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COMPREHENSIVE DESIGN OF AN ORDER PICKING LINE BY SIMULATION
COMPREHENSIVE DESIGN OF AN ORDER PICKING LINE BY SIMULATION Lukasz Kawczynski and Ruth Aguilar-Sommar Department of Production Economics Linköping’s Institute of Technology 581 83 Linköping, Sweden Tel: +46.13.285785 email: [email protected]; [email protected]
Abstract: Successful order picking is necessary for fast order shipment to the customer. This paper presents a methodology for a complete analysis to design a picking line. This investigation considered a global view of the line: station layout, storage policy, picking policy, replenishment process and sorting solution. Size and number of the stations to be installed were also investigated. Arena simulation model was developed. The developed methodology resulted in solutions that clearly are better in performance: number of orders and units served, number of staff required, length of the conveyor needed, single picker utilization and total time in the system. Copyright © 2006 IFAC Key words: computer simulation, system analysis, computer aided manufacturing, manufacturing processes, system models.
1.
INTRODUCTION
In times of e-commerce and mail order companies, year by year effective order picking process is becoming more important. Successful order picking is necessary for fast order shipment to customer. On market which is full of competitors, fast shipment becomes an order qualifier, i.e. a market requirement, or an order winner, i.e. a competitive advantage. There is a few literature describing different shapes, sizes and configurations of manual picking systems. Especially, analysis of size of the station receives little attention. The size analysis of the picking zone is developed by Petersen, et. al. (2002). Author also gives analysis of picking zone configuration and considers different storage policies within the zones. The clustering algorithm for products assignment to particular zone is given by Chin-Chia and Yih-Wenn (2003). Che-Hung and Iuan-Yuan (1999) among others examine order picking strategies which are widely examined by Hinojosa (2003), as well. Byuing-In, et. al. (2003) has looked into replenishment process of picking zones. The order picking performance as the result of the product
skewness was analyzed by Petersen, et. al. (2004). Sorting issues are widely discussed by Geinzer and Meszaros (1990), Gang Jing, et. al. (1998), Meller (1997), Choi (1996), Masel and Goldsmith (1997) by using a simulation model. The picking system in mail order companies was chosen as a subject of an analysis, because there is no publication which gathered wide and global scope concerning picking process as seen above. The analysis considers an order picking system in an integrated way. In the picking process there is still scope for improvements. The Arena simulation models provide the knowledge about key performance of each of the configurations. This allows choosing solution which is the least labour extensive and requires the shortest length of the conveyor dedicated for a buffer. The simulation model also provides a better understanding of the analysed process. The analysis creates a manual of the order picking system design. Moreover, the model might be used as an expert system for managers. It not only allows to design the order picking system, but also to examine configurations
for further developed. The aim of the analysis is to provide the best layout of the packaging line considering for assumed sales values. The present work step by step shows how to deal with such a problem, from early formulation to the final layout specification. The packaging line design is divided into smaller areas. The paper is focused in the concurrent issues of (1) picking station configuration and performance, (2) product location – storage policies, (3) picking strategies, (4) replenishment system, (5) convey system configuration. The paper starts with some background of the study followed by the description of the methodology used, which explains how to design an order picking line in an integrated way by using simulation. The list of constrains and suppositions in the model as well as the scope of the analysis are given. Next section discusses the simulation model: possible system configurations, the reference model and sub models. Section 5 focuses on the simulation analysis, i.e. the order picking system design, and the results of testing different solutions. Last section provides conclusions and further research areas. 2.
PROPOSAL METHODOLOGY
The case described in this paper addresses the design of a line in the packaging department of a cosmetic company. The hypothetic company runs the business on the basis of mail order. There is a need to pack the products according to specific customer order. Each order is customized and unique. Customer does not have any limitations in placing the orders (no quantity limit, no minimum order border, nor order value). Thus, number of staff needed to serve the line is analysed. The result of the analysis by a simulation in Arena of the line under consideration is the rough layout. The order picking system covered by the layout has to be able to serve assumed demand data. Since the picking line is a sophisticated system, the analysis is divided into smaller pieces. Analysis follows a bottom-up approach described in figure 1. At each of the steps except replenishment section, a model for each of the solutions considered is created, its performance is examined and then the best one is chosen. For the replenishment section a kanban system with economical order quantity (EOQ) and reorder point (ROP) is analysed. In first step of the methodology, the analysis starts from the station layout, which is the smallest part of the system. During the simulation an influence of station shape on the picking performance is found. The shapes considered are highlighted in figure 1. Influence on the performance of multiple numbers of stations in the line is also analysed. In second step, the storage policy is examined. Examined storage policies are depicted in figure 1. Third step comprises the analysis of different picking policies with and without batching policy. As fourth step, the replenishment process under Kanban system with
S ta tio n la y o u t a n a ly s is
S to ra g e p o lic y a n a ly s is
P ic k in g p o lic y a n a ly s is
R e fe re n c e s ta tio n la yo u t
W ith in th e a is le
A c ro s s th e a is le
P e rim e te r
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D ia g o n a l
R e c ta n g u la r
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W ith in b a tc h
R e p le n is h m e n t p ro c e s s a n a ly s is
S o rtin g s o lu tio n a n a y s is
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K anban s ys te m
W ith o u t b yp a s s s o lu tio n
W ith b y-p a s s s o lu tio n
Fig. 1. Bottom-up analysis methodology economical order quantity and reorder point is analyzed. Finally, to complete the analysis in the fifth step, the sorting solution is examined. The analysis of each of the five steps is done separately for each of the product zones. For each of the solutions separate sub models in Arena are created. The solutions are evaluated. The best solution from lower level of bottom–up analysis becomes an input to the upper part of the analysis. Moreover, in order to have a basis for the comparison of performance, a reference model is created. The reference model is created in four of the steps of analysis excluding replenishment process. The main criteria assessed are the number of staff and length of the conveyor needed. Preferable solution requires less staff and shorter conveyor as buffers which will result in lower costs. The simulation models developed are descriptive models. In some way each model simplifies the real system. In each model a list of basic processes is simulated. Each process is using statistical data gathered from the literature, concerned with the walking times, picking times, dropping times, see (Hinojosa, 2003). The list of the assignment and decision modules is implemented in order to characterize system logic. The simplifications appear in lasting times of processes and in the architecture of system logic. The picking station in terms of dimensions/distances is built based on data from “Human dimension atlas” (2001). One limitation is that the vertical location of the products has not been examined. Picking from the bottom of the rack is equal to picking from the level of the shoulders. Nor the differences between workers are simulated. It is assumed that all the workers in the line have the same picking times which are described by statistical functions. Due to lack of the sample of real orders the batching policy analysis does not examine specific batching algorithm. Along the analysis it is assumed that 33% of line orders work to be batched. The assumed support level is rather poor, so it should be easily reachable for the company. Validation of whole model is performed by providing validity of component activities. If parts of the process are valid,
the whole model is valid. The validation of components activities is done through comparison of presented performance to the performance of similar activity presented by the literature. Moreover in some cases the experience of the authors with the order picking systems became a validation tool. 3.
SIMULATION MODEL
As mentioned before, the analysis covers five areas of an order picking system. For each of the areas a few possible solutions are examined. At the beginning it is necessary to get and define input data and limitations. The most important input data is the table with monthly product demands, and is created by statistical variation considering split between zones. The table is a key to establish the probability of picking from particular bin. The size of a line is established to be 1400 products, as result of table with monthly sale values. The simulation model can not be easily changed after number of products in the line has been changed significantly. In order to maintain some differences between quantities of high and low product demand product groups assuming the percentage split between stations are created. The number of product zones is assumed to be 3: 140 high, 210 medium and 1050 low sale products. This methodology allowed to have a few high demand products and many low demand products, which is quite similar to reality of most companies. The percentage split is a variable, and might be changed in the simulation model, which might be useful when a manufacturer is observing so called sale flattening. The number of products per order is variable, and it is assumed to be described by exponential distribution with mean value of 6. The number of products per order might be easily changed and will strongly influence system performance Station layout examines sub models for high and medium sale products zone: reference station – depicted, tunnel station, and the U-shape station – figure 2. In reference arrangement 6 bins are
gathering times are same for all of the models. The reference model does not need to consider process of turning around by the picker, since there are no bins available at the back. The simulation model considers set of the activities performed sequentially, which together gives picking process. The activities include travelling, turning around, gathering, dropping product, gathering, dropping, reading picking list, releasing carton, stopping carton. Considering Caron, et al. (2000) there are two main station arrangements for rare picking operations: horizontal and vertical one, which are depicted in figure 3. The solutions
Horizontal Fig.
3. Slow moving arrangement.
Vertical products
zone
station
will differ from each other for all the amount of the products available in particular walking distance. The second part of the analysis considered couple of stations arranged in the line. The analysis examined 1, 2, 3 and 4 stations in the raw arrangements. This experiment makes single station smaller, since there is defined number of products within the zone. Hence, the split of products available in the particular distance from the home location of the station differ between different solutions. Simulation model with the extended number of stations (5 and more) was not examined due to limitations of academic version of Arena. Storage policy section tests for the best station layout from previous section and different storage policies. In high and medium sale products no other policy than ‘within the aisle’ can be implemented, since the station has only one raw of the racks on the back of the picker. The storage policy models are created for low sale value products zone. The sub models are created in order to examine: within the aisle, across the aisle, diagonal, perimeter, and rectangular storage policies – the summary is given in the figure 4.
Reference
Reference
Tunnel
U-shape
Fig. 2. Stations layouts. available in the hand area, while under U-shape station are 18 bins are available. The dropping and
Fig. 4. Storage policies examined in the slow moving products zone. The within the aisle policy is similar to across the aisle, but the products are arranged vertically instead of horizontally. The simulation models differ from
each other, in the percentages of picks available in different areas available from the home base of the station. Within the picking policy section, a sub model with implemented batching policy in high sale products zone is examined. Due to batching process the simulation model for batched orders does not consider times associated with some of the activities which are slack due to batching method. The time reduction is done due to the picker travels within the station to pick the product only once for two orders. The reduction is also due to putting activity all information about picked products on one picking list so it is picked only one time for two orders from the box. The simulation model contains additional process - separation of picked quantity for orders that being batched. The model logic attaches an attribute to each order, which states whether the order is being the second order from the batch. The variable that might be changed is the percentage of order lines that is being considered as a batched. The simulation model was examined under assumed value of 33 %. Rising up the variable value will improve system performance. Replenishment process sub model examines kanban system with implemented economical order quantity (EOQ) and reorder point (ROP). The simulation model gathers the data about the frequency of needed from previous sections. Each time when the product stock drops below ROP the system sends the information of needs to replenish within EOQ. The replenishment process considers the transportation of goods from central warehouse to the replenishment zone. The Arena Process analyzer is used in order to determine number of staff needed. The established goal is to keep short lead time: 1,5 hour. The sorting solution sub model brings the best solution from previous sections and adds the sorting logic. The sorting logic is required in order to provide smooth movement of boxes between stations. There are two models created: one which arranges all the station in raw – reference sorting solution, and second which provides equal workload between stations. The solution continuously examines the cumulated zone and single station line lengths and chooses the one with the minimum values. The examination is possible due to implementation of variables which are monitored online by system logic. The building and verification activities for each of the model sections are conducted simultaneously. This means that a specific section is not considered until it is not entirely verified. All codes and features needed by the simulation program had to be thoroughly inspected in order to check for logic and syntax errors. Arena has a built-in function that automatically checks for syntax errors when trying to run a model. Arena verification was used in order to validate the code. After constructing the sub models, they are run for a specific time to see their behaviour in order to validate them. While running the sub models different things are checked. In order to make
sure about their behaviour, they were demonstrated to experts. For each of the station layout, simulation is run for the time equal to the two shifts work – 15 effective working hours (EWH). In order to avoid the influence of the statistical disturbances each simulation is run 10 times, which is equal to ten working days. Both time values can be easily changed, simulating for the same time at different working conditions. Considering mail order company that every month changes the offer of a product, the simulation conditions examined cover half of the time for that particular line of the products which is sold. Proposed simulation time provides simulation credibility. The extended number of simulations does not influence the results significantly. The warm up period was not implemented since in reality when the work starts it should start with an empty system. Each picker has the same performance, including the failure rate. The simulation was limited by maximum number of entities / orders (150), due to academic version of Arena. The high sale products zone is simulated within the variable that, in average 4 % of boxes does not include any item to pick from the particular station. The characteristic might be changed. For detailed description of the simulation model and analysis see (Kawczynski, 2005). 4.
ANALYSIS AND RESULTS
Tunnel arrangement serves 0,5 % more orders than the U shape station, and 1,5 % more than reference arrangement. When considering average times, the best from considered solutions is U shape station. The detailed results are given in table 1. Table 1. The summary of the fast moving products zone simulation. Statistic Stations in the raw Time between arrivals (Random EXPO) General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker 1 utilization Picker 2 utilization Picker 3 utilization
The reference model 3 9,6
The tunnel 3 9,6
The U shape 3 9,6
5 519 27725
5 624 28253
5 593 28097
506,21 27,87 478,33
82,58 23,01 59,57
60,00 20,80 39,20
95,80% 94,76% 95,11%
80,12% 79,44% 80,19%
71,73% 71,12% 72,72%
The time between arrivals might decrease to 7,2 seconds, comparing to 9,6 seconds in case of reference solution. Therefore, the best solution for the picking activity in the fast moving products zone is the U shape station layout. The results of the analysis for the number of stations in the raw within the fast moving products zone are given in table 2. Time between arrivals of orders might be reduced from 32,3 seconds in case of single station up to 5,5 seconds for four stations arrangement. According to assumed demand data, the high sale products zone
Table 2. The summary of the simulation of number of stations within fast moving products zone 1 32,3
The U shape 2 3 32,3 32,3
4 32,3
1 598 6 753
1 689 7138
1 637 6 920
1663 7027
1437,18 33,37 1403,81
33,78 23,29 10,49
27,80 23,02 4,78
23,61 20,29 3,32
Table 4. The summary of the simulation of different storage policies within slow moving products zone
98,74% 36,23% 23,14% 15,45% 36,66% 23,27% 15,50% 23,41% 15,74% 15,77%
needs to be able to pack 56000 units of products per day. According to figure 5, it is possible to pack this quantity of units with nine lines within one station in the raw, this means hiring nine picking persons. The same amount of units might be packed with three lines of two stations in the raw. This solution requires six pickers. In order to serve the demand within three stations in the raw, two lines are required – six pickers. Respectively, the same situation requires almost only one line of the four stations in the raw arrangement. For the medium sale products zone the
Statistic Time between arrivals (Random EXPO) General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker 1 utilization
The horizonal model Within the Across the aisle aisle Rectangular Perimeter 14,6 14,6 14,6 14,6 3 664 3 772
3 643 3 751
3 679 3 787
3 673 3 781
160,08 13,91 146,17
219,62 14,11 205,51
386,92 14,45 372,47
348,21 14,26 333,96
77,25 13,07 64,17
95,20%
95,76%
97,47%
97,14%
88,93%
adjustment are depicted in figure 6. The diagonal policy provides lowest number of orders queuing in buffer and the lowest single picker utilization. This leads to the conclusion that the best from considered storage policies under our conditions is diagonal storage policy. In fast moving products zone, time 600.00
Average total time (left scale)
1400.00
Average waiting time (left scale)
600.00
20,000
4,100
Units out (right scale)
4,000 3,900
300.00
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30,000
800.00
Average waiting time (left scale)
400.00
40,000
1000.00
4,200
Average total time (left scale)
500.00
50,000
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1200.00 Time (seconds)
60,000
Time (seconds)
1600.00
Diagonal 14,6
3 696 3 805
400.00
3,800
200.00
3,700
100.00
3,600
0.00 10,000
200.00 0.00
3,500 Within the aisle
Across the aisle
Rectangular
Perimeter
Diagonal
0 1
2
3 Stations in raw
4
Fig. 6. The storage policies main performance.
The U shape
Fig. 5. The performance summary. results appear to be similar as for top sale products zone. The best form considered is U-shape station. The number of stations in the raw is established to be two, since system does not require more capacity. The vertical and horizontal station arrangements in slow moving products zone serve the same amount of orders. The details are given in table 3. Table 3. The summary of the simulation of station layout within slow moving products zone Statistic Stations in the raw Time between arrivals (Random EXPO) General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker 1 utilization
The vertical model 1 14,2
The horizontal model 1 14.2
3 756 3 861
3 766 3 871
577,32 14,17 563,15
327,65 13,93 313,72
98,58%
97,15%
Adjusting of times between arrivals drops by 0,1 to 14,1 seconds in case of horizontal arrangement. Number of stations in the raw does not have to be examined since one station in the raw is able to pack
between arrivals batching policy offers around 0,5 % more orders served comparing to the system without implemented batching policy. The average total time is reduced by 60 % comparing to referenced system. The 15 % reduction of time is done in core picking activity. The average waiting time is reduced by 70 %. Single picker utilization in system with implemented batching policy is around 77 %, and comparing to system without batching policy it is been reduced. Within top sale products zone, after adjusting time between arrivals, the batching policy offers 19% improvements in amount of served orders. The line length is dropped in average by 38%. The replenishment process of the line need to be served by at least 69 persons. The number of staff is necessary in order to maintain short lead time and low stock level. The maximum number of “need to replenish orders” waiting in queue under this condition is 24. After considering implemented safety lead time, with 69 person hired the system will be replenished on time. The proposed sorting solution provides smoother workload between the pickers. The details of system performance are given in table 5. The queue reduction for medium and low sale products zone is lower because the utilization within these zones is also very low. High line
Units out
Statistic Stations in the raw Time between arrivals (Random EXPO) General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker 1 utilization Picker 2 utilization Picker 3 utilization Picker 4 utilization
desired amount of orders. Analysis of storage policies of low sale value products zone, depicts that under each storage policy the zone is able to serve more or less the same amount of orders. The all performance of each storage policy are depicted in table 4. The results of time between arrivals
Table 5. The summary of the simulation of sorting solutions. Statistic General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker A1 utilization Picker A2 utilization Picker A3 utilization Picker A4 utilization Picker B1 utilization Picker B2 utilization Picker C1 utilization
Reference Proposed sorting solution sorting solution
9 254
9 268
55521
55609
259,3 33,6 225,7
237,9 33,9 204,0
95,1% 95,4% 94,7% 91,9% 71,9% 72,3% 55,7%
98,8% 95,1% 95,3% 92,5% 72,3% 72,5% 55,7%
reduction in top sale products zone is possible due to high utilization of the pickers. Moreover, higher than the average number of queuing orders to the first station in the top sale products zone, is the cause of implemented station selection rule. 5.
solution at the step i. This will expand the analysis and at the same time will totally exclude the probability of appearing at the situation described above.
CONCLUSION AND FURTHER RESEARCH
This paper showed a methodology to totally design order picking systems by means of simulation. The built simulation model permitted the comparison of the performance of different solutions in station layout, storage policies, picking process, replenishment process and sorting solution; and thus, choosing the best one. This five steps methodology permits in a simple way to test and determine the main order picking system features and thus design it totally. Analysis proved that the U-shape station layout offers 10 % better productivity comparing to tunnel layout, due to reduction of travelling distances. For the slow moving products zone, the diagonal storage policy provides the greatest reduction of time for picking. Through comparison of different picking policies, the benefits of batching algorithm were shown. The entire model highlighted benefits of the even workload distribution. Further research should be done to analyse sample of orders performance for particular batching algorithm. The influence of the picking list length might be also analyzed. Moreover, further research should be done in order to find out the model behaviour under extended simulation conditions. The above simulation approach is constrained by the limitations of academic version of Arena. Further research should be done in order to measure influence of different techniques of passing information: picking list, pick to voice, pick to screen, pick to light. Hence, further research might be done in order to establish likelihood of order errors under each of the solution. The errors have not been taken into consideration since in order picking systems they mostly depend on human factor. The next step of the analysis will include “matrix analysis” – enhanced analysis of combined solutions, which will exclude the probability that poor solution of step i produces finally a better solution at the step i+1 than the better
REFERENCES: Byuing-In, K., S.S. Heragu, R.J. Graves, and A. St. Onge (2003). “Realization of short cycle time in warehouse replenishment and order picking”. International Journal of Production Research, vol. 41, no. 2, 349-364. Che-Hung, L. and L. Iuan-Yuan (1999). “The procedure of determining the order picking strategies in distribution centre”. International Journal Production Economics, 60-61, 301-307. Chin-Chia, J. and L. Yih-Wenn (Jan2003). „A clustering algorithm for item assignment in a synchronized zone order picking system“. O.R. Applications. Choi, B.K. (Sep1996). “International Journal of Computer Integrated Manufacturing”. Event graph modelling of automated sorting and buffering system, vol. 9, n 5, p 369-380. Caron, F., G. Marchet and A. Perego (2000). “Layout design in manual picking systems: a simulation approach”. Integrated Manufacturing Systems, vol. 11, n 2, p 94-104. Gang Jing, G., D.W. Kelton, J.C. Arantes and A.A. Houshmand (1998). „Modelling a controlled conveyor network with merging configuration“. Proceedings of the 30th conference on Winter simulation, p 1041-1048. Geinzer, C.M. and J.P. Meszaros (1990). „Modelling high volume conveyor sorting systems“. Proceedings of the 22nd conference on Winter simulation, p 714-719. Hinojosa, A. (Feb2003). “Picking up the pieces”, Industrial Engineer: IE, vol. 35, Issue 2, p39, 6p, 1 graph, 3c. “Human dimension atlas” (2001). (Centralny Instytut Ochrony Pracy. (Ed)), Warsaw. Kawczynski, L. (Sep2005). “The design of a packaging line in a manufacturing company”. Internal publication: LITH-IPE-EX—05/762—SE, Department of Production Economics of Linköping’s Institute of Technology, Sweden. Masel, D. and D. Goldsmith (1997). “Using a simulation model to evaluate the configuration of a sortation facility”. Proceeding of the 1997 Winter Simulation Conference. Meller, R.D. (Apr1997). “Optimal order-to-lane assignments in an order accumulation/sortation system”. IIE transactions, vol. 29, n 4, p 293-301. Petersen, C.G. and G. Aase (Nov2002). „A comparison of picking, storage, and routing policies in manual order picking“. Petersen, C.G, G. Aase and D.R. Heiser (2004). “Improving order-picking performance through the implementation of class-based storage”.
Abstract: Successful order picking is necessary for fast order shipment to the customer. This paper presents a methodology for a complete analysis to design a picking line. This investigation considered a global view of the line: station layout, storage policy, picking policy, replenishment process and sorting solution. Size and number of the stations to be installed were also investigated. Arena simulation model was developed. The developed methodology resulted in solutions that clearly are better in performance: number of orders and units served, number of staff required, length of the conveyor needed, single picker utilization and total time in the system. Copyright © 2006 IFAC Key words: computer simulation, system analysis, computer aided manufacturing, manufacturing processes, system models.
1.
INTRODUCTION
In times of e-commerce and mail order companies, year by year effective order picking process is becoming more important. Successful order picking is necessary for fast order shipment to customer. On market which is full of competitors, fast shipment becomes an order qualifier, i.e. a market requirement, or an order winner, i.e. a competitive advantage. There is a few literature describing different shapes, sizes and configurations of manual picking systems. Especially, analysis of size of the station receives little attention. The size analysis of the picking zone is developed by Petersen, et. al. (2002). Author also gives analysis of picking zone configuration and considers different storage policies within the zones. The clustering algorithm for products assignment to particular zone is given by Chin-Chia and Yih-Wenn (2003). Che-Hung and Iuan-Yuan (1999) among others examine order picking strategies which are widely examined by Hinojosa (2003), as well. Byuing-In, et. al. (2003) has looked into replenishment process of picking zones. The order picking performance as the result of the product
skewness was analyzed by Petersen, et. al. (2004). Sorting issues are widely discussed by Geinzer and Meszaros (1990), Gang Jing, et. al. (1998), Meller (1997), Choi (1996), Masel and Goldsmith (1997) by using a simulation model. The picking system in mail order companies was chosen as a subject of an analysis, because there is no publication which gathered wide and global scope concerning picking process as seen above. The analysis considers an order picking system in an integrated way. In the picking process there is still scope for improvements. The Arena simulation models provide the knowledge about key performance of each of the configurations. This allows choosing solution which is the least labour extensive and requires the shortest length of the conveyor dedicated for a buffer. The simulation model also provides a better understanding of the analysed process. The analysis creates a manual of the order picking system design. Moreover, the model might be used as an expert system for managers. It not only allows to design the order picking system, but also to examine configurations
for further developed. The aim of the analysis is to provide the best layout of the packaging line considering for assumed sales values. The present work step by step shows how to deal with such a problem, from early formulation to the final layout specification. The packaging line design is divided into smaller areas. The paper is focused in the concurrent issues of (1) picking station configuration and performance, (2) product location – storage policies, (3) picking strategies, (4) replenishment system, (5) convey system configuration. The paper starts with some background of the study followed by the description of the methodology used, which explains how to design an order picking line in an integrated way by using simulation. The list of constrains and suppositions in the model as well as the scope of the analysis are given. Next section discusses the simulation model: possible system configurations, the reference model and sub models. Section 5 focuses on the simulation analysis, i.e. the order picking system design, and the results of testing different solutions. Last section provides conclusions and further research areas. 2.
PROPOSAL METHODOLOGY
The case described in this paper addresses the design of a line in the packaging department of a cosmetic company. The hypothetic company runs the business on the basis of mail order. There is a need to pack the products according to specific customer order. Each order is customized and unique. Customer does not have any limitations in placing the orders (no quantity limit, no minimum order border, nor order value). Thus, number of staff needed to serve the line is analysed. The result of the analysis by a simulation in Arena of the line under consideration is the rough layout. The order picking system covered by the layout has to be able to serve assumed demand data. Since the picking line is a sophisticated system, the analysis is divided into smaller pieces. Analysis follows a bottom-up approach described in figure 1. At each of the steps except replenishment section, a model for each of the solutions considered is created, its performance is examined and then the best one is chosen. For the replenishment section a kanban system with economical order quantity (EOQ) and reorder point (ROP) is analysed. In first step of the methodology, the analysis starts from the station layout, which is the smallest part of the system. During the simulation an influence of station shape on the picking performance is found. The shapes considered are highlighted in figure 1. Influence on the performance of multiple numbers of stations in the line is also analysed. In second step, the storage policy is examined. Examined storage policies are depicted in figure 1. Third step comprises the analysis of different picking policies with and without batching policy. As fourth step, the replenishment process under Kanban system with
S ta tio n la y o u t a n a ly s is
S to ra g e p o lic y a n a ly s is
P ic k in g p o lic y a n a ly s is
R e fe re n c e s ta tio n la yo u t
W ith in th e a is le
A c ro s s th e a is le
P e rim e te r
U s h a p e s ta tio n la yo u t
D ia g o n a l
R e c ta n g u la r
W ith o u t b a tc h
W ith in b a tc h
R e p le n is h m e n t p ro c e s s a n a ly s is
S o rtin g s o lu tio n a n a y s is
T u n n e l s ta tio n la yo u t
K anban s ys te m
W ith o u t b yp a s s s o lu tio n
W ith b y-p a s s s o lu tio n
Fig. 1. Bottom-up analysis methodology economical order quantity and reorder point is analyzed. Finally, to complete the analysis in the fifth step, the sorting solution is examined. The analysis of each of the five steps is done separately for each of the product zones. For each of the solutions separate sub models in Arena are created. The solutions are evaluated. The best solution from lower level of bottom–up analysis becomes an input to the upper part of the analysis. Moreover, in order to have a basis for the comparison of performance, a reference model is created. The reference model is created in four of the steps of analysis excluding replenishment process. The main criteria assessed are the number of staff and length of the conveyor needed. Preferable solution requires less staff and shorter conveyor as buffers which will result in lower costs. The simulation models developed are descriptive models. In some way each model simplifies the real system. In each model a list of basic processes is simulated. Each process is using statistical data gathered from the literature, concerned with the walking times, picking times, dropping times, see (Hinojosa, 2003). The list of the assignment and decision modules is implemented in order to characterize system logic. The simplifications appear in lasting times of processes and in the architecture of system logic. The picking station in terms of dimensions/distances is built based on data from “Human dimension atlas” (2001). One limitation is that the vertical location of the products has not been examined. Picking from the bottom of the rack is equal to picking from the level of the shoulders. Nor the differences between workers are simulated. It is assumed that all the workers in the line have the same picking times which are described by statistical functions. Due to lack of the sample of real orders the batching policy analysis does not examine specific batching algorithm. Along the analysis it is assumed that 33% of line orders work to be batched. The assumed support level is rather poor, so it should be easily reachable for the company. Validation of whole model is performed by providing validity of component activities. If parts of the process are valid,
the whole model is valid. The validation of components activities is done through comparison of presented performance to the performance of similar activity presented by the literature. Moreover in some cases the experience of the authors with the order picking systems became a validation tool. 3.
SIMULATION MODEL
As mentioned before, the analysis covers five areas of an order picking system. For each of the areas a few possible solutions are examined. At the beginning it is necessary to get and define input data and limitations. The most important input data is the table with monthly product demands, and is created by statistical variation considering split between zones. The table is a key to establish the probability of picking from particular bin. The size of a line is established to be 1400 products, as result of table with monthly sale values. The simulation model can not be easily changed after number of products in the line has been changed significantly. In order to maintain some differences between quantities of high and low product demand product groups assuming the percentage split between stations are created. The number of product zones is assumed to be 3: 140 high, 210 medium and 1050 low sale products. This methodology allowed to have a few high demand products and many low demand products, which is quite similar to reality of most companies. The percentage split is a variable, and might be changed in the simulation model, which might be useful when a manufacturer is observing so called sale flattening. The number of products per order is variable, and it is assumed to be described by exponential distribution with mean value of 6. The number of products per order might be easily changed and will strongly influence system performance Station layout examines sub models for high and medium sale products zone: reference station – depicted, tunnel station, and the U-shape station – figure 2. In reference arrangement 6 bins are
gathering times are same for all of the models. The reference model does not need to consider process of turning around by the picker, since there are no bins available at the back. The simulation model considers set of the activities performed sequentially, which together gives picking process. The activities include travelling, turning around, gathering, dropping product, gathering, dropping, reading picking list, releasing carton, stopping carton. Considering Caron, et al. (2000) there are two main station arrangements for rare picking operations: horizontal and vertical one, which are depicted in figure 3. The solutions
Horizontal Fig.
3. Slow moving arrangement.
Vertical products
zone
station
will differ from each other for all the amount of the products available in particular walking distance. The second part of the analysis considered couple of stations arranged in the line. The analysis examined 1, 2, 3 and 4 stations in the raw arrangements. This experiment makes single station smaller, since there is defined number of products within the zone. Hence, the split of products available in the particular distance from the home location of the station differ between different solutions. Simulation model with the extended number of stations (5 and more) was not examined due to limitations of academic version of Arena. Storage policy section tests for the best station layout from previous section and different storage policies. In high and medium sale products no other policy than ‘within the aisle’ can be implemented, since the station has only one raw of the racks on the back of the picker. The storage policy models are created for low sale value products zone. The sub models are created in order to examine: within the aisle, across the aisle, diagonal, perimeter, and rectangular storage policies – the summary is given in the figure 4.
Reference
Reference
Tunnel
U-shape
Fig. 2. Stations layouts. available in the hand area, while under U-shape station are 18 bins are available. The dropping and
Fig. 4. Storage policies examined in the slow moving products zone. The within the aisle policy is similar to across the aisle, but the products are arranged vertically instead of horizontally. The simulation models differ from
each other, in the percentages of picks available in different areas available from the home base of the station. Within the picking policy section, a sub model with implemented batching policy in high sale products zone is examined. Due to batching process the simulation model for batched orders does not consider times associated with some of the activities which are slack due to batching method. The time reduction is done due to the picker travels within the station to pick the product only once for two orders. The reduction is also due to putting activity all information about picked products on one picking list so it is picked only one time for two orders from the box. The simulation model contains additional process - separation of picked quantity for orders that being batched. The model logic attaches an attribute to each order, which states whether the order is being the second order from the batch. The variable that might be changed is the percentage of order lines that is being considered as a batched. The simulation model was examined under assumed value of 33 %. Rising up the variable value will improve system performance. Replenishment process sub model examines kanban system with implemented economical order quantity (EOQ) and reorder point (ROP). The simulation model gathers the data about the frequency of needed from previous sections. Each time when the product stock drops below ROP the system sends the information of needs to replenish within EOQ. The replenishment process considers the transportation of goods from central warehouse to the replenishment zone. The Arena Process analyzer is used in order to determine number of staff needed. The established goal is to keep short lead time: 1,5 hour. The sorting solution sub model brings the best solution from previous sections and adds the sorting logic. The sorting logic is required in order to provide smooth movement of boxes between stations. There are two models created: one which arranges all the station in raw – reference sorting solution, and second which provides equal workload between stations. The solution continuously examines the cumulated zone and single station line lengths and chooses the one with the minimum values. The examination is possible due to implementation of variables which are monitored online by system logic. The building and verification activities for each of the model sections are conducted simultaneously. This means that a specific section is not considered until it is not entirely verified. All codes and features needed by the simulation program had to be thoroughly inspected in order to check for logic and syntax errors. Arena has a built-in function that automatically checks for syntax errors when trying to run a model. Arena verification was used in order to validate the code. After constructing the sub models, they are run for a specific time to see their behaviour in order to validate them. While running the sub models different things are checked. In order to make
sure about their behaviour, they were demonstrated to experts. For each of the station layout, simulation is run for the time equal to the two shifts work – 15 effective working hours (EWH). In order to avoid the influence of the statistical disturbances each simulation is run 10 times, which is equal to ten working days. Both time values can be easily changed, simulating for the same time at different working conditions. Considering mail order company that every month changes the offer of a product, the simulation conditions examined cover half of the time for that particular line of the products which is sold. Proposed simulation time provides simulation credibility. The extended number of simulations does not influence the results significantly. The warm up period was not implemented since in reality when the work starts it should start with an empty system. Each picker has the same performance, including the failure rate. The simulation was limited by maximum number of entities / orders (150), due to academic version of Arena. The high sale products zone is simulated within the variable that, in average 4 % of boxes does not include any item to pick from the particular station. The characteristic might be changed. For detailed description of the simulation model and analysis see (Kawczynski, 2005). 4.
ANALYSIS AND RESULTS
Tunnel arrangement serves 0,5 % more orders than the U shape station, and 1,5 % more than reference arrangement. When considering average times, the best from considered solutions is U shape station. The detailed results are given in table 1. Table 1. The summary of the fast moving products zone simulation. Statistic Stations in the raw Time between arrivals (Random EXPO) General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker 1 utilization Picker 2 utilization Picker 3 utilization
The reference model 3 9,6
The tunnel 3 9,6
The U shape 3 9,6
5 519 27725
5 624 28253
5 593 28097
506,21 27,87 478,33
82,58 23,01 59,57
60,00 20,80 39,20
95,80% 94,76% 95,11%
80,12% 79,44% 80,19%
71,73% 71,12% 72,72%
The time between arrivals might decrease to 7,2 seconds, comparing to 9,6 seconds in case of reference solution. Therefore, the best solution for the picking activity in the fast moving products zone is the U shape station layout. The results of the analysis for the number of stations in the raw within the fast moving products zone are given in table 2. Time between arrivals of orders might be reduced from 32,3 seconds in case of single station up to 5,5 seconds for four stations arrangement. According to assumed demand data, the high sale products zone
Table 2. The summary of the simulation of number of stations within fast moving products zone 1 32,3
The U shape 2 3 32,3 32,3
4 32,3
1 598 6 753
1 689 7138
1 637 6 920
1663 7027
1437,18 33,37 1403,81
33,78 23,29 10,49
27,80 23,02 4,78
23,61 20,29 3,32
Table 4. The summary of the simulation of different storage policies within slow moving products zone
98,74% 36,23% 23,14% 15,45% 36,66% 23,27% 15,50% 23,41% 15,74% 15,77%
needs to be able to pack 56000 units of products per day. According to figure 5, it is possible to pack this quantity of units with nine lines within one station in the raw, this means hiring nine picking persons. The same amount of units might be packed with three lines of two stations in the raw. This solution requires six pickers. In order to serve the demand within three stations in the raw, two lines are required – six pickers. Respectively, the same situation requires almost only one line of the four stations in the raw arrangement. For the medium sale products zone the
Statistic Time between arrivals (Random EXPO) General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker 1 utilization
The horizonal model Within the Across the aisle aisle Rectangular Perimeter 14,6 14,6 14,6 14,6 3 664 3 772
3 643 3 751
3 679 3 787
3 673 3 781
160,08 13,91 146,17
219,62 14,11 205,51
386,92 14,45 372,47
348,21 14,26 333,96
77,25 13,07 64,17
95,20%
95,76%
97,47%
97,14%
88,93%
adjustment are depicted in figure 6. The diagonal policy provides lowest number of orders queuing in buffer and the lowest single picker utilization. This leads to the conclusion that the best from considered storage policies under our conditions is diagonal storage policy. In fast moving products zone, time 600.00
Average total time (left scale)
1400.00
Average waiting time (left scale)
600.00
20,000
4,100
Units out (right scale)
4,000 3,900
300.00
Units out
30,000
800.00
Average waiting time (left scale)
400.00
40,000
1000.00
4,200
Average total time (left scale)
500.00
50,000
Units out (right scale)
1200.00 Time (seconds)
60,000
Time (seconds)
1600.00
Diagonal 14,6
3 696 3 805
400.00
3,800
200.00
3,700
100.00
3,600
0.00 10,000
200.00 0.00
3,500 Within the aisle
Across the aisle
Rectangular
Perimeter
Diagonal
0 1
2
3 Stations in raw
4
Fig. 6. The storage policies main performance.
The U shape
Fig. 5. The performance summary. results appear to be similar as for top sale products zone. The best form considered is U-shape station. The number of stations in the raw is established to be two, since system does not require more capacity. The vertical and horizontal station arrangements in slow moving products zone serve the same amount of orders. The details are given in table 3. Table 3. The summary of the simulation of station layout within slow moving products zone Statistic Stations in the raw Time between arrivals (Random EXPO) General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker 1 utilization
The vertical model 1 14,2
The horizontal model 1 14.2
3 756 3 861
3 766 3 871
577,32 14,17 563,15
327,65 13,93 313,72
98,58%
97,15%
Adjusting of times between arrivals drops by 0,1 to 14,1 seconds in case of horizontal arrangement. Number of stations in the raw does not have to be examined since one station in the raw is able to pack
between arrivals batching policy offers around 0,5 % more orders served comparing to the system without implemented batching policy. The average total time is reduced by 60 % comparing to referenced system. The 15 % reduction of time is done in core picking activity. The average waiting time is reduced by 70 %. Single picker utilization in system with implemented batching policy is around 77 %, and comparing to system without batching policy it is been reduced. Within top sale products zone, after adjusting time between arrivals, the batching policy offers 19% improvements in amount of served orders. The line length is dropped in average by 38%. The replenishment process of the line need to be served by at least 69 persons. The number of staff is necessary in order to maintain short lead time and low stock level. The maximum number of “need to replenish orders” waiting in queue under this condition is 24. After considering implemented safety lead time, with 69 person hired the system will be replenished on time. The proposed sorting solution provides smoother workload between the pickers. The details of system performance are given in table 5. The queue reduction for medium and low sale products zone is lower because the utilization within these zones is also very low. High line
Units out
Statistic Stations in the raw Time between arrivals (Random EXPO) General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker 1 utilization Picker 2 utilization Picker 3 utilization Picker 4 utilization
desired amount of orders. Analysis of storage policies of low sale value products zone, depicts that under each storage policy the zone is able to serve more or less the same amount of orders. The all performance of each storage policy are depicted in table 4. The results of time between arrivals
Table 5. The summary of the simulation of sorting solutions. Statistic General Orders out Units out Times (seconds) Average total time Average picking time Average waiting time Resources Picker A1 utilization Picker A2 utilization Picker A3 utilization Picker A4 utilization Picker B1 utilization Picker B2 utilization Picker C1 utilization
Reference Proposed sorting solution sorting solution
9 254
9 268
55521
55609
259,3 33,6 225,7
237,9 33,9 204,0
95,1% 95,4% 94,7% 91,9% 71,9% 72,3% 55,7%
98,8% 95,1% 95,3% 92,5% 72,3% 72,5% 55,7%
reduction in top sale products zone is possible due to high utilization of the pickers. Moreover, higher than the average number of queuing orders to the first station in the top sale products zone, is the cause of implemented station selection rule. 5.
solution at the step i. This will expand the analysis and at the same time will totally exclude the probability of appearing at the situation described above.
CONCLUSION AND FURTHER RESEARCH
This paper showed a methodology to totally design order picking systems by means of simulation. The built simulation model permitted the comparison of the performance of different solutions in station layout, storage policies, picking process, replenishment process and sorting solution; and thus, choosing the best one. This five steps methodology permits in a simple way to test and determine the main order picking system features and thus design it totally. Analysis proved that the U-shape station layout offers 10 % better productivity comparing to tunnel layout, due to reduction of travelling distances. For the slow moving products zone, the diagonal storage policy provides the greatest reduction of time for picking. Through comparison of different picking policies, the benefits of batching algorithm were shown. The entire model highlighted benefits of the even workload distribution. Further research should be done to analyse sample of orders performance for particular batching algorithm. The influence of the picking list length might be also analyzed. Moreover, further research should be done in order to find out the model behaviour under extended simulation conditions. The above simulation approach is constrained by the limitations of academic version of Arena. Further research should be done in order to measure influence of different techniques of passing information: picking list, pick to voice, pick to screen, pick to light. Hence, further research might be done in order to establish likelihood of order errors under each of the solution. The errors have not been taken into consideration since in order picking systems they mostly depend on human factor. The next step of the analysis will include “matrix analysis” – enhanced analysis of combined solutions, which will exclude the probability that poor solution of step i produces finally a better solution at the step i+1 than the better
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