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Automotive Returnable Container Management with RFID:A Simulation Approach
9th IFAC Conference on Manufacturing Modelling, Management and 9th IFAC Conference on Manufacturing Modelling, Management and Control 9th IFAC Conference on Manufacturing Modelling, Management and Control Available online at www.sciencedirect.com 9th IFAC Conference on Manufacturing Modelling, Management and 9th IFAC Conference on Manufacturing Manufacturing Modelling, Management Management and and Berlin, Germany, August 28-30, 2019 Control 9th IFAC Conference on Modelling, Berlin, Germany, August 28-30, 2019 Control Control Berlin, Germany, August 28-30, 2019 Control Berlin, Germany, August 28-30, 2019 Berlin, Germany, Germany, August August 28-30, 28-30, 2019 2019 Berlin,
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IFAC PapersOnLine 52-13 (2019) 325–330
Automotive Returnable Container Management with RFID: Automotive Returnable Container Management with RFID: Automotive Returnable Container Management with RFID: Automotive Returnable Container Management with RFID: A Simulation Approach with RFID: A Simulation Approach Automotive Returnable Container Management A Simulation Approach A Simulation Approach A Simulation Approach Benedetto Benedetto Giubilato*, Giubilato*, Guoqing Guoqing Zhang** Zhang**
Benedetto Giubilato*, Guoqing Zhang** Zhang** Arianna Alfieri*** Benedetto Giubilato*, Guoqing Arianna Alfieri*** Benedetto Giubilato*, Guoqing Zhang** Zhang** Benedetto Giubilato*, Guoqing Arianna Alfieri*** Arianna Arianna Alfieri*** Alfieri*** Arianna Alfieri*** *Supply Chain and and Logistics Logistics Optimization Optimization Centre, Centre, University University of of Windsor Windsor *Supply Chain *Supply Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). *Supply Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). *Supply and Logistics Optimization Centre, University of Windsor *Supply Chain Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). **Supply Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). **Supply Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). Canada (e-mail: [email protected]). **Supply Chain and Logistics Optimization Centre, University of Windsor Canada (Tel: 519-253-3000 ext. 2637; e-mail: [email protected]) **Supply Chain and Logistics Optimization Centre, University of Windsor Canada (Tel: 519-253-3000 ext. 2637; e-mail: [email protected]) **Supply Chain and Logistics Centre, University **Supply Chain and Logistics Optimization Optimization Centre, University of of Windsor Windsor Canada (Tel: 519-253-3000 ext. 2637; e-mail: [email protected]) *** Politecnico di Torino,Turin, Torino,Turin, Italy (e-mail: [email protected]) Canada (Tel: ext. 2637; e-mail: [email protected]) *** Politecnico di (e-mail: [email protected]) Canada (Tel: 519-253-3000 519-253-3000 ext.Italy 2637; e-mail: [email protected]) Canada (Tel: 519-253-3000 ext. 2637; e-mail: [email protected]) *** Politecnico di Torino,Turin, Italy (e-mail: [email protected]) *** *** Politecnico Politecnico di di Torino,Turin, Torino,Turin, Italy Italy (e-mail: (e-mail: [email protected]) [email protected]) *** Politecnico di Torino,Turin, Italy (e-mail: [email protected]) Returnable containers are essential and critical for manufacturing operation operation and and logistics logistics in in the the Returnable containers are essential and critical for manufacturing Returnable containers are essential and critical for manufacturing operation and logistics in the automotive industry. However, container management is still affected by problems such as container Returnable containers are and critical for manufacturing and logistics in the automotive industry. However, container management still affected operation by problems as container Returnable containers are essential essential and critical for is manufacturing operation andsuch logistics inabout the Returnable containers are essential and critical for manufacturing and logistics in the automotive industry. However, container management is still by problems such as container shortage, losses losses or inefficient inefficient handling. These problems lead to affected a lack lack of ofoperation accurate and timely data automotive industry. However, container management is still affected by problems such as container shortage, or handling. These problems lead to a accurate and timely data about automotive industry. However, container management is still affected by problems such as container automotive industry. However, container management is still affected by problems such as container shortage, losses or inefficient handling. These problems lead to a lack of accurate and timely data about container losses flow throughout throughout the handling. automotive supply chain. In In thistowork, work, weofstudy study RFID based automotive automotive shortage, or inefficient These problems lead aa lack accurate and timely data about container flow the automotive supply chain. this aa RFID based shortage, losses or inefficient handling. These problems lead towork, lackwe ofstudy accurate andevaluate timely dataimpact about shortage, losses or inefficient handling. These problems lead to aapproach lack of accurate and timely data about container flow throughout the automotive supply chain. In this we aa RFID based automotive supply network, and a discrete-event simulation (DES) is used to the container flow throughout the automotive supply chain. In this work, we study RFID based automotive supply network, and a discrete-event simulation (DES) approach is used to evaluate the impact container flow throughout the automotive supply chain. In this work, we study aa RFID based container flowreal throughout theautomotive automotive supply chain. In (DES) this supply work, we study RFID based automotive automotive supply network, and a discrete-event simulation approach is used to evaluate the impact of RFID on case of an returnable container chain. The simulation model and container supply and aa discrete-event simulation (DES) approach is used to evaluate model the impact of RFID on real network, case of an automotive returnable container supply chain. The simulation and container supply network, and simulation (DES) approach is to the impact container supply network, and a discrete-event discrete-event simulation (DES) approach is used used to evaluate evaluate the impact of RFID on real case of an automotive returnable container supply chain. The simulation model and method provide the tool to analyze container supply chain performances. Computational results show that of RFID on real automotive returnable container supply chain. The simulation and method provide thecase toolof to an analyze container supply chain performances. Computational resultsmodel show that of RFID on real case of automotive returnable container supply The model and of RFID oncan realthe case ofto an an automotive returnable container supply© chain. chain. The simulation simulation model and method provide tool analyze container supply chain performances. Computational results show that Copyright 2019 IFAC using RFID bring relevant benefits to the supply chain. method provide the tool to analyze container supply chain performances. Computational results show that using RFID can bring relevant benefits to the supply chain. Copyright © 2019 IFAC method provide the tool to analyze container supply chain performances. Computational results show that method provide the tool to analyze container supply chain performances. Computational results show that using RFID can bring relevant benefits to the supply chain. Copyright © 2019 IFAC using RFID can bring relevant benefits to the chain. Copyright © 2019 IFAC Keywords: Supply chain management, simulation, RFID, © 2019, IFAC (International Federation Control) Hosting by Elsevier Ltd. All rights reserved. using RFID can bring relevant benefits of toAutomatic the supply supply chain.automotive, Copyright ©container. 2019 IFAC Keywords: Supply chain management, simulation, RFID, automotive, container. using RFID can bring relevant benefits to the supply chain. Copyright © 2019 IFAC Keywords: Supply chain management, simulation, RFID, automotive, container. Keywords: Supply chain management, simulation, RFID, automotive, container. Keywords: Supply Supply chain chain management, management, simulation, simulation, RFID, RFID, automotive, automotive, container. container. Keywords: •• Inventory Inventory accuracy: accuracy: container container inventory inventory is is often often • Inventory accuracy: container inventory is often 1. INTRODUCTION inaccurate, and the needed container quantity might be not 1. INTRODUCTION •• Inventory accuracy: container inventory is often inaccurate, and the needed container quantity might be not Inventory accuracy: container inventory is often 1. INTRODUCTION • Inventory accuracy: container inventory is often inaccurate, and the needed container quantity might be not properly estimated. A returnable container is a packaging solution used to avoid 1. INTRODUCTION inaccurate, and the needed container quantity might be not properly estimated. A returnable container is a packaging solution used to avoid 1. INTRODUCTION INTRODUCTION inaccurate, and 1. inaccurate, and the the needed needed container container quantity quantity might might be be not not properly estimated. A returnable container is aa packaging solution used to avoid disposing of costly shipping material each time aa product is properly estimated. A returnable container is packaging solution used to avoid disposing of costly shipping material each time product is •properly Inefficient estimated. A returnable container is packaging solution used to properly estimated. handling A returnable container is aa location. packaging solution used to avoid avoid • Inefficient handling leads leads to to excess excess inventory inventory at at disposing of costly shipping material each time a product is distributed to a customer Most automotive parts disposing of costly shipping material each time product is •• Inefficient handling leads to excess inventory at distributed a customer location. Most automotive parts some locations and operations time. disposing ofto costly shipping material eachusing time aaa returnable product is Inefficient handling leads to excess at disposing of costly shipping material each time product is some locations and increased increased operations time. inventory distributed to a customer location. Most automotive parts are stored, shipped and consumed • Inefficient handling leads to excess inventory distributed aa customer Most automotive parts •some locations Inefficient handling operations leads to excess at and increased increased time. inventory at are stored,to shipped andlocation. consumed using returnable distributed to customer location. Most automotive parts some locations and operations time. distributed to a customer location. Most automotive parts are stored, shipped and consumed using returnable containers (Twede & Clarke, 2005). •some Inaccurate tracking: trailers loaded with containers some locations and increased operations time. are stored, shipped and consumed using returnable locations and increased operations time. containers (Twede & Clarke, 2005). • Inaccurate tracking: trailers loaded with containers are stored,(Twede shipped and 2005). consumed using using returnable returnable •might sitInaccurate are stored, shipped and consumed containers & Clarke, tracking: trailers loaded with containers in while considered in transit, thus containers (Twede & 2005). •• tracking: loaded with containers might sitInaccurate in the the yard yard whiletrailers considered in transit, thus Empty returnable shipped containers (Twedecontainers & Clarke, Clarke, are 2005). Inaccurate tracking: trailers loaded with containers containers (Twede & Clarke, 2005). Inaccurate tracking: trailers loaded with containers sit in the yard while considered in transit, thus Empty returnable containers are shipped from from aa warehouse warehouse •might resulting in large pickup time fluctuations, delays and might sit in the yard while considered in transit, resulting inin large pickup timeconsidered fluctuations, delays thus and Empty returnable containers are shipped from a warehouse to the supplier, where they are filled with automotive parts might sit the yard while in transit, thus Empty returnable containers are shipped from a warehouse might sit in the yard while considered in transit, thus resulting in large pickup time fluctuations, delays and to the supplier, where they are filled with automotive parts missed shipments. Empty returnable containers are shipped from a warehouse resulting in large pickup time fluctuations, delays and Empty returnable containers arefilled shipped from a line. warehouse missed shipments. to the supplier, where they are with automotive parts and shipped back to manufacturer’s assembly Once resulting in large pickup time fluctuations, delays and to the supplier, where they are filled with automotive parts resulting in large pickup time fluctuations, delays and missed shipments. and shipped back to manufacturer’s assembly line. Once to theshipped supplier, where they are are filled with with automotive parts missed shipments. to the supplier, where they filled automotive parts and back to manufacturer’s assembly line. toOnce Once parts have consumed, containers are the to missed shipments. and shipped back to manufacturer’s line. missed shipments. parts have been been consumed, containersassembly are shipped shipped toOnce the According According to (Sheffi, (Sheffi, 2003), 2003), those those issues issues are are caused caused by by lack lack and shipped back to manufacturer’s assembly line. and shipped back to manufacturer’s assembly line. Once parts have been consumed, containers are shipped to the According to (Sheffi, 2003), those issues are caused by lack warehouse (often operated by a third-party logistics of accurate and timely data from suppliers and service parts have been consumed, containers are shipped to the According to (Sheffi, 2003), those issues are caused by lack warehouse (often operated by a third-party logistics of accurate and timely data from suppliers and service parts have been consumed, containers are shipped to the According to (Sheffi, 2003), those issues are caused by lack parts have been consumed, containers are shipped to the According to (Sheffi, 2003), those issues are caused by lack warehouse (often operated by a third-party logistics of accurate and timely data from suppliers and service company) where they are treated and providers about to where shipments are, the current warehouse (often operated by aa third-party logistics of accurate and from suppliers company) where they are sorted, sorted, treated and refurbished, refurbished, providers about totimely wheredata shipments are, what what and the service current warehouse (often operated by third-party logistics of accurate and timely data from suppliers and service warehouse (often operated byto treated athe third-party logistics of accurate andis, timely data from suppliers and service company) where they are sorted, and refurbished, providers about to where shipments are, what the current ready to be shipped out again supplier (Lunani & inventory level and where is it located. In particular, aa company) they are sorted, and refurbished, providers about to where shipments are, what the current ready to bewhere shipped out again to treated the supplier (Lunani & inventory level is, and where is it located. In particular, company) where they are sorted, treated and providers about to where shipments are, the current company) where theyout areagain sorted, treated and refurbished, refurbished, providers about to where shipments are, what what the currenta ready to be shipped to the supplier (Lunani & inventory level is, and where is it located. In particular, major source of variability is the current data acquisition Hanebeck, 2008). ready to be2008). shipped out again to the supplier (Lunani & major inventory level and where it located. In particular, source ofis, variability is is the current data acquisitionaa Hanebeck, ready to shipped level is, and where is it In ready to be be2008). shipped out out again again to to the the supplier supplier (Lunani (Lunani & & inventory inventory level is,variability andmanual whereisoperations. isthe it located. located. In particular, particular, major source of current data data acquisitiona Hanebeck, practice that relies on major source of variability is the current acquisition Hanebeck, 2008). practice that relies on manual operations. Containers are source of is the Hanebeck, major source of variability variability isoperations. the current current data data acquisition acquisition Hanebeck, 2008). practice that relies on manual Containers 2008). are critical critical to to ensure ensure the the quality quality of of major that manual Containers areoperations critical (Foster, to ensure ensure the &quality quality of practice manufacturing Sindhu, Blundell, Radio (RFID) practice that relies relies on onIdentification manual operations. operations. Containers critical to the of practice that relies on manual operations. manufacturingare operations (Foster, Sindhu, &quality Blundell, Radio Frequency Frequency Identification (RFID) is is an an AutoID AutoID Containers are critical to ensure the of Containers are critical to containers ensure the quality of technology manufacturing operations (Foster, Sindhu, & Blundell, Radio Frequency Identification (RFID) is an AutoID 2006). OEM must ensure that are in the right that allows for automatic extraction of the object manufacturing operations (Foster, Sindhu, & Blundell, Radio Frequency Identification (RFID) is an 2006). OEM must ensure that containers are in the right technology that allows for automatic extraction of theAutoID object manufacturing operations (Foster, Sindhu, & Blundell, Radio Frequency Identification (RFID) is an AutoID manufacturing operations (Foster, Sindhu, & Blundell, Radio Frequency Identification (RFID) is of an AutoID 2006). OEM must ensure that containers are in the right technology that allows for automatic extraction the object place at the right time. However, there are some issues identity at key points along the supply chain, without the 2006). OEM must ensure that containers are in the right technology that allows for automatic extraction of the object place at the right time. However, there are some issues identity at key points along the supply chain, without the 2006). OEM must ensure that are in the right that for automatic extraction of the object 2006). OEM must ensureautomotive that containers containers are some in container theissues right technology technology that allows allows for automatic extraction of the object place at the right time. However, there are identity at key points along the supply chain, without the related to the current returnable need of manual operations. This technology does not place at the right time. However, there are some issues identity at key points along the supply chain, without the related to the current automotive returnable container need of manual operations. This technology does not place at the right time. However, there are issues identity at key points along the supply chain, without the place atchain thethe right time. automotive However, there are some some issues identity atmanual keycontact points along the supply chain, without the related to current returnable container need of operations. This technology does not supply management (Lunani & Hanebeck, 2008, require visual or clear line of sight, as it uses radio related to the current automotive returnable container need of manual operations. This technology does not supply chain management (Lunani & Hanebeck, 2008, require visual contact or clear line of sight, as it uses radio related to the current automotive returnable container need of manual operations. This technology does not related to themanagement current automotive returnable container need ofvisual manual operations. Thisof technology doesradio not supply chain (Lunani & Hanebeck, 2008, require contact or clear line sight, as it uses Chism, 2010): signals. supply chain visual Chism, 2010): signals. supply chain management management (Lunani (Lunani & & Hanebeck, Hanebeck, 2008, 2008, require require visual contact contact or or clear clear line line of of sight, sight, as as it it uses uses radio radio supply chain management (Lunani & Hanebeck, 2008, require visual contact or clear line of sight, as it uses radio Chism, 2010): signals. Chism, 2010): signals. •Chism, Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event 2010): signals.technology can automatically generate event data •Chism, 2010): Shortage: wrong shipments and misplacement lead signals. RFID data that that •• 15-20% Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event data that that to container losses and 20-25% excess purchased digitally describe how physical entities move through the Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event data to 15-20% container losses and 20-25% excess purchased digitally describe how physical entities move through the ••containers. Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event data Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event data that that to 15-20% container losses and 20-25% excess purchased digitally describe how physical entities move through the Containers might also be used as work in supply chain processes (and, also, across different parties). to 15-20% container losses and 20-25% excess purchased digitally describe how physical entities move through the containers. Containers might also be used as work in supply chain processes (and, also, across different parties). to 15-20% container losses andalso 20-25% excess purchased digitally describe how (and, physical entities move through through the to 15-20% container losses and 20-25% excess purchased digitally describe how physical move the containers. Containers might be used as work in supply chain processes also,entities across different different parties). process (WIP) storage at supplier facilities. containers. Containers might also be used as work in supply chain processes (and, also, across parties). process (WIP) storage at supplier facilities. The aim of the present work is to use Discrete Event containers. Containers might also be used used as as work work in in supply supply chain processes (and, also, across different parties). containers. Containers might also be chain processes (and, also, across different parties). process (WIP) storage at supplier facilities. The aim of the present work is to use Discrete Event (WIP) storage at facilities. The aim of the present work is to use Discrete Event •process Substitute cost: for lost (DES) to how RFID can the (WIP) storage at supplier supplier facilities. The aim of the present work is to use Discrete Event process storage at supplier facilities. •process (WIP) Substitute cost: for each each lost container, container, it it is is Simulation Simulation (DES) to investigate investigate how RFID can reduce reduce the The aim of the work to use Event The aim of(DES) the present present workinis is toautomotive use Discrete Discrete Event •• Substitute cost: for each lost container, it is Simulation to investigate how RFID can reduce the necessary to provide suppliers with an expendable impact of inventory inaccuracy the returnable Substitute cost: for each lost container, it is Simulation (DES) to investigate how RFID can reduce the necessary to provide suppliers with an expendable impact of inventory inaccuracy in the automotive returnable •cardboard cost: for lost container, it (DES) to investigate how RFID can reduce the •necessarySubstitute Substitute cost: for each each lost cost container, it is is Simulation Simulation (DES) chain. toinaccuracy investigate how RFID can reduce the to provide suppliers with an expendable impact of inventory in the automotive returnable packaging as backup. The total is a relevant container supply The relative effect of inventory necessary to provide suppliers an expendable impact of inaccuracy in the cardboard packaging as backup. The with total cost a relevant container supply chain. The relative effect of returnable inventory necessary to OEM, provide suppliers with an is expendable impact of inventory inventory inaccuracy in the automotive automotive returnable necessary to provide suppliers with an expendable impact of inventory inaccuracy in the automotive returnable cardboard packaging as backup. The total cost is aa relevant container supply chain. The relative effect of inventory loss for the especially if an urgent shipping is inaccuracy on container stock level is compared to cardboard packaging as backup. The total cost is relevant container supply chain. The relative effect of loss for the OEM, especially if an urgent shipping is inaccuracy on container stock level is compared to other other cardboard packaging as backup. The The total cost is isshipping relevant container supply chain. stock The relative relative effect of inventory inventory cardboard packaging as backup. total cost aa relevant container supply chain. The effect of inventory loss for the OEM, especially if an urgent is inaccuracy on container level is compared to other necessary. loss for the OEM, especially if an urgent shipping is inaccuracy on container stock level is compared to necessary. loss for for the the OEM, OEM, especially especially if if an an urgent urgent shipping shipping is is inaccuracy inaccuracy on on container container stock stock level level is is compared compared to to other other loss other necessary. necessary. necessary. necessary.
2405-8963 © IFAC (International Federation of Automatic Control) Copyright © 2019, 2019 IFAC 330Hosting by Elsevier Ltd. All rights reserved. Copyright 2019 responsibility IFAC 330Control. Peer review© of International Federation of Automatic Copyright ©under 2019 IFAC 330 Copyright © 2019 IFAC 330 10.1016/j.ifacol.2019.11.127 Copyright © 2019 IFAC 330 Copyright © 2019 IFAC 330
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sources of variability in the supply chain, such as lead time and demand fluctuation.
3. THE EMPIRICAL CONTEXT AND A DES SIMULATION MODEL
The DES model was developed starting from a real OEM supply chain, made of a warehouse and a supplier. By means of factorial design, the main influencing variables are identified, and system performance with and without RFID are compared.
A simulation model has been developed to study the introduction of RFID in a container supply chain of major OEM (the Company, in the following) that will remain anonymous for confidentiality reasons.
The remainder of the paper is structured as follows: Section 2 reviews the relevant literature. The simulation model is presented in Section 3. Section 4 presents the computational results and the Section 5 concludes the paper.
3.1 The company containers supply chain overview Fig. 1 shows an overview of OEM supply chain. The stages in the supply chain includes: • Assembly plant • Plant cross-dock • Regional cross-dock • Suppliers.
2. LITERATURE REVIEW There are several works related to automotive containers tracking. Foster et al. (2006) developed a case study with a leading OEM regarding RFID tagging of high value automotive stillages. The authors developed a comprehensive study of the entire stillage supply chain to assess the feasibility of an RFID tracking system. Lunani and Hanebeck (2008) implemented an RFID system over an automotive returnable container supply chain, reporting positive effects on performances indicators. In particular, 80% reduction in expendable cost and 5-15% reduction in fleet shrinkage was observed. Velandia et al. (2016) proposed an RFID system for managing crankshafts manufacturing and assembly. They concluded RFID can provide several advantages to the manufacturing process, but a careful integration process of this technology is necessary, involving both technologists and management.
Fig. 1. Container supply chain overview.
Khan et Al. (2006) presented a very interesting application of RFID for vehicle components recycling: using RFID to control closed-loop recycling would allow for automotive dump reduction.
Containers can either travel full with parts (black arrows) or empty (green) from multiple suppliers and across several handling points. For the sake of this simulation work, a subset of this system has been considered. This subset is composed by: • Assembly plant (AP). • Empty container Warehouse (EW) • Single supplier (SP)
Kirch and Poenicke (2015) studied RFID application to confirm the completion of automotive assembly processes: a wristband is proposed as viable solution to integrate automatic confirmation of tasks and operations. Herrmann et al. (2015) investigated the application of RFID on finished vehicle distribution. The authors identified the current final processing of Finished Vehicle as a weak point of the supply chain. An RFID transponder solution is evaluated, to enable an optimized finished vehicle steering process.
Fig. 2 shows a schematic of the system under study.
Tabanli and Ertay (2012) realized an RFID-based Kanban system to be used by an automotive safety components supplier. Traditional Kanban cards are replaced with RFID Kanban cards, to enable real time visibility on inventory. Thoroe et al. (2009) analysed the impact of RFID on container management using a deterministic inventory model. The analysis is based on a general reverse logistics model. They also studied the profitability of the RFID system and determined relevant relations of costs and benefits. Alternatives of implementing an RFID-based container tracking system was also discussed.
Fig. 2. Simulation system overview
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Let us describe how those parameters were derived from real world data.
Table 1 summarizes the main variables used to model the system. Table 1. Simulation model variables Name SEW SSP SSEW SSSP FSP FEW LTEW,SP LTSP,AP LTAP,EW LTAP OULSP RAP RSP ε α DAP
Description EW containers stock level SP containers stock level EW safety stock level SP safety stock level SP delivery frequency EW delivery frequency Lead time EW to SP Lead time SP to AP Lead time AP to EW AP transit time SP order-up-to-level AP containers replenishment SP containers replenishment Error magnitude Container counting accuracy AP demand
Each time containers received at SP do not match what SP required, a document called Return Discrepancy Report (RDR) is issued from SP to OEM. RDRs is a measure counting mistake occurrence. If total RDRs are indicated as TRDR and total shipment are indicated as T SHIP, we can define α as follows:
Variable Type Controllable Controllable Controllable Controllable Controllable Controllable Random Random Random Controllable Controllable Controllable Controllable Random Controllable Random
α=
α = α(αSP , αEOP ) It is assumed manual counting accuracy is the same for EW and SP: α = (αSP × αEOP ) = (αMan )2 Based on eight months OEM data, αMan = 83%. RDR also reports received vs. shipped quantity, so that for each RDR is possible to derive error magnitude as fraction of received quantity, according to:
Let us describe the container flow in the model:
• • •
•
•
•
TSHIP − TRDR × 100 TSHIP
Notice that this value is the effect of both shipping and receiving location counting accuracy.
Variables have been split between two kinds • Controllable: they can be controlled in real world to modify supply chain performance • Random: affected by uncertainty and based on statistical distribution
•
327
𝜀𝜀𝑖𝑖 = According to FSP, SP consumes containers stock to fulfill demand DAP. Outbound qty. is deducted from stock SSP and shipped to AP. After time LTSP,AP , containers arrive to AP, and spend a transit time LTAP inside the plant. The containers are delivered from AP to EW with lead time LTAP,EW. Once at EW, inbound containers are manually counted (accuracy αM) and SEW is increased with counted quantity According to FEW, containers are consumed from stock SEW to fulfill SP demand DSP. Outbound qty. is counted with accuracy αM, deducted from stock SEW and shipped to SP After time LTEW,SP , containers arrive to SP and are manually counted with accuracy αM. Counted quantity is added to SSP. Containers are moved to SP storage closing the loop.
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖 − 𝑆𝑆ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 × 100 𝑆𝑆ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
Based on OEM data sample, ε is normally distributed. 3.1.2 Supplier replenishment policy The supplier uses a periodic review inventory policy, i.e., an order is placed such that the level of current inventory plus the replenishment lot size equals a pre-specified level called order-up-to-level (OUL) (Chopra and Meindl, 2016), i.e., OULSP = DAP (FSP + LTEW,SP ) + SSSP Empty containers flow from EW to SP each time the supplier places and order, i.e., each R time units. The replenishment quantity is defined as: R SP = OULSP − IP Where IP is supplier’s inventory position, defined as IP = OH + IT Where OH is the current on-hand inventory and IT current intransit quantity.
A special effort has been made to realistically model the manual counting process occurring at SP and EW, so to show the benefit deriving from RFID automation.
3.1.3 Performance indicators System performance is evaluated by the following indicators: • Supplier Type I service level. It considers the total number of containers stock-out occurred and the total number of shipments:
3.1.1 Manual counting process The empty container manual counting process is defined by two parameters (as mentioned in Table 1). • α : Counting accuracy • ε : Error magnitude
SPSL1 =
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Total supplier shipments − Total Stockout occured × 100 Total supplier shipments
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•
SSP will either be updated with actual truckload or affected by error ε.
Supplier Type II service level (Fill rate). This indicator measures the total fraction of parts demand that has been shipped using containers: SPSL2 =
•
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3.2.3 EW operations & shipping manager
SP Total containers shipped × 100 SP Total containers requirement
Fig. 4 shows schematic of EW shipping agent.
EW Type II service level (Fill rate). Measures the total fraction of replenishment quantity that has been satisfied by EW: EWSL2 =
EW Total containers shipped × 100 EW Total containers requirement
3.2 DES Model description
Fig. 4. EW operations agent
To reproduce this system by means of DES, the following agents have been defined: • EW shipping manager • EW operations • SP shipping Manager • SP Operations
Every FEW, an order entity is generated and triggers the creation of SP replenishment requirement. If OUL SP is less than zero, no container will be shipped. According to α, R SP is either initialized to zero or to ε. The initialization of the variable affects how many container entities are loaded by EW operations.
Three main entities simulation entities are employed: Container entity, Truckload entity, Order entity.
Upon EW replenishment manager signal, container release to SP is started, and entity is released from storage until set shipping quantity REW is reached.
In the following section an overall description of the model implementation is provided. The model has been developed using ARENA® by Rockwell Automation.
Fig. 5 shows schematic of EW operations agent.
3.2.1 SP shipping manager An order entity is generated according to shipping frequency F2. This entity triggers the creation of RAP according to DAP and lot size. The agent verifies SP stock and trigger containers release. If stock level is not sufficient, the agent will set as RAP what available in stock.
Fig. 5. EW operations agent 3.2.4 RFID simulation parameters
Quantity RAP is released from storage and consolidated into truck entity. Truck entity will experience a lead time from SP to EW according to:
The application of RFID is simulated by increasing counting accuracy α to three different levels, as reported in Table 2: Table 2. RFID counting accuracy
𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆,𝐸𝐸𝐸𝐸 = 𝐿𝐿𝑇𝑇𝑆𝑆𝑆𝑆,𝐴𝐴𝐴𝐴 + 𝐿𝐿𝐿𝐿𝐴𝐴𝐴𝐴 + 𝐿𝐿𝐿𝐿𝐴𝐴𝐴𝐴,𝐸𝐸𝐸𝐸 Where LTAP is the transit time containers spend at AP to be used and emptied again.
αRFID
100%
99.9%
98%
Description
Ideal case
Realistic value Rahmati et al. (2007)
Worst-case scenario.
3.2.2 SP operations 3.4 Handling time reduction
Fig. 3 shows schematic of EW operations agent.
As a complement to the simulation work, potential handling time reduction by means of the RFID automatic counting has been estimated. Average container counting time, was measured by means of stopwatch analysis, performed at OEM warehouses. Assuming RFID counting occurs instantaneously, total time saving (Tsav) is defined by the following equation
Fig. 3. SP receiving operations agent A truckload entity arrives from EW operations to SP operation agent after time LTEW,SP. According to accuracy α,
TSav = Avg. Stack counting time × Total stacks. Results are presented in Section 4. 333
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4. COMPUTATIONAL RESULTS
329
Set2 SPSL2 3.20%11.20%
Among the possible experimentation strategies, a Design of Experiment (DOE) based on a full factorial design with two levels (i.e., factorial 2k) was chosen. Results are analysed by means of ANOVA.
26.10% 59.40%
Choice of factors for each set is summarized by Table 2, together with the rationale for the experimental scenarios setting. DAP
ε
LTEW,SP
Error
Table 3. Experimental scenarios Fig. 7. Set 2 results
SET 1 SET 2
SSEW SSSP α FEW DAP LTEW ε
Experiment objective
Relative effect of increasing counting accuracy (manual to RFID level) compared to other controllable factors
Max Improvement for EW SL2 13.5
Relative effect of counting error magnitude compared to other variability sources (Lead time and demand)
% Improvement
Factor (2k)
Each set is repeated for two different dataset and three different levels of RFID accuracy. The simulation length is 365 days, with 5 days warm-up time. For each factor combination, 50 replications have been made.
13
RFID 100% accuracy
12.5 12 11.5
RFID 99.9% Accuracy RFID 98% accuracy
11 10.5
Fig. 6 shows SET1 simulation results with datasets 1 and 2, respectively, in the case counting reading accuracy is 100%.
Fig. 8a. Maximum Improvement for EWSL2 .
Max Improvement for SP SL2 % Improvement
1.5 1
RFID 100% accuracy RFID 99.9% accuracy
0.5
RFID 98% accuracy
0 Fig. 6. Set 1 results Fig. 8b. Maximum Improvement for SPSL2 .
For both datasets, increasing reading accuracy to RFID level, influences the performance indicators up to 33% in dataset1 and 26.2% in dataset 2.
A 13% EWSL2 increase can be achieved by means of 100% accurate counting process. Even for the less accurate case, the improvement is still around 11.5%. For SPSL2 the improvement is less significant and reaches a maximum of 1.5%.
Fig. 7 shows result From SET2 for dataset 1. For SPSL2 , uncertainty on the lead time (ULT) contributes to 62%, while UE accounts for 10.6%. This result depends on the specific supply chain under study, in which the lead time from SP to AP is much larger than that from EW to SP. As consequence, reducing counting error at EW will improve service level much more than in SP.
The application of RFID can result in potential safety stock reduction, as shown by the Fig. 9. It is possible to keep the same SP service level while reducing the container stock at SP, as shown by Fig. 9. As shown in the figure, considering a 100% accurate reading system, the same service level can be achieved with one day less of stock at supplier. This suggests that using RFID disclose interesting supply chain optimization opportunities.
Fig. 8 (a & b) show the potential improvement on the performance indicators as consequence of increased reading accuracy.
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REFERENCES Chism, C. (2010). Optimizing and Benchmarking Returnable Container Processes within an Automotive. RTI scholar works. Chopra S and Meindl, P. (2016). Supply Chain Management: Strategy, Planning, and Operation, sixth edition. Pearson. Foster, P., Sindhu, A., & Blundell, D. (2006). A case study to track high value stillages using RFID for an automobile OEM and its supply chain in the manufacturing industry. In 2006 4th IEEE International Conference on Industrial Informatics, 56-60 Herrmann, S., Rogers, H., Gebhard, M., & Hartmann, E. (2015). Co-creating value in the automotive supply chain: an RFID application for processing finished vehicles. Production Planning & Control. 26 (12), 981993. Khan, O., Scotti, A., Leverano, A., Bonino, F., Ruggiero, G., & Dörsch, C. (2006). RFID in automotive: A closedLoop approach. 2006 IEEE International Technology Management Conference (ICE),1-8 Kirch, M., & Poenicke, O. (2015). Using the RFID Wristband for Automatic Identification in Manual Processes-The RFID Wristband in the Automotive Industry. European Conference on Smart Objects, Systems and Technologies,1-7. Thoroe, L., Melski, A., & Schumann, M. (2009). The impact of RFID on management of returnable containers. Electronic Markets, 19(2-3), 115-124. Lunani, M., & Hanebeck, C. (2008). RFID-enabled returnable container management: Solution to a chronic and wasteful automotive industry problem. IBM Global services. Rahmati, A., Zhong, L., Hiltunen, M., & Jana, R. (2007). Reliability Techniques for RFID-Based Object Tracking Applications. 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07), 113-118. Sheffi, M. A. (2003). The Impact of Automatic Identification on Supply Chain Operations. The International Journal of Logistics Management, 14(1), 117. Tabanli, R., & Ertay, T. (2012). Value stream mapping and benefit–cost analysis applicationfor value visibility of a pilot project on RFID investmentintegrated to a manual production control system—a case study. Int J Adv Manuf Technol. 66(5-8), 987-1002. Twede, D., & Clarke, R. (2005). Supply chain issues in reusable packaging. Journal of Marketing Channels.12(1), 7-26 Velandia, S., Kaur, N., Whittow, W., Conway, P., & West, A. (2016). Towards industrial internet of things: Crankshaft monitoring,traceability and tracking using RFID. Robotics and Computer-Integrated Manufacturing. 41, 66-77
Fig. 9. Potential safety stock reduction 4.1. Handling time reduction As explained in Section 3.4, by means of stopwatch analysis it is possible to estimate the manpower hour reduction enabled by RFID automatic counting. Results for a specific supplier/assembly plant combination are reported in Table 4. Table 4. Time reduction for standard pooled container. Average Stack counting time Total shipped containers Total Flow Attrition Actual total flow Stack size Stacks (rounded) Yearly handling time reduction
8.54 Sec 884057 1768114 6.3% 1656723 9 98229 430 Hours
Those data are related to only the several container types used by the OEM. 5. CONCLUSIONS A Discrete-Event simulation model of an automotive returnable container supply chain was presented to evaluate the impact of the RFID introduction. The model has been developed in collaboration with a major OEM, and the simulation results show that RFID can bring interesting supply chain optimization opportunities in terms on service level increase, stock reduction and handling time reduction. The model logic is flexible, and it can be easily adjusted to simulate different kinds of supply chains. One of the possible extensions to the current work is to consider a multiple supplier case, where several containers are shared by several suppliers. Also, the simulation model could be integrated with optimization models to provide a solid base to proof the benefit of RFID to the future supply chain 4.0. ACKNOWLEDGMENT This research is partially supported by the Natural Sciences and Engineering Research Council of Canada discovery grant (RGPIN-2014-03594).
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Automotive Returnable Container Management with RFID: Automotive Returnable Container Management with RFID: Automotive Returnable Container Management with RFID: Automotive Returnable Container Management with RFID: A Simulation Approach with RFID: A Simulation Approach Automotive Returnable Container Management A Simulation Approach A Simulation Approach A Simulation Approach Benedetto Benedetto Giubilato*, Giubilato*, Guoqing Guoqing Zhang** Zhang**
Benedetto Giubilato*, Guoqing Zhang** Zhang** Arianna Alfieri*** Benedetto Giubilato*, Guoqing Arianna Alfieri*** Benedetto Giubilato*, Guoqing Zhang** Zhang** Benedetto Giubilato*, Guoqing Arianna Alfieri*** Arianna Arianna Alfieri*** Alfieri*** Arianna Alfieri*** *Supply Chain and and Logistics Logistics Optimization Optimization Centre, Centre, University University of of Windsor Windsor *Supply Chain *Supply Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). *Supply Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). *Supply and Logistics Optimization Centre, University of Windsor *Supply Chain Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). **Supply Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). **Supply Chain and Logistics Optimization Centre, University of Windsor Canada (e-mail: [email protected]). Canada (e-mail: [email protected]). **Supply Chain and Logistics Optimization Centre, University of Windsor Canada (Tel: 519-253-3000 ext. 2637; e-mail: [email protected]) **Supply Chain and Logistics Optimization Centre, University of Windsor Canada (Tel: 519-253-3000 ext. 2637; e-mail: [email protected]) **Supply Chain and Logistics Centre, University **Supply Chain and Logistics Optimization Optimization Centre, University of of Windsor Windsor Canada (Tel: 519-253-3000 ext. 2637; e-mail: [email protected]) *** Politecnico di Torino,Turin, Torino,Turin, Italy (e-mail: [email protected]) Canada (Tel: ext. 2637; e-mail: [email protected]) *** Politecnico di (e-mail: [email protected]) Canada (Tel: 519-253-3000 519-253-3000 ext.Italy 2637; e-mail: [email protected]) Canada (Tel: 519-253-3000 ext. 2637; e-mail: [email protected]) *** Politecnico di Torino,Turin, Italy (e-mail: [email protected]) *** *** Politecnico Politecnico di di Torino,Turin, Torino,Turin, Italy Italy (e-mail: (e-mail: [email protected]) [email protected]) *** Politecnico di Torino,Turin, Italy (e-mail: [email protected]) Returnable containers are essential and critical for manufacturing operation operation and and logistics logistics in in the the Returnable containers are essential and critical for manufacturing Returnable containers are essential and critical for manufacturing operation and logistics in the automotive industry. However, container management is still affected by problems such as container Returnable containers are and critical for manufacturing and logistics in the automotive industry. However, container management still affected operation by problems as container Returnable containers are essential essential and critical for is manufacturing operation andsuch logistics inabout the Returnable containers are essential and critical for manufacturing and logistics in the automotive industry. However, container management is still by problems such as container shortage, losses losses or inefficient inefficient handling. These problems lead to affected a lack lack of ofoperation accurate and timely data automotive industry. However, container management is still affected by problems such as container shortage, or handling. These problems lead to a accurate and timely data about automotive industry. However, container management is still affected by problems such as container automotive industry. However, container management is still affected by problems such as container shortage, losses or inefficient handling. These problems lead to a lack of accurate and timely data about container losses flow throughout throughout the handling. automotive supply chain. In In thistowork, work, weofstudy study RFID based automotive automotive shortage, or inefficient These problems lead aa lack accurate and timely data about container flow the automotive supply chain. this aa RFID based shortage, losses or inefficient handling. These problems lead towork, lackwe ofstudy accurate andevaluate timely dataimpact about shortage, losses or inefficient handling. These problems lead to aapproach lack of accurate and timely data about container flow throughout the automotive supply chain. In this we aa RFID based automotive supply network, and a discrete-event simulation (DES) is used to the container flow throughout the automotive supply chain. In this work, we study RFID based automotive supply network, and a discrete-event simulation (DES) approach is used to evaluate the impact container flow throughout the automotive supply chain. In this work, we study aa RFID based container flowreal throughout theautomotive automotive supply chain. In (DES) this supply work, we study RFID based automotive automotive supply network, and a discrete-event simulation approach is used to evaluate the impact of RFID on case of an returnable container chain. The simulation model and container supply and aa discrete-event simulation (DES) approach is used to evaluate model the impact of RFID on real network, case of an automotive returnable container supply chain. The simulation and container supply network, and simulation (DES) approach is to the impact container supply network, and a discrete-event discrete-event simulation (DES) approach is used used to evaluate evaluate the impact of RFID on real case of an automotive returnable container supply chain. The simulation model and method provide the tool to analyze container supply chain performances. Computational results show that of RFID on real automotive returnable container supply chain. The simulation and method provide thecase toolof to an analyze container supply chain performances. Computational resultsmodel show that of RFID on real case of automotive returnable container supply The model and of RFID oncan realthe case ofto an an automotive returnable container supply© chain. chain. The simulation simulation model and method provide tool analyze container supply chain performances. Computational results show that Copyright 2019 IFAC using RFID bring relevant benefits to the supply chain. method provide the tool to analyze container supply chain performances. Computational results show that using RFID can bring relevant benefits to the supply chain. Copyright © 2019 IFAC method provide the tool to analyze container supply chain performances. Computational results show that method provide the tool to analyze container supply chain performances. Computational results show that using RFID can bring relevant benefits to the supply chain. Copyright © 2019 IFAC using RFID can bring relevant benefits to the chain. Copyright © 2019 IFAC Keywords: Supply chain management, simulation, RFID, © 2019, IFAC (International Federation Control) Hosting by Elsevier Ltd. All rights reserved. using RFID can bring relevant benefits of toAutomatic the supply supply chain.automotive, Copyright ©container. 2019 IFAC Keywords: Supply chain management, simulation, RFID, automotive, container. using RFID can bring relevant benefits to the supply chain. Copyright © 2019 IFAC Keywords: Supply chain management, simulation, RFID, automotive, container. Keywords: Supply chain management, simulation, RFID, automotive, container. Keywords: Supply Supply chain chain management, management, simulation, simulation, RFID, RFID, automotive, automotive, container. container. Keywords: •• Inventory Inventory accuracy: accuracy: container container inventory inventory is is often often • Inventory accuracy: container inventory is often 1. INTRODUCTION inaccurate, and the needed container quantity might be not 1. INTRODUCTION •• Inventory accuracy: container inventory is often inaccurate, and the needed container quantity might be not Inventory accuracy: container inventory is often 1. INTRODUCTION • Inventory accuracy: container inventory is often inaccurate, and the needed container quantity might be not properly estimated. A returnable container is a packaging solution used to avoid 1. INTRODUCTION inaccurate, and the needed container quantity might be not properly estimated. A returnable container is a packaging solution used to avoid 1. INTRODUCTION INTRODUCTION inaccurate, and 1. inaccurate, and the the needed needed container container quantity quantity might might be be not not properly estimated. A returnable container is aa packaging solution used to avoid disposing of costly shipping material each time aa product is properly estimated. A returnable container is packaging solution used to avoid disposing of costly shipping material each time product is •properly Inefficient estimated. A returnable container is packaging solution used to properly estimated. handling A returnable container is aa location. packaging solution used to avoid avoid • Inefficient handling leads leads to to excess excess inventory inventory at at disposing of costly shipping material each time a product is distributed to a customer Most automotive parts disposing of costly shipping material each time product is •• Inefficient handling leads to excess inventory at distributed a customer location. Most automotive parts some locations and operations time. disposing ofto costly shipping material eachusing time aaa returnable product is Inefficient handling leads to excess at disposing of costly shipping material each time product is some locations and increased increased operations time. inventory distributed to a customer location. Most automotive parts are stored, shipped and consumed • Inefficient handling leads to excess inventory distributed aa customer Most automotive parts •some locations Inefficient handling operations leads to excess at and increased increased time. inventory at are stored,to shipped andlocation. consumed using returnable distributed to customer location. Most automotive parts some locations and operations time. distributed to a customer location. Most automotive parts are stored, shipped and consumed using returnable containers (Twede & Clarke, 2005). •some Inaccurate tracking: trailers loaded with containers some locations and increased operations time. are stored, shipped and consumed using returnable locations and increased operations time. containers (Twede & Clarke, 2005). • Inaccurate tracking: trailers loaded with containers are stored,(Twede shipped and 2005). consumed using using returnable returnable •might sitInaccurate are stored, shipped and consumed containers & Clarke, tracking: trailers loaded with containers in while considered in transit, thus containers (Twede & 2005). •• tracking: loaded with containers might sitInaccurate in the the yard yard whiletrailers considered in transit, thus Empty returnable shipped containers (Twedecontainers & Clarke, Clarke, are 2005). Inaccurate tracking: trailers loaded with containers containers (Twede & Clarke, 2005). Inaccurate tracking: trailers loaded with containers sit in the yard while considered in transit, thus Empty returnable containers are shipped from from aa warehouse warehouse •might resulting in large pickup time fluctuations, delays and might sit in the yard while considered in transit, resulting inin large pickup timeconsidered fluctuations, delays thus and Empty returnable containers are shipped from a warehouse to the supplier, where they are filled with automotive parts might sit the yard while in transit, thus Empty returnable containers are shipped from a warehouse might sit in the yard while considered in transit, thus resulting in large pickup time fluctuations, delays and to the supplier, where they are filled with automotive parts missed shipments. Empty returnable containers are shipped from a warehouse resulting in large pickup time fluctuations, delays and Empty returnable containers arefilled shipped from a line. warehouse missed shipments. to the supplier, where they are with automotive parts and shipped back to manufacturer’s assembly Once resulting in large pickup time fluctuations, delays and to the supplier, where they are filled with automotive parts resulting in large pickup time fluctuations, delays and missed shipments. and shipped back to manufacturer’s assembly line. Once to theshipped supplier, where they are are filled with with automotive parts missed shipments. to the supplier, where they filled automotive parts and back to manufacturer’s assembly line. toOnce Once parts have consumed, containers are the to missed shipments. and shipped back to manufacturer’s line. missed shipments. parts have been been consumed, containersassembly are shipped shipped toOnce the According According to (Sheffi, (Sheffi, 2003), 2003), those those issues issues are are caused caused by by lack lack and shipped back to manufacturer’s assembly line. and shipped back to manufacturer’s assembly line. Once parts have been consumed, containers are shipped to the According to (Sheffi, 2003), those issues are caused by lack warehouse (often operated by a third-party logistics of accurate and timely data from suppliers and service parts have been consumed, containers are shipped to the According to (Sheffi, 2003), those issues are caused by lack warehouse (often operated by a third-party logistics of accurate and timely data from suppliers and service parts have been consumed, containers are shipped to the According to (Sheffi, 2003), those issues are caused by lack parts have been consumed, containers are shipped to the According to (Sheffi, 2003), those issues are caused by lack warehouse (often operated by a third-party logistics of accurate and timely data from suppliers and service company) where they are treated and providers about to where shipments are, the current warehouse (often operated by aa third-party logistics of accurate and from suppliers company) where they are sorted, sorted, treated and refurbished, refurbished, providers about totimely wheredata shipments are, what what and the service current warehouse (often operated by third-party logistics of accurate and timely data from suppliers and service warehouse (often operated byto treated athe third-party logistics of accurate andis, timely data from suppliers and service company) where they are sorted, and refurbished, providers about to where shipments are, what the current ready to be shipped out again supplier (Lunani & inventory level and where is it located. In particular, aa company) they are sorted, and refurbished, providers about to where shipments are, what the current ready to bewhere shipped out again to treated the supplier (Lunani & inventory level is, and where is it located. In particular, company) where they are sorted, treated and providers about to where shipments are, the current company) where theyout areagain sorted, treated and refurbished, refurbished, providers about to where shipments are, what what the currenta ready to be shipped to the supplier (Lunani & inventory level is, and where is it located. In particular, major source of variability is the current data acquisition Hanebeck, 2008). ready to be2008). shipped out again to the supplier (Lunani & major inventory level and where it located. In particular, source ofis, variability is is the current data acquisitionaa Hanebeck, ready to shipped level is, and where is it In ready to be be2008). shipped out out again again to to the the supplier supplier (Lunani (Lunani & & inventory inventory level is,variability andmanual whereisoperations. isthe it located. located. In particular, particular, major source of current data data acquisitiona Hanebeck, practice that relies on major source of variability is the current acquisition Hanebeck, 2008). practice that relies on manual operations. Containers are source of is the Hanebeck, major source of variability variability isoperations. the current current data data acquisition acquisition Hanebeck, 2008). practice that relies on manual Containers 2008). are critical critical to to ensure ensure the the quality quality of of major that manual Containers areoperations critical (Foster, to ensure ensure the &quality quality of practice manufacturing Sindhu, Blundell, Radio (RFID) practice that relies relies on onIdentification manual operations. operations. Containers critical to the of practice that relies on manual operations. manufacturingare operations (Foster, Sindhu, &quality Blundell, Radio Frequency Frequency Identification (RFID) is is an an AutoID AutoID Containers are critical to ensure the of Containers are critical to containers ensure the quality of technology manufacturing operations (Foster, Sindhu, & Blundell, Radio Frequency Identification (RFID) is an AutoID 2006). OEM must ensure that are in the right that allows for automatic extraction of the object manufacturing operations (Foster, Sindhu, & Blundell, Radio Frequency Identification (RFID) is an 2006). OEM must ensure that containers are in the right technology that allows for automatic extraction of theAutoID object manufacturing operations (Foster, Sindhu, & Blundell, Radio Frequency Identification (RFID) is an AutoID manufacturing operations (Foster, Sindhu, & Blundell, Radio Frequency Identification (RFID) is of an AutoID 2006). OEM must ensure that containers are in the right technology that allows for automatic extraction the object place at the right time. However, there are some issues identity at key points along the supply chain, without the 2006). OEM must ensure that containers are in the right technology that allows for automatic extraction of the object place at the right time. However, there are some issues identity at key points along the supply chain, without the 2006). OEM must ensure that are in the right that for automatic extraction of the object 2006). OEM must ensureautomotive that containers containers are some in container theissues right technology technology that allows allows for automatic extraction of the object place at the right time. However, there are identity at key points along the supply chain, without the related to the current returnable need of manual operations. This technology does not place at the right time. However, there are some issues identity at key points along the supply chain, without the related to the current automotive returnable container need of manual operations. This technology does not place at the right time. However, there are issues identity at key points along the supply chain, without the place atchain thethe right time. automotive However, there are some some issues identity atmanual keycontact points along the supply chain, without the related to current returnable container need of operations. This technology does not supply management (Lunani & Hanebeck, 2008, require visual or clear line of sight, as it uses radio related to the current automotive returnable container need of manual operations. This technology does not supply chain management (Lunani & Hanebeck, 2008, require visual contact or clear line of sight, as it uses radio related to the current automotive returnable container need of manual operations. This technology does not related to themanagement current automotive returnable container need ofvisual manual operations. Thisof technology doesradio not supply chain (Lunani & Hanebeck, 2008, require contact or clear line sight, as it uses Chism, 2010): signals. supply chain visual Chism, 2010): signals. supply chain management management (Lunani (Lunani & & Hanebeck, Hanebeck, 2008, 2008, require require visual contact contact or or clear clear line line of of sight, sight, as as it it uses uses radio radio supply chain management (Lunani & Hanebeck, 2008, require visual contact or clear line of sight, as it uses radio Chism, 2010): signals. Chism, 2010): signals. •Chism, Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event 2010): signals.technology can automatically generate event data •Chism, 2010): Shortage: wrong shipments and misplacement lead signals. RFID data that that •• 15-20% Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event data that that to container losses and 20-25% excess purchased digitally describe how physical entities move through the Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event data to 15-20% container losses and 20-25% excess purchased digitally describe how physical entities move through the ••containers. Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event data Shortage: wrong shipments and misplacement lead RFID technology can automatically generate event data that that to 15-20% container losses and 20-25% excess purchased digitally describe how physical entities move through the Containers might also be used as work in supply chain processes (and, also, across different parties). to 15-20% container losses and 20-25% excess purchased digitally describe how physical entities move through the containers. Containers might also be used as work in supply chain processes (and, also, across different parties). to 15-20% container losses andalso 20-25% excess purchased digitally describe how (and, physical entities move through through the to 15-20% container losses and 20-25% excess purchased digitally describe how physical move the containers. Containers might be used as work in supply chain processes also,entities across different different parties). process (WIP) storage at supplier facilities. containers. Containers might also be used as work in supply chain processes (and, also, across parties). process (WIP) storage at supplier facilities. The aim of the present work is to use Discrete Event containers. Containers might also be used used as as work work in in supply supply chain processes (and, also, across different parties). containers. Containers might also be chain processes (and, also, across different parties). process (WIP) storage at supplier facilities. The aim of the present work is to use Discrete Event (WIP) storage at facilities. The aim of the present work is to use Discrete Event •process Substitute cost: for lost (DES) to how RFID can the (WIP) storage at supplier supplier facilities. The aim of the present work is to use Discrete Event process storage at supplier facilities. •process (WIP) Substitute cost: for each each lost container, container, it it is is Simulation Simulation (DES) to investigate investigate how RFID can reduce reduce the The aim of the work to use Event The aim of(DES) the present present workinis is toautomotive use Discrete Discrete Event •• Substitute cost: for each lost container, it is Simulation to investigate how RFID can reduce the necessary to provide suppliers with an expendable impact of inventory inaccuracy the returnable Substitute cost: for each lost container, it is Simulation (DES) to investigate how RFID can reduce the necessary to provide suppliers with an expendable impact of inventory inaccuracy in the automotive returnable •cardboard cost: for lost container, it (DES) to investigate how RFID can reduce the •necessarySubstitute Substitute cost: for each each lost cost container, it is is Simulation Simulation (DES) chain. toinaccuracy investigate how RFID can reduce the to provide suppliers with an expendable impact of inventory in the automotive returnable packaging as backup. The total is a relevant container supply The relative effect of inventory necessary to provide suppliers an expendable impact of inaccuracy in the cardboard packaging as backup. The with total cost a relevant container supply chain. The relative effect of returnable inventory necessary to OEM, provide suppliers with an is expendable impact of inventory inventory inaccuracy in the automotive automotive returnable necessary to provide suppliers with an expendable impact of inventory inaccuracy in the automotive returnable cardboard packaging as backup. The total cost is aa relevant container supply chain. The relative effect of inventory loss for the especially if an urgent shipping is inaccuracy on container stock level is compared to cardboard packaging as backup. The total cost is relevant container supply chain. The relative effect of loss for the OEM, especially if an urgent shipping is inaccuracy on container stock level is compared to other other cardboard packaging as backup. The The total cost is isshipping relevant container supply chain. stock The relative relative effect of inventory inventory cardboard packaging as backup. total cost aa relevant container supply chain. The effect of inventory loss for the OEM, especially if an urgent is inaccuracy on container level is compared to other necessary. loss for the OEM, especially if an urgent shipping is inaccuracy on container stock level is compared to necessary. loss for for the the OEM, OEM, especially especially if if an an urgent urgent shipping shipping is is inaccuracy inaccuracy on on container container stock stock level level is is compared compared to to other other loss other necessary. necessary. necessary. necessary.
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sources of variability in the supply chain, such as lead time and demand fluctuation.
3. THE EMPIRICAL CONTEXT AND A DES SIMULATION MODEL
The DES model was developed starting from a real OEM supply chain, made of a warehouse and a supplier. By means of factorial design, the main influencing variables are identified, and system performance with and without RFID are compared.
A simulation model has been developed to study the introduction of RFID in a container supply chain of major OEM (the Company, in the following) that will remain anonymous for confidentiality reasons.
The remainder of the paper is structured as follows: Section 2 reviews the relevant literature. The simulation model is presented in Section 3. Section 4 presents the computational results and the Section 5 concludes the paper.
3.1 The company containers supply chain overview Fig. 1 shows an overview of OEM supply chain. The stages in the supply chain includes: • Assembly plant • Plant cross-dock • Regional cross-dock • Suppliers.
2. LITERATURE REVIEW There are several works related to automotive containers tracking. Foster et al. (2006) developed a case study with a leading OEM regarding RFID tagging of high value automotive stillages. The authors developed a comprehensive study of the entire stillage supply chain to assess the feasibility of an RFID tracking system. Lunani and Hanebeck (2008) implemented an RFID system over an automotive returnable container supply chain, reporting positive effects on performances indicators. In particular, 80% reduction in expendable cost and 5-15% reduction in fleet shrinkage was observed. Velandia et al. (2016) proposed an RFID system for managing crankshafts manufacturing and assembly. They concluded RFID can provide several advantages to the manufacturing process, but a careful integration process of this technology is necessary, involving both technologists and management.
Fig. 1. Container supply chain overview.
Khan et Al. (2006) presented a very interesting application of RFID for vehicle components recycling: using RFID to control closed-loop recycling would allow for automotive dump reduction.
Containers can either travel full with parts (black arrows) or empty (green) from multiple suppliers and across several handling points. For the sake of this simulation work, a subset of this system has been considered. This subset is composed by: • Assembly plant (AP). • Empty container Warehouse (EW) • Single supplier (SP)
Kirch and Poenicke (2015) studied RFID application to confirm the completion of automotive assembly processes: a wristband is proposed as viable solution to integrate automatic confirmation of tasks and operations. Herrmann et al. (2015) investigated the application of RFID on finished vehicle distribution. The authors identified the current final processing of Finished Vehicle as a weak point of the supply chain. An RFID transponder solution is evaluated, to enable an optimized finished vehicle steering process.
Fig. 2 shows a schematic of the system under study.
Tabanli and Ertay (2012) realized an RFID-based Kanban system to be used by an automotive safety components supplier. Traditional Kanban cards are replaced with RFID Kanban cards, to enable real time visibility on inventory. Thoroe et al. (2009) analysed the impact of RFID on container management using a deterministic inventory model. The analysis is based on a general reverse logistics model. They also studied the profitability of the RFID system and determined relevant relations of costs and benefits. Alternatives of implementing an RFID-based container tracking system was also discussed.
Fig. 2. Simulation system overview
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Let us describe how those parameters were derived from real world data.
Table 1 summarizes the main variables used to model the system. Table 1. Simulation model variables Name SEW SSP SSEW SSSP FSP FEW LTEW,SP LTSP,AP LTAP,EW LTAP OULSP RAP RSP ε α DAP
Description EW containers stock level SP containers stock level EW safety stock level SP safety stock level SP delivery frequency EW delivery frequency Lead time EW to SP Lead time SP to AP Lead time AP to EW AP transit time SP order-up-to-level AP containers replenishment SP containers replenishment Error magnitude Container counting accuracy AP demand
Each time containers received at SP do not match what SP required, a document called Return Discrepancy Report (RDR) is issued from SP to OEM. RDRs is a measure counting mistake occurrence. If total RDRs are indicated as TRDR and total shipment are indicated as T SHIP, we can define α as follows:
Variable Type Controllable Controllable Controllable Controllable Controllable Controllable Random Random Random Controllable Controllable Controllable Controllable Random Controllable Random
α=
α = α(αSP , αEOP ) It is assumed manual counting accuracy is the same for EW and SP: α = (αSP × αEOP ) = (αMan )2 Based on eight months OEM data, αMan = 83%. RDR also reports received vs. shipped quantity, so that for each RDR is possible to derive error magnitude as fraction of received quantity, according to:
Let us describe the container flow in the model:
• • •
•
•
•
TSHIP − TRDR × 100 TSHIP
Notice that this value is the effect of both shipping and receiving location counting accuracy.
Variables have been split between two kinds • Controllable: they can be controlled in real world to modify supply chain performance • Random: affected by uncertainty and based on statistical distribution
•
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𝜀𝜀𝑖𝑖 = According to FSP, SP consumes containers stock to fulfill demand DAP. Outbound qty. is deducted from stock SSP and shipped to AP. After time LTSP,AP , containers arrive to AP, and spend a transit time LTAP inside the plant. The containers are delivered from AP to EW with lead time LTAP,EW. Once at EW, inbound containers are manually counted (accuracy αM) and SEW is increased with counted quantity According to FEW, containers are consumed from stock SEW to fulfill SP demand DSP. Outbound qty. is counted with accuracy αM, deducted from stock SEW and shipped to SP After time LTEW,SP , containers arrive to SP and are manually counted with accuracy αM. Counted quantity is added to SSP. Containers are moved to SP storage closing the loop.
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖 − 𝑆𝑆ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 × 100 𝑆𝑆ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
Based on OEM data sample, ε is normally distributed. 3.1.2 Supplier replenishment policy The supplier uses a periodic review inventory policy, i.e., an order is placed such that the level of current inventory plus the replenishment lot size equals a pre-specified level called order-up-to-level (OUL) (Chopra and Meindl, 2016), i.e., OULSP = DAP (FSP + LTEW,SP ) + SSSP Empty containers flow from EW to SP each time the supplier places and order, i.e., each R time units. The replenishment quantity is defined as: R SP = OULSP − IP Where IP is supplier’s inventory position, defined as IP = OH + IT Where OH is the current on-hand inventory and IT current intransit quantity.
A special effort has been made to realistically model the manual counting process occurring at SP and EW, so to show the benefit deriving from RFID automation.
3.1.3 Performance indicators System performance is evaluated by the following indicators: • Supplier Type I service level. It considers the total number of containers stock-out occurred and the total number of shipments:
3.1.1 Manual counting process The empty container manual counting process is defined by two parameters (as mentioned in Table 1). • α : Counting accuracy • ε : Error magnitude
SPSL1 =
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Total supplier shipments − Total Stockout occured × 100 Total supplier shipments
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•
SSP will either be updated with actual truckload or affected by error ε.
Supplier Type II service level (Fill rate). This indicator measures the total fraction of parts demand that has been shipped using containers: SPSL2 =
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3.2.3 EW operations & shipping manager
SP Total containers shipped × 100 SP Total containers requirement
Fig. 4 shows schematic of EW shipping agent.
EW Type II service level (Fill rate). Measures the total fraction of replenishment quantity that has been satisfied by EW: EWSL2 =
EW Total containers shipped × 100 EW Total containers requirement
3.2 DES Model description
Fig. 4. EW operations agent
To reproduce this system by means of DES, the following agents have been defined: • EW shipping manager • EW operations • SP shipping Manager • SP Operations
Every FEW, an order entity is generated and triggers the creation of SP replenishment requirement. If OUL SP is less than zero, no container will be shipped. According to α, R SP is either initialized to zero or to ε. The initialization of the variable affects how many container entities are loaded by EW operations.
Three main entities simulation entities are employed: Container entity, Truckload entity, Order entity.
Upon EW replenishment manager signal, container release to SP is started, and entity is released from storage until set shipping quantity REW is reached.
In the following section an overall description of the model implementation is provided. The model has been developed using ARENA® by Rockwell Automation.
Fig. 5 shows schematic of EW operations agent.
3.2.1 SP shipping manager An order entity is generated according to shipping frequency F2. This entity triggers the creation of RAP according to DAP and lot size. The agent verifies SP stock and trigger containers release. If stock level is not sufficient, the agent will set as RAP what available in stock.
Fig. 5. EW operations agent 3.2.4 RFID simulation parameters
Quantity RAP is released from storage and consolidated into truck entity. Truck entity will experience a lead time from SP to EW according to:
The application of RFID is simulated by increasing counting accuracy α to three different levels, as reported in Table 2: Table 2. RFID counting accuracy
𝐿𝐿𝐿𝐿𝑆𝑆𝑆𝑆,𝐸𝐸𝐸𝐸 = 𝐿𝐿𝑇𝑇𝑆𝑆𝑆𝑆,𝐴𝐴𝐴𝐴 + 𝐿𝐿𝐿𝐿𝐴𝐴𝐴𝐴 + 𝐿𝐿𝐿𝐿𝐴𝐴𝐴𝐴,𝐸𝐸𝐸𝐸 Where LTAP is the transit time containers spend at AP to be used and emptied again.
αRFID
100%
99.9%
98%
Description
Ideal case
Realistic value Rahmati et al. (2007)
Worst-case scenario.
3.2.2 SP operations 3.4 Handling time reduction
Fig. 3 shows schematic of EW operations agent.
As a complement to the simulation work, potential handling time reduction by means of the RFID automatic counting has been estimated. Average container counting time, was measured by means of stopwatch analysis, performed at OEM warehouses. Assuming RFID counting occurs instantaneously, total time saving (Tsav) is defined by the following equation
Fig. 3. SP receiving operations agent A truckload entity arrives from EW operations to SP operation agent after time LTEW,SP. According to accuracy α,
TSav = Avg. Stack counting time × Total stacks. Results are presented in Section 4. 333
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4. COMPUTATIONAL RESULTS
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Set2 SPSL2 3.20%11.20%
Among the possible experimentation strategies, a Design of Experiment (DOE) based on a full factorial design with two levels (i.e., factorial 2k) was chosen. Results are analysed by means of ANOVA.
26.10% 59.40%
Choice of factors for each set is summarized by Table 2, together with the rationale for the experimental scenarios setting. DAP
ε
LTEW,SP
Error
Table 3. Experimental scenarios Fig. 7. Set 2 results
SET 1 SET 2
SSEW SSSP α FEW DAP LTEW ε
Experiment objective
Relative effect of increasing counting accuracy (manual to RFID level) compared to other controllable factors
Max Improvement for EW SL2 13.5
Relative effect of counting error magnitude compared to other variability sources (Lead time and demand)
% Improvement
Factor (2k)
Each set is repeated for two different dataset and three different levels of RFID accuracy. The simulation length is 365 days, with 5 days warm-up time. For each factor combination, 50 replications have been made.
13
RFID 100% accuracy
12.5 12 11.5
RFID 99.9% Accuracy RFID 98% accuracy
11 10.5
Fig. 6 shows SET1 simulation results with datasets 1 and 2, respectively, in the case counting reading accuracy is 100%.
Fig. 8a. Maximum Improvement for EWSL2 .
Max Improvement for SP SL2 % Improvement
1.5 1
RFID 100% accuracy RFID 99.9% accuracy
0.5
RFID 98% accuracy
0 Fig. 6. Set 1 results Fig. 8b. Maximum Improvement for SPSL2 .
For both datasets, increasing reading accuracy to RFID level, influences the performance indicators up to 33% in dataset1 and 26.2% in dataset 2.
A 13% EWSL2 increase can be achieved by means of 100% accurate counting process. Even for the less accurate case, the improvement is still around 11.5%. For SPSL2 the improvement is less significant and reaches a maximum of 1.5%.
Fig. 7 shows result From SET2 for dataset 1. For SPSL2 , uncertainty on the lead time (ULT) contributes to 62%, while UE accounts for 10.6%. This result depends on the specific supply chain under study, in which the lead time from SP to AP is much larger than that from EW to SP. As consequence, reducing counting error at EW will improve service level much more than in SP.
The application of RFID can result in potential safety stock reduction, as shown by the Fig. 9. It is possible to keep the same SP service level while reducing the container stock at SP, as shown by Fig. 9. As shown in the figure, considering a 100% accurate reading system, the same service level can be achieved with one day less of stock at supplier. This suggests that using RFID disclose interesting supply chain optimization opportunities.
Fig. 8 (a & b) show the potential improvement on the performance indicators as consequence of increased reading accuracy.
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REFERENCES Chism, C. (2010). Optimizing and Benchmarking Returnable Container Processes within an Automotive. RTI scholar works. Chopra S and Meindl, P. (2016). Supply Chain Management: Strategy, Planning, and Operation, sixth edition. Pearson. Foster, P., Sindhu, A., & Blundell, D. (2006). A case study to track high value stillages using RFID for an automobile OEM and its supply chain in the manufacturing industry. In 2006 4th IEEE International Conference on Industrial Informatics, 56-60 Herrmann, S., Rogers, H., Gebhard, M., & Hartmann, E. (2015). Co-creating value in the automotive supply chain: an RFID application for processing finished vehicles. Production Planning & Control. 26 (12), 981993. Khan, O., Scotti, A., Leverano, A., Bonino, F., Ruggiero, G., & Dörsch, C. (2006). RFID in automotive: A closedLoop approach. 2006 IEEE International Technology Management Conference (ICE),1-8 Kirch, M., & Poenicke, O. (2015). Using the RFID Wristband for Automatic Identification in Manual Processes-The RFID Wristband in the Automotive Industry. European Conference on Smart Objects, Systems and Technologies,1-7. Thoroe, L., Melski, A., & Schumann, M. (2009). The impact of RFID on management of returnable containers. Electronic Markets, 19(2-3), 115-124. Lunani, M., & Hanebeck, C. (2008). RFID-enabled returnable container management: Solution to a chronic and wasteful automotive industry problem. IBM Global services. Rahmati, A., Zhong, L., Hiltunen, M., & Jana, R. (2007). Reliability Techniques for RFID-Based Object Tracking Applications. 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07), 113-118. Sheffi, M. A. (2003). The Impact of Automatic Identification on Supply Chain Operations. The International Journal of Logistics Management, 14(1), 117. Tabanli, R., & Ertay, T. (2012). Value stream mapping and benefit–cost analysis applicationfor value visibility of a pilot project on RFID investmentintegrated to a manual production control system—a case study. Int J Adv Manuf Technol. 66(5-8), 987-1002. Twede, D., & Clarke, R. (2005). Supply chain issues in reusable packaging. Journal of Marketing Channels.12(1), 7-26 Velandia, S., Kaur, N., Whittow, W., Conway, P., & West, A. (2016). Towards industrial internet of things: Crankshaft monitoring,traceability and tracking using RFID. Robotics and Computer-Integrated Manufacturing. 41, 66-77
Fig. 9. Potential safety stock reduction 4.1. Handling time reduction As explained in Section 3.4, by means of stopwatch analysis it is possible to estimate the manpower hour reduction enabled by RFID automatic counting. Results for a specific supplier/assembly plant combination are reported in Table 4. Table 4. Time reduction for standard pooled container. Average Stack counting time Total shipped containers Total Flow Attrition Actual total flow Stack size Stacks (rounded) Yearly handling time reduction
8.54 Sec 884057 1768114 6.3% 1656723 9 98229 430 Hours
Those data are related to only the several container types used by the OEM. 5. CONCLUSIONS A Discrete-Event simulation model of an automotive returnable container supply chain was presented to evaluate the impact of the RFID introduction. The model has been developed in collaboration with a major OEM, and the simulation results show that RFID can bring interesting supply chain optimization opportunities in terms on service level increase, stock reduction and handling time reduction. The model logic is flexible, and it can be easily adjusted to simulate different kinds of supply chains. One of the possible extensions to the current work is to consider a multiple supplier case, where several containers are shared by several suppliers. Also, the simulation model could be integrated with optimization models to provide a solid base to proof the benefit of RFID to the future supply chain 4.0. ACKNOWLEDGMENT This research is partially supported by the Natural Sciences and Engineering Research Council of Canada discovery grant (RGPIN-2014-03594).
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