that the process has a greater redundancy. Suddenly changing point of process time happens in fine /rough turning of combustion chamber operated by L6 and finishing turning of cylindrical operated by L7, those processes are mostly near the entire cycle time. It is referred that those processes may

7200 14400 21600 28800 36000 43200 50400 57600 64800 72000 79200 86400

1 2 3 4 5 6 7 8 9 10 11

Equipment utilization reflects the load of each machine. Under different simulation times, machine utilization is shown in Fig. 4.14. It is known that: when the line is in a steady state, the machine utilization does not change over time on the whole. But the difference of the utilization on each machine tool is much larger, namely: balance of this production line is very poor. The utilizations of M1, M3, M4, M6, M7, M8, M10, M11 and M12 are much higher, and utilization of finishing turning of cylindrical processes machined by M11 and

Fig. 4.13. Relationship between process time and actual cycle time of each unit

Process number of every labor(L1-L11)

cycle time of every process stable value of actual cycle time

Simulation time(s)

cycle time(s)

be the most serious bottleneck process in the system.

part output actual cycle time stable value of cycle time

Fig. 4.12. Relationship between simulation time and cycle time

35

43

51

Process time(s)

3. Equipment utilization

59

67

75

Part output

M12 is the highest, up to 94.6%. Considering the output of the all product line, cycle time, and machine utilization, it can be diagnosed that: the finishing turning of the outside round is the most serious bottleneck process. When adjusting and redesigning the production line, the adjustment should be begun from this process. Constrained by the bottleneck process, utilizations of M13 ~ M16 are relatively low. If adjusting those bottleneck processes, the utilizations of M13 ~ M16 can be significantly improved to carry out the purpose of logistics balance and increasing output of the whole production line.

Fig. 4.14. Equipment utilization at different simulation time

Fig. 4.15. Labor utilization at different simulation time

4. Utilizations of labor

Labor utilizations are shown in the labor intensity during the piston manufacturing. The utilization of each labor at different times after the simulation data is shown in Fig. 4.15.

Fig. 4.15 shows: labor utilization of L2 (responsible for finishing turning iron groove process) is the highest, up to 23.9%, followed by labor utilization of L5. But labor utilization of the bottleneck process operated by L7 (responsible for finishing cylindrical) is lower because the percentage of machine processing time is larger. It leads to decrease the

Virtual Design of Piston Production Line 27

2. Combine the fine and rough turning of the combustion chamber to reduce the auxiliary time, the process can save 9.5s. At this time, the process time of the fine and rough

1 2 3 4 5 6 7 8 9 10 11

Through above methods, it is modified the virtual dynamic logic model, and simulated the redesign model again, the results could be gained, the piston production per shift in this piston production line increase from 336 to 516, and the most serious bottleneck processes have been weakened. Figure 4.18 and 4.19 show respectively the machine utilization and labor utilization before and after eliminating the bottleneck. It is indicated that: after taking the bottleneck reducing measures, the machine and labor utilization of each process after the bottleneck is improved, reduces the utilizations of machine and labor before the bottleneck

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Equipment number(M1-M16)

labor number(L1-L11)

after optimizated original

turning of the combustion chamber is 72.06s, but the process needs two devices.

500

Fig. 4.17. Walking distance of labors before and after optimization

to reduce bottlenecks by making the production line in balance direction.

original

after optimized

Fig. 4.18. Utilizations of machine tool before and after optimized

1500

2500

3500

walking distance(m)

20

40

60

Equipment utilization(

%

80

100

4500

percentage of operating time of labor. During this condition, it is often no longer allowed this labor to operate other machines in order to guarantee machining precision of the bottleneck process. The labor utilization of L10 (responsible for the rolling of the pin-hole process) is lower. It is indicated that the labor has the capacity to operate other machines, to reduce the number of the labor.

#### **4.3.4 Re-design of the production line**

Therefore, when redesigning the piston production line, based on the bottleneck diagnosis and analysis with adjusting the process and equipment, or optimizing the layout parameters, it can reduce labor intensity, improve equipment utilization, and reduce the layout area to make the piston production line more balanced.

After re-adjusting the layout, the arrangement of the machine layout is shown in Fig. 4.16. Fig. 4.2 and fig. 4.16 show that: the layout of the optimized piston production line is more compact, which fully takes advantage of the spare station. Optimized layout area S' = 171.603m2, and the layout area is reduced by 36.827 m2 than that of before optimization.

Under the same conditions (the same process, the same operating frequency and other parameters) walking paths before and after the optimization are shown in Figure 4.17, it is indicated that: the optimized walking distance of the workers have been shortened at different degree, and the machine arrangement is more compact as well as operating range of the workers is more reasonable. Before and after optimization in this production line, significantly shorter walking paths are: L3 (responsible for finishing turning aluminum tank), L4 (responsible for semi-finishing boring pin hole process), L8 (responsible for the fine turning roof surface process), because the layout optimization is mainly carried out on the machines that they are responsible for it.

Fig. 4.16. Virtual layout after optimization

Through the analysis for the cycle time, the balance of the process time, utilizations of machines and labors, the most serious bottleneck process in this production line are fine turning of cylindrical, followed by rough and fine turning of the combustion chamber. But the production line's performance is improved evidently, after taking those optimization measures as following:

1. Improve the feed of finishing turning of cylindrical. Increasing the feed of finishing cylindrical to 0.12mm / r, the process can save time 22.7s.

percentage of operating time of labor. During this condition, it is often no longer allowed this labor to operate other machines in order to guarantee machining precision of the bottleneck process. The labor utilization of L10 (responsible for the rolling of the pin-hole process) is lower. It is indicated that the labor has the capacity to operate other machines, to

Therefore, when redesigning the piston production line, based on the bottleneck diagnosis and analysis with adjusting the process and equipment, or optimizing the layout parameters, it can reduce labor intensity, improve equipment utilization, and reduce the

After re-adjusting the layout, the arrangement of the machine layout is shown in Fig. 4.16. Fig. 4.2 and fig. 4.16 show that: the layout of the optimized piston production line is more compact, which fully takes advantage of the spare station. Optimized layout area S' = 171.603m2, and the layout area is reduced by 36.827 m2 than that of before optimization.

Under the same conditions (the same process, the same operating frequency and other parameters) walking paths before and after the optimization are shown in Figure 4.17, it is indicated that: the optimized walking distance of the workers have been shortened at different degree, and the machine arrangement is more compact as well as operating range of the workers is more reasonable. Before and after optimization in this production line, significantly shorter walking paths are: L3 (responsible for finishing turning aluminum tank), L4 (responsible for semi-finishing boring pin hole process), L8 (responsible for the fine turning roof surface process), because the layout optimization is mainly carried out on

Through the analysis for the cycle time, the balance of the process time, utilizations of machines and labors, the most serious bottleneck process in this production line are fine turning of cylindrical, followed by rough and fine turning of the combustion chamber. But the production line's performance is improved evidently, after taking those optimization

1. Improve the feed of finishing turning of cylindrical. Increasing the feed of finishing

cylindrical to 0.12mm / r, the process can save time 22.7s.

reduce the number of the labor.

**4.3.4 Re-design of the production line** 

the machines that they are responsible for it.

Fig. 4.16. Virtual layout after optimization

measures as following:

layout area to make the piston production line more balanced.

2. Combine the fine and rough turning of the combustion chamber to reduce the auxiliary time, the process can save 9.5s. At this time, the process time of the fine and rough turning of the combustion chamber is 72.06s, but the process needs two devices.

Fig. 4.17. Walking distance of labors before and after optimization

Through above methods, it is modified the virtual dynamic logic model, and simulated the redesign model again, the results could be gained, the piston production per shift in this piston production line increase from 336 to 516, and the most serious bottleneck processes have been weakened. Figure 4.18 and 4.19 show respectively the machine utilization and labor utilization before and after eliminating the bottleneck. It is indicated that: after taking the bottleneck reducing measures, the machine and labor utilization of each process after the bottleneck is improved, reduces the utilizations of machine and labor before the bottleneck to reduce bottlenecks by making the production line in balance direction.

Fig. 4.18. Utilizations of machine tool before and after optimized

Virtual Design of Piston Production Line 29

Dirk Rantzau et al..(1999).Industrial virtual reality engineering applications, *The Symposium* 

Ford, H., (1926). *Today and Tomorrow (special reprint,1988)*. Productivity Press, ISBN-

FAN Xiu-min et al.(2001).Research & Application of Simulation & Optimization

GAO Chonghui et al.(2010).Research and application of virtual simulation of automatic

H.T. Papadopolous, C. Heavey, J. Browne.(1993).*Queuing theory in manufacturing systems* 

J.S. Smith.(2003).Survey on the use of simulation for manufacturing system design and

Kotler, P.(1989). From mass marketing to mass customization. *Planning Review* 17(5), 10–13. Lee, H., Peleg, B.&Whang, S.(2005).*Toyota: Demand Chain Management*. Harvard Business

M.J. Ashworth, K.M. Carley.(2007).Can tools help unify organization theory? Perspectives

*Organization Theory*,Vol.13 ,No.1(August 2006),pp.89–111,ISSN 1381-298X Par Klingstam, Per Gullander.(1999).Overview of simulation tools for computer-aided production engineering *Computers in Industry*. Vol.38,pp.173-186,ISSN: 0166-3615 S.H. Han, et al.. (2012). Automated post-simulation visualization of modular building

Shao Li et al.(2000). Virtual integrated design of production line. *Industrial engineering and* 

S B Yoo et al.(1994).An Object-Oriented approach to integrated control of flexible

S.M. Shafer, T.L. Smunt.(2004).Empirical simulation studies in operations management:

*Production Economics*, Vol.131,No.1 (May 2011),pp.183–193. ISSN 0925-5273 T.S. Baines, D.K. Harrison.(1999).Opportunity for system dynamics in manufacturing system modeling, *Production Planning and Control*,Vol.10,No.6 (1999),pp.542–552. Yong-Sik Kim, Jeongsam Yang&Soonhung Han.(2006).A multichannel visualization module

*Management*,Vol.22,No.4 (August 2004),pp.345–354, ISSN: 0272-6963 Thomas Volling , Thomas S. Spengler.(2011).Modeling and simulation of order-driven

*Exposition (IMECE'99)*, Vol. 5, pp. 49-56 ,Chicago, USA. 1999.

*analysis and design* Chapman & Hall. ISBN 978-041-2387-20-3

School Publishing, Boston, MA, Case No.GS-42.

*management*, No.6(June 2000),pp.1-4,ISSN 1006-5429

*Integrated Manufacturing System*.No.1,pp.383-386

2006),pp.653–662,ISSN 0166-3615

100915299364 ISBN-13 978-091-5299-36-2, Cambridge.

0956-5515

ISSN 0278-6125

236,ISSN:0926-5805.

2010),pp.146-148,ISSN 1000-3940

*on Industrial virtual Reality and the 1999 ASME Mechanical Engineering Congress and* 

Technologies in Virtual Product Manufacturing Planning.*Computer Integrated Manufacturing Systems-CIMS*,Vol.7,No.8(August 2001), pp.41-43,ISSN 1006-5911 F.T.S. Chan, H.K. Chan.(2004).A comprehensive survey and future trend of simulation study

on FMS scheduling, *Journal of Intelligent Manufacturing*,Vol.15,No.1,pp.87–102. ISSN

press line for automobile panel. *Forging &Stamping Technology*, Vol.35,No.1(January

operation, *Journal of Manufacturing Systems*,Vol.22,No.2 (March 2004),pp.157–171,

on the state of computational modeling, *Computational and Mathematical* 

production assembly line, .*Automation in Construction*. Vol.21, pp.229-

manufacturing systems.*International Symposium on Advances in Intelligent Computer* 

Context, trends, and research opportunities, *Journal of Operations* 

planning policies in build-to-order automobile production. *International Journal of* 

for virtual manufacturing.*Computers in Industry*,Vol.57,No.7 (September

Fig. 4.19. Utilizations of labor before and after optimized

#### **5. Conclusions**

Compared to the traditional design method of the production line, virtual design of the production line integrates many advanced technologies, and applies the unified manufacturing process modeling, analysis and dynamic optimization and other advanced design tools to get multiple-objective optimal results. Meanwhile, it is easier to get corresponding design data and decision-making data. Based on those simulation results, it can gain some methods quickly to redesign the real production line's parameters in the early design stage. So, this design method is a prospective powerful tool for the manufacturing planning.

With object-oriented technology, combined with the advantages of QUEST and UML, it is established the simulation model of the piston production line mapping from real to virtual environment with UML class diagram of the physical equipment, process, logic control and system interaction classes. The virtual model of this production line is divided to steps: static modeling is described with the hierarchy of various resources objects in piston production line and other static characteristics, and dynamic modeling defined with behavioral and control logic processes.

With this virtual design method, an instance of this piston production line is used to not only present the virtual design procedure, but also compare those design results quickly with the virtual model. From those simulation results, it is shown that the optimized production line can greatly reduce the labor intensity, improves equipment utilization, decreases the layout area, and makes all processes more balanced. With this method, all design procedures are achieved in a virtual environment with a short design cycle before carrying out the real production line. It is easy to avoid wasting the resource and making the system design more reliable and effective during the beginning planning for the piston production line, which can save the design cost and time to improve the design performance success.

#### **6. References**

Da Silveira, G., Borenstein, D.&Fogliatto, F.S.(2001). Mass customization. *International Journal of Production Economics*, Vol.72,No.1, (June 2001),pp.1–13. ISSN 0925-5273

1 2 3 4 5 6 7 8 9 10 11

Compared to the traditional design method of the production line, virtual design of the production line integrates many advanced technologies, and applies the unified manufacturing process modeling, analysis and dynamic optimization and other advanced design tools to get multiple-objective optimal results. Meanwhile, it is easier to get corresponding design data and decision-making data. Based on those simulation results, it can gain some methods quickly to redesign the real production line's parameters in the early design stage. So, this design method is a prospective powerful tool for the manufacturing planning.

With object-oriented technology, combined with the advantages of QUEST and UML, it is established the simulation model of the piston production line mapping from real to virtual environment with UML class diagram of the physical equipment, process, logic control and system interaction classes. The virtual model of this production line is divided to steps: static modeling is described with the hierarchy of various resources objects in piston production line and other static characteristics, and dynamic modeling defined with

With this virtual design method, an instance of this piston production line is used to not only present the virtual design procedure, but also compare those design results quickly with the virtual model. From those simulation results, it is shown that the optimized production line can greatly reduce the labor intensity, improves equipment utilization, decreases the layout area, and makes all processes more balanced. With this method, all design procedures are achieved in a virtual environment with a short design cycle before carrying out the real production line. It is easy to avoid wasting the resource and making the system design more reliable and effective during the beginning planning for the piston production line, which can

Da Silveira, G., Borenstein, D.&Fogliatto, F.S.(2001). Mass customization. *International Journal of Production Economics*, Vol.72,No.1, (June 2001),pp.1–13. ISSN 0925-5273

save the design cost and time to improve the design performance success.

labor number(L1-L11)

original after optimizated

5

behavioral and control logic processes.

Fig. 4.19. Utilizations of labor before and after optimized

10

15

labor utilization (%)

**5. Conclusions** 

**6. References** 

20

25


**2** 

*1The University of Newcastle* 

*2University of Salford* 

*2United Kingdom* 

*1Australia* 

**Changing Skills in Changing Environments:** 

**Skills Needed in Virtual Construction Teams** 

This book focuses on virtual reality. In the context of design, virtual reality is an emerging technology that not only allows designers and other stakeholders to gain a threedimensional appreciation of the artifact being designed, it also has the potential to significantly alter the manner in which design occurs. Internet-based technologies have made it possible for designers in different locations to collaborate in developing and refining their designs. Virtual reality has contributed to this environment (Maher, 2005) by allowing designers in geographically-dispersed locations to interact with each other. Software applications have been developed to assist and facilitate these collaborative activities (including Shyamsundar and Gadh (2001) and Lau, Mak and Lu (2003)) but comparatively speaking, little research has been conducted into the people-related issues of collaboration

Recent developments in virtual communication technologies have the potential to dramatically improve collaboration in the construction industry (Gameson & Sher, 2002). Furthermore, virtual teams "hold significant promise for organizations that implement them because they enable unprecedented levels of flexibility and responsiveness" (Powell, Piccoli, & Ives, 2004, p. 6). Some authors observe that virtual teams are here to stay (Bell & Kozlowski, 2002) and that organisations will be forced to "embrace virtual collaboration to enhance their competitiveness" (Abuelmaatti & Rezgui, 2008, p. 351). Indeed, current research proposes that "(g)lobally disbursed project teams are the new norm in every industry today" (Daim et al., 2012). However, the skills required to work productively in virtual environments have been theoretically defined but not assessed in the real world. Indeed, many of the studies that have been conducted (e.g. Hatem, Kwan and Miles (2011) and Rezgui (2007)) into virtual teamwork have involved tertiary-level students. Abelmaatti and Rezgui (2008) consider that the challenges of virtual teamwork in the real world substantially outweigh the relative ease with which academics can research and develop virtual team solutions. Furthermore, the differences between virtual and face-to-face teamwork means that an overt and explicit effort is needed to design new work processes to

via the Internet. Some of these are the issues addressed in this chapter.

make it successful (Nunamaker, Reinig, & Briggs, 2009).

**1. Introduction** 

Willy Sher1, Sue Sherratt1, Anthony Williams1 and Rod Gameson2

