24
This article was downloaded by: [University of Chicago Library] On: 07 October 2014, At: 20:56 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Journal of Production Research Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tprs20 Incorporating ARIMA forecasting and service-level based replenishment in RFID-enabled supply chain S.-J. Wang a , C.-T. Huang a , W.-L. Wang a & Y.-H. Chen a a Department of Industrial Engineering and Management , National Chin-Yi University of Technology , Taichung, Taiwan, ROC Published online: 18 Mar 2010. To cite this article: S.-J. Wang , C.-T. Huang , W.-L. Wang & Y.-H. Chen (2010) Incorporating ARIMA forecasting and service-level based replenishment in RFID-enabled supply chain, International Journal of Production Research, 48:9, 2655-2677, DOI: 10.1080/00207540903564983 To link to this article: http://dx.doi.org/10.1080/00207540903564983 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms- and-conditions

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Page 1: Incorporating ARIMA forecasting and service-level based replenishment in RFID-enabled supply chain

This article was downloaded by: [University of Chicago Library]On: 07 October 2014, At: 20:56Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

International Journal of ProductionResearchPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tprs20

Incorporating ARIMA forecasting andservice-level based replenishment inRFID-enabled supply chainS.-J. Wang a , C.-T. Huang a , W.-L. Wang a & Y.-H. Chen aa Department of Industrial Engineering and Management , NationalChin-Yi University of Technology , Taichung, Taiwan, ROCPublished online: 18 Mar 2010.

To cite this article: S.-J. Wang , C.-T. Huang , W.-L. Wang & Y.-H. Chen (2010) Incorporating ARIMAforecasting and service-level based replenishment in RFID-enabled supply chain, InternationalJournal of Production Research, 48:9, 2655-2677, DOI: 10.1080/00207540903564983

To link to this article: http://dx.doi.org/10.1080/00207540903564983

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Page 2: Incorporating ARIMA forecasting and service-level based replenishment in RFID-enabled supply chain

International Journal of Production ResearchVol. 48, No. 9, 1 May 2010, 2655–2677

Incorporating ARIMA forecasting and service-level based replenishment

in RFID-enabled supply chain

S.-J. Wang*, C.-T. Huang, W.-L. Wang and Y.-H. Chen

Department of Industrial Engineering and Management, National Chin-Yi University ofTechnology, Taichung, Taiwan, ROC

(Revision received November 2009)

This paper focuses on the global supply chain of a company in Taiwan (referredto as Company A), which manufactures thin film transistor liquid crystal display(TFT-LCD) products. A simulated experiment is made 640 times, and acomparison of output analysis is also made. In the experiment, the keyperformance indicators are the total inventory cost, the inventory turnoverrate, and the bullwhip effect. Four supply chain replenishment policies, fourcustomer demand forecasting methods, radio frequency identification (RFID)and non-RFID system are the experimental factors and their levels, whichgenerate 16 combinations of the Taguchi experiment. From the result, we findthat the RFID-enabled R-SCIARIMA supply chain model which integrates the(s,Q) replenishment policy based on the ARIMA forecasting method andservice level is the best: the total inventory cost has a 35.43% reduction, and theinventory turnover rate has a 61.36% increase, compared with that of thenon-RFID SCIARIMA model.

Keywords: global supply chain; replenishment policy; ARIMA forecasting; RFID

1. Introduction

In the traditional supply chain, the upstream manufacturers deal with customer demandson the basis of orders actually received downstream. Because much longer time is neededto reflect market changes, it does not satisfy the changing demand styles and causes theinventory to expire. In addition, barriers are generated while information is beingprocessed in every tier. Information of demands is distorted during the movement amongdifferent supply chain tiers, and this leads to the increase of variation in supply chainorders, which produces the bullwhip effect. To solve the problem of bullwhip effect, thesharing and exchanging of information is necessary. One of the ways to achieve this goal isto use the RFID system. With the real-time product visibility and traceability of RFID,the communication of information among tiers can be accelerated, and the lead time ofproduct delivery can be shortened. Moreover, the impact which results from errors indemand forecasting can be reduced, and the effectiveness of inventory management is thusincreased. The RFID technology can provide item-level data along with stock keeping unit(SKU) and gain access to relative data with the unique electronic product code (EPC).

*Corresponding author. Email: [email protected]

ISSN 0020–7543 print/ISSN 1366–588X online

� 2010 Taylor & Francis

DOI: 10.1080/00207540903564983

http://www.informaworld.com

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In January 2005, Wal-Mart and the US Department of Defense asked their suppliers to

implement RFID technology, in order to shorten the lead time, save total costs and makedecisions through data provided by RFID tags. For supply chain members, the

implementation of the RFID technology does generate lots of effectiveness.The purpose of this research is to establish a simulated model of a global supply chain,

which incorporates the autoregressive integrated moving average (ARIMA) demand

forecasting method and its service level as the basic inventory replenishment policy, for the

TFT-LCD Company A, to verify that the implementation of an RFID system can bestimprove its effectiveness.

The remainder of this paper is organised as follows. Section 2 provides a literature

review on the supply chain demand forecasting, RFID applications, supply chainsimulation and agent model applications, and the impact of the bullwhip effect in a supply

chain. The TFT-LCD global supply chain and modelling of Company A is presented inSection 3. The Taguchi methods/design of experiments and verification by simulation are

depicted in Section 4. In Section 5, an analysis and comparison of the experimental results

of KPI simulation output is made. We conclude the research in Section 6.

2. Literature review

2.1 Supply chain demand forecasting

This research uses Company A’s historical data of customer sales to make good

predictions of the future situations. As a result, the time series method is adopted as thecustomer demand forecasting method. The most common time series methods include

naıve, moving average (MA), exponential smoothing (ES), and autoregressive integratedmoving average (ARIMA). Dhahri and Chabchoub (2007) researched issues of the

increase of the inventory level and the decrease of the service level, which result from the

adoption of methods to weaken the bullwhip effect. Aburto and Weber (2007) researched amixed system combining ARIMA and a neutral network, which showed improvement on

the accuracy of forecasting. They also designed a replenishment system, which leads to lesssales loss and lower inventory level. Sun and Ren (2005) studied the impact of demand

forecasting on the bullwhip effect in supply chain management (SCM). The result showed

that an increase in the lead time will increase its variation, and the degree of impact isdetermined by the forecasting methods adopted. The study of Chandra and Grabis (2005)

indicated that the existence of the bullwhip effect and the increase in the variation oforders result in a lack of efficiency in inventory management. The result showed that

parameters considered in the autoregressive models do effectively reduce the bullwhip

effect, but the variation of parameters will also affect the degree of the bullwhip effect.Zhang’s (2004) study focused on the impact of forecasting methods on the bullwhip effect.

The result showed that an increase in the lead time and underlying parameters of thedemand process will strengthen the bullwhip effect, and the degree of effect varies

according to different forecasting methods.In conclusion, these studies all show that the ARIMA demand forecasting method can

reduce the bullwhip effect in a supply chain. However, they do not take the integration of

RFID and the factor of real-time information sharing into consideration. This research

combines a supply chain with the real-time and fast responding character of RFID inexpectation of enhancing the effectiveness of demand forecasting, in order to make further

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studies on issues of the service level in replenishment management. Also, the applicabilityof ARIMA will be simulated and compared with other forecasting methods.

2.2 RFID applications

Delen et al. (2007) analysed RFID data collected from retailers and suppliers in a supplychain to know how to estimate the time needed from the logistics centre to retailersthrough RFID. Saygin et al. (2007) designed methods for establishing an RFID supplychain system and emphasised the communications infrastructure necessary to provideseamless data and information flow in order to achieve RFID data-based decision-makingat all levels of the supply chain. Mills-Harris et al. (2007) produced a simulated study onthe inventory management of time-sensitive materials, based on data collected by RFID.The forecast integrated inventory model was developed based on a trend adjustedexponential smoothing algorithm. The result showed that a proper adjustment of the twosmoothing parameters (� and �) can achieve the system performance demanded.Moreover, in January, 2005, a successful trial of the RFID/EPC system on tagged palletsand cases was done by Wal-Mart and its top 100 suppliers. The University of Arkansasanalysed Wal-Mart’s success and found that after adopting the RFID/EPC system, therewas a 16% decrease in the out-of-stock rate (Wal-Mart Stores Inc. 2005). Hardgrave et al.(2005) researched 24 retailers of Wal-Mart, divided into two groups, each group consistingof 12 retailers. The result showed that the group which implements RFID has a 26%decrease in the out-of-stock rate and has improved 63% compared to the group withoutRFID. Lee et al. (2004) of IBM proved the potential effectiveness of RFID in decreasingthe inventory and enhancing the service level with simulation methods based on real data,and the subject is a three-tier supply chain.

The studies above only involve parts of the supply chain tiers without taking thecomplexity of the whole supply chain into account. Therefore, this research takesCompany A’s global supply chain of TFT-LCD as an example, designs a platform forthe RFID network database of products to simulate the operation model of theRFID-enabled global supply chain, and uses experimental design methods to prove thepotential effectiveness of RFID for the improvement on the supply chain inventorymanagement.

2.3 Supply chain simulation and agents applications

Kleijnen (2005) surveyed four types of supply chain simulation: spreadsheet, systemdynamics (SD), discrete-event dynamic system (DEDS), and business games. The surveyconcluded that the DEDS simulation is an important method in SCM. It can representindividual events and incorporates uncertainties. Borshchev and Filippov (2004) suggestedthat the system being modelled contain active objects (people, products, stocks, businessunits, etc.) with timing and event ordering and that it is suitable to add an agent basedmodel to the DEDS simulation background. Ozbayrak et al. (2007) established a four-tiersupply chain. The performance evaluated was mainly based on inventory, the WIP level,backlogged orders, and customer satisfaction. Li and Wei (2007) offer technology whichintegrates RFID and a multi-agent to monitor the location of transferring goods and theinformation of the environment. Liang and Huang (2006) established a multi-agent systemin a supply chain, in which the inventory system is operated through different agents.

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The result of the agent-based system showed the reduction of total cost and the smoothingof orders variation curve. Emerson and Piramuthu (2004) established an agent-baseddynamic supply chain infrastructure. They also proved through real cases that theperformance of a dynamic supply chain infrastructure is better than that of a static supplychain infrastructure.

The traditional management system has difficulty in practising supply chain manage-ment due to its complexity, so observation and testing of the system simulation isneeded. Therefore, the dynamic system simulation method is used for establishing anRFID-enabled supply chain model, which consists of agents of planning management,stock control and executive operation. Moreover, the RFID agent and the demandforecasting agent are added in order to figure out the most appropriate replenishmentpolicy and demand forecasting method for the RFID-enabled supply chain.

2.4 The bullwhip effect in the supply chain

Lee et al. (1997) concluded that the bullwhip effect results from the distortion ofinformation during the process of communication in the supply chain. They defined thatthe factors which lead to the bullwhip effect include demand forecasting, order batching,price fluctuation, rationing and shortage gaming. Four methods should be adopted toeffectively weaken the bullwhip effect, and they are: avoiding multiple demand forecastupdates, breaking order batches, stabilising prices, and eliminating gaming in shortagesituations. Luong (2007) concluded that the bullwhip effect will appear when informationof demand moves upstream. In their supply chain, the impact of autoregressive coefficientand lead time on the bullwhip effect is studied through a first-order autoregressive modelby the retailers, based on inventory policy. Xu et al. (2001) used a demand model andtime series to prove the bullwhip effect really exists in the three tiers of supply chain, andthat the bullwhip effect can be effectively weakened through information exchangeand consistent forecasting. Kelle and Milne (1999) thought the bullwhip effect is aphenomenon that results from demand forecasting and batch orders. Small frequentorders can reduce the effect of high variability and the resulting uncertainty, thereforeeffectively weakening the bullwhip effect.

In conclusion, due to changing customer demand, the supply chain becomes instableand hard to control. Thus, it is difficult to forecast the change of orders. So, this researchuses some demand forecasting methods, like ARIMA, to forecast the future demand in thehope of effectively weakening the bullwhip effect.

3. Global supply chain and modelling of Company A

3.1 TFT-LCD industry

According to the Materialsnet (2008), the Industry Economic Knowledge Center ofIndustrial Technology Research Institute in Taiwan produced statistics showing that thetotal production value of the flat display panel in Taiwan was 40.3 billion USD in the yearof 2007, which overtakes the 34.5 billion USD in South Korea and the 22.5 billion USD inJapan. This makes Taiwan the real top flat display panel manufacturer. At the same time,the total production value of the flat display industry of 2007 in Taiwan was 1.78 trillionNTD, which is a 39.8% increase compared to that of 2006.

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The structures of the upstream, midstream, and downstream of the TFT-LCD industryare quite enormous. The components manufactured upstream include crystal, glasssubstrate, colour filter, driver IC, polariser, and back light. They are assembled in themidstream, and then can be applied in electronic appliance, consumer products,communication, transportation, computer, and business products.

3.2 Company A

In this case study, the global operations structure of Company A includes eight branchwarehouses, three regional distribution centres, five LCD monitor manufactories, and fourLCD panel manufactories. Its global inventory is computed every day, and the data istransmitted to the headquarters in Taiwan to be organised. In order to solve the problemin inventory management, this research establishes a demand forecasting model based onan inventory replenishment policy with a certain service level. The purpose is to forecastthe future demands of customers and to solve the problem in inventory management byimplementing RFID to transmit instant information.

3.3 Global supply chain simulation modelling

This research adopts AnyLogic, a system simulation tool, to establish a mechanism ofsupply chain automatic replenishment simulation with a demand forecasting model.AnyLogic is professional simulation software that can be applied for discrete, continuousand hybrid system modelling. The interface of the simulation model R-SCIARIMA

established in this research is shown in Figure 1.

Figure 1. The simulation main screen of R-SCIARIMA model.

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3.3.1 Design of supply chain agents

In this research, the mechanism of inventory replenishment simulation is operated throughfunctions of agents to monitor the entire supply chain system and collect instantinformation with RFID. The agents can be sorted into three categories: planningmanagement, stock control, and executive operation. The detailed R-SCIARIMA

multi-agents simulation procedures are described as follows:

(1) When a number of units for customer demand generated by the system with aWeibull distribution model, the order check agent will notify the demandforecasting agent after the demand is confirmed.

(2) The demand forecasting agent will process the calculation of forecasted demandbased on the ARIMA model and then offer the number of forecasted units forcustomer demand to the order management agent.

(3) The order management agent then asks the finished goods agent to release finishedgoods with required forecasted units. If the finished goods on-hand units are inshort supply, existing finished goods will be issued at once. Besides, the delayedtime of the issuing operation will be generated.

(4) A real-time monitoring of releasing finished goods is performed through theRFID agent simultaneously. The stock units monitor agent will obtain real-timetransactions information of the finished goods on-hand units. The elapsed time ofretrieving and transferring RFID tagged data is assumed to be 0.025 week. On thecontrary, it is enlarged to be 0.15 week for the bar-code system.

(5) The out-of-stock units will be recorded by the order management agent and theproduction management agent as notified by the stock units monitor agent. Thus,the out-of-stock units must be released first when newly finished goods arereceived.

(6) In the simulation run, the supply chain management agent will publish theinventory replenishment policy (s,Q) to the stock units monitor agents of each tierin the supply chain. The amount of maximum raw materials on-hand S for anytier’s member is also assigned by the supply chain management agent.

(7) The stock units monitor agent monitors the transactions of on-handunits constantly. If the on-hand units are found to be fewer than thereordering point s, the requirement of replenishment Q is sent to the ordermanagement agent.

(8) The order management agent sends replenishment demands (¼Q purchasing units)to the upper tier’s supplier in the supply chain.(The procedure of demand orders handling in each tier of the supply chain followsthat of steps 2 to 8.)

(9) When the replenishment of raw materials arrives, the stock units monitor agent ofeach tier will receive the information of the gain of on-hand units sent by the RFIDagent. Then, the total of the present on-hand units and the scheduled receipt unitswill be checked to see if it exceeds the amount of the maximum raw materialson-hand units. The purchased units agent is only responsible for the release andcarrying operation of the raw materials in stock.

(10) If the stock units monitor agent finds that the total of the raw materials on-handunits and the scheduled receipt units exceeds the assigned maximum raw materialson-hand units S, it will notify the order management agent to cancel thepurchasing orders which have not been released in the preceding tier.

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(11) When the production management agent of the manufacturing plant tier receivesthe information of an out-of-stock in the finished goods stock, it will ask theproduction agent to begin production.

(12) When the production operation is being performed by the production managementagent in the manufacturing plant, the work-in-process units increase and the rawmaterials units decrease. There will be delayed time in the production operation.The units of finished goods will be increased at the end of the production process.

(13) The stock units monitor agents will be simultaneously informed of thework-in-process units by the production management agent.

3.3.2 RFID-enabled supply chain network platform

This research is based on the global supply chain operation process of Company A, withan RFID mechanism in addition. The receiving and shipping point of each location isequipped with RFID, including one reader and two antennas. The tagged EPC data ofeach product, affiliated with RFID tagged records like the receiving time and shippingtime, is transmitted to the reader. The data is then transmitted through local networks andsaved in a MySQL database. The tagged EPC code is disassembled into the RFID EPCrecords, and the EPC records saved in each location are then transmitted to the centralserver via the internet for tracking, tracing and stock units update of the supply chain. Thisplatform uses Labview as a software development tool. The whole RFID-enabledapplication infrastructure is shown in Figure 2. In this research, the RFID mechanism inthe global supply chain simulation model of Company A established by AnyLogic will begradually simulated with reference to the network platform operation flow above.

3.3.3 Design of simulation model input parameters

The case of Company A is a multi-tier global supply chain model. The simulation periodT is set to be 52 weeks. Based on the actual demand in 52 weeks of the 17-inch TFT-LCDof Company A, the statistics distribution model analysis tool, Stat:Fit, is used to make ananalysis. The result shows the customer demand statistics distribution model of CompanyA approximates the Weibull (min¼ 6980, �¼ 4.13, �¼ 128).

In this research, the inventory replenishment policies include the continuous review(s,Q), (s,S ) and the periodic review (R, s,S ), (R,S ), in reference to the formulae designedby Chopra and Meindl (2001), and Simchi-Levi et al. (2000), with the prerequisite of thedesired cycle service level (CSL) to be 95%. The reorder point (s), order-up-to level (S )and order quantity (Q) are calculated individually. The result is shown in Table 1.The continuous review policies of (s,Q), (s,S ) are described as follows:

DL ¼ D� L

�L ¼ffiffiffiffiLp� �

ss ¼ ZðCSLÞ � � �ffiffiffiffiLp

s ¼ DL þ ss

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Figure

2.Conceptualstructure

ofRFID

-enabledsupply

chain

applications.

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S ¼MAXðD,DLÞ þ ZðCSLÞ � � �ffiffiffiffiLp

Q ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2�D� PC

CC

r:

Where:

D average weekly demand faced by each tier member;� standard deviation of weekly demand;L lead time for replenishment;

DL average weekly demand during lead time;�L standard deviation of weekly demand during lead time;ss safety stock;s reorder point;S order-up-to-level;

PC purchasing cost;CC carrying cost.

3.4 Demand forecasting models

This research focuses on the forecasting of the end customer’s order demand from theretailers. It is based on the actual customer demand offered by Company A, and theforecasting model consists of ARIMA, trend-corrected exponential smoothing, simpleexponential smoothing and the naıve forecasting methods. The model is used forcalculating the customer demand of the next week.

3.4.1 Autoregressive integrated moving average (ARIMA) model

This research uses SPSS to establish the ARIMA model, based on the four steps by Boxand Jenkins (1970): identification, estimation, diagnosis and forecasting. The completeestablishment flow of the model as shown in Figure 3 is as follows:

(1) Identification

First, the time series chart will be observed through its autocorrelation coefficientto check whether the chart is stationary or semi-stationary. The autocorrelation

Table 1. Input parameters of (s,Q) and (s,S ) replenishment policy with 95% CSL.

Tier’sname

LCD panelmanufactories

LCD monitormanufactories

RegionalDCs

Branchwarehouses Retailers Unit

D 14,763 11,694 19,286 7165 7094 Piece/week� 76 60 100 37 36.63 Piece/weekL 0.5 0.4 0.2 0.1 0.1 WeekDL 7382 4677 3859 716 709 Piece/week�L 54 38 45 12 12 Piece/weekss 89 63 74 19 19 Piece/weeks 7471 4740 3933 735 728 PieceQ 1955 1630 2423 1,448 1187 PieceS 14,852 11,757 19,370 7,184 7113 Piece

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function (ACF) and partial autocorrelation function (PACF) will be used to check

the orders of p and q ( p is an autoregressive order, and q is a moving average

order). In the check of the time series, the result shows that the actual customer

demand data of Company A is of trend-affiliated time series. So, the

first-difference is calculated to wipe out the trend to make it a stationary time

series. The difference d is 1, as observed in the ACF and PACF graph, and

candidate models ( p, d, q) are generated such as (1, 1, 0), (1, 1, 1), (1, 1, 2), (2, 1, 0),

(2, 1, 1) and (2, 1, 2). The Akaihere’s information criteria (AIC) value of each

candidate model is quite close. Therefore, residual ACF and PACF graphs are

generated to check if the residual is white noise. The result shows that the ACF and

PACF fall in the confidential interval when ( p, d, q)¼ (2, 1, 2) and

( p, d, q)¼ (1, 1, 1). The AIC value of (1, 1, 1) is 540.882, which is larger than the

536.614 of (2, 1, 2). Therefore, the ARIMA¼ (2, 1, 2) is selected as the customer

demand forecasting model.

(2) Estimation

The parameters estimation will be carried out to find out the degree of impact of

each lag variable on the forecasting series. The model of the ARIMA (2, 1, 2) can

be induced by using first-difference model Dx(t)¼ x(t)�x(t� 1) and ARIMA (2, 2)

model:

xðtÞ � constant ¼ a1 xðt� 1Þ � constant� �þ a2 xðt� 2Þ � constant

� �þ uðtÞ � b1uðt� 1Þ � b2uðt� 1Þ:

Therefore, the forecasting model is as follows:

xðtÞ � xðt� 1Þ� �

þ 0:485 ¼ 0:686� xðt� 1Þ � xðt� 2Þ½ � þ 0:485� �

� 0:015� xðt� 2Þ � xðt� 3Þ½ � þ 0:485� �

þ uðtÞ � 1:704� uðt� 1Þ þ 0:897� uðt� 1Þ:

Suppose the forecasting value is the current value, x(t) is the current value, x(t� 1)

is the last period, x(t� 2) is the period before last, x(t� 3) is the two periods before

the last, and u(t) is the residual, then u(t)�N(0, �2).

Yes

Yes

No

Demand data ofcompany A

Observe & depicttime series graph

Stationary ? Identify p, q

orders

Identify model byAIC criteria

Parameters estimation

Residual testForecasting

modelARIMA forecasting inAnyLogic simulation

Difference

No

Figure 3. The flowchart of ARIMA forecasting model.

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(3) Diagnosis

A test of model fitness will be carried out to make a check with its residualdistribution. This research tests the acceptability of ARIMA¼ (2, 1, 2) with thePortmanteau Q-test model suggested by Ljung-Box:

Q� ¼ nðnþ 2ÞXKk¼1

ðn� K Þ�1r2kðaÞ

¼ 59� ð59þ 2Þ �ð�0:187Þ2

59� 1þ � � � þ

ð�0:079Þ2

59� 16

� �¼ 12:38:

In this formula, n is the number of actual residual (n¼N� d¼ 60� 1¼ 59, N is thenumber of time series data, d is the order of difference), k is the number of residualACF, rkðaÞ is the residual (k¼ 1, 2, . . . ,K ). Finally:

�20:05ð16� 2� 2Þ ¼ �20:05ð12Þ ¼ 21:034 12:38ð¼Q�Þ

shows that there is no significant relation between residuals. Therefore, theforecasting model should be ARIMA (2, 1, 2).

(4) Forecasting

We can design the forecasting model as follows:

xðtÞ � xðt� 1Þ� �

þ 0:485 ¼ 0:686� xðt� 1Þ � xðt� 2Þ½ � þ 0:485� �

� 0:015� xðt� 2Þ � xðt� 3Þ½ � þ 0:485� �

þ uðtÞ � 1:704� uðt� 1Þ þ 0:897� uðt� 1Þ:

The forecasting value of ARIMA (2, 1, 2) is calculated as follows:

xðt, 1Þ ¼ xðtÞ � 0:485þ 0:686� xðtÞ � xðt� 1Þ½ � þ 0:485� �

� 0:015� xðtÞ � xðt� 1, 1Þ½ � þ 0:485� �

� 1:704� xðtÞ � xðt� 1, 1Þ� �

þ 0:897� xðt� 1Þ � xðt� 2, 1Þ� �

:

According to the forecasting value of period 53 for Company A, the actual demand ofperiod 52 was xðtÞ ¼ 7011, the actual demand of period 51 was xðt� 1Þ ¼ 7009, theforecasting value of period 52 was xðt� 1, 1Þ ¼ 7005:69, the forecasting value of period 51was xðt� 2, 1Þ ¼ 7005:15, therefore, the forecasting value of period 53 is as follows:

xðt, 1Þ ¼ 7011� 0:485þ 0:686� 7011� 7009½ � þ 0:485� �

� 0:015� 7011� 7005:69½ � þ 0:485� �

� 1:704� 7011� 7005:69f g

þ 0:897� 7009� 7005:15f g

¼ 7006:538:

3.4.2 Trend-corrected exponential smoothing (Holt-ES) model

According to the trend-corrected exponential smoothing method designed by Chopra andMeindl (2001), from the demand Dt and the time period of linear regression, the level and

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the initial value of the trend appear as follows: Dt¼ atþ b. With the constant b, the

demand of period t¼ 0 can be estimated, which is also an estimate of the initial level L0.

The constant a represents the change rate of each period’s demand and the initial estimate

of trend T0. With the regression function of Excel, the value of initial level L0 can be

estimated from the INTERCEPT worksheet function, and the value of trend T0 can be

estimated from the parameter of variable t. In the case of Company A, the result is

L0¼ 7105.038 and T0¼�1.34332. � and � are the smoothing coefficients of the level and

trend, and there are nine combinations with the values of 0.2, 0.5 and 0.8. From the

calculation of mean squared error (MSE), the MSE is the smallest when �¼ 0.2 and

�¼ 0.2. In the case of Company A, the result of the forecasting estimate of the 53rd week

based on the actual demand of t¼ 52 is as follows:

L52 ¼ ��D52 þ ð1� �Þ � ðL51 þ T51Þ

¼ 0:2� 7011þ ð1� 0:2Þ � 7002:988

¼ 7004:59

T52 ¼ �� ðL52 � L51Þ þ ð1� �Þ � T51

¼ 0:2� 0:387þ ð1� 0:2Þ � ð�1:21505Þ

¼ �0:89457

F53 ¼ L52 þ T52

¼ 7004:59� 0:89457

¼ 7003:696:

3.4.3 Simple exponential smoothing (SES) model

According to the simple exponential smoothing method designed by Chopra and Meindl

(2001), the initial estimation of level L0 can be calculated with the average of all historic

data. Based on the demand data with period n¼ 52, the initial estimation can be presented

as follows:

L0 ¼1

52

X52t¼1

Dt

¼ 7064:067:

The selection of the smoothing coefficient � is determined by the smallest value of

MSE, which falls at the value when �¼ 0.9. Therefore, the estimation of the level of the

52nd week calculated with equation is as follows:

L52 ¼ ��D52 þ ð1� �Þ � L51

¼ 0:9� 7011þ ð1� 0:9Þ � 7009:124

¼ 7010:812:

As a result, the forecasting value of period 53 is F53 ¼ L52 ¼ 7010:812.

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3.4.4 Naıve forecasting model

The naıve forecasting method uses the single value of the preceding period in the timeseries as the basis of forecasting. It can be applied in stationary time series, seasonalvariation or trends. In other words, the forecasting value of any period equals to the actualvalue of the preceding period: Ft ¼ Dt�1. In the case of Company A, the forecasting valueof period 53 is F53 ¼ D52 ¼ 7011.

3.5 Global supply chain performance evaluation

This research assigns the total inventory cost, the inventory turnover rate and the bullwhipeffect as key performance indicators (KPIs). The subject for simulation is the five tiers ofthe supply chain of Company A, and the simulation time lasts for 52 weeks. The KPIequations are as follows:

. Total inventory cost¼ production cost þ inventory replenishment costþbackorder costþdelivery cost:

TC ¼XNn¼1

XTt¼1

ðPAQnt þ IAQntÞSCn þMAQntðMCn þMSCnÞ þ PAPQntPCn

� �

þXNn¼1

XTt¼1

IAQntACn þOAQntMOCnð Þ

þXNn¼1

XTt¼1

IASQntSOCnð Þ

þXNn¼1

XTt¼1

QAQntShCnð Þ: ð1Þ

. Inventory turnover rate¼ sales amount� inventory cost:

ITR ¼

PNn¼1

PTt¼1 DntSCnPN

n¼1

PTt¼1 MAQntMCn þ PAPQntPCþ IAIQntGCnð Þ

: ð2Þ

. Bullwhip effect:

BE ¼VarðQNÞ

VarðDÞ, ð3Þ

where Var(QN) represents the deviation of demand orders issued by the Nth tier inthe supply chain, and Var(D) represents the deviation of end customer demand.

The following notation is used in the global supply chain simulation model.

N the set of members in each tier of the supply chain;PAQnt the throughput units of the production management agent of

manufacturer n in period t;IAQnt the issued units by the finished goods agent of manufacturer n in

period t;SCn the unit sale price of finished goods produced by manufacturer n;

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MAQnt the raw materials on-hand units monitored by the purchased unitsagent of manufacturer n in period t;

MCn the raw materials unit cost of manufacturer n;MSCn the raw materials carrying cost per unit of manufacturer n;

PAPQnt the work-in-process units of the production management agent ofmanufacturer n in period t;

PCn the work-in-process cost per unit of manufacturer n;ACn the issuing activity unit cost of manufacturer n;

OAQnt the raw materials purchased units issued by the order managementagent of manufacturer n in period t;

MOCn the raw materials purchasing unit cost of manufacturer n;IASQnt the backorder units recorded by the finished goods agent of manu-

facturer n in period t;SOCn the backorder unit cost of the finished goods of manufacturer n;QAQnt the scheduled shipping units monitored by the stock units control

agent of manufacturer n in period t;ShCn the shipping cost per unit of manufacturer n;

IAIQnt the amount of finished goods on-hand units monitored by the finishedgoods agent of manufacturer n in period t;

GCn the finished goods unit cost of manufacturer n;Dnt the demand units received by the order management agent of

manufacturer n in period t.

4. Taguchi experiment design and simulation verification

4.1 Taguchi experiment

This research adopts the Taguchi method to carry out experiments effectively with fewercombinations of experiments. There are three phases in the Taguchi method: planning,execution, and the analysis and verification of the output.

(1) Experimental planning phase

In this experiment, the quality characteristic of the global supply chain model ofCompany A is the total inventory cost. Its characteristic is the smaller-the-better,meaning that the cost should be smaller. The definition of the smaller-the-bettersignal-to-noise (SN ) ratio is as follows:

SN ¼ �10� log10ðMSDÞ ¼ �10� log101

n

Xni¼1

y2i

!:

In the equation, MSD is the mean square deviation and yi is the output value. Thecontrol factors and their levels are listed in Table 2. In this experiment, there aretwo level-4 factors and one level-2 factor in Table 3, which is an L16(4)

3

combination known from Minitab.

(2) Experimental implementation phase

The 16 combinations determined by the last step are being experimentedand replicated 40 times, and the total simulation experiment times are16�40¼ 640 times. The summarised table of the experimental output data isshown in Table 4.

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(3) Experimental outputs analysis and confirmation

The experimental result is shown in the last column of Table 4, and the total

average of the 16 SN ratios is:

�T ¼1

16

X16i¼1

�i

¼1

16�175:514� 179:684� � � � � 185:224� 176:782ð Þ

¼ �181:76:

The average SN ratios of the three factor levels can be calculated by using the SN

ratios in Table 5. The average SN ratio of level-1 factor A is �A1 ¼ �177:7; theaverage SN ratio of level-1 factor B is �B1 ¼ �180:4; the average SN ratio of level-1factor C is �C1 ¼ �179:8, and the others likewise. According to the definition of theSN ratio, the larger the SN ratio is, the better the quality is. So, a level of larger SNratio is selected. The optimal level combination of this research is A1B1C1. The

Table 3. L16(4)3 experiment combinations.

# A B C

1 1 1 12 1 2 23 1 3 34 1 4 45 2 1 26 2 2 17 2 3 48 2 4 39 3 1 310 3 2 411 3 3 112 3 4 213 4 1 414 4 2 315 4 3 216 4 4 1

Note: C factor is RFID, dummy level for levels 3and 4, level 3 to be as RFID, level 4 to be asnon-RFID during experiments.

Table 2. Control factors and levels in Taguchi experiments.

Levels

Factors 1 2 3 4

A. Replenishment policy (s,Q) (s,S ) (R, s,S ) (R,S )B. Forecasting model ARIMA Holt SES NaıveC. RFID Yes None

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result of the analysis of variance (ANOVA) shows the F value of factor B is 0.3886,

and the F value of factor C is 0.4594. Both are smaller than the significance level

of 0.05. Thus, factors B and C are significance factors, and factor A is not a

significant factor and is to be merged as errors. The SN ratio under the optimal

conditions is

SN ¼ �Tþ ð �B1 � �T Þ þ ð �C1 � �T Þ

¼ �B1 þ �C1 � �T

¼ �180:4þ ð�179:8Þ � ð�181:76Þ

¼ �178:44:

In order to effectively estimate the observed values, the confidence interval

(CI) must be calculated. The confirmation of the expected average value of

experiments is:

CI ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiF�;1,v2 � Ve �

1

neffþ1

r

� �s

¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi5:12� 45:348�

7

16þ1

5

� �s

¼ 12:166:

Table 4. Partial experimental output data.

Control factors Observation values (total inventory cost)

# A B C 1 2 . . . 40 SN

1 1 1 1 591307416 609425262 . . . 591660504 �1¼�175.5142 1 2 2 971240631 977042254 . . . 923378995 �2¼�179.6843 1 3 3 589164613 609227193 . . . 616479656 �3¼�175.6754 1 4 4 1059057694 998050536 1036118855 �4¼�179.977... ..

. ... ..

. ... ..

. ... ..

. ...

16 4 4 1 702302339 692145943 . . . 683317736 �16¼�176.782

Table 5. The simulation results of four models.

Modelname

Productioncost

Inventoryreplenishment

costBackorder

costDeliverycost

Totalinventory

cost

R-SCIARIMA 593,077,452 18,052 1,045 3,347,006 596,443,555SCIARIMA 911,094,142 21,397 1,273 3,271,127 914,387,939R-SCI 675,954,816 18,003 1,057 3,336,281 679,310,157SCI 1,091,657,485 18,886 1,375 3,507,069 1,095,184,815

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That is, it can be concluded with 95% confidence in this experiment that theboundary of the expected SN ratio is �178:44 12:166. The SN ratio average ofthe five experiment confirmations is �175.446, which falls in the confidenceinterval above. This means that the selected factors B and C and their levels areadequate. When the success in the experiment is confirmed, the combination of theoptimal level of control factors is included in Company A’s global supply chainsystem. The specification is to implement the (s,Q) inventory replenishment policy,ARIMA demand forecasting method and the RFID-enabled system. It is called theR-SCIARIMA (RFID-enabled supply chain inventory demand forecasting: ARIMA).

4.2 The compared global supply chain models

This research plans to verify if the R-SCIARIMA is the optimal model through four globalsupply chain inventory management models.

. SCI (supply chain inventory)

This model represents the current environment and simulates the currentinventory operation of Company A. The current operation implements the(s,Q) replenishment policy and checks the weekly inventory level of each supplychain member to increase the purchasing orders to Q units.

. R-SCI (RFID-enabled supply chain inventory)

This model supposes each inventory item is recorded on an RFID tag, and itsvisibility is 100%. The fixed baseline is similar to that of the SCI model,and RFID is only for monitoring rather than dynamically modifying theinventory level.

. SCIARIMA (supply chain inventory demand forecasting: ARIMA)

This model implements the (s,Q) replenishment policy and the ARIMA demandforecasting method. It uses forecasting methods to estimate the future demand inorder to reduce the inventory cost by decreasing variation of orders.

. R-SCIARIMA (RFID-enabled supply chain inventory demand forecasting:ARIMA)

In addition to using ARIMA demand forecasting methods, this model improvesdemand management with the immediacy of RFID. Forecasts can be done andinformation of demand can be received at any time on the basis of instant demandinformation. However, this research uses a simulation method, in which the timeof the simulation process is short, and the data RFID can read is quite large.Therefore, the virtual RFID system method is implemented here.

4.3 RFID system cost

In this research, RFID equipment, including two antennas and a reader, are set in thereceiving/shipment location of each supply chain tier. The price of each antenna is about$290, and the price of each reader is about $995. The proposed life of RFID is five years,

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the salvage value is 10%, the simulation time is one year, the maintenance expense ofRFID is 12%, and the operation cost is 15%.

The RFID equipment cost of each supply chain tier is 2� 290þ 1� 995¼ $1575. Basedon the accelerated depreciation method, the salvage value of one RFID system at the endof the fifth year will be 1575� 10%¼ $157.5. The maintenance cost is 1575� 12%¼ $189,the operation cost is 1575� 15%¼ $236.25, and the depreciation cost equals (1575�157.5)� [(5� 1þ 1) / (1þ 2þ 3þ 4þ 5)]¼ $472.5. Thus, the total cost of one-year simu-lation for a set of RFID equipment¼ the maintenance costþ the operation costþ thedepreciation cost¼ $189þ $236.25þ $472.5¼ $897.75. The cost of one tag is about $1,and the average throughput in one year of LCD panel manufactories is 2,701,171 pieces.The attached tags are for entire usage of the supply chain, so the cost of RFID tags isabout $2,701,171. The individual RFID cost of each tier is: LCD panel man-ufactories¼ 897.75� 4� 1þ 2,701,171¼ $2,704,762, LCD monitor manufactories¼897.75� 5� 2¼ $8977.5, regional distribution centre¼ 897.75� 3� 2¼ $5,386.5, branchwarehouses¼ 897.75� 8� 2¼ $14,364, and retailers¼ 897.75� 8� 2¼ $14,364.

5. Simulation output analysis and comparison

The simulation output analysis and comparison of the R-SCIARIMA model is carried outwith the Bernoulli experiment, the value of each KPI and the sensitivity analysis, to verifyif the R-SCIARIMA model is the optimal.

5.1 Significance test

From the result of the analysis of the normal distribution graph with Minitab, thetotal inventory costs and inventory turnover rates of R-SCIARIMA and SCIARIMA fallin the 95% confidence interval, which are in accordance with a normal distribution.The Bernoulli experiment is carried out in order to test the difference between theaverage total inventory costs of R-SCIARIMA and SCIARIMA, and the test statistics areas follows:

� �x1� �x2 ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi�21n1þ�22n2

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffið14898852Þ2

40þð10110842Þ2

40

s

¼ 2846949:757

Z ¼ð �x1 � �x2Þ � ð�1 � �2Þ

� �x1� �x2

¼596443556� 914387864ð Þ � 0

2846949:757

¼ �111:6789:

Under two-tailors test (�/2¼ 0.05) in the 95% confidence level, Z¼�111.6789, whichis less than Z(�/2¼ 0.025)¼�1.96 and falls in the reject area. Therefore, we accept thealternative hypothesis H1: there are differences between the total inventory costs of the twogroups.

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The Bernoulli experiment is carried out in order to test the difference between the

average inventory turnover rates of R-SCIARIMA and SCIARIMA, and the test statistics are

as follows:

� �x1� �x2 ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi�21n1þ�22n2

s

¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0:02078ð Þ

2

40þ

0:01632ð Þ2

40

s

¼ 0:004178

Z ¼ð �x1 � �x2Þ � ð�1 � �2Þ

� �x1� �x2

¼ð1:136� 0:522Þ � 0

0:004178

¼ 146:96:

Z¼ 146.96, which is larger than Z (�/2¼ 0.025)¼ 1.96 and falls in the reject area, does

not accept the null hypothesis H0: no difference exists in the average inventory turnover

rate of the two models. In conclusion, at a 95% confidence level, there are differences

between the inventory turnover rates of the two models.

5.2 The comparison of KPI simulation outputs

5.2.1 Total inventory cost comparison

From the simulation outputs shown in Table 5, based on the experiment output of

R-SCIARIMA, it can be known that the R-SCIARIMA is the best of the four models. It hasthe lowest production cost, replenishment cost, backorder cost and total inventory cost.

Table 6 shows the improvement rate of each tier member in the supply chain model.

It shows that the costs in every subject are improved from the comparison of the

R-SCIARIMA and SCIARIMA models. Among them, the improvement rates of production

cost and backorder (out-of-stock) cost relatively increase a lot. Take retailers for example,

in the R-SCIARIMA model, the production cost has a 28.42% decrease, and the backordercost has a 50.32% decrease. The total inventory cost of the R-SCIARIMA model has a

31.93% decrease. Table 7 shows that the costs in every tier are improved from the

comparison of the R-SCIARIMA and R-SCI models.

5.2.2 Inventory turnover rate comparison

Table 8 shows the improved value of the inventory turnover rate of each tier member in the

supply chain model. It shows that the inventory turnover rate in each tier is improved from

the comparison of the R-SCIARIMA and SCIARIMA or R-SCI models. Take retailers for

example: the inventory turnover rate has a 10.65% and a 3.95% increase, respectively.Take branch warehouses for example: the inventory turnover rate has a 90.13% and a

14.40% increase, respectively.

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5.2.3 Bullwhip effect analysis

According to the definition of the bullwhip effect, the bullwhip effect values of the

R-SCIARIMA, SCIARIMA, R-SCI, and SCI models are calculated in Table 9, respectively.

Take LCD panel manufactories for example. In the R-SCIARIMA model, the bullwhip

effect value¼LCD panel manufactories demand variation� customer order devia-

tion¼ 904,601/26,870¼ 33.67. Table 9 shows the output of the calculation of the bullwhip

effect of each model according to the definition. In conclusion, the RFID system and

ARIMA demand forecasting method both being implemented together in the supply chain

can achieve a significant decrease degree of bullwhip effect.

5.3 (s, Q) parameters sensitivity analysis

This research carries out the analysis of the sensitivity of lead time (LT) and service level

(SL) with the optimal model: the R-SCIARIMA model. The lead time is analysed by

the method of the original setting LT 5%, which affects the reorder point (s). From the

Table 8. The improved inventory turnover rate of each tier by R-SCIARIMA vs SCIARIMA andR-SCI.

LCD panelmanufactories

LCD monitormanufactories

RegionalDC

Branchwarehouses Retailers

R-SCIARIMA (1) 0.7753 0.9629 0.8127 1.8937 0.2331SCIARIMA (2) 0.3700 0.2312 0.4721 0.9924 0.1266R-SCI (3) 0.7715 0.8544 0.6872 1.7497 0.1936Improved rate (1)–(2) 40.53% 73.17% 34.06% 90.13% 10.65%Improved rate (1)–(3) 0.38% 10.85% 12.55% 14.40% 3.95%

Table 6. The R-SCIARIMA vs SCIARIMA improved costs of each tier’s member.

Cost itemsLCD panel

manufactoriesLCD monitormanufactories Regional DCs

Branchwarehouses Retailers

Production �52.24% �75.92% �42.66% �65.73% �28.42%Replenishment �1.39% �0.06% 2.75% 5.24% 6.42%Delivery �1.22% �0.08% 2.69% 5.49% 6.71%Backorder �38.43% �30.55% �15.07% �29.24% �50.32%

Table 7. The R-SCIARIMA vs R-SCI improved costs of each tier’s member.

Cost itemsLCD panel

manufactoriesLCD monitormanufactories Regional DCs

Branchwarehouses Retailers

Production �0.12% 4.29% �15.61% �15.11% �17.21%Replenishment 0.12% 0.09% 0.46% 0.32% 0.27%Delivery 0.18% 0.10% 0.47% 0.81% 0.39%Backorder �1.80% �2.18% �2.75% �3.04% �4.32%

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comparison of total inventory costs in Table 10, it can be known that if the lead timeincreases, the total inventory cost increases.

From the comparison of inventory turnover rates in Table 11, it can be known that theresult of the variation is the same as that of the total inventory cost.

The analysis of sensitivity is carried out with service levels of 99%, 97%, 95%, 93%, and90%. As service level increases, the risk of backorder will decrease. From Table 12, it can beknown that the higher the service level is, the less the out-of-stock cost and risk will be.

6. Conclusion

This research focused on the simulation analysis of the TFT-LCD global supply chainmodel of Company A. It was found that the existence of the bullwhip effect will affect theeffectiveness of the whole supply chain, especially in the domain of inventory management.From the result of the Taguchi experiment, we learn that there is a combination of optimallevels of the control factors in the total inventory cost of the R-SCIARIMA model.The R-SCIARIMA has a 35.43% decrease in the total inventory cost and a 61.36% increasein the inventory turnover rate, in comparison with the SCIARIMA model. This shows thatthe implementation of an RFID system has a significant effect of improvement on thesupply chain. The R-SCIARIMA has a 13.08% decrease in the total inventory cost and an11.08% increase in the inventory turnover rate, in comparison with the R-SCI model.This shows the adoption of the ARIMA forecasting method in addition to RFID caneffectively enhance the performance of forecasting and gain significant effectiveness ofimprovement in inventory management. In the evaluation of the bullwhip effect, theestablishment of the R-SCIARIMA model in Company A can weaken the bullwhip effectand gain optimal effectiveness of improvement. Finally, we conclude that the real-time

Table 9. The comparison of bullwhip effect by four supply chain inventory models.

Endcustomer Retailers

Branchwarehouses

RegionalDC

LCD monitormanufactories

LCD panelmanufactories

R-SCIARIMA (1)

Var(Qk) * 103 26,870 300,148 33,121 62,146 121,854 904,601

BW a 1 11.17 1.23 2.31 4.53 33.67

SCIARIMA (2)

Var(Qk) * 103 28,019 367,007 42,770 89,641 173,218 1,312,085

BW b 1 13.10 1.53 3.20 6.18 46.83

R-SCI (3)Var(Qk) * 10

3 17,151 342,005 36,854 56,037 113,600 868,342BW c 1 19.94 2.15 3.27 6.63 50.63

SCI (4)

Var(Qk) * 103 17,522 131,274 22,458 46,477 196,346 1,530,903

BW d 1 7.49 1.28 2.65 11.21 87.37

(1) vs (2)¼[(b� a)/b] * 100

14.72 19.24 27.71 26.64 28.11

(1) vs (3)¼[(c� a)/c] * 100

43.98 42.63 29.21 31.53 33.50

(1) vs (4)¼[(d� a)/d] * 100

–49.09 3.82 12.78 59.53 61.47

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transmitting capability and visibility for acquiring RFID-enabled tagged product data

across a supply chain can synchronously trigger the ARIMA forecasting process and

eventually decrease the supply chain total inventory cost.

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