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Competence Requirements and Their Impact on Manufacturing System Performance
Competence Requirements and Their Impact on Manufacturing System Performance Anders Kinnander, Torbjorn Ilar, Ulf Eriksson Lulea University of Technology, Department of Manufacturing Engineering, Lulea, Sweden Submitted by Gunnar Sohlenius (1) The Royal Institute of Technology, Stockholm, Sweden Received on January 8,1998
Abstract Research into "Next generation manufacturing systems" stresses the use of semi-automated systems to handle short product life cycles and customer oriented production. Despite increasing dependency on technology, the importance of humans is expected to increase and to provide a realistic basis for decision support, both technical and organisational processes must be included in simulation models. In a rapidly changing environment, skill development is also important and should be considered when developing simulation models. This paper describes two case studies where Competence and skill development were modelled using learning curves to obtain a more representative simulation of system performance.
Keywords Manufacturing, simulation, performance, evaluation.
Introduction Ongoing research into so called "next generation manufacturing systems" stresses the importance of semiautomated systems, i.e. systems where humans play a significant role (Gaines & Norrie el al, 1995). The absolute productivity of such systems will be determined by the ability of the organisation to adapt to and use them. One way to support this adaptation is to use an extended implementation plan when installing new manufacturing systems thus give the organisation opportunity to develop in phase with the technical implementation (Towill & Cherrington, 1994). A significant drawback with this approach is the long project lead time required which is contrary to the general situation in industry today where rapid and successful implementation is necessary to take advantage of "market windows". The use of various simulation tools such as discrete event simulation for decision support when developing manufacturing systems is now quite commonplace. However, their use is usually limited to evaluating alternative technical configurations and seldom takes organisational aspects into account. In many cases, the results from such "technical" simulations are of limited benefit to the project as a whole as they correlate poorly with the behaviour of the real system where organisational factors affect system Performance.
One approach to modelling skill and competence development within an industrial organisation is the use of a theoretical learning curve. Learning curve theory has been applied to a wide range of industrial applications that include human operators in complex tasks such as assembly (Naime & Towill et at, 1993). The use of simulation incorporating learning curve theory to model the behaviour of manufacturing systems is exptored in the following two case studies. Case studv I The aim of the work presented in this case study was to increase understanding of the impact of worker competence on overall productivity in a recently commissioned highly automated press line. The simulation model developed included both technical (main) and organisational (supporting) processes and focused on the interaction between these (Figure 1). Whilst production simulation had been used as a decision support tool in the early phases of this project, these initial models covered only the main processes (filled symbols in Figure 1). The effects of operator learning and interaction effects associated with the sub-processes were neglected. These initial models, in retrospect, indicated performance / production levels far higher than seen in practice.
Production simulation models of semi-automated systems should therefore model both the technical configuration and the organisations ability to handle the system.
Annals of the ClRP Val. 47/1/1998
3135
I I
I I I I I I
I I I
I
Easy --> Difficult Figure 1, Basic layout of and flowchart of the activities associated with the press line In the present study process mapping was used to identify the processes to be included in the discrete event The simulation model (Ilar & Kinnander, 1997). processes chosen were the main process as well as tool preparation, set-up and maintenance processes all of which had the greatest impact on overall system performance. To be able to model the 'learning' or increase in operator skill associated with the supporting processes, a competence profile and learning curve was included in the model for two tasks: the tool related process and the maintenance-relatedprocess.
A number of different formulae have been developed to model learning. (Naime & Towill ET al, 1994). The one used in this case was based on the "time constant" model which is probably the most widely used.
T,(x)=
I
T,,,, + A T x r
L
where:
J
Tc(x) is the operation time for index x: TOPIis the operation time X+W: (T,,,+AT) is operation time at x=O and: C, is the learning curve factor.
In the simulation model, the competence index for each operator was updated according to this formulae each time s/he performed an activity. The initial competence index for each operator was obtained through discussions with the group supervisor. Two different human management philosophies were evaluated; one using dedicated operators and another developing broad operator competence and hence flexibility. The impact of the management philosophy used was evaluated for both short term productivity as well as after a learning period of 10 weeks. Different levels of work load, operator availability and product batch sizes were investigated. A factorial design was used to plan the simulation which required 8 combinations to achieve a three factor, two level, full factorial experiment. (Law & Kelton, 1991.)
386
Results Table 1 shows the relative production rate (RPR) in YO, defined as the production rate from this simulation study, vs. the production rate from the previous decision support simulation run. The results are based on simulation of 10 weeks production preceded by a 10 week competence build up period and with a highest level of workload.
1
Management philosophy broad competence dedicated operator
I
I
Mean
Min
30.18 28.81
I
29.43 28.35
Max
I
30.93 29.27
Table 1, Relative production rate: mean, min, and max based on 95% confidence interval. The study indicated that the highest initial RPR is achieved using dedicated operators However, developing "broad competence" offers advantages after the build up period and when the workload is increased. (Figure 2.) Shortterm (two shift) measurements of the RPR also shows a much lower variation for the broad competence approach compered to the dedicated operator approach.
100 -
J a e L & simulation study
f
c
n Competence
/
spread
Dedicated
I time Figure 2, Outline of the RPR development over time for the two alternatives.
It is important to note that the production rate decreases by approximately 70% for both alternatives compared to predictions from the earlier study which focused almost entirely on the technical configuration. About one third of this decrease is explained by the more frequent setups in the present study. The remainder is due to the introduction of organisational factors and the inclusion of more realistic breakdown data. This indicates the importance of including both main and supporting process. These results were validated through presentation and discussions with the project team rather than through direct comparison with actual system performance. Case studv II The model in this case study is of an assembly line for manufacture aircraft wing beams. The line has, at this point in time, produced 31 batches of beams.
included in the model. The use of learning curves for estimating assembly lead time is common in the aircraft industry where relatively short production runs and high dependence on worker skill mean that competence development and learning play a significant role. The "learning curve" equation in use at the company, which is different to that used in the first case studv was used in the simulation model. (Parametric Cost Estimating Handbook, 1997-12-23):
y = a*xb
y =time per unit; a = time to complete the first unit; x = unit number and b = learning curve coefficient
where,
The study used comparison between alternatives (Law & Kelton, 1991). The alternatives used were: Curves I
The line is currently undergoing a period of reorganisation and improvement due to the likelihood of winning a major export order which is expected to result in an increase of approx. 50% in order volume. In order to complete this order on schedule, the 'door to door' lead time must be reduced. The workers in the assembly line are currently divided into four groups; mechanical sub-assembly, mechanical final assembly, assembly of electronic equipment, and final seal and dry. Five different types of beams are assembled in the line (Figure 3.) In order to meet the demands of increased order volume and reduced door to door time, it has been suggested that the two mechanical assembly groups, sub assembly and final assembly, are combined into one multi-skilled group in order to minimise the delay at hand-over between these two stages. This has close similarities with the use of different management philosophies seen in case one.
Management Philosophy
I
No
iI
Yes
I
1
1
3
I
Separate assembly groups
I
,
Results This study is still ongoing but some initial results can be presented. Table 2 shows a comparison between confidence intervals of door to door times (Hours) and shows that there is a significant difference between the four alternatives evaluated.
1 2 3
87.93 98.81 67.42
87.34 97.40 67.1 9
88.52 100.22 67.69
The principle performance measures used in the study was door to door time, however, there was a secondary goal of keeping labour utilisation at less than 70%. This relatively low figure was chosen to compensate for other tasks and operators unavailability, which were not
pccnmhlw
Beam 1 out
Seal, dry
Beam 2,3 out
Part
I
I
Figure 3. Flowchart of the activities associated with the assembly line.
387
The most striking difference is seen in the simulation results with and without learning curve effects.(1-3, 2-4) The model covers manufacturing from the 31I‘ batch onwards and the slope in the learning curve is rather flat but still has a significant effect. There is also an indication that the spread in door to door time decreases when using the separate assembly groups. (1-3) The study will be extended in order to develop the model to include limited resources in the form of jigs and also storage space. Neither of these were included in the present study. Another improvement is to increase the quality of the data, for instance accuracy of operator competence profiles. The results obtained thus far, as well as those from further research, will be used by management to decide how to proceed in the real system. Conclusions This paper presents two case studies which demonstrate the effects of operator and team skill development and the need to include these characteristics when using simulation modelling in order to obtain a realistic evaluation of advanced semi-automated manufacturing systems. In both case studies technical as well as organisational factors were modelled; technical factors being related to automated activities and organisational factors related to learning curve effects as the number of times that task is carried out increases. From these studies it can be seen that simulation modelling including learning theory is important when modelling manufacturing systems characterised by frequent re-configurations and where productivity is directly effected by the organisations ability to adapt. Including technical as well as organisational factors is of great importance for understanding the effects of humadmachine interaction on the overall system performance. The most important application area for this technique is to support development of manufacturing systems consisting of highly automated main processes and organisational oriented supporting process as found in Case Study 1. Traditional project management associated with this type of manufacturing system is usually very technically oriented and often neglects supporting process. Nevertheless, overall performance is often highly dependent on the performance of the supporting process. Case study two shows that even systems which are well established can behave in what might appear an unpredictablemanner when subjected to change. Future research will include more sophisticated output analysis methods which will allow an increased number of factors to be considered and thus allow more complex competence development patterns to be studied. Another important area of research is the integration of financial modelling to support a realistic and dynamic life cycle cost analysis of the different systems.
388
Acknowledaements The work presented here was carried out as part of the PlDUS research project, which addresses the design, implementation, commissioning, management and maintenance of production system. The authors would like to thank Eur.lng. David Legge and other co-workers in the Department of Manufacturing Engineering, the industrial partners used for the case studies. Special thanks must go to the Swedish National Board for Industrial and Technical Development (NUTEK) for funding this work. References 1. Gaines B.R., Norrie D.H. “Knowledge Systematization in the International IMS Research Program.“ http://ksi.cpsc.ucalgary.calKSl/KSI.html(1995) 2. llar T, Kinnander A. “A new Modelling Approach to Support Identificationand Developmentof Secondary Processes in an Industrial AMT Implementation”. Proceeding of the 4Ih ICCIM 1997: ~ ~ 3 6 1 - 3 7 0 3. Law, A. M. Kelton. W. D. “SIMULATION MODELLING &ANALYSIS.“ New York, McGraw-Hill (1991) 4. Naim M.M., Towill D.R. “Modelling and Forecasting Industrial Innovations via the Transfer Function S-Shaped Learning Curve.” International Journal of Advanced Manufacturing Technology (1993) no 8 pp329343 5. Parametric Cost Estimating Handbook -The Learning Curve http://www.jsc.nasa.gov/bu2/PCEHHTMUpceh4 5.htm (23/12/97). 6. Towill, D.R., Cherrington, J.E. “Learning Curve Models for Prediction the Performance of AMT.“. International Journal of Advanced Manufacturing Technology (1994) 9:ppl95-203
Abstract Research into "Next generation manufacturing systems" stresses the use of semi-automated systems to handle short product life cycles and customer oriented production. Despite increasing dependency on technology, the importance of humans is expected to increase and to provide a realistic basis for decision support, both technical and organisational processes must be included in simulation models. In a rapidly changing environment, skill development is also important and should be considered when developing simulation models. This paper describes two case studies where Competence and skill development were modelled using learning curves to obtain a more representative simulation of system performance.
Keywords Manufacturing, simulation, performance, evaluation.
Introduction Ongoing research into so called "next generation manufacturing systems" stresses the importance of semiautomated systems, i.e. systems where humans play a significant role (Gaines & Norrie el al, 1995). The absolute productivity of such systems will be determined by the ability of the organisation to adapt to and use them. One way to support this adaptation is to use an extended implementation plan when installing new manufacturing systems thus give the organisation opportunity to develop in phase with the technical implementation (Towill & Cherrington, 1994). A significant drawback with this approach is the long project lead time required which is contrary to the general situation in industry today where rapid and successful implementation is necessary to take advantage of "market windows". The use of various simulation tools such as discrete event simulation for decision support when developing manufacturing systems is now quite commonplace. However, their use is usually limited to evaluating alternative technical configurations and seldom takes organisational aspects into account. In many cases, the results from such "technical" simulations are of limited benefit to the project as a whole as they correlate poorly with the behaviour of the real system where organisational factors affect system Performance.
One approach to modelling skill and competence development within an industrial organisation is the use of a theoretical learning curve. Learning curve theory has been applied to a wide range of industrial applications that include human operators in complex tasks such as assembly (Naime & Towill et at, 1993). The use of simulation incorporating learning curve theory to model the behaviour of manufacturing systems is exptored in the following two case studies. Case studv I The aim of the work presented in this case study was to increase understanding of the impact of worker competence on overall productivity in a recently commissioned highly automated press line. The simulation model developed included both technical (main) and organisational (supporting) processes and focused on the interaction between these (Figure 1). Whilst production simulation had been used as a decision support tool in the early phases of this project, these initial models covered only the main processes (filled symbols in Figure 1). The effects of operator learning and interaction effects associated with the sub-processes were neglected. These initial models, in retrospect, indicated performance / production levels far higher than seen in practice.
Production simulation models of semi-automated systems should therefore model both the technical configuration and the organisations ability to handle the system.
Annals of the ClRP Val. 47/1/1998
3135
I I
I I I I I I
I I I
I
Easy --> Difficult Figure 1, Basic layout of and flowchart of the activities associated with the press line In the present study process mapping was used to identify the processes to be included in the discrete event The simulation model (Ilar & Kinnander, 1997). processes chosen were the main process as well as tool preparation, set-up and maintenance processes all of which had the greatest impact on overall system performance. To be able to model the 'learning' or increase in operator skill associated with the supporting processes, a competence profile and learning curve was included in the model for two tasks: the tool related process and the maintenance-relatedprocess.
A number of different formulae have been developed to model learning. (Naime & Towill ET al, 1994). The one used in this case was based on the "time constant" model which is probably the most widely used.
T,(x)=
I
T,,,, + A T x r
L
where:
J
Tc(x) is the operation time for index x: TOPIis the operation time X+W: (T,,,+AT) is operation time at x=O and: C, is the learning curve factor.
In the simulation model, the competence index for each operator was updated according to this formulae each time s/he performed an activity. The initial competence index for each operator was obtained through discussions with the group supervisor. Two different human management philosophies were evaluated; one using dedicated operators and another developing broad operator competence and hence flexibility. The impact of the management philosophy used was evaluated for both short term productivity as well as after a learning period of 10 weeks. Different levels of work load, operator availability and product batch sizes were investigated. A factorial design was used to plan the simulation which required 8 combinations to achieve a three factor, two level, full factorial experiment. (Law & Kelton, 1991.)
386
Results Table 1 shows the relative production rate (RPR) in YO, defined as the production rate from this simulation study, vs. the production rate from the previous decision support simulation run. The results are based on simulation of 10 weeks production preceded by a 10 week competence build up period and with a highest level of workload.
1
Management philosophy broad competence dedicated operator
I
I
Mean
Min
30.18 28.81
I
29.43 28.35
Max
I
30.93 29.27
Table 1, Relative production rate: mean, min, and max based on 95% confidence interval. The study indicated that the highest initial RPR is achieved using dedicated operators However, developing "broad competence" offers advantages after the build up period and when the workload is increased. (Figure 2.) Shortterm (two shift) measurements of the RPR also shows a much lower variation for the broad competence approach compered to the dedicated operator approach.
100 -
J a e L & simulation study
f
c
n Competence
/
spread
Dedicated
I time Figure 2, Outline of the RPR development over time for the two alternatives.
It is important to note that the production rate decreases by approximately 70% for both alternatives compared to predictions from the earlier study which focused almost entirely on the technical configuration. About one third of this decrease is explained by the more frequent setups in the present study. The remainder is due to the introduction of organisational factors and the inclusion of more realistic breakdown data. This indicates the importance of including both main and supporting process. These results were validated through presentation and discussions with the project team rather than through direct comparison with actual system performance. Case studv II The model in this case study is of an assembly line for manufacture aircraft wing beams. The line has, at this point in time, produced 31 batches of beams.
included in the model. The use of learning curves for estimating assembly lead time is common in the aircraft industry where relatively short production runs and high dependence on worker skill mean that competence development and learning play a significant role. The "learning curve" equation in use at the company, which is different to that used in the first case studv was used in the simulation model. (Parametric Cost Estimating Handbook, 1997-12-23):
y = a*xb
y =time per unit; a = time to complete the first unit; x = unit number and b = learning curve coefficient
where,
The study used comparison between alternatives (Law & Kelton, 1991). The alternatives used were: Curves I
The line is currently undergoing a period of reorganisation and improvement due to the likelihood of winning a major export order which is expected to result in an increase of approx. 50% in order volume. In order to complete this order on schedule, the 'door to door' lead time must be reduced. The workers in the assembly line are currently divided into four groups; mechanical sub-assembly, mechanical final assembly, assembly of electronic equipment, and final seal and dry. Five different types of beams are assembled in the line (Figure 3.) In order to meet the demands of increased order volume and reduced door to door time, it has been suggested that the two mechanical assembly groups, sub assembly and final assembly, are combined into one multi-skilled group in order to minimise the delay at hand-over between these two stages. This has close similarities with the use of different management philosophies seen in case one.
Management Philosophy
I
No
iI
Yes
I
1
1
3
I
Separate assembly groups
I
,
Results This study is still ongoing but some initial results can be presented. Table 2 shows a comparison between confidence intervals of door to door times (Hours) and shows that there is a significant difference between the four alternatives evaluated.
1 2 3
87.93 98.81 67.42
87.34 97.40 67.1 9
88.52 100.22 67.69
The principle performance measures used in the study was door to door time, however, there was a secondary goal of keeping labour utilisation at less than 70%. This relatively low figure was chosen to compensate for other tasks and operators unavailability, which were not
pccnmhlw
Beam 1 out
Seal, dry
Beam 2,3 out
Part
I
I
Figure 3. Flowchart of the activities associated with the assembly line.
387
The most striking difference is seen in the simulation results with and without learning curve effects.(1-3, 2-4) The model covers manufacturing from the 31I‘ batch onwards and the slope in the learning curve is rather flat but still has a significant effect. There is also an indication that the spread in door to door time decreases when using the separate assembly groups. (1-3) The study will be extended in order to develop the model to include limited resources in the form of jigs and also storage space. Neither of these were included in the present study. Another improvement is to increase the quality of the data, for instance accuracy of operator competence profiles. The results obtained thus far, as well as those from further research, will be used by management to decide how to proceed in the real system. Conclusions This paper presents two case studies which demonstrate the effects of operator and team skill development and the need to include these characteristics when using simulation modelling in order to obtain a realistic evaluation of advanced semi-automated manufacturing systems. In both case studies technical as well as organisational factors were modelled; technical factors being related to automated activities and organisational factors related to learning curve effects as the number of times that task is carried out increases. From these studies it can be seen that simulation modelling including learning theory is important when modelling manufacturing systems characterised by frequent re-configurations and where productivity is directly effected by the organisations ability to adapt. Including technical as well as organisational factors is of great importance for understanding the effects of humadmachine interaction on the overall system performance. The most important application area for this technique is to support development of manufacturing systems consisting of highly automated main processes and organisational oriented supporting process as found in Case Study 1. Traditional project management associated with this type of manufacturing system is usually very technically oriented and often neglects supporting process. Nevertheless, overall performance is often highly dependent on the performance of the supporting process. Case study two shows that even systems which are well established can behave in what might appear an unpredictablemanner when subjected to change. Future research will include more sophisticated output analysis methods which will allow an increased number of factors to be considered and thus allow more complex competence development patterns to be studied. Another important area of research is the integration of financial modelling to support a realistic and dynamic life cycle cost analysis of the different systems.
388
Acknowledaements The work presented here was carried out as part of the PlDUS research project, which addresses the design, implementation, commissioning, management and maintenance of production system. The authors would like to thank Eur.lng. David Legge and other co-workers in the Department of Manufacturing Engineering, the industrial partners used for the case studies. Special thanks must go to the Swedish National Board for Industrial and Technical Development (NUTEK) for funding this work. References 1. Gaines B.R., Norrie D.H. “Knowledge Systematization in the International IMS Research Program.“ http://ksi.cpsc.ucalgary.calKSl/KSI.html(1995) 2. llar T, Kinnander A. “A new Modelling Approach to Support Identificationand Developmentof Secondary Processes in an Industrial AMT Implementation”. Proceeding of the 4Ih ICCIM 1997: ~ ~ 3 6 1 - 3 7 0 3. Law, A. M. Kelton. W. D. “SIMULATION MODELLING &ANALYSIS.“ New York, McGraw-Hill (1991) 4. Naim M.M., Towill D.R. “Modelling and Forecasting Industrial Innovations via the Transfer Function S-Shaped Learning Curve.” International Journal of Advanced Manufacturing Technology (1993) no 8 pp329343 5. Parametric Cost Estimating Handbook -The Learning Curve http://www.jsc.nasa.gov/bu2/PCEHHTMUpceh4 5.htm (23/12/97). 6. Towill, D.R., Cherrington, J.E. “Learning Curve Models for Prediction the Performance of AMT.“. International Journal of Advanced Manufacturing Technology (1994) 9:ppl95-203