RFID-based material delivery method for mixed-model automobile assembly

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RFID-based material delivery method for mixed-model automobile assembly Yu Zhenga, Siqi Qiua, , Fei Shenb, Changpeng Hec ⁎

a b c

Institute of Intelligent Manufacturing and Information Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China ANJI Automotive Logistics Co., Ltd, 12-17F No.100 Jiangpu Rd., Shanghai 200092, PR China Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, PR China

ARTICLE INFO

ABSTRACT

Keywords: RFID Materials handling Mixed model sequencing Material delivery Material demand model Line feeding

In order to deal with the increasingly intensive market competition and make rapid response to customers, the automobile manufacturing enterprises in China begin to explore new methods of material supply to meet the management requirements of precision and efficient production. For this purpose, an RFID-based material delivery method for mixed-model automobile assembly is proposed in this paper. The assembly line material classification and its distribution pattern are analyzed, and the production progress data acquisition scheme with RFID is designed. Based on the periodic inventory monitoring strategy, a buffer inventory demand model and an optimized delivery plan are formed to provide accurate material feeding service for the assembly line. In addition, an RFID-based material distribution prototype software for automobile mixed-model assembly line is developed and applied. This software has realized the RFID information collection, buffer material requirements management, as well as the logistics distribution and replenishment plan. It provides a feasible method and solution for automobile enterprises.

1. Introduction With the trend of micro-growth in the global automotive market, increasingly intense competitions have arisen in Chinese automotive industry. The OEMs (original equipment manufacturer) of automobile industry have been launching new car models continuously, which makes the life cycle of automotive products shorter and the upgrade of products faster. Meanwhile, customers are more and more eager to the personalized products. To meet the increasing personalized needs, automobile manufacturers have been trying to provide all the external options, like selecting your own desired colour, interior, etc., which leads to the results that automobile manufacturers have to introduce mixed-mode assembly production lines (cf. Franz, Koberstein, & Suhl, 2015; Cortez & Costa, 2015). Mixed-model assembly production lines and fast assemblies require the manufacturers conduct line feeding according to the dynamic needs of the assembly line of materials, while organizing a large number of suppliers to deliver a large quantities of materials frequently and just in time. It is really a great management challenge for the automobile OEMs, especially in materials inventory control, production management, and materials distribution management. At present, the material distribution processes and methods of mixed-model assembly lines are mostly after treatment or passive

⁎

activities according to the assembly line requirements. Usually, when buffer stock balance arrives at the safe stock level, the demand for the materials will be invoked to the workshop warehouse, then the workers pick, sort and delivery the materials to the destination stations according to the demand. Material consumption information is obtained by artificial line inspection, manual empty containers’ barcode scanning and Andon system. This method relies on manual operations, and may easily lead to many problems such as high labour cost, low operation efficiency, and slow response. Due to the subjective factors of the workers, abnormalities including material pulling earlier, later or in an unbalanced are usually caused. The barcodes, which only contain limited information, cannot be used repeatedly, and need to be manually operated in the modification or updated with high labour cost and time consumption. In addition, the barcodes are easily got lost, damaged, polluted and unrecognizable, and consequently result in the loss of data. Therefore, an automatic, rapid, accurate and reliable information transmission and acquisition method is really required. Compared with barcodes, RFID has many advantages including larger amount of data storage, reusable, as well as more efficient and convenient recognition (Ngai, 2010). With RFID, it becomes possible to get the real-time material consumption information in order to provide frequent, small batch, and active replenishment service for assembly lines to reduce the pressure on work-in-process buffers.

Corresponding author. E-mail address: [email protected] (S. Qiu).

https://doi.org/10.1016/j.cie.2019.106023

0360-8352/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Yu Zheng, et al., Computers & Industrial Engineering, https://doi.org/10.1016/j.cie.2019.106023

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In order to improve the operation management efficiency, this paper studies a new material distribution and delivery method for mixed-model assembly lines by using the RFID technology in production data acquisition. The related research work is reviewed in Section 2. Current status and existing problem of material delivery for mixedmodel automobile assembly is analyzed in Section 3. An RFID-based mixed-model automobile assembly buffer material delivery method is proposed in Section 4. And in Section 5, the corresponding software is developed, and applied in an automobile OEMs.

Just-In-Sequence (JIS) on the fluidity of supply chains and processes of logistics suppliers, and proposed a genetic algorithm to reduce risks of supply disruptions at assembly line of finished goods. In summary, many scholars have done a lot of detailed and useful work on material distribution problem of mixed-model assembly line, covering distribution method and algorithms to solve the distribution model, and formed a relatively complete, systematic theoretical system and methodology. However, existing studies are mainly focused on the static materials distribution problem, which conducts the pre-calculation of the material consumption information, including the types, quantity and the corresponding demand time window, based on every shift of production plan, and then completes the appropriate resources allocation and distribution plan. However, study on the dynamic material distribution problem is relatively fewer, in which the material consumption information is estimated according to the real-time production progress.

2. Literature review 2.1. Material distribution problems of mixed-model assembly line The study of the material distribution problems of mixed-model assembly line includes material demand generation, transportation resources (such as trucks, trailers and labours) distribution control, line feeding and workshop scheduling. Some scholars study the smooth and precise schedule of mixed-model production in order to make different kinds of material consumption and demand relatively stable, thereby to reduce the difficulty of material distribution, and the realize a simple batch distribution (Boysen, Fliedner, & Scholl, 2009a, 2009b, 2009c). However, material consumption rate is actually not smooth, and precise schedule is almost impossible to achieve. The material distribution problem of mixed-model assembly line has a certain similarity to the Vehicle Scheduling Problem (VSP); in addition, the former is more sensitive to time and more dependent on distribution demands. Studies are concentrated on the optimization objectives, like distribution costs, the total delivery time, inventory control and service level. The main goal is to deliver the right materials to the right buffer by assembly line in right quantities, at right time, and with the constraints of time windows as well as conveyors’ quantity and capacity. The studies mainly include following aspects.

2.2. Application of RFID technology in manufacturing operation management The studies on the application of RFID technology in operation management can be classified into following three aspects. (1) Production status monitoring and visualization With the development of computer integrated manufacturing technology in the past 40 years, new concepts and systems have been emerging as shown in Fig. 1, such as Product Data Management (PDM), Simulation Data Management (SDM), Enterprise Resource Management (ERP), Manufacturing Execution System (MES), Test Data Management (TDM), etc. ERP focuses on enterprise level resource planning and management, and MES focuses on process execution management at the workshop level. RFID technology provides a means for the real-time data acquisition which is an important function of MES. Costa, Carvalho, Fernandes, Alves, and Silva (2017), Wang (2014), Cao, Jiang, Lu, Liu, and Jiang (2017), Hu, Lewis, Gan, Phua, and Aw (2014), Ding, Jiang, and Su (2018) studied and developed RFID-based real-time monitoring systems that made the production process transparent, and material flow and information flow synchronized. Yin, Tserng, Wang, and Tsai (2009), Oner, Ustundag, and Budak (2017), Abdullah, Ismail, Halim, and Zulkifli (2013) also studied RFID-based production management systems, and applied them to enhance materials and equipment identification, quality tracking and production process monitoring, which indeed improved the production performance, and consummated the manufacturing process workflow management and information collection. Velandia, Kaur, Whittow, Conway, and West (2016), Zhu, Tan, Ren, Ni, and Guan (2012), Yang, Xu, Wong, and Wang (2015), Yang, Zhang, and Chen (2016) studied the Manufacturing Execution System (MES) with RFID for crankshaft manufacturers, household electrical appliances manufacturers and mass customization enterprises, which is to track the manufactured objects, collect real-time manufacturing information, and identify fluctuations or unbalance. In addition, the system is integrated with the Enterprise Resources Planning (ERP) system, leading to the great improvement in production closed-loop control, management decision-making, production efficiency and product quality.

(1) Determination of the distribution time, stations and quantities Andriolo, Battini, Persona, and Sgarbossa (2016) proposed a cost model of different materials feeding processes (kitting, line storage, and just in time delivery), considering the impact of parts features on the total delivery cost of materials to assembly line workstations as a criterion to directly select the feeding method for each part type. The best materials delivery strategy can be determined based on the economic basis. Lu, Zhu, Han, and Hu (2018) proposed an integrated decisionmaking mathematical model with the objective of minimising the number of deliveries. The material supply problem of aircraft assembly line is studied on the basis of material batching and tow-trains scheduling problems. A hybrid endocrine-immune algorithm (HEIA) was proposed to jointly make decisions on the delivery batch, delivery time and storage positions of each job’s materials. (2) Algorithms to solve the distribution model In order to select the right quantity of each part to be supplied at the right time under a set of constraints, Fathi, Alvarez, and Rodríguez (2014) proposed a new memetic ant colony optimization-based heuristic algorithm. A performance measure called SI (smoothness index) was also used to recognize all the possible differences among the solutions. Triki, Mellouli, and Masmoudi (2017) proposed a new extension of SALBP-2, which aims to minimize both the cycle time and the cost per time unit (hour) of a line for a fixed number of stations to satisfy the constraints of precedence between tasks and compatibility between resources. And a new version of multi-objective genetic algorithm (MOGA) called hybrid MOGA (HMOGA) is elaborated. Braekers, Ramaekers, and Van Nieuwenhuyse (2016) summarized the related algorithms for VRP including exact algorithms and heuristic algorithms. Gaston, Dario, Giovanni, Jesus, and Arturo (2017) studied the effect of

(2) Closed-loop production planning and control The calculation of material requirements in the existing ERP system is based on static data with a fixed lead time. Because the actual state of the resources cannot be obtained in real time, existing ERP systems cannot effectively respond to emergencies. Kwon, Kang, Yoon, Sohn, and Chung (2014) proposed an advanced process management method for RFID data mining and applied it to a real time process control system connected to the RFID-based Enterprise Information System 2

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Enterprise Operation Management

Workshop Operation Control

Master Production Plan Material Requirement Plan Procurement Management Cost Control ……

Workshop Manufacturing Plan Plans Execution Management Real-time Data Acquisition Quality Control ……

ERP

MES

PDM/PLM

…… SDM

Function and ability of systems

TDM

Computer Integrated Manufacturing(CIM) System

Information interaction between systems

Fig. 1. ERP and MES concept in CIM context.

Most of the current research and application focus on information acquisition and monitoring with experimental application, and is lack of further exploration and application of the information collected by RFID. Existing research has less consideration of the uncertainty of the dynamic production situation in discrete manufacturing process, especially in accurate forecast and control of production scheduling with the transparent information. With the RFID technology and real-time production information, the accurate material distribution can be actively implemented, and the buffer material accumulation or shortage can be avoided. Based on the previous research, this paper proposes a more precise material distribution model for mixed-model automobile assembly line, which is different from the traditional materials distribution according to the static original production plan or line side inventory.

(EIS). The system was able to perform predicting and tracking of real time process and inventory control. Zhang, Tang, Huang, and Xu (2016) introduced a paradigm of distributed intelligent manufacturing control system based on agent, which can make the real-time decision based on the operating status of the system. According to the scheduling and control architectures currently employed in flexible assembly lines (FALs) that lack the flexibility and reconfiguration capacity to manage disturbances when they occur, Barenji, Barenji, and Hashemipour (2016) examined the potential enhancement of FAL performance through the use of a radio-frequency-identification-enabled multi-agent scheduling and control system (RFID-enabled MASCS). The uptime productivity and production rate of a FAL can be increased. (3) Workshop materials distribution and logistics control optimization Zhong et al. (2015) creatively introduced RFID-Cuboids to establish a data warehouse. A holistic Big Data approach is proposed to excavate frequent trajectory and it can support further decision-makings such as logistics planning and scheduling. Fang, Huang, and Li (2013) built up a multi-agent-based and event-driven WIP management platform for the pervasive manufacturing, taking use of RFID technology to track the real-time production and distribution process in dynamic workshop. According to the characteristics of multi-frequency material requirements under the mode of Just-in-time (JIT) and by-order, Huang, Qu, Zhang, and Yang (2012), Poon, Choy, Chan, and Lau (2011), Poon et al. (2011) developed the real time operation decision support system and warehouse operation planning system. The systems are based on the RFID technology and aim at the unpredictable risk of material demanding and the difficulties in preparing additional material inventory. Genetic algorithms are applied to find the solution of stochastic material requirements, and to work out the optimum of small batch replenishment and distribution planning. In the research work above, the application of RFID technology in production management has attracted a great deal of attention, which offers a new way and solution for manufacturers to manage market challenges. RFID technology can replace existing identification method, and make the manufacturing resources and work in process (WIP) intelligible. Through RFID technology, the manufacturing process data and information are perceived and analyzed to assist production and logistics operation automatically and intelligently.

3. Current status and existing problem of material delivery for mixed-model automobile assembly 3.1. Current status of material delivery for mixed-model automobile assembly Lean thinking stems from the Japanese Toyota Production System (TPS) (Chiarini, Baccarani, & Mascherpa, 2018) and was first coined by Krafcik (1988). Lean production provides a total system view of the manufacturing process to maintain efficiency, quality, and flow through the system (Womack, Jones, & Roos, 1990). Lean production is an integrated socio-technical system whose main objective is to eliminate waste by concurrently reducing or minimizing supplier, customer, and internal variability (Shah & Ward, 2007). JIT practices, waste reduction, improvement strategies, defect-free production, and work standardization are the principal characteristics of lean manufacturing (Botti, Mora, & Regattieri, 2017). Currently, lean thinking is widely used in supply chain and in-plant supply. Mácsay and Bányai (2017) studied the TPS, as a precursor of the lean philosophy, in the milkrun based in-plant supply. Material delivery for mixed-model automobile assembly is based primarily on the material delivery method, which applies lean manufacturing principle and combines manufacturing resource planning (MRP II) and JIT strategy. Material delivery is driven by the combination of a daily material demand plan, which is related to the daily production plan developed by the production planning 3

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MRPII push production information flow JIT pull production information flow material flow

Material purchasing plan

Production Department Weekly production plan

Workshop Department

Material daily requirements plan

vehicle queue material requirements

Daily production plan

Supply

VMI Warehouse

Supplier

replenishment

Workshop internal stock painting offline/ artificially scanning/to-beassembled vehicle queue

Material delivery

Emergency delivery

ANDON

Buffer

Buffer

Buffer

Buffer

artificially line inspection/ manually barcode scanning

Buffer

Fig. 2. Typical material delivery mode for mixed-model automobile assembly.

department, and material consumption pulling, which is related to the production process at the assembly site. This typical material delivery mode in mixed-model automobile assembly is schematized in Fig. 2. The details are as follows: Material delivery for mixed-model automobile assembly primarily uses the material pulling delivery method, which is based on JIT ideas such as the material ANDON system, KANBAN technology, and the production pulling system (PPS). The material ANDON system involves a material call button near the assembly station. When an assembly worker observes that the buffer material quantity is less than a predefined value, the worker presses a call button to send out a material request. As a result, electronic display systems at the assembly site and the workshop internal stock department instantly report material demand in the assembly line. Subsequently, internal inventory logistics personnel prepare the material and forklift operators, by following the vehicular terminal’s instruction, deliver the materials to the production buffers that sent out the material requests in the specified time, which completes the material delivery online task. Plant logistics personnel periodically inspect the mixed-model assembly line and scan the material barcodes on empty material containers. Electronic data interchange (EDI) data are transmitted wirelessly to report buffer material demands to the workshop internal stock department that coordinates the material pulling orders. Internal stock logistics personnel prepare the materials, and forklift operators deliver the materials to the production buffers in the specified time by following the vehicular terminal’s instructions to complete material delivery online demands. Additionally, the buffer zone between painting assembly offline and general assembly online has the capacity to buffer 70–100 vehicle bodies waiting for assembly. In some automobile manufacturing enterprises, a vehicle identification number (VIN) scan station is set before the general assembly line for manual scanning to get the assembly

queue. The PPS generates the material pulling information according to the production cycle and the bill of materials (BOM) breakdown table to facilitate internal stock material preparation and to arrange material delivery. 3.2. Problem of material delivery for existing mixed-model automobile assembly At present, material consumption status is collected by the methods such as the material ANDON system, manual line inspection and empty container barcode scanning, which rely excessively on manual operation and suffer from relatively high cost, low degree of automation, low collection efficiency, and inadequate agility. Furthermore, subjective factors associated with scan personnel may easily lead to abnormal problems such as material early pull, late pull, and imbalanced pull. Moreover, in the actual assembly process, personal reasons and emergent situations (such as machine failures and part quality issues) lead to inevitable fluctuation in assembly production. Therefore, errors and deviations are inevitable in the material pulling signal generated by the PPS system. The assembly progress of the entire production cannot be effectively monitored, leading to a lack of proactivity and agility in material delivery, improperly handled emergent situations, and low management efficiency. Workshop internal stock material delivery is mostly handled afterward and assembly line demand is addressed passively, which can lead to buffer materials accumulation or shortage. Additionally, realtime working conditions in the plant cannot be addressed effectively, material delivery is inflexible, and urgent delivery is often required. To summarize, material delivery for mixed-model automobile assembly requires an information collection method that can automatically obtain assembly progress status and material consumption status at the production, and a buffer material delivery method that is expected to improve buffer material delivery flexibility and accuracy, 4

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and better serve the material delivery requirements of mixed-model automobile assembly.

4. RFID-based mixed-model automobile assembly buffer material delivery method

3.3. RFID-based material delivery model for mixed-model automobile assembly

4.1. Material classification Normally, 20–30 vehicle types are produced simultaneously in a mixed-model automobile assembly line. Different vehicle models and types require significantly different types of materials (approximately 1200 in total). Some vehicle models require as many as 2300 types of materials. These materials are typically stored in production buffer material racks, and the storage quantity varies for different types of materials. Based on material process characteristics and logistics characteristics, the materials can be divided into the following categories:

This paper proposes the application of RFID technology in information collection and progress monitoring for mixed-model automobile assembly. Vehicle model information is saved in an RFID tag attached to the to-be-assembled vehicle body. An RFID read-write device is installed at the entrance of each assembly station to track every to-be-assembled vehicle body that moves along the mixed assembly line and to monitor the operation status of the entire mixed assembly line. Real-time feedback of assembly progress status and assembly line status are combined with a plant daily production plan and the product process BOM to calculate buffer material consumption status and buffer inventory balance to generate buffer material demand. The workshop internal stock department emphasizes proactive service, focusing on the station and providing buffer material delivery promptly, accurately and proactively. Thus, stable and orderly production in the plant is guaranteed. The structure of the RFID-based material delivery model for mixedmodel automobile assembly is shown in Fig. 3. In this model, RFID technology is applied in the mixed-model automobile assembly line to automatically collect assembly status and track assembly progress status in real-time. Combining with the product process BOM data, the material delivery system automatically deduces material type and quantity currently consumed at each assembly station and updates the buffer inventory balance in real time. Based on the current assembly progress and assembly line status, the system can also accurately forecast the assembly line’s upcoming status along with the upcoming assembly progress status. Then, based on the buffer inventory balance and process BOM data, the system can accurately build the buffer material demand for every station and delivery plan for each logistics.

(1) Supplier-directly-delivered materials (SDD material) SDD material refers to material that is directly delivered to the production buffer in the specified time by the supplier according to material demand. This type of material is generally associated with high storage cost and high value, and generally a long-term strategic partnership with the supplier has been formed. Normally, the supplier should fully support the automobile manufacturing enterprise, deliver materials to the requesting station in the specified time according to the quantity and type of the materials requested by mixed-model automobile assembly line, and guarantee the material quality pass rate to be 100%. (2) Workshop-Internal-Stock materials (WIS material) WIS material is delivered by the Third-Part Logistics (TPL) from the supplies’ or TPL’s warehouse to the OEM’s workshop internal stock house, then delivered to the assembly line buffer form the workshop internal stock according to the line consumption. WIS material is not appropriate for supplier direct delivery, because these kinds of materials may have higher logistics distribution requirements, or one

Material purchasing plan

Production Department

MRPII push production information flow JIT pull production information flow material flow

Weekly production plan Material daily requirement plan

Workshop Department Daily production plan

BOM/ Production cycle/ Safety stock

RFID-based material distribution management system

VMI Warehouse

Material requirements Delivery plan

Real-time Requirements/ delivery order

supply

Supplier replenishment

Workshop internal stock

Initiative delivery

Real-time assembly progressing status

Buffer

Buffer

RFID Device Fig. 3. Structure of RFID-based material delivery model for mixed-model automobile assembly. 5

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Automobile Product

Assembly material Work station 1

Assembly process

Work station n

Quality

Material 1

Generic material

s

Material 2

SDD material

t

procedure2

Material 3

WIS material

r

procedure n

Material n

Generic material

u

procedure1

. . . . . .

Material type

Fig. 4. Structure of assembly-process-based material delivery BOM.

material involves multiple suppliers, and direct delivery is likely to create chaotic situation. Therefore, internal stock delivery is necessary. These WIS materials generally include optional assembly materials for a specific vehicle model and configuration, materials of large size (such as bumpers), essential materials that affect automobile performance (such as engines), and bundle materials (such as door decorative strips).

obtained according to the product process BOM data. Subsequently the buffer inventory balance at this station in the database is updated because of the upcoming consumption. Then the buffer material demand is calculated and a buffer material delivery arrangement is generated to guide the workshop internal stock department to deliver the material proactively to the corresponding station. However, after the material demand request is generated, it takes time for internal stock logistics worker to deliver the required material to the buffer of the corresponding station, and it is impossible to replenish the buffer inventory immediately. Therefore, it is necessary to determine the buffer material delivery lead time.

(3) Generic materials Generic materials refer to the materials of high versatility, low cost and high volume of consumption, such as bolts or nuts. Generic materials are also delivered to the assembly line buffer form the workshop internal stock. This paper focuses mainly on material demand and material delivery method of the latter two types of materials. Fig. 4 shows an assembly-process-based material delivery BOM. WIS material is delivered by the Just-In-Sequence (JIS) idea according to the vehicle model information in production, and the quantity of each delivery is limited. In the case of generic materials, delivery is normally determined by buffer inventory balance. Each delivery of generic materials is with the form of a work bin that contains a large quantity of materials, and it can satisfy material consumption at the station over a long period of time.

4.2.1. Buffer material delivery lead time determination Buffer material delivery is the process occurring after the workshop internal stock department has received the buffer material delivery request. At this point, logistics worker starts to sort, load, and transport materials to the destination station at the specified time. Therefore, there is a time difference between the receiving of demand by the workshop internal stock department and the arrival of material to the requesting station as shown in Fig. 5. Fig. 5 shows that the buffer material delivery lead time is LT = Tmaterial preparation + Tonline + Tsafety. In the equation: Tmaterial preparation—material preparation time, which consists mainly of material preparation order converting and printing time (t1), material preparation and packaging time (t2), and loading time (t3), after the workshop internal stock department has received the material delivery request; Tonline—online time, which consists mainly of the transport time (t4) for workshop logistics to deliver the material by forklift to the destination buffers, and the unloading time (t5);

4.2. Production progress data acquisition scheme with RFID As mentioned in Section 3.3, in a mixed-model automobile assembly plant, a RFID read-write device is installed at the entrance of each assembly station. When the to-be-assembled vehicle body with an RFID tag moves to a station, the RFID read-write device collects vehicle model information contained in the tag in real time. The required type and quantity of material by the to-be-assembled vehicle body is

Arrival to the destination

Material delivery request Buffer material delivery lead time Material preparation order converting and printing

Material preparation and packaging

Transportation time

Loading

Unloading

Material online time

Material preparation time

Fig. 5. Buffer material delivery lead time. 6

Safety time Safety time

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Tsafety—safety time, which refers to the time reserved for emergencies and uncertain problems.

mainly includes material demand uncertainty and material delivery lead time uncertainty. The relationship between the buffer material safety stock, the standard deviation of material actual demand, and customer service level related is given as follows (Lin & Ma, 2003):

4.2.2. Data acquisition cycle and data fetch cycle In order to guarantee timely collection of the assembly line dynamic production progress status, the RFID data acquisition cycle has to be set less than on production cycle. In this paper, the RFID data acquisition cycle is set as one half of the production cycle. As long as the tag is in the read/write range, the RFID read/write device will always collect information. Thus, this situation may result in a large amount of redundant data of one station that is saved in the database. Before being used by the delivery system, the data should be filtered. Therefore, to reduce the difficulty in data processing as well as the impact of redundant data, the RFID data fetch cycle is set as Ta. It is assumed that the plant assembly line moves continuously at a fixed speed and the production cycle is t. Within a material delivery lead time LT, the number of stations passing through the assembly line is LT n = t , where denotes rounding up, i.e., the smallest integer not less than the argument. Therefore, the buffer material delivery RFID data LT fetch cycle is Ta = n· t = t · t LT . For an assembly station, material demand can be calculated and predicted according to the situation of corresponding preceding assembly station. And each delivery can at least satisfy material consumption in one lead time, the assembly line status that changes in each data fetch cycle will not affect material demand calculations or material delivery. To calculate material demand at the initial station in a mixed-model automobile assembly line, n virtual stations should be installed before the initial station with RFID read-write devices. When the to-be-assembled vehicle body with an RFID tag passes through these virtual stations, the collected vehicle model information can be used to calculate material demand at several initial stations. Virtual stations are mainly used to collect assembly vehicle model queue information in advance, and they don’t appear in material consumption calculations.

SSi = k LT

2 iD

+

2 2 L Di

(1)

In the formula:

• SSi—buffer safety stock of material i; • k—safety coefficient of expected customer service level; • LT—material delivery lead time; • Di—average demand of material i; • —standard deviation of demand of material i; • —standard deviation of material delivery lead time. iD

L

Because the mixed-model automobile assembly line uses RFID to collect assembly progress and buffer material consumption status, then generates material demand, so the entire assembly production progress and material consumption become observable and transparent, which eliminates error in material demand. However, after the workshop internal stock department received the buffer material demand request, during the entire material delivery process, which includes material sorting, packaging, loading and delivery to production buffer, uncertainties in delay error and other parameters may exist. Therefore, error is inevitable concerning the buffer material delivery lead time. Therefore, the formula for buffer material safety stock can be converted to:

SSi = k

2 2 L Di

= k L Di

(2)

In this expression, the standard deviation of the buffer material delivery lead time should be calculated based on historical data of buffer material delivery. 4.3.3. Periodic inventory monitoring strategy based buffer material demand model To meet the material demand of assembly line production and avoid buffer material accumulation or shortage, the buffer inventory should be closely monitored to decide when to generate a buffer material demand request, as well as the quantity of material to be delivered. A buffer material demand from the RFID real-time production progress status is generated through the following steps. First, the assembly line real-time production progress and status are obtained, and the buffer material stock balance is updated in real time. Then, based on the stock balance, the buffer material demand is forecasted accurately, and the buffer inventory balance is compared with the buffer material demand to automatically make a decision on whether to generate the buffer material demand. To reduce the delivery system’s data processing cost, avoid duplicated buffer material demand determination, and improve the controllability of workshop material delivery, this paper employs a periodic inventory monitoring strategy, or (t, s, S) strategy. The buffer stock balance is updated in real-time. However, inventory is monitored periodically to check if current inventory can satisfy material consumption in at least one delivery lead time. Then, the result is used to decide whether to generate the buffer material demand and send a material demand request to the internal stock department. According to the material classification method described in Section 4.1, different types of material have different demand generation logic.

4.3. Buffer material demand model base on the production progress status RFID-based production progress data acquisition makes assembly status transparent, and makes it possible to collect buffer material consumption statistics, and update the buffer inventory balance. This provides precise information to support buffer material demand generation and material delivery. 4.3.1. Buffer inventory balance update based on real-time information Buffer inventory balance update involves two processes, i.e., material consumption and material replenishment. Material consumption is a subtraction operation on buffer inventory based on real-time production status; and material replenishment is an addition operation on buffer inventory based on material online confirmation and material delivery status change. In the case of station i, when the RFID read-write device has collected the production status of a to-be-assembled vehicle body, the type and quantity of material required at station i will be estimated with the process BOM, and the buffer inventory of the corresponding material will be reduced automatically. In the process of buffer material delivery by the workshop internal stock department, when the material online operation succeeds, the status of the delivered material changes from stock-in-transit to bufferstock and the buffer inventory of the corresponding material will automatically be increased and updated.

(1) Generic materials

4.3.2. Precise demand-based buffer material safety stock Buffer material safety stock is used as a buffer inventory whose purpose is to overcome interference factors such as production fluctuations and to cope with uncertainty in actual material demand, which

In the case of generic materials, when the RFID read-write device at each station in the assembly line has collected vehicle model information from the to-be-assembled vehicle body, based on the required type and quantity of material at this station in the delivery BOM, the 7

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Moving direction of mixed-model automobile assembly line One delivery lead time Work station (i-5)

Work station (i-4)

One delivery lead time

Work station (i-3)

Work station (i-2)

Work station (i-1)

j-3 car

j-2 car

j-1 car

Work station i

RFID

j-5 car

j-4 car

j car

Fig. 6. Diagram of assembly status in a mixed-model automobile assembly line.

material delivery system reduces the corresponding quantity from the buffer inventory. When the buffer inventory balance is less than the demand at this station in one delivery lead time (including safety stock), the material is pulled from internal stock for delivery. Fig. 6 shows the assembly status of a mixed-model automobile assembly line at one moment. The material delivery lead time is approximately two cycles. At this moment, for station i, some types of generic materials’ inventory balance at station i should be checked to see if it can meet j-2 and j-1, which are the demands for materials at station i in the future required by two to-be-assembled vehicle bodies. If those demands cannot be met, then the workshop internal stock department should replenish buffer at station i. Therefore, the generic material delivery quantity and delivery time are calculated by using parameters and detailed calculation logic listed below.

- 1) TS(m = ij

LT t

- 1) - 1) - 1) N (m = IN (m + SN (m ij ij ij

O(m) = ij

Pijk

(5)

k

This is the accumulated demand of material j at station i in the future by all to-be-assembled vehicle bodies at stations from the first virtual station to station i. In actual delivery, when the material unit package quantity limit is taken into account, the whole container delivery is normally used in principle to reduce the material sorting cost and to improve the convenience of the actual operation. Therefore, if the delivery demand of material j at station i, i.e., is not an integral multiple of the unit package quantity Xj, then the minimum integral multiple of the unit package quantity that satisfies the demand of material j at station i will be delivered; i.e., the actual delivery number is

;

ijk

N (m) = ij

j

O(m) ij Xj

Xj

j

(m) ij

•

(6)

In the formula, denotes rounding up as defined previously.

ij

•

(4)

SSj + TS(m) When L ij ij , the theoretical demand of material j delivered by the workshop internal stock department to station i is

• P —quantity of material j consumed by vehicle body k at station i; • SS —buffer safety stock of material j; • X —quantity of material j in a single package; • L —buffer stock of material j at station i; • TS —accumulated future demand for material j at station i in the • •

(3)

In the case of an initial station in the assembly line, n virtual stations are required before this initial station. Additionally, we can write

• i—station No.; • j—material ID; • k—assembly vehicle body ID; • m—RFID data fetch cycle No.; • LT—buffer material delivery lead time; • t—average production cycle; • n—the number of stations passed by the assembly line in one delivery lead time LT, n =

Pijk k TS

start of the (m)th data fetch cycle, by all n to-be-assembled vehicle bodies at station i in the previous delivery lead time; TS is the set of n to-be-assembled vehicle bodies; O(m) ij —theoretical demand for material j at station i in the (m)th data fetch cycle; N (m) ij —actual quantity of material j that should be delivered by the workshop internal stock department to station i, in the (m)th data fetch cycle; IN (m) ij —in-transit quantity of material j delivered by the workshop internal stock department to station i in the (m)th data fetch cycle period; theoretically, at the start of a new data fetch cycle, material that should be delivered in the previous data fetch cycle has been successfully logged online; i.e., at this moment the in-transit quantity is 0; SN (m) ij —quantity of material j delivered by the workshop internal stock department to the buffer of station i in the (m)th data fetch cycle period.

(2) WIS material For the WIS material, it is impossible to deliver an excessive amount of materials to the buffer at one time; instead, the technique involves quantitative delivery or on-demand delivery. In the (m)th data fetch cycle, the demand of material j at station i in the future by all n to-beassembled vehicle bodies in the moment preceding delivery lead time at station i is

O(m) = ij

n

Pijk k=1

(7)

Then, the actual delivery quantity is

N (m) = O(m) ij ij

(8)

Taking the WIS material for station i in Fig. 6 as an example, all n tobe-assembled vehicle bodies in the moment preceding delivery lead time at station i (i.e., two vehicle bodies at j-3, j-4), and the quantity of JIT-delivered material for station i in the future should be calculated.

Therefore, the following formula is obtained: 8

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4.4. Optimized delivery plan for buffer material

(m)th data fetch cycle; (m) —the total weight of buffer materials (including the material container) that should be delivered after the (m)th data fetch cycle.

•Q

After the delivery system generates the buffer material demand, the workshop internal stock logistics personnel prepare, package and load the material based on demands.

Therefore, the actual loading weight of material j (including the material container) delivered to station i from the workshop internal stock department is

(1) Quantity of material delivery container

q(m) = n(m) + N (m) ij ij ij

Buffer material is primarily carried in material containers. Because each material’s shape, dimension and weight are different, the necessary material container size and type also vary, which makes it more difficult to load material containers. This paper assumes that all material is loaded in the same type of standard material container and that different materials are loaded in different quantities in each single package. In the case of material j, the relevant parameters are defined as follows:

After this data fetch cycle, the total weight of buffer materials (including the material container) that should be delivered is

Therefore, after the (m)th data fetch cycle, the workshop internal stock department prepares the material based on the buffer material demand, combines the material required by adjacent stations and loads that onto the transport bogie for delivery. Additionally, for each bogie, the total volume of all loaded material containers should not exceed the capacity limit, and the total weight should not exceed the load limit. Based on these criteria, a load plan can be calculated so that the maximum number of material containers can be carried in transport bogies and reasonable buffer material delivery can be arranged.

j

(m) ij

the (m)th data fetch cycle.

(3) Optimized delivery plan for transport bogie

If material j is a generic material, the number of containers of material j required at station i is:

First, the value of

N (m) ij (9)

Xj

(13)

i,j

(m) ij

=

(n(m) + N (m) j) ij ij

Q(m) =

• N —actual quantity of material j delivered by the workshop internal stock department to station i after the (m)th data fetch cycle; X • —quantity of material j in a single package in a carrying container; • n —number of containers of material j required at station i after

n(m) ij

(12)

j

= max

If material j is a WIS material, the number of containers of material j required at station i is:

l1 l2

×

w1 w2

is calculated:

×

h1 l , 1 h2 w2

×

w1 l2

×

h1 h2

(14)

(m) ij

In this formula, ⌊·⌋ denotes rounding down, i.e., the maximum integer not greater than the argument. If the result is the first item, then the material container placement is l1//l2 (i.e., the material container length direction is parallel to the transport bogie length direction); if the result is the second item, then the material container placement is l1//w2 (i.e., the material container length direction is parallel to the transport bogie width direction). Next, material containers with to-be-delivered material are arranged based on the distances to destination stations. The material container intended for the farthest station is the first to be loaded in the transport bogie for delivery; also, whenever possible, material containers to adjacent stations should be placed in the same transport bogie. Finally, the total weight of the first material containers in delivery queue is calculated to see if that value exceeds the transport bogie’s weight limit Q1. If it is over the limit, then the material containers in the transport bogie should be reduced until the total load does not exceed Q1; if it is not over the limit, then is the maximum number of material containers that can be loaded in this transport bogie. These material containers with material are loaded into the transport bogie in the order from bottom to top and from inside to outside. That is, the material container intended for the last station is placed at the innermost and bottommost place in the delivery bogie. After the innermost and bottommost layer is completed, the second layer at the innermost place is assembled. Following the same rule, the containers are placed successively upward and then outward, as shown in Fig. 7. After the current transport bogie is filled with the maximum number of material containers (total volume does not exceed its capacity limit, and total weight does not exceed its load limit), the next transport bogie is loaded and the operation continues until all to-be-delivered material containers are loaded.

(m) ij

(4) Buffer material inventory update

n(m) = ij

N (m) ij Xj

(10)

In this formula, denotes rounding up as defined previously. Therefore, after the (m)th data fetch cycle, the total containers of buffer materials to be delivered to the station from the workshop internal stock department is:

N (m) ij

n(m) = ij

N= i,j

j generic

Xj

N (m) ij

+ j WIS

Xj

(11)

(2) Buffer material loading weight To reduce the delivery cost of buffer material, each transport bogie should load as many material containers as possible to reduce the required number of transport bogies. This paper assumes that all transport bogies for buffer material delivery are identical; therefore, the relevant parameters are defined as follows:

• σ —unit weight of material j; • Q —maximum load of the transport bogie; • β—weight of the material container; • —maximum number of material containers that the transport bogie can carry; • l * w * h —maximum capacity of the transport bogie; • l * w * h —dimension of the material container; • N —actual quantity of material j delivered to station i from the workshop internal stock department after the (m)th data fetch cycle; n • —number of containers of material j required at station i after the (m)th data fetch cycle; q • —loading weight of material j (including material container) j

1

1

1

1

2

2

2

(m) ij

delivered to station i from the internal stock department after the

After the material is sorted and loaded by the workshop internal 9

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Height direction

buffer stock management, buffer demand management, and buffer delivery management. Buffer stock management is used to query current buffer inventory status. Buffer demand management is used to count current buffer material demand requests and requests fulfilment status. Buffer delivery management generates material delivery orders based on information such as buffer demand requests and workshop delivery resources to guide internal stock logistics personnel in preparing and delivering material online, and it can also assist management personnel in understanding the current delivery status of buffer material.

First arrival Last arrival Layer One

headstock

Length Direction

5.2. Application example

Layer Two

An automobile OEM’s real assembly production line data, BOM data and workshop logistics data are used to develop and verify the prototype of the RFID-based material distribution system. The assembly production line is shared to produce three kinds’ vehicle models, in which seven stations are included and 49 types of materials are used. The production cycle takes approximately 120 s, and data collection cycle of the RFID devices is set to one half of the production cycle. Buffer material delivery information obtained from system operation is shown in Fig. 9. After application development and customization, the system and the methods were adopted by an automobile OEM’s in its assembly line, for the distribution of all large size parts of the GLT model and small size parts of KLT model. More than 200 kinds of large parts of GLT model and more than 1000 kinds of small size parts of KLT model are delivered intelligent by the system. Figs. 10 and 11 separately show a comparison of results of the new method proposed and the traditional method of GLT model and KLT model. The blue line represents the pull amount of the traditional way, the red line represents the material demand amount by the new method, and the green line represents the mean of the amount. Standard deviations (SDs) of Figs. 10 and 11 are calculated and shown in Table 1. SDs of material demand using the proposed method are much less than that using traditional method, indicating the balance of material pulling has been greatly improved, as well as the accuracy of material delivery. The inspection workload is reduced and labour productivity is improved. The amount of material in buffer is reduced. The results show that this delivery model and system can properly monitor the production status of a mixed-model automobile assembly plant, monitor buffer material consumption, and coordinate buffer material demand and delivery. Thus, it can help the workshop internal stock department to deliver material to the buffer and provide a feasible plan with the real-time precise delivery for mixed-model automobile assembly.

tailstock

Fig. 7. Transport bogie material container placement rules.

stock department and until it is loaded into the buffer material rack, it is regarded as stock in transit. When loaded into the buffer material rack, delivery personnel use a handheld RFID read-write device to collect data from the RFID tag on the material container that carries this material to confirm successful online material transfer. Once this information is entered into the system database, the status of this material changes from stock in transit to online, and the buffer inventory is also updated correspondingly. 4.5. Synthesis This section introduces the methods to drive the RFID-based material delivery model for mixed-model automobile assembly. In the first part, mixed-model automobile assembly buffer material is classified and the research mainly focuses on WIS materials and generic materials. To determine the data acquisition scheme, the second part proposes algorithms to calculate the delivery lead time determination, data acquisition cycle and data fetch cycle. The third part proposes the buffer material demand model. Firstly, the buffer inventory balance update process is described. Then the buffer material safety stock calculation formula is discussed here, and the demand formula of generic materials and WIS materials is deduced. In the last part, the container number of buffer materials is discussed and the total weight formula of buffer materials is given. Then, the formula to calculate maximum number of material containers that the transport bogie can carry is described and an optimized delivery plan for buffer material is presented. 5. Delivery system and its application

6. Conclusion

5.1. Software system development

Due to intense competition in automotive industry, the overall automobile industry market in China has entered a state of micro growth. In the face of increasing diverse personalized user requirements, as well as the rising resources and management cost, automobile OEMs have to find a new way to reduce the product cost, make quick response to the customers and ensure the production controllable, which includes the production management mode, material inventory control strategies, and material distribution mode. However, currently line feeding in automobile mixed-model assembly relies on artificial patrol very much, which leads to passive material requirement information acquisition and interaction. This manner is lack of initiative and real time. It is also difficult to effectively monitor workshop production progressing and handle unexpected situation, as well as to meet the requirements on fast and efficient material delivery of a mixed flow assembly line and the rhythm of material supply. Therefore, to satisfy real-time demand of buffer material delivery, an RFID-based method is proposed in this paper, which can automatically obtain the information of the production progress and

In this section, based on the mixed-model automobile assembly buffer material delivery method above, a .NET-based prototype software system is developed. The system prototype UI (user interface) is shown in Fig. 8 and includes four parts: basic information management, parameter configuration, buffer material delivery and workshop internal stock replenishment. The basic information management module consists of six functions: material information management, assembly process information management, transport bogie information management, buggy information management, package container information management, and supplier information management. This module is used to query, update and maintain relevant basic information in the database. The parameter configuration module consists of three functions: vehicle model setting, collection station setting, and RFID data fetch cycle, and these functions serve as an interface for system parameter configuration. The buffer material delivery module consists of three functions: 10

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Fig. 8. Main UI of the prototype system.

Fig. 9. Buffer material delivery management UI.

material consumption status. In order to improve the transparency of assembly line, automation level and accuracy of material delivery, realtime material delivery problem of mixed-model automobile assembly is studied in this paper. The main work and contributions include the following points.

classification and distribution pattern analysis, the production progress data acquisition scheme with RFID is designed. Based on the periodic inventory monitoring strategy, buffer material demand model and optimized delivery plan are formed to provide accurate material feeding service for assembly line. (2) An RFID-based material distribution prototype software for automobile mixed-model assembly line is developed and applied. This software has realized the RFID information collection, buffer

KLT

(1) An RFID-based material delivery method for mixed-model automobile assembly is proposed. With the assembly line material

100 90 80 70 60 50 40 30 20 10 0

Comparison of application result of GLT material delivery 87 55

55

53 40 41 42 33 35 34

28 30

39

49 40 42 29

46 49

34 10

15

14

16

13

52 32

57 42 43

38 31

14

56 31 27 11

Fig. 10. Comparison of application results of GLT material delivery. 11

traditional method new method mean of material demand

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Comparison of application result of KLT material delivery 700

623

600

KLT

500

380

400 300

410

235

297 252

200

184

100

258

214

208 101

232 270

131 199 214

0

0

327

356 229

65

321 273

107

306 193 196

178 181 47

92

193 199

traditional method

240 98

new method

202 3

mean of material demand

0

Fig. 11. Comparison of application results of KLT material delivery. Table 1 SDs of the traditional method and the proposed method. Model

SD of the traditional method

SD of the proposed method

GLT KLT

18.91 149.82

5.89 34.61

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material requirements management, as well as the logistics distribution and replenishment plan, and provides a feasible method and solution for automobile enterprises. Further work in intensive analysis and application of information acquire by RFID can be conducted to expand this research. Especially the further research work can be explored in the control and management method of the closed-loop production plan and production scheduling by the integration and fusion between RFID information and enterprise management information system. Acknowledgement This research is partially supported by the Shanghai Key Laboratory of Advanced Manufacturing Environment and the Science and Technology Commission of Shanghai Municipality under grant 11dz1120100, and National Key Technologies R&D Program under grant 2015BAF18B00. References Abdullah, S., Ismail, W., Halim, Z. A., & Zulkifli, C. Z. (2013). Integrating ZigBee-based mesh network with embedded passive and active RFID for production management automation. In RFID-Technologies and Applications (RFID-TA), 2013 IEEE International Conference (pp. 1–6). . Andriolo, A., Battini, D., Persona, A., & Sgarbossa, F. (2016). A new bi-objective approach for including ergonomic principles into the EOQ model. International Journal of Production Research, 54(9), 2610–2627. Barenji, A. V., Barenji, R. V., & Hashemipour, M. (2016). Flexible testing platform for employment of RFID-enabled multi-agent system on flexible assembly line. Advances in Engineering Software, 91, 1–11. Botti, L., Mora, C., & Regattieri, A. (2017). Integrating ergonomics and lean manufacturing principles in a hybrid assembly line. Computers & Industrial Engineering, 111, 481–491. Boysen, N., Fliedner, M., & Scholl, A. (2009a). Level scheduling for batched JIT supply. Flexible Services and Manufacturing Journal, 21(1), 31–50. Boysen, N., Fliedner, M., & Scholl, A. (2009b). Sequencing mixed-model assembly lines: Survey, classification and model critique. European Journal of Operational Research, 192(2), 349–373. Boysen, N., Fliedner, M., & Scholl, A. (2009c). Production planning of mixed-model assembly lines: Overview and extensions. Production Planning and Control, 20(5), 455–471. Braekers, K., Ramaekers, K., & Van Nieuwenhuyse, I. (2016). The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 99, 300–313. Cao, W., Jiang, P., Lu, P., Liu, B., & Jiang, K. (2017). Real-time data-driven monitoring in job-shop floor based on radio frequency identification. The International Journal of Advanced Manufacturing Technology, 92, 2099–2120. Chiarini, A., Baccarani, C., & Mascherpa, V. (2018). Lean production, Toyota Production

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Knowledge based Engineering, etc. She undertakes and participates in a number of research project funded by NSFC, MOST and Shanghai government, etc. Zheng received the third prize of Shanghai Science and Technology Progress Award in 2013. She has published more than 20 papers and 1 book.

Yu Zheng is an associate professor in the Institute of Intelligent Manufacturing and Information Engineering, Shanghai Jiao Tong University. She received her Ph.D. degree in Mechanical Engineering from Shanghai Jiao Tong University in 2015. Her Research interests include Product Lifecycle Management, Product Information Engineering, and

Changpeng He is Ph.D candidate in Sino-US Global Logistics Institute, Shanghai Jiao Tong University. His Research interests include production logistics and distribution system.

Siqi Qiu is currently an assistant professor at Shanghai Jiao Tong University. She received her Ph.D. degree from Compiegne University of Technology in 2014. Her research interests include system reliability, non-probabilistic uncertainty theories, smart manufacturing, etc. She is granted by Shanghai Pujiang Talent Program in 2016. She has published more than 20 journal articles and conference papers, and she is a reviewer for several international journals. Fei Shen is currently the deputy general manager of ANJI automobile logistics co., LTD., with nearly 20 years work experience in third-party logistics. He also is an expert in auto parts logistics, vehicle logistics and after-sales parts logistics.

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