<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">IB</journal-id><journal-title-group><journal-title>iBusiness</journal-title></journal-title-group><issn pub-type="epub">2150-4075</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/ib.2010.21009</article-id><article-id pub-id-type="publisher-id">IB-1444</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Business&amp;Economics</subject></subj-group></article-categories><title-group><article-title>
 
 
  Influence of Lateral Transshipment Policy on Supply Chain Performance: A Stochastic Demand Case
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>ingxian</surname><given-names>Chen</given-names></name><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Jianxin</surname><given-names>Lu</given-names></name></contrib></contrib-group><author-notes><corresp id="cor1">* E-mail:<email>carl.jxchen@hotmail.com(IC)</email>;</corresp></author-notes><pub-date pub-type="epub"><day>26</day><month>03</month><year>2010</year></pub-date><volume>02</volume><issue>01</issue><fpage>77</fpage><lpage>86</lpage><history><date date-type="received"><day>September</day>	<month>14th,</month>	<year>2009</year></date><date date-type="rev-recd"><day>October</day>	<month>25th,</month>	<year>2009</year>	</date><date date-type="accepted"><day>December</day>	<month>2nd,</month>	<year>2009.</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  Considering the supply chain consists of one supplier and two retailers, we construct the system’s dynamic models which face stochastic demand in the case of non-lateral transshipment (NLT), unidirectional lateral transshipment (ULT) and bidirectional lateral transshipment (BLT). Numerical example simulation experiments of these models were run on Venple. We adopt customer demand satisfaction rate and total inventory as performance indicators of supply chain. Through the comparative of the simulation results with the NLT policy, we analyze the influence of ULT policy and BLT policy on system performance. It shows that, if retailers face the same random distribution demand, lateral transshipment policy can effectively improve the performance of supply chain system; if the retailers face different random distribution demand, lateral transshipment policy cannot effectively improve the performance of supply chain systems, even reduce system’s customer demand satisfaction rate, and increase system inventory variation.
 
</p></abstract><kwd-group><kwd>Supply Chain</kwd><kwd> Inventory System</kwd><kwd> Lateral Transshipment</kwd><kwd> Performance</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>Lateral transshipment, an important inventory replenishment policy, has gained a common concern of the academics and business managers in recent years. There are numerous researches about this issue. Lateral transshipment is defined as the redistribution of stock from retailers with stock on hand to retailers that cannot meet customer demands or to retailers that expect significant losses due to high risk [<xref ref-type="bibr" rid="scirp.1444-ref1">1</xref>]. The early pioneering work of Krishnan and Rao [<xref ref-type="bibr" rid="scirp.1444-ref2">2</xref>] examine a periodic review policy in a single-echelon, single-periodic setting [<xref ref-type="bibr" rid="scirp.1444-ref2">2</xref>]. Robinson [<xref ref-type="bibr" rid="scirp.1444-ref3">3</xref>] extends the research to a multi-period case, and establishes the system’s lateral transshipment model [<xref ref-type="bibr" rid="scirp.1444-ref3">3</xref>]. The emergency lateral transshipment model of repairable product was analyzed by Lee [<xref ref-type="bibr" rid="scirp.1444-ref4">4</xref>] for the two-echelon inventory system case [<xref ref-type="bibr" rid="scirp.1444-ref4">4</xref>]. Axs&#228;ter [<xref ref-type="bibr" rid="scirp.1444-ref5">5</xref>] studies the emergency lateral transshipment problem of the multi-level repairable product inventory system, and gets some interesting conclusions different from Lee’s [<xref ref-type="bibr" rid="scirp.1444-ref5">5</xref>]. Archibald et al. [<xref ref-type="bibr" rid="scirp.1444-ref6">6</xref>] develop a lateral transshipment model among the multi-retailer based on Markov decision-making methods [<xref ref-type="bibr" rid="scirp.1444-ref6">6</xref>]. Grahovac and Chakravarty [<xref ref-type="bibr" rid="scirp.1444-ref7">7</xref>] limit the research object to low demanded expensive product and analyze the lateral transshipments model in a multiechelon supply chain system [<xref ref-type="bibr" rid="scirp.1444-ref7">7</xref>]. Kukreja et al. [<xref ref-type="bibr" rid="scirp.1444-ref8">8</xref>] consider a single-echelon continuous review inventory system which contains n depots, and takes the expensive consumable product as object, study the one-to-one lateral transshipment model [<xref ref-type="bibr" rid="scirp.1444-ref8">8</xref>]. Rudi et al. [<xref ref-type="bibr" rid="scirp.1444-ref9">9</xref>] work on optimal order policy of the vendors in the existence of lateral transshipment circumstances [<xref ref-type="bibr" rid="scirp.1444-ref9">9</xref>]. Minner and Silver [<xref ref-type="bibr" rid="scirp.1444-ref10">10</xref>] provide a new decision rule of system’s lateral transshipment, and prove it can figure out the size of transshipment as well as some important problems [<xref ref-type="bibr" rid="scirp.1444-ref10">10</xref>]. Xu et al. [<xref ref-type="bibr" rid="scirp.1444-ref11">11</xref>] analyze emergency lateral transshipment policy between the two-echelon continuous review inventory system that use <img src="9-8601007\f24bfced-1e66-4d51-a92f-98d4069fee80.jpg" /> policy [<xref ref-type="bibr" rid="scirp.1444-ref11">11</xref>]. Banerjee et al. [<xref ref-type="bibr" rid="scirp.1444-ref12">12</xref>] study lateral transshipment of two-echelon supply chain systems which include multiple retailers and single supplier based on DOE [<xref ref-type="bibr" rid="scirp.1444-ref12">12</xref>]. Xu and Luo [<xref ref-type="bibr" rid="scirp.1444-ref13">13</xref>] use Expect Cost Method to analyze lateral transshipment policy in cross-docking system [<xref ref-type="bibr" rid="scirp.1444-ref13">13</xref>]. Xu and Xiong [<xref ref-type="bibr" rid="scirp.1444-ref14">14</xref>] analyze the best time for one-off transshipments in a cross-docking system with stochastic demand [<xref ref-type="bibr" rid="scirp.1444-ref14">14</xref>]. Wang et al. [<xref ref-type="bibr" rid="scirp.1444-ref15">15</xref>] conduct a quantitative analysis to the value of lateral transshipment policy of regional inventory distribution systems, which consist of a distribution center and multiple retail points [<xref ref-type="bibr" rid="scirp.1444-ref15">15</xref>]. Huo and Li [<xref ref-type="bibr" rid="scirp.1444-ref16">16</xref>] develop batch ordering policy in a single-echelon, multi-location transshipment inventory system [<xref ref-type="bibr" rid="scirp.1444-ref16">16</xref>]. Li et al. [<xref ref-type="bibr" rid="scirp.1444-ref17">17</xref>] study inventory management model of the cluster supply chain system with the existence of emergency lateral transshipment [<xref ref-type="bibr" rid="scirp.1444-ref17">17</xref>].</p><p>Most of the papers above dealing with transshipment assume that lateral transshipment already exists in system. However, lateral transshipment will make the problem complicate and tend to be very difficult to analyze analytically, especially BLT [<xref ref-type="bibr" rid="scirp.1444-ref18">18</xref>]. Hence, will lateral transshipment really need? This paper handles this problem from the performance measurement point. We consider one supply chain system consists of one supplier and two retailers, allowing a retailer transship from the other one for inventory replenishment besides order from supplier. System’s models were developed by system dynamics assume that all the members use the order-up-to policy, and numerical experiment was run on Venple platform.</p><p>The paper is organized as follows: in Section 2, the models with lateral transshipment were developed, as well as without lateral transshipment. The accuracy of the model is tested against simulation in Section 3; Section 4 deals with the influence of ULT and BLT. The conclusion of this paper was presented in Section 5.</p></sec><sec id="s2"><title>2. Model Description</title><p>We consider two retailers facing independent stochastic customer demand and one supplier (<xref ref-type="fig" rid="fig1">Figure 1</xref>). Without lateral transshipment, retailers order from suppliers to replenish inventory in case of stock out. In order to respond to customer demand quickly, they can use lateral transshipment policy besides order from the supplier, which means they replenish inventory from the other one if there exist surplus stock on hand.</p><sec id="s2_1"><title>2.1 Model Assumption</title><p>Development of the model needs the following assumptions.</p><p>• Customer1 and Customer2 face independent stochastic demand;</p><p>• Both retailers adopt order-up-to policy, the ordering period is constant;</p><p>• Lateral transshipments take no time;</p><p>• Transshipments take place when there are surplus stocks. That is, if retailer 1 needs transshipment from retailer 2, retailer 2 only transships the redundant stock.</p></sec><sec id="s2_2"><title>2.2 System Model</title><p>As a modeling and simulation technology, system dynamics has a wide range of applications since its birth, especially in dealing with long-term, chronic, dynamic management problems [<xref ref-type="bibr" rid="scirp.1444-ref19">19</xref>]. Forrester [<xref ref-type="bibr" rid="scirp.1444-ref20">20</xref>] applies system dynamics in industrial business management, addressing issues such as fluctuations in production and employees, instability of market shares and market growth [<xref ref-type="bibr" rid="scirp.1444-ref20">20</xref>]. Logistics and Supply Chain Management is an important area of System Dynamics. Sterman [<xref ref-type="bibr" rid="scirp.1444-ref21">21</xref>]designs the well-known beer game by System Dynamics, and carries out detailed analysis on feedback loops, nonlinear, time-delay and management behavior in the system [<xref ref-type="bibr" rid="scirp.1444-ref21">21</xref>]. Diseny et al. [<xref ref-type="bibr" rid="scirp.1444-ref22">22</xref>] analyze VMI in transport operation by system dynamics [<xref ref-type="bibr" rid="scirp.1444-ref22">22</xref>]. Marquez [<xref ref-type="bibr" rid="scirp.1444-ref23">23</xref>] establishes a model for measuring financial and operational performance in the supply chain based on System Dynamics [<xref ref-type="bibr" rid="scirp.1444-ref23">23</xref>], and so on.</p><p>Generally speaking, a complete system dynamics model usually consists of three parts: model variables, causal loop diagrams and mathematical description. We analyze the three part of model in turn as follows.</p><sec id="s2_2_1"><title>2.2.1 Model Variables</title><p>The structure of a system dynamics model contains stock, smoothed stock, flow rate, auxiliary variables and constants. Stock variables are used to describe the cumulative effect of the system. Smoothed stock variables are the expected values of specific variables obtained by exponential smoothing techniques. Flow rate describes the rate of the cumulative effect of the system. Auxiliary variables are the middle variables which express the decision-making process. Constants change little or relatively do not change during the study period. The fundamental notations of the model are following:</p><p><img src="9-8601007\4e0548fb-a98d-4632-b618-26ef6f49c6ac.jpg" /></p></sec><sec id="s2_2_2"><title>2.2.2 Causal Loop Diagrams</title><p>Causal loop diagram is a tool that expresses the structure of the system, playing an extremely important role in system dynamics. There are two reasons for that. First, during model development, they serve as preliminary sketches of causal hypotheses and secondly, they can simplify the representation of a model.</p><p>The first step of our analysis is to capture the relationship among the system operations in a system dynamics manner and to construct the appropriate causal loop diagram.</p><p><xref ref-type="fig" rid="fig2">Figure 2</xref> describes the causal loop of the supply chain without lateral transshipment.</p><p>The system structure in <xref ref-type="fig" rid="fig2">Figure 2</xref> contains supplier and retailers. For the supplier, <img src="9-8601007\06c4abe9-dc44-4228-8397-d74e3ed93d8a.jpg" />is decided by <img src="9-8601007\f6f793bc-cfa7-4dad-991c-a51d734dae13.jpg" /> and<img src="9-8601007\95b0480a-271e-4f75-b613-ef96a23461dc.jpg" />. <img src="9-8601007\02b35ae5-30a3-449f-9519-6deb321a0908.jpg" />is determined commonly by <img src="9-8601007\2830c9d5-7920-4372-b6c7-ea8576751d28.jpg" /> and<img src="9-8601007\c4bc6b72-8dfb-46ae-adee-b6de3380a5cc.jpg" />. Delivery rate <img src="9-8601007\f3e0ddd2-fd25-4fef-b226-68ead2d541d6.jpg" /> is determined by <img src="9-8601007\d01af35e-67b7-46ee-80b9-246e8ca3fa7a.jpg" /> and<img src="9-8601007\a8f0093a-9d65-48a3-9e44-2417a28e967f.jpg" />. Supplier adjust inventory level by setting<img src="9-8601007\b46d1571-facf-4675-965d-d064afbffbcb.jpg" />, together with <img src="9-8601007\a42e75d6-fb41-4e3f-b424-695e275618f2.jpg" /> determine<img src="9-8601007\fd7513db-6450-4760-887c-062dcd11a424.jpg" />. <img src="9-8601007\3351fe7a-106e-44e5-a792-9269742302c6.jpg" />and <img src="9-8601007\1d882daf-e14c-4f78-9a84-cd835e58bf58.jpg" /> determine<img src="9-8601007\1ec94dd5-bf4f-4c18-99c7-67bb4b594362.jpg" />, in turn, <img src="9-8601007\88350698-502c-4705-a58b-a8562cd0412e.jpg" />has a direct impact on</p><p><img src="9-8601007\49c0a515-4eb5-489d-a1e7-a82112795547.jpg" />and an indirect impact on<img src="9-8601007\2743aab2-7094-4b66-980b-17239496d3ec.jpg" />. For retailers, <img src="9-8601007\ac9f037c-cada-4f89-9bb7-6fe4ea716bf4.jpg" />is determined by <img src="9-8601007\7e0d69c7-7dfa-4b08-a9dd-326a94fc5092.jpg" /> and<img src="9-8601007\c2d2e83b-dc39-44e4-bd2d-8e3a39bc022e.jpg" />. <img src="9-8601007\500c0926-1a11-490b-8e87-0816674bfddc.jpg" />is the delay of<img src="9-8601007\e457fa4b-a88e-4313-8973-4a5c8e0618eb.jpg" />, delay time is<img src="9-8601007\e4b0bd83-964e-4113-a46e-1d31cd5d8a87.jpg" />. <img src="9-8601007\de571ffe-f44d-45b6-9ed2-3f2a4fa723b4.jpg" />is decided by <img src="9-8601007\731e0994-0c04-4941-9a04-b4b01de86054.jpg" /> and<img src="9-8601007\1f27ae48-15c2-4a71-9811-fc0af2c8015c.jpg" />. <img src="9-8601007\8e055d01-fa99-47a0-85b3-72f5c6d318a1.jpg" />is obtained from <img src="9-8601007\e974c78a-ee5e-46b0-8dba-8dec881602b4.jpg" /> after the time<img src="9-8601007\8f277e7b-7f1d-4648-a633-eb6595961cf5.jpg" />. Retailers also adjust inventory level by setting<img src="9-8601007\35db1b7b-5a6b-4752-b513-59566e50883e.jpg" />. <img src="9-8601007\da549440-614b-4291-9d20-c4c4fe075e28.jpg" />is decided by <img src="9-8601007\efa37f4f-b47d-4f01-9909-2b05dc239605.jpg" /> and<img src="9-8601007\e7cef540-f6cf-4af4-a928-14902d7acd99.jpg" />. <img src="9-8601007\04285a0c-fbd7-4b8f-bcef-cdb06a0e73e2.jpg" />and <img src="9-8601007\11977998-a54a-435b-b60e-3a94c3a06e0d.jpg" /> jointly determine<img src="9-8601007\7fbeafd5-a0af-4ca4-a034-42d82f22f6b6.jpg" />. <img src="9-8601007\69f44dee-d285-4b90-8095-8dd3e9b07c23.jpg" />and <img src="9-8601007\c1123951-38fa-49cf-b44a-3db93fb21ba6.jpg" />commonly determine<img src="9-8601007\1ce8b934-4c5d-46f5-ac68-7a309a8be1de.jpg" />. If <img src="9-8601007\81ab5ca6-f8e6-4fb0-9fbd-722caa4e943a.jpg" /> is greater than 0, Retailer sent orders to suppliers, order quantity <img src="9-8601007\e0fdac69-1d53-4c9b-8633-c06786396c35.jpg" /> is decided by</p><p><img src="9-8601007\61b26042-704e-4e33-af9b-4beac75d8569.jpg" />.<img src="9-8601007\2fc07c09-cedd-42c1-82a8-2a69e85842d2.jpg" />, <img src="9-8601007\ba313062-f741-45c1-876b-1cdd383c91b0.jpg" />, and <img src="9-8601007\e2ffc9b1-fe81-4cda-b35d-e8fed2326586.jpg" /> determine<img src="9-8601007\e4ba1c85-4cc6-479b-93ef-a314acf9047d.jpg" />. There are two performance variables, customer demand satisfaction rate <img src="9-8601007\6ed08944-cc2c-43e1-a4b2-d7fcbc299662.jpg" />and total inventory<img src="9-8601007\24c2c779-6675-43da-90aa-54b45a5197a3.jpg" />. <img src="9-8601007\d7e4e7cd-7e63-41bd-b090-834976af510c.jpg" />is decided by inventory <img src="9-8601007\f050e93a-c260-4bfb-98e6-fa3f9b54c914.jpg" /> and customer demands<img src="9-8601007\6fa7ea7b-6ae0-4a10-a8b9-60b150126c5f.jpg" />, <img src="9-8601007\a171ff5d-c483-48f1-814b-63d92d462da2.jpg" />is a accumulation sum of supplier inventory <img src="9-8601007\f693aad8-68f4-40e0-b11f-c53522adf13b.jpg" /> and<img src="9-8601007\14cfe52f-6b11-4b03-a0e6-2f805e4063c6.jpg" />.</p><p><xref ref-type="fig" rid="fig3">Figure 3</xref> describes the causal loop diagram of supply chain with ULT. <img src="9-8601007\084a63f4-bb90-49bb-bfc0-32d6f62164d9.jpg" />means the transshipment from retailer 2 to retailer 1. It is a flow rate variable and means that when retailer 1 out of stock, retailer 2 will replenish retailer 1 by transshipment on condition that it has surplus stock. <img src="9-8601007\d6148886-7db3-48ad-84db-40dd03af9b64.jpg" />is decided by <img src="9-8601007\7a8b78ed-18a9-4bc0-8a20-0af01a112135.jpg" /> and<img src="9-8601007\3e9efffa-7a31-44fa-8fe2-c35d83571ace.jpg" />, and influence<img src="9-8601007\ba108f20-665b-4e3e-89a7-ce5a6be2a9a6.jpg" />.</p></sec></sec></sec></body><back><ref-list><title>References</title><ref id="scirp.1444-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">G. Tagaras and M. A. Cohen, “Pooling in two-location inventory systems with nonnegligible replenishment lead times,” Management Science, Vol. 38, pp. 1067–1083, 1992. 
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