- Email: [email protected]
Design of RFID-enabled Aircraft Reverse Logistics Network Simulation
7th IFAC Conference on Manufacturing Modelling, Management, and Control International Federation of Automatic Control June 19-21, 2013. Saint Petersburg, Russia
Design of RFID-enabled Aircraft Reverse Logistics Network Simulation Kehinde O. Adetiloye*. Anjali Awasthi** Amin Hammad*** *Concordia Institute of Information Systems Engineering, Concordia University, Montreal QC H3G 1M8 Canada (Tel: 438993-3884; e-mail: k_adetil@ encs.concordia.ca). **Concordia Institute of Information Systems Engineering, Concordia University, Montreal QC H3G 1M8 Canada (Tel: 514848-2424; ext. 5622; e-mail: [email protected]). *** Concordia Institute of Information Systems Engineering, Concordia University, Montreal QC H3G 1M8 Canada (Tel: 514-848-2424; ext. 5800; e-mail: [email protected]).
Abstract: The reverse logistics (RL) of aircrafts pose a big challenge to its owners due to the complexity of its RL network, and the inherit problems of realizing a reliable system for efficiently monitoring and tracking the numerous parts of end-of-life (EOL) aircrafts in the RL network. Radio frequency identification (RFID) technology, through its automatic and wireless data capture capability, offers great potential for counteracting this problem. Widespread and cost-effective, traditional barcode systems, unlike RFID technology, requires manual scanning and line-of-sight for its use. In this research, we performed simulated studies of barcode tags, used at case-level, and passive tags used at item-level, case-level and pallet-level in the EOL aircraft RL network in order to comparatively analyze the Return-On-Investment (ROI) of RFID technology relative to bar-coding. The results of our study demonstrate that the use of RFID technology in EOL aircraft RL network offers great potential compared to barcode technology; however, the high initial investment cost of RFID technology deployment may necessitate proper planning, such as business process re-engineering (BPR), tag reuse and phased implementation, to achieve more positive ROI. Keywords: EOL aircrafts, reverse logistics, Radio Frequency Identification (RFID), simulation model, Return-On-Investment (ROI) 1. INTRODUCTION In the past few years, industrial trends and research directions have revealed, renewed and invigorated interests in reverse logistics (RL). These interests are spurred by the rising global awareness on the need for environmental protection, which has resulted in the enactment of diverse environmental protection laws (e.g. EU Directive 2000/53/EC) by Governments as well as influx of investments aimed at mitigating the impact of environmental pollution and greenhouse effect.
Manufacturer
Retail shops
Distribution Center
Collection Point
Forward Logistics
Reuse Remanufacture Recycle Dispose
Process facilities Reverse Logistics
Product flow
When compared to forward logistics (FL), RL presents more complicated network due to the uncertainties inherent in product returns, complex nature of re-processing, and high implementation costs of RL systems. Hence, optimization of RL networks (e.g. collection-points maximization and cost minimization), and development of efficient information management systems (IMS) is required. RL differs from FL also in the way information and product flow in the logistics networks, as shown in Figure 1. In FL, information and products flow in opposite direction in between the manufacturers, distribution centers and retailers while in RL information and product flow in the same direction in between the process facilities, distribution centers and collection points. 978-3-902823-35-9/2013 © IFAC
Distribution center
Information Flow
Figure 1: Information and product flow In regards to the development of efficient Information Management Systems (IMS) for RL network, Radio Frequency Identification (RFID) technology is gaining increasing popularity as foremost support technology for solving RL network problems. Its use is prevalent in research studies and in industries in both pilot and full scale implementations. RFID, through its automatic wireless and data capture capabilities, can expedite the process of collecting data and identifying returned products, enhance traceability of returned products, reduce mismatch between 289
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2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
physical inventory and information inventory, eliminate time consuming process that can result from use of manual barcode technology instead of RFID, and enhance the information sharing across the entire RL network. In turn, these process improvements, resulting from RFID technology integration, can lead to significant gain in savings from costs due to shrinkage, inventory inaccuracy and mismatch, and manual operations.
2.2 RFID-enabled aviation logistics Most of the research on RFID-enabled avaition logistics are in the area of aircraft Maintenance-Repair-Operations (MRO) e.g. Chang et al (2006), Harun et al. (2008), Ramudhin et al. (2008), and Swedberg, 2012. A recent study on RFIDenabled aircraft RL simulation is presented in Adetiloye (2012). 2.3 RFID-enabled Logistics Simulations
End-of-life (EOL) products consist of parts that can be reused, remanufactured, recycled or properly disposed at the end of a product lifecycle. Hence, the RL of EOL products is important to ensure sustainability and reduction of environmental pollution.
A number of studies use simulations to analyze problems in RFID-enabled supply chains e.g. inventory inaccuracy and bullwhip effect, and justify investment in RFID-enabled supply chains. These studies include the works of Lee et al. (2004), Fleisch and Tellkamp (2005), Al Kattan and AlKhudairi (2007), Tu et al.(2009), Karagiannaki (2010), and Su and Roan (2011).
The RL network of most EOL products (Conventional RL) is characterized by large number of collection centres due to the uncertainty of product returns. For EOL aircraft (Aircraft RL), however, the complex nature of its logistic network, the high cost of constructing the network, and the fairly predictable return rates of EOL aircrafts trivialize the need for constructing many collection centres.
3. PROBLEM DESCRIPTION In order to justify investment of RFID technology in EOL aircraft RL network, the problem investigated in this paper involves assessing Return-On-Investment (ROI) of RFID technology relative to barcode technology in a hypothesized aircraft RL network.
A project for the eco-efficient management of End of-LifeAircraft, the PAMELA-LIFE project, was started by Airbus in 2005 (Airbus, 2008). Also, a best management practice manual for recycling of EOL aircraft has been published by the Aircraft Fleet Recycling Association (AFRA) (AFRA, 2009).
4. METHODS We conduct a discrete event simulation based study to assess the ROI of RFID technology vs barcode. A hypothetical aircraft RL network is considered. Figure 2 presents a hypothesized as-is process map in which case-level barcode tagging is employed and the routing decision is based on aircraft part (AP) types and RL types. The (AP) types are materials, components, air-frame systems, avionics and power systems (Airframer index, 2012) and the RL types are reuse, remanufacture, recycle, and dispose. The as-is process map serves as the baseline or as-is model.
2. RELATED WORKS 2.1 RFID-enabled reverse logistics Lee and Chan (2009) proposed an RFID-based RL framework based on Genetic Algorithm (GA), a metaheuristic approach, to determine locations of collection points that maximize the coverage of customers. RFID was used to collect data on returned products for the GA. Trappey et al. (2010) presented a forecasting and decision support system, developed with Fuzzy Cognitive Map (FCM) and GA, for RFID-based RL system. They used RFID technology to collect real-time data for the operation of the system. Arrival of EOL Aircraft
Start
Disassemble Aircraft
Sort parts
Pack multiple parts into cases
Attach barcodes with AP and RL types information
Move to physical store
Pick cases
Manually scan cases and update information store
Add cases to pallets
Ship pallets
Manually scan cases and update information store
Reuse Arrive at Reuse Depot
Unload pallets
Manually scan cases and update information store
Move cases to physical store
Pick cases
Manually scan cases and update information store
Unpack cases
Prepare parts for process line
Process parts according to part’s information
Move parts to physical store
Arrive at Remanufacturing Plant
Unload pallets
Manually scan cases and update information store
Move to cases physical store
Pick cases
Manually scan cases and update information store
Unpack cases
Prepare parts for process Line
Process parts according to part’s information
Move parts to physical store
Arrive at Recycling Plant
Unload pallets
Manually scan cases and update information store
Move cases to physical store
Pick cases
Manually scan cases and update information store
Unpack cases
Prepare parts for process Line
Process parts according to part’s information
Move parts to physical store
Arrive at Disposition Plant
Unload pallets
Move cases to physical store
Pick cases
Manually scan cases and update information store
Unpack cases
Prepare parts for process Line
Dispose part according to part’s information
RL type? Remanufacture
Recycle
Dispose
Manually scan cases and update information store
Figure 2: Process map for case-level barcode tagging 290
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2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
Through qualitative analysis of the reference process map in Figure 2, we identified the Business Process Reengineering (BPR) (Banks et al., 2007) required to develop three similar process maps that employ passive RFID tags used at itemlevel, case- level and pallet-level. The four process maps are then simulated in Arena simulation software to analysis the ROIs.
4.5 Cost calculations According to Banks et al. (2007), the cost components of RFID technology can be classified as RFID and Wi-Fi hardware (H/W) costs, network and application H/W and software(S/W) costs, integration costs, and personnel costs (Banks et al., 2007). For the EOL aircraft RL network, the network and application H/W and S/W costs include the cost of a 3-tier client-server architecture configuration with web server, application server, and database server. The capacities of the servers need to be properly determined by experts to ensure that the servers can adequately meet the demands of data-processing and service requests from client computers.
4.1 Process map for case-level barcode tagging The process map for the EOL aircraft RL with case-level barcode (CLB) tagging is shown in Figure 2. In the system, barcode tags are attached to cases containing multiple parts after the process of disassembling, sorting and casing, and are scanned manually with barcode scanners for identification, tracking, and inventory-level monitoring.
For the EOL aircraft RL network, the total cost is calculated as:
4.2 Item-level RFID tagging
Total cost = operating costs + overhead costs = (ICi=0 + VCi) + (VACi + WCi + NVACi)
The BPR we identified for item-level RFID (ILR) and applied to the baseline model in Figure 2 are:
(1)
Where, ICi=0 is the investment for year i = 0, i Ti, where Ti is a set of yearly periods, VCi is the variable cost for period i VACi is the value added cost for period i WCi is the waiting cost for period i NVACi is the non-value added cost for period i
(i) One part is packed per case, (ii) Passive RFID tags, rather than barcodes, are attached to cases containing single part after the process of disassembling, sorting and casing, (iii) RFID tag contain information about the single part, (iv) Data on RFID tags are automatically read and stored in information store, (v) Inventory management is enhanced with RFID technology, (vi) Transport logistics is enhanced with RFID technology (vii) Processing of parts on process lines is enhanced with RFID technology
For the operating cost, VCi = Cm * Q m for i in Ti (2) Where Cm is the unit cost of RFID technology component m, and Q m is the quantity of m. Since Cm is a constant and Qm is a variable, VCi varies with Qm.
4.3 Case-level RFID tagging The BPR we identified for case-level RFID (CLR) tagging and applied to the baseline model in Figure 2 are:
For the overhead cost, VACi for a given period i is calculated as:
(i) Two or more similar parts are packed per case, (ii) Passive RFID tags are attached to cases containing multiple parts after the process of disassembling, sorting and casing, (iii) RFID tags contain information about the multiple part in cases, (iv) Data on RFID tags are automatically read and stored on information store, (v) Inventory management is enhanced with RFID technology, (vi) Transport logistics is enhanced with RFID technology, (vii) Processing of parts on process lines is enhanced with RFID technology
VACi = h x VATi + ( ∑ ) x VATi + ( ∑ = (h + ∑ ) x VATi + ( ∑ ) (Rossetti, 2010; pg 452)
) (3)
Where, h is the holding cost rate VATi is the value added time for period i uj is the usage cost associated with jth resource used during an activity bj is the busy cost associated with jth resource used during an activity Ri is the set of resources used by the entity in an activity period Waiting cost, WCi and non-value-added cost, NVACi for a given period i are calculated as:
4.4 Pallet-level RFID tagging The BPR we identified for pallet-level RIFD (PLR) and applied to the baseline model in Figure 2 are: (i) One or more similar parts are packed per case (ii)Passive RFID tags are attached to pallets with multiple cases after the process of disassembling, storing, sorting, casing and picking, (iii) RFID tags contain information about the multiple cases in pallets, (iv) Data on RFID tags are automatically read and stored in information store, (v) Inventory management is enhanced with RFID technology, (vi) Transport logistics is enhanced with RFID technology, (vii) Processing of parts on process lines is enhanced with RFID technology
WCi = h x WTi
(4)
NVACi = h x NVATi Where, WTi is the waiting time for period i NVATi is the non-value add time for period i
(5)
VACi, WCi, and NVACi are obtained as results in Arena simulation software..
1
Parts, particularly the reuse and remanufacture types, are cased to reduce interference and absorption of RFID signals,and protect them for damage.
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2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
5. NUMERICAL APPLICATION
Resource cost represents the cost of utilizing resources in the RL network. The resource costs are given in Table 2.
The numerical application demonstrated in this section uses hypothetical values which are presented for illustration purposes only. Based on a number of constraints and assumption, cost components, parts and time distributions, the process maps were simulated using Arena simulation software.
Table 2: Resource cost Resource Disassembly machine Processing plant RFID reader Barcode scanner Human labour Shippers Movers Disassembly machine
In eight hours daily operations lasting 356 days (2848 hours), EOL aircrafts arrived at a collection center exponentially at the rate of 1 aircraft per 20 days. Each aircraft was parted into 10% materials, 20% components, 20% airframes, 30% avionics and 20% power systems. Each AP type has four RL types with 25% being reused, 25% remanufactured, 25% recycled, and 25% disposed. 5.2 Constraints and assumptions Constraints 1.
Experiment is conducted in a lab environment with simulation models Disassembly, inventory, shipping, receiving and process facilities already exist; hence, their setup costs are not included in the ROI analyzes.
2.
All parts are contained in special casing that helps to protect the parts and prevent interference and absorption of RFID signals.
3.
Passive tags are used in the to-be systems.
4.
Data on RFID tag memory is shared across the EOL aircraft RL network.
5.
Simulation clock runs 8 hours per day for 365 days (Rosetti, 2010, Kelton et al., 2009)
Per usage cost (uj) ($) 10
100 5 40 40 50 15 100
10 2 10 10 10 5 10
5.6 Time distributions The time distributions for processing CLB, ILR, CLR, and PLR tagging are presented in Table 3 and Table 4.
Assumptions: 1.
Busy cost per hour (bj) ($/hr) 100
Table 3: Time distributions for processing case-level barcode tagging Processes Disassembly Sorting of parts Casing of parts Attach barcodes with RL types information Manually scan barcode and add data to information store Move case parts to physical store Pick cases Add cases to pallet Ship pallets Arrive at reuse depot Unload pallets Unpack cases Prepare parts for process lines Process parts according to part's information
5.3 Investment cost The investment cost in year 0 is $1,026,700 for barcode technology and $1,062,700 for RFID technology. 5.4 Variable cost The variable cost includes mainly the cost of tags used each year. The tag cost for CLB, ILR, CLR, and PLR tagging are presented in Table 1. Table 1: Tag costs for case-level barcode (CLB), item-level RFID (ILR), case-level RFID (CLR) and pallet-level RFID (PLR).
Time distributions TRIA(5,6,7) TRIA(5,8,10) TRIA(15,20,25)
Time Unit days minutes minutes
TRIA(5,8,10)
minutes
TRIA(3,5,7)
minutes
TRIA(20,22,25)
minutes
TRIA(20,22,25) TRIA(20,22,25) TRIA(20,22,25) EXPO(10) TRIA(20,22,25) TRIA(20,22,25)
minutes minutes minutes minutes minutes minutes
TRIA(20,22,25)
minutes
TRIA(20,22,25)
minutes
5.5 Resource cost Components CLB ILR CLR PLR
Unit cost ($) 0.5 10 10 10
Quantity 1,500 1,500 750 375
Keys:
Total cost ($) 750 15,000 7,500 3,750
TRIA (Min, Mode, Max) - Triangular distribution with minimum, modal, and maximum values EXPO (Mean) - Exponential distribution with mean value
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2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
Table 4 Time distributions for processing item-level RFID (ILR), case-level RFID (CLR) and pallet-level RFID (PLR) tagging. Time Unit days minutes minutes minutes
TRIA(5,8,10)
seconds
TRIA(20,22,25)
minutes
TRIA(20,22,25) TRIA(20,22,25) TRIA(20,22,25) EXPO(10) TRIA(20,22,25) TRIA(20,22,25)
minutes minutes minutes minutes minutes minutes
5
15000
3.7
x 10
3.6
3.5 Value added cost
10000 Variable cost
5000
3.4
3.3
3.2
0 CLB
ILR CLR Technology tagging levels
3.1 CLB
PLR
ILR
CLR
PLR
CLR
PLR
Tagging levels
a)
b) 5
3.7
3400
TRIA(20,22,25)
minutes
3200
x 10
3.6
3000 3.5
2800
TRIA(20,22,25)
minutes
Overhead cost
Disassembly Sorting of parts Casing of parts Attaching of RFID tags Automatically read data and update information store Move cased parts to physical store Pick cases Add cases to pallet Ship pallets Arrive at reuse depot Unload pallets Unpack cases Prepare parts for process lines with special RFID technology Process parts according to part's information
Time distributions TRIA(5,6,7) TRIA(5,8,10) TRIA(15,20,25) TRIA(5,8,10)
Wait cost
Processes
The ROI calculations for CLR tagging are shown in Table 5, and a summary of all the results in which the variable cost, valued added cost, wait cost and overhead cost of ILR, CLR and PLR tagging are compared with CLB tagging is presented in Table 6. The results show negative ROI values for all the tagging levels. The negative value means that these projects are not profitable. The low negative value is due to the high cost, formed mainly by initial investment cost, relative to benefit. PLR tagging has the most positive ROI, followed by CLR tagging and ITR tagging.
2600 2400
3.4
3.3
2200 3.2
2000 1800 CLB
5. EXPERIMENTAL RESULTS
3.1 CLB
ILR
CLR
ILR
PLR
Tagging levels
Tagging levels
c)
Plots of variable cost, value added cost, wait cost and overhead costs for CLB, ILR, CLR and PLR for the numerical problem is shown in Figure 3.
d)
Figure 3: Plot of a) Variable cost b) Value added cost c) Wait cost d) Overhead cost
Table 5: ROI calculation for case-level RFID (CLR) tagging Year Discount rate = 5% Cost ($) Investment cost Variable cost (Tags and labels) Overhead cost Total= Discounted cost
0 1
1 0.9524
2 0.907
3 0.8638
4 0.0829
5 0.7835
1,062,450
0
0
0
0
0
0 0 1,062,450
7,500 334,808 342,308
7,500 334,808 342,308
7,500 334,808 342,308
7,500 334,808 342,308
7,500 334,808 342,308
1,062,450.00
326,014.14
310,473.36
295,685.65
28,377.33
268,198.32
31,896.95
31,896.95
31,896.95
31,896.95
31,896.95
31,896.95
Savings from delays Savings from correcting errors on entered data
279 5000
279 5000
279 5000
279 5000
279 5000
279 5000
Savings from shrinkage
5000
5000
5000
5000
5000
5000
Total =
42175.51
42175.51
42175.51
42175.51
42175.51
42175.51
Discounted benefit
40,167.96
38,253.19
36,431.21
3,496.35
33,044.51
40,167.96
Benefits Savings from data capturing
ROI =
-0.932 293
2,291,198.80
151,393.21
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
Table 6: Summary of results Performance measured relative to case-level barcode tagging Tagging levels
Variable cost
Value added cost
Wait cost
Overhead cost
ROI
Case-level barcode tagging
750
344,665
2,231
346,896
-0.931
Item-level RFID tagging
1900% increase
0.06% increase
0.06% decrease
0.07 % increase
-0.938
Case-level RFID tagging
900% increase
0.09% decrease
0.12% decrease
0.08% decrease
-0.932
Pallet-level RFID tagging
400% increase
0.07% decrease
0.47% increase
0.06% decrease
-0.931
6. CONCLUSIONS Fleisch, E. and Tellkamp C. (2005). Inventory inaccuracy and supply chain performance: a simulation study of a retail supply chain. International Journal of Production Economics, 95, pp. 373-385. Harun, K., Cheng, K. and Wibbelmann, M. (2008). RFIDenabled aerospace manufacturing: theoretical models, simulation and implementation issues. Proceedings of the IEEE IEEM, Singapore, pp. 1824 - 1829. Karagiannaki, A., Katerina, P. and Doukidis G. (2010). Using simulation to design and evaluate RFID implementation in the supply chain. Proceedings of Operation Research Society Simulation Workshop. Worcestershire. Lee, C.K.M and Chan, T.M. (2009). Development of RFIDbased reverse logistics system. Expert Systems with Applications, 36, pp. 9299-9307. Lee, Y.M., Cheng, F. and Leung, Y.T. (2004). Exploring the impact of RFID on supply chain dynamics. Proceedings of the 2004 Winter Simulation Conference, Washington, pp. 1145-1152. Leung, Y.T, Cheng, F., Lee, Y. M. and Hennessy, J.J. (2007). A tool set for exploring the value of RFID in a supply chain in trends in: Supply chain design and management. Springer Series in Advanced Manufacturing, pp. 49-70. Ramudhin, A., Paquet, M., Artiba, A.,Dupré, P., Varvaro, D., and Thomson, V. (2008). A generic framework to support the selection of an RFID-based control system with application to the MRO activities of an aircraft engine manufacturer. Production Planning and Control, 19(2), pp. 183-196. Rossetti, M. (2010). Simulation modeling and Arena. Hoboken: John Wiley & Sons. Su, Y. and Roan, J. (2011). Investigating the impacts of RFID application on supply chain dynamics with chaos theory. WSEAS Transaction on Information Science and Application, 8(1). Swedberg, C. (2012, January 11). Boeing to launch RFID programs for Airlines in Febuary. Retrieved March 17, 2013, from RFID Journal: http:// www. Rfidjournal .com / article /view/9107 Tu, Y., Zhou, W. and Piramuthu, S. (2009). Identifying RFID-embedded objects in pervasive healthcare applications. Decision Support Systems, 46, pp. 586-593
In this paper, we present a discrete event simulation based approach to determine the ROIs for RFID on a hypothesized EOL aircraft RL network, with barcode tags used in the as-is model at case-level and RFID tags used in the to-be models at item, case and pallet levels. The experimental results showed that RFID has greater potential to reduce the running costs of the network than barcode. Also, the ROIs are low due to the high initial investment costs compared to the savings. Since the results are derived from hypothetical data, they are currently useful for academic studies, not industrial. Interested readers can find complete details of the presented work in Adetiloye (2012). In future works, our simulations will be validated with industrial data, and the impacts of tag reuse, BPR, and phase implementation on the ROIs will be investigated. REFERENCES
Adetiloye, K.O. (2012). Design of RFID-enabled aircraft reverse logistics network simulations. Master’s thesis. Retrieved from Spectrum Concordia University Research Repository. Airbus. (2008). Eco-efficient ways to manage end-of-life aircraft. Accessed April 8, 2013 from http://videos. airbus.com/ video/iLyROoafIOU9.html Airframer Index. (2012). Boeing 787 Dreamliner. Retrieved 04 16, 2012, from http://www.airframer.com /pdf _progs/airframer_ prog8.pdf. Aircraft Fleet Recycling Association (AFRA). (2009). Best management practice for management of used aircraft parts and assemblies version 2.0. AFRA. Retrieved April 2, 2012, from http://www.afraassociation.org /AFRAPartsBMPv2_2.pdf Al Kattan, I. and Al-Khudairi, T. (2007), Improving Supply Chain Management effectiveness using RFID, 2007 IEEE International Engineering Management Conference, pp.191-198. Banks, J., Hanny, D., Pachano, M.A and Thompson, L.G. (2007). RFID Applied. John Wiley & Sons, Inc. Chang, Y.S, Chang, H.O., Whang, Y.S., Lee, J.J, Kwon, J.A, Kang, M.S and Park, J.S. (2006). Development of RFID enabled aircraft maintenance system. Proceedings of IEEE International Conference on Industrial Informatics, Singapore, pp. 224-229.
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Design of RFID-enabled Aircraft Reverse Logistics Network Simulation Kehinde O. Adetiloye*. Anjali Awasthi** Amin Hammad*** *Concordia Institute of Information Systems Engineering, Concordia University, Montreal QC H3G 1M8 Canada (Tel: 438993-3884; e-mail: k_adetil@ encs.concordia.ca). **Concordia Institute of Information Systems Engineering, Concordia University, Montreal QC H3G 1M8 Canada (Tel: 514848-2424; ext. 5622; e-mail: [email protected]). *** Concordia Institute of Information Systems Engineering, Concordia University, Montreal QC H3G 1M8 Canada (Tel: 514-848-2424; ext. 5800; e-mail: [email protected]).
Abstract: The reverse logistics (RL) of aircrafts pose a big challenge to its owners due to the complexity of its RL network, and the inherit problems of realizing a reliable system for efficiently monitoring and tracking the numerous parts of end-of-life (EOL) aircrafts in the RL network. Radio frequency identification (RFID) technology, through its automatic and wireless data capture capability, offers great potential for counteracting this problem. Widespread and cost-effective, traditional barcode systems, unlike RFID technology, requires manual scanning and line-of-sight for its use. In this research, we performed simulated studies of barcode tags, used at case-level, and passive tags used at item-level, case-level and pallet-level in the EOL aircraft RL network in order to comparatively analyze the Return-On-Investment (ROI) of RFID technology relative to bar-coding. The results of our study demonstrate that the use of RFID technology in EOL aircraft RL network offers great potential compared to barcode technology; however, the high initial investment cost of RFID technology deployment may necessitate proper planning, such as business process re-engineering (BPR), tag reuse and phased implementation, to achieve more positive ROI. Keywords: EOL aircrafts, reverse logistics, Radio Frequency Identification (RFID), simulation model, Return-On-Investment (ROI) 1. INTRODUCTION In the past few years, industrial trends and research directions have revealed, renewed and invigorated interests in reverse logistics (RL). These interests are spurred by the rising global awareness on the need for environmental protection, which has resulted in the enactment of diverse environmental protection laws (e.g. EU Directive 2000/53/EC) by Governments as well as influx of investments aimed at mitigating the impact of environmental pollution and greenhouse effect.
Manufacturer
Retail shops
Distribution Center
Collection Point
Forward Logistics
Reuse Remanufacture Recycle Dispose
Process facilities Reverse Logistics
Product flow
When compared to forward logistics (FL), RL presents more complicated network due to the uncertainties inherent in product returns, complex nature of re-processing, and high implementation costs of RL systems. Hence, optimization of RL networks (e.g. collection-points maximization and cost minimization), and development of efficient information management systems (IMS) is required. RL differs from FL also in the way information and product flow in the logistics networks, as shown in Figure 1. In FL, information and products flow in opposite direction in between the manufacturers, distribution centers and retailers while in RL information and product flow in the same direction in between the process facilities, distribution centers and collection points. 978-3-902823-35-9/2013 © IFAC
Distribution center
Information Flow
Figure 1: Information and product flow In regards to the development of efficient Information Management Systems (IMS) for RL network, Radio Frequency Identification (RFID) technology is gaining increasing popularity as foremost support technology for solving RL network problems. Its use is prevalent in research studies and in industries in both pilot and full scale implementations. RFID, through its automatic wireless and data capture capabilities, can expedite the process of collecting data and identifying returned products, enhance traceability of returned products, reduce mismatch between 289
10.3182/20130619-3-RU-3018.00231
2013 IFAC MIM June 19-21, 2013. Saint Petersburg, Russia
physical inventory and information inventory, eliminate time consuming process that can result from use of manual barcode technology instead of RFID, and enhance the information sharing across the entire RL network. In turn, these process improvements, resulting from RFID technology integration, can lead to significant gain in savings from costs due to shrinkage, inventory inaccuracy and mismatch, and manual operations.
2.2 RFID-enabled aviation logistics Most of the research on RFID-enabled avaition logistics are in the area of aircraft Maintenance-Repair-Operations (MRO) e.g. Chang et al (2006), Harun et al. (2008), Ramudhin et al. (2008), and Swedberg, 2012. A recent study on RFIDenabled aircraft RL simulation is presented in Adetiloye (2012). 2.3 RFID-enabled Logistics Simulations
End-of-life (EOL) products consist of parts that can be reused, remanufactured, recycled or properly disposed at the end of a product lifecycle. Hence, the RL of EOL products is important to ensure sustainability and reduction of environmental pollution.
A number of studies use simulations to analyze problems in RFID-enabled supply chains e.g. inventory inaccuracy and bullwhip effect, and justify investment in RFID-enabled supply chains. These studies include the works of Lee et al. (2004), Fleisch and Tellkamp (2005), Al Kattan and AlKhudairi (2007), Tu et al.(2009), Karagiannaki (2010), and Su and Roan (2011).
The RL network of most EOL products (Conventional RL) is characterized by large number of collection centres due to the uncertainty of product returns. For EOL aircraft (Aircraft RL), however, the complex nature of its logistic network, the high cost of constructing the network, and the fairly predictable return rates of EOL aircrafts trivialize the need for constructing many collection centres.
3. PROBLEM DESCRIPTION In order to justify investment of RFID technology in EOL aircraft RL network, the problem investigated in this paper involves assessing Return-On-Investment (ROI) of RFID technology relative to barcode technology in a hypothesized aircraft RL network.
A project for the eco-efficient management of End of-LifeAircraft, the PAMELA-LIFE project, was started by Airbus in 2005 (Airbus, 2008). Also, a best management practice manual for recycling of EOL aircraft has been published by the Aircraft Fleet Recycling Association (AFRA) (AFRA, 2009).
4. METHODS We conduct a discrete event simulation based study to assess the ROI of RFID technology vs barcode. A hypothetical aircraft RL network is considered. Figure 2 presents a hypothesized as-is process map in which case-level barcode tagging is employed and the routing decision is based on aircraft part (AP) types and RL types. The (AP) types are materials, components, air-frame systems, avionics and power systems (Airframer index, 2012) and the RL types are reuse, remanufacture, recycle, and dispose. The as-is process map serves as the baseline or as-is model.
2. RELATED WORKS 2.1 RFID-enabled reverse logistics Lee and Chan (2009) proposed an RFID-based RL framework based on Genetic Algorithm (GA), a metaheuristic approach, to determine locations of collection points that maximize the coverage of customers. RFID was used to collect data on returned products for the GA. Trappey et al. (2010) presented a forecasting and decision support system, developed with Fuzzy Cognitive Map (FCM) and GA, for RFID-based RL system. They used RFID technology to collect real-time data for the operation of the system. Arrival of EOL Aircraft
Start
Disassemble Aircraft
Sort parts
Pack multiple parts into cases
Attach barcodes with AP and RL types information
Move to physical store
Pick cases
Manually scan cases and update information store
Add cases to pallets
Ship pallets
Manually scan cases and update information store
Reuse Arrive at Reuse Depot
Unload pallets
Manually scan cases and update information store
Move cases to physical store
Pick cases
Manually scan cases and update information store
Unpack cases
Prepare parts for process line
Process parts according to part’s information
Move parts to physical store
Arrive at Remanufacturing Plant
Unload pallets
Manually scan cases and update information store
Move to cases physical store
Pick cases
Manually scan cases and update information store
Unpack cases
Prepare parts for process Line
Process parts according to part’s information
Move parts to physical store
Arrive at Recycling Plant
Unload pallets
Manually scan cases and update information store
Move cases to physical store
Pick cases
Manually scan cases and update information store
Unpack cases
Prepare parts for process Line
Process parts according to part’s information
Move parts to physical store
Arrive at Disposition Plant
Unload pallets
Move cases to physical store
Pick cases
Manually scan cases and update information store
Unpack cases
Prepare parts for process Line
Dispose part according to part’s information
RL type? Remanufacture
Recycle
Dispose
Manually scan cases and update information store
Figure 2: Process map for case-level barcode tagging 290
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Through qualitative analysis of the reference process map in Figure 2, we identified the Business Process Reengineering (BPR) (Banks et al., 2007) required to develop three similar process maps that employ passive RFID tags used at itemlevel, case- level and pallet-level. The four process maps are then simulated in Arena simulation software to analysis the ROIs.
4.5 Cost calculations According to Banks et al. (2007), the cost components of RFID technology can be classified as RFID and Wi-Fi hardware (H/W) costs, network and application H/W and software(S/W) costs, integration costs, and personnel costs (Banks et al., 2007). For the EOL aircraft RL network, the network and application H/W and S/W costs include the cost of a 3-tier client-server architecture configuration with web server, application server, and database server. The capacities of the servers need to be properly determined by experts to ensure that the servers can adequately meet the demands of data-processing and service requests from client computers.
4.1 Process map for case-level barcode tagging The process map for the EOL aircraft RL with case-level barcode (CLB) tagging is shown in Figure 2. In the system, barcode tags are attached to cases containing multiple parts after the process of disassembling, sorting and casing, and are scanned manually with barcode scanners for identification, tracking, and inventory-level monitoring.
For the EOL aircraft RL network, the total cost is calculated as:
4.2 Item-level RFID tagging
Total cost = operating costs + overhead costs = (ICi=0 + VCi) + (VACi + WCi + NVACi)
The BPR we identified for item-level RFID (ILR) and applied to the baseline model in Figure 2 are:
(1)
Where, ICi=0 is the investment for year i = 0, i Ti, where Ti is a set of yearly periods, VCi is the variable cost for period i VACi is the value added cost for period i WCi is the waiting cost for period i NVACi is the non-value added cost for period i
(i) One part is packed per case, (ii) Passive RFID tags, rather than barcodes, are attached to cases containing single part after the process of disassembling, sorting and casing, (iii) RFID tag contain information about the single part, (iv) Data on RFID tags are automatically read and stored in information store, (v) Inventory management is enhanced with RFID technology, (vi) Transport logistics is enhanced with RFID technology (vii) Processing of parts on process lines is enhanced with RFID technology
For the operating cost, VCi = Cm * Q m for i in Ti (2) Where Cm is the unit cost of RFID technology component m, and Q m is the quantity of m. Since Cm is a constant and Qm is a variable, VCi varies with Qm.
4.3 Case-level RFID tagging The BPR we identified for case-level RFID (CLR) tagging and applied to the baseline model in Figure 2 are:
For the overhead cost, VACi for a given period i is calculated as:
(i) Two or more similar parts are packed per case, (ii) Passive RFID tags are attached to cases containing multiple parts after the process of disassembling, sorting and casing, (iii) RFID tags contain information about the multiple part in cases, (iv) Data on RFID tags are automatically read and stored on information store, (v) Inventory management is enhanced with RFID technology, (vi) Transport logistics is enhanced with RFID technology, (vii) Processing of parts on process lines is enhanced with RFID technology
VACi = h x VATi + ( ∑ ) x VATi + ( ∑ = (h + ∑ ) x VATi + ( ∑ ) (Rossetti, 2010; pg 452)
) (3)
Where, h is the holding cost rate VATi is the value added time for period i uj is the usage cost associated with jth resource used during an activity bj is the busy cost associated with jth resource used during an activity Ri is the set of resources used by the entity in an activity period Waiting cost, WCi and non-value-added cost, NVACi for a given period i are calculated as:
4.4 Pallet-level RFID tagging The BPR we identified for pallet-level RIFD (PLR) and applied to the baseline model in Figure 2 are: (i) One or more similar parts are packed per case (ii)Passive RFID tags are attached to pallets with multiple cases after the process of disassembling, storing, sorting, casing and picking, (iii) RFID tags contain information about the multiple cases in pallets, (iv) Data on RFID tags are automatically read and stored in information store, (v) Inventory management is enhanced with RFID technology, (vi) Transport logistics is enhanced with RFID technology, (vii) Processing of parts on process lines is enhanced with RFID technology
WCi = h x WTi
(4)
NVACi = h x NVATi Where, WTi is the waiting time for period i NVATi is the non-value add time for period i
(5)
VACi, WCi, and NVACi are obtained as results in Arena simulation software..
1
Parts, particularly the reuse and remanufacture types, are cased to reduce interference and absorption of RFID signals,and protect them for damage.
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5. NUMERICAL APPLICATION
Resource cost represents the cost of utilizing resources in the RL network. The resource costs are given in Table 2.
The numerical application demonstrated in this section uses hypothetical values which are presented for illustration purposes only. Based on a number of constraints and assumption, cost components, parts and time distributions, the process maps were simulated using Arena simulation software.
Table 2: Resource cost Resource Disassembly machine Processing plant RFID reader Barcode scanner Human labour Shippers Movers Disassembly machine
In eight hours daily operations lasting 356 days (2848 hours), EOL aircrafts arrived at a collection center exponentially at the rate of 1 aircraft per 20 days. Each aircraft was parted into 10% materials, 20% components, 20% airframes, 30% avionics and 20% power systems. Each AP type has four RL types with 25% being reused, 25% remanufactured, 25% recycled, and 25% disposed. 5.2 Constraints and assumptions Constraints 1.
Experiment is conducted in a lab environment with simulation models Disassembly, inventory, shipping, receiving and process facilities already exist; hence, their setup costs are not included in the ROI analyzes.
2.
All parts are contained in special casing that helps to protect the parts and prevent interference and absorption of RFID signals.
3.
Passive tags are used in the to-be systems.
4.
Data on RFID tag memory is shared across the EOL aircraft RL network.
5.
Simulation clock runs 8 hours per day for 365 days (Rosetti, 2010, Kelton et al., 2009)
Per usage cost (uj) ($) 10
100 5 40 40 50 15 100
10 2 10 10 10 5 10
5.6 Time distributions The time distributions for processing CLB, ILR, CLR, and PLR tagging are presented in Table 3 and Table 4.
Assumptions: 1.
Busy cost per hour (bj) ($/hr) 100
Table 3: Time distributions for processing case-level barcode tagging Processes Disassembly Sorting of parts Casing of parts Attach barcodes with RL types information Manually scan barcode and add data to information store Move case parts to physical store Pick cases Add cases to pallet Ship pallets Arrive at reuse depot Unload pallets Unpack cases Prepare parts for process lines Process parts according to part's information
5.3 Investment cost The investment cost in year 0 is $1,026,700 for barcode technology and $1,062,700 for RFID technology. 5.4 Variable cost The variable cost includes mainly the cost of tags used each year. The tag cost for CLB, ILR, CLR, and PLR tagging are presented in Table 1. Table 1: Tag costs for case-level barcode (CLB), item-level RFID (ILR), case-level RFID (CLR) and pallet-level RFID (PLR).
Time distributions TRIA(5,6,7) TRIA(5,8,10) TRIA(15,20,25)
Time Unit days minutes minutes
TRIA(5,8,10)
minutes
TRIA(3,5,7)
minutes
TRIA(20,22,25)
minutes
TRIA(20,22,25) TRIA(20,22,25) TRIA(20,22,25) EXPO(10) TRIA(20,22,25) TRIA(20,22,25)
minutes minutes minutes minutes minutes minutes
TRIA(20,22,25)
minutes
TRIA(20,22,25)
minutes
5.5 Resource cost Components CLB ILR CLR PLR
Unit cost ($) 0.5 10 10 10
Quantity 1,500 1,500 750 375
Keys:
Total cost ($) 750 15,000 7,500 3,750
TRIA (Min, Mode, Max) - Triangular distribution with minimum, modal, and maximum values EXPO (Mean) - Exponential distribution with mean value
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Table 4 Time distributions for processing item-level RFID (ILR), case-level RFID (CLR) and pallet-level RFID (PLR) tagging. Time Unit days minutes minutes minutes
TRIA(5,8,10)
seconds
TRIA(20,22,25)
minutes
TRIA(20,22,25) TRIA(20,22,25) TRIA(20,22,25) EXPO(10) TRIA(20,22,25) TRIA(20,22,25)
minutes minutes minutes minutes minutes minutes
5
15000
3.7
x 10
3.6
3.5 Value added cost
10000 Variable cost
5000
3.4
3.3
3.2
0 CLB
ILR CLR Technology tagging levels
3.1 CLB
PLR
ILR
CLR
PLR
CLR
PLR
Tagging levels
a)
b) 5
3.7
3400
TRIA(20,22,25)
minutes
3200
x 10
3.6
3000 3.5
2800
TRIA(20,22,25)
minutes
Overhead cost
Disassembly Sorting of parts Casing of parts Attaching of RFID tags Automatically read data and update information store Move cased parts to physical store Pick cases Add cases to pallet Ship pallets Arrive at reuse depot Unload pallets Unpack cases Prepare parts for process lines with special RFID technology Process parts according to part's information
Time distributions TRIA(5,6,7) TRIA(5,8,10) TRIA(15,20,25) TRIA(5,8,10)
Wait cost
Processes
The ROI calculations for CLR tagging are shown in Table 5, and a summary of all the results in which the variable cost, valued added cost, wait cost and overhead cost of ILR, CLR and PLR tagging are compared with CLB tagging is presented in Table 6. The results show negative ROI values for all the tagging levels. The negative value means that these projects are not profitable. The low negative value is due to the high cost, formed mainly by initial investment cost, relative to benefit. PLR tagging has the most positive ROI, followed by CLR tagging and ITR tagging.
2600 2400
3.4
3.3
2200 3.2
2000 1800 CLB
5. EXPERIMENTAL RESULTS
3.1 CLB
ILR
CLR
ILR
PLR
Tagging levels
Tagging levels
c)
Plots of variable cost, value added cost, wait cost and overhead costs for CLB, ILR, CLR and PLR for the numerical problem is shown in Figure 3.
d)
Figure 3: Plot of a) Variable cost b) Value added cost c) Wait cost d) Overhead cost
Table 5: ROI calculation for case-level RFID (CLR) tagging Year Discount rate = 5% Cost ($) Investment cost Variable cost (Tags and labels) Overhead cost Total= Discounted cost
0 1
1 0.9524
2 0.907
3 0.8638
4 0.0829
5 0.7835
1,062,450
0
0
0
0
0
0 0 1,062,450
7,500 334,808 342,308
7,500 334,808 342,308
7,500 334,808 342,308
7,500 334,808 342,308
7,500 334,808 342,308
1,062,450.00
326,014.14
310,473.36
295,685.65
28,377.33
268,198.32
31,896.95
31,896.95
31,896.95
31,896.95
31,896.95
31,896.95
Savings from delays Savings from correcting errors on entered data
279 5000
279 5000
279 5000
279 5000
279 5000
279 5000
Savings from shrinkage
5000
5000
5000
5000
5000
5000
Total =
42175.51
42175.51
42175.51
42175.51
42175.51
42175.51
Discounted benefit
40,167.96
38,253.19
36,431.21
3,496.35
33,044.51
40,167.96
Benefits Savings from data capturing
ROI =
-0.932 293
2,291,198.80
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Table 6: Summary of results Performance measured relative to case-level barcode tagging Tagging levels
Variable cost
Value added cost
Wait cost
Overhead cost
ROI
Case-level barcode tagging
750
344,665
2,231
346,896
-0.931
Item-level RFID tagging
1900% increase
0.06% increase
0.06% decrease
0.07 % increase
-0.938
Case-level RFID tagging
900% increase
0.09% decrease
0.12% decrease
0.08% decrease
-0.932
Pallet-level RFID tagging
400% increase
0.07% decrease
0.47% increase
0.06% decrease
-0.931
6. CONCLUSIONS Fleisch, E. and Tellkamp C. (2005). Inventory inaccuracy and supply chain performance: a simulation study of a retail supply chain. International Journal of Production Economics, 95, pp. 373-385. Harun, K., Cheng, K. and Wibbelmann, M. (2008). RFIDenabled aerospace manufacturing: theoretical models, simulation and implementation issues. Proceedings of the IEEE IEEM, Singapore, pp. 1824 - 1829. Karagiannaki, A., Katerina, P. and Doukidis G. (2010). Using simulation to design and evaluate RFID implementation in the supply chain. Proceedings of Operation Research Society Simulation Workshop. Worcestershire. Lee, C.K.M and Chan, T.M. (2009). Development of RFIDbased reverse logistics system. Expert Systems with Applications, 36, pp. 9299-9307. Lee, Y.M., Cheng, F. and Leung, Y.T. (2004). Exploring the impact of RFID on supply chain dynamics. Proceedings of the 2004 Winter Simulation Conference, Washington, pp. 1145-1152. Leung, Y.T, Cheng, F., Lee, Y. M. and Hennessy, J.J. (2007). A tool set for exploring the value of RFID in a supply chain in trends in: Supply chain design and management. Springer Series in Advanced Manufacturing, pp. 49-70. Ramudhin, A., Paquet, M., Artiba, A.,Dupré, P., Varvaro, D., and Thomson, V. (2008). A generic framework to support the selection of an RFID-based control system with application to the MRO activities of an aircraft engine manufacturer. Production Planning and Control, 19(2), pp. 183-196. Rossetti, M. (2010). Simulation modeling and Arena. Hoboken: John Wiley & Sons. Su, Y. and Roan, J. (2011). Investigating the impacts of RFID application on supply chain dynamics with chaos theory. WSEAS Transaction on Information Science and Application, 8(1). Swedberg, C. (2012, January 11). Boeing to launch RFID programs for Airlines in Febuary. Retrieved March 17, 2013, from RFID Journal: http:// www. Rfidjournal .com / article /view/9107 Tu, Y., Zhou, W. and Piramuthu, S. (2009). Identifying RFID-embedded objects in pervasive healthcare applications. Decision Support Systems, 46, pp. 586-593
In this paper, we present a discrete event simulation based approach to determine the ROIs for RFID on a hypothesized EOL aircraft RL network, with barcode tags used in the as-is model at case-level and RFID tags used in the to-be models at item, case and pallet levels. The experimental results showed that RFID has greater potential to reduce the running costs of the network than barcode. Also, the ROIs are low due to the high initial investment costs compared to the savings. Since the results are derived from hypothetical data, they are currently useful for academic studies, not industrial. Interested readers can find complete details of the presented work in Adetiloye (2012). In future works, our simulations will be validated with industrial data, and the impacts of tag reuse, BPR, and phase implementation on the ROIs will be investigated. REFERENCES
Adetiloye, K.O. (2012). Design of RFID-enabled aircraft reverse logistics network simulations. Master’s thesis. Retrieved from Spectrum Concordia University Research Repository. Airbus. (2008). Eco-efficient ways to manage end-of-life aircraft. Accessed April 8, 2013 from http://videos. airbus.com/ video/iLyROoafIOU9.html Airframer Index. (2012). Boeing 787 Dreamliner. Retrieved 04 16, 2012, from http://www.airframer.com /pdf _progs/airframer_ prog8.pdf. Aircraft Fleet Recycling Association (AFRA). (2009). Best management practice for management of used aircraft parts and assemblies version 2.0. AFRA. Retrieved April 2, 2012, from http://www.afraassociation.org /AFRAPartsBMPv2_2.pdf Al Kattan, I. and Al-Khudairi, T. (2007), Improving Supply Chain Management effectiveness using RFID, 2007 IEEE International Engineering Management Conference, pp.191-198. Banks, J., Hanny, D., Pachano, M.A and Thompson, L.G. (2007). RFID Applied. John Wiley & Sons, Inc. Chang, Y.S, Chang, H.O., Whang, Y.S., Lee, J.J, Kwon, J.A, Kang, M.S and Park, J.S. (2006). Development of RFID enabled aircraft maintenance system. Proceedings of IEEE International Conference on Industrial Informatics, Singapore, pp. 224-229.
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