How To Make A Profit From A Customer Order Book

Transcription

1 Internatonal Journal of Computer Scence and Applcatons, Technomathematcs Research Foundaton Vol. 13, No. 1, pp , 2016 ORDER ALLOCATION FOR SERVICE SUPPLY CHAIN BASE ON THE CUSTOMER BEST DELIVERY TIME UNDER THE BACKGROUND OF BIG DATA LIU Yan-Qu School of scence, Shenyang Unversty of Technology, Shenyang , Chna WANG Hao School of scence, Shenyang Unversty of Technology, Shenyang , Chna E-mal: We manly studed order allocaton of logstcs servce supply chan under the background of bg data. Accordng to the characterstc of predcton of bg data, desgned the two-phase soluton of predcton of bg data stage and model optmzaton stage n logstcs servce supply chan. The frst stage through analyss the massve amounts of customer s locaton data and clcks to predct the best delvery tme and the number of customer s demand. The second stage s a three-echelon order allocaton model wth the obectve of the logstcs servce cost s establshed. And select the supplers to offer servce whch the customer was satsfed wth base on the analyss of bg data and the customer s requrements. The result shows that the predcton result of bg data s more accurate and better provde decson-makng bass for next stage n order allocaton. So t can mnmze the costs whle the customer satsfacton was guaranteed. Keywords: bg data; logstcs servce supply chan; order allocaton; assocaton analyss; delvery tme predcton; clcks rato; locaton data. 1. Introducton Logstcs servce supply chan optmzaton problem was frst proposed by Ellram n the artcle Journal of supply chan management n 2004 and defne the concept of logstcs servce supply chan. The man structure of the logstcs servce supply chan s logstcs servce ntegrator, functonal logstcs servce provders and customers. The customers demand orders are ntegrated by the Logstcs servce ntegrator after recevng the order from the customers. The orders are allocated to the functonal logstcs servce provders whch provde the correspondng logstcs servce to fulfll the customers. Ths process s called the order demand allocaton. Wth the rapd development of nformaton technology, such as the Internet, and people s lvng standard s hgher and hgher, people has hgher requrement and more ndvdual need to the servce level of logstcs. In the era of bg data, n order to mprove the reputaton and compettveness of enterprse, we should analyze customer nterest accordng to the customers data and make order allocaton decsons based on the customer s nterests. And then to optmze the cost of the whole logstcs servce supply chan. So t can brng long-term benefts for the enterprse. In the study of logstcs servce supply chan optmzaton, we manly focus on the problem of order allocaton. References [1], [2] consder transacton costs between logstcs servce ntegrator and subcontractors n order allocaton and solved wth the 84

2 Order allocaton for servce supply chan 85 genetc algorthm. Lu et al. [3-4] bult three-echelon logstcs servce supply chan of order allocaton, mnmzed the costs of the logstcs servce ntegrator and maxmzed satsfacton of the functon logstcs servce provders as the goal. Fan et al. [5] proposed a dynamc nteger-programmng model wth multple tasks and targets n multple perods. [6] ntroduced the model of Stackelberg Game nto the logstcs servce supply chan. Fan et al [7] consdered the characterstcs of the bad nto the supply chan and bult a mxed nteger programmng model wth multple targets. They combned Smulated Annealng algorthm wth a heurstcs rule to solve ths model. [8] proposed the value-orented operaton process model for servce supply chan. Order Allocaton Model of supplers selected based on fuzzy lnear programmng was presented by L et al [9]. L Yang Zhen[10] ntroduced the tme relablty nto the logstcs servces supply chan and bult the model of proft maxmzaton based on the constrants of logstc capacty, whch consdered the qualty level of logstcs servces and the relablty needs of our customers. The present study on the optmzaton of the logstc servce supply chan rarely combned wth bg data.for ths reason; ths paper presented two mprovements n logstcs servce supply chan order allocaton under the background of bg data. On the one hand, nstead of usng probablty dstrbutons to antcpate customer s need, we predct the results accordng to the vast amounts of data of the customers. And the result s closer to the actual. Therefore ths paper predcts the number of customer need base on analyzng the correlaton among customers Internet clcks rato, browsng tme and sales. However, prevous studes usually shorten the delvery tme to mprove the qualty of servce n logstcs servce supply chan, but shorten the delvery tme may not be the best delvery tme for customers. Therefore, ths artcle wll predct best delvery tme of customer base on analyss the behavor of customer whch accordng to vast amounts of data of customer s locaton. On the other hand, t s based on the predctons of bg data and the ndvdual needs of customers to match the functonal logstcs servce provders wth customers. Thus we can select approprate supplers whch customers are most satsfed wth can mprove customer servce qualty. So t can brng more profts for the long-term development of enterprses. 2. Problem Formulaton and Modelng In order to mprove servce levels of customer, ths paper ntroduces customer personalzed demand and predcted result whch based on the analyss of bg data nto the logstcs servce supply chan, and establshes a three-echelon order allocaton model wth the obectves of the total servce cost of logstcs servce ntegrator. Accordng to the order allocaton model we select functonal logstcs servce provders to provde servce whch the customer was most satsfed wth Notatons descrpton and assumptons Assumptons are as follows: (1)Assume that the qualty and category of the product has no dfference. (2)A dstrbutor can provde servce formultple clents, but a clent can accept only one dstrbutor s servce. (3)Assume that the data of locaton of customers s known.

3 86 Lu & Wang Notatons for the model as follows: n denotes the number of functonal logstcs servce provder, m denotes the number of customers, A denotes the attracton whch the th functonal logstcs servce provder appeal to the th customer, q denotes the servce number of functonal logstcs servce provder, c denotes servce costs of functonal logstcs servce provder, p denotes the servce quote of the th functonal logstcs servce provder to the th customer, Q denotes the servce level of the th functonal logstcs servce provder to the th customer, p denotes the hghest acceptable prce of customer, Q denotes the expected level of servce of customer, denotes the senstvty coeffcent of customer to the prce change, denotes the senstvty coeffcent of customer to the level of servce, H denotes the proft of the functonal logstcs servce provder, S denotes the mnmum proft of functonal logstcs servce provder can partcpate n order allocaton, x denotes the maxmum servce capacty of functonal logstcs servce provder, c denotes the prce of the product. Decson varables 1 under the U =1, n =1, m y 0 else U maxa 2.2. Model buldng In ths model, the total servce cost of the whole logstcs servce ntegrator s composed of the quote of functonal logstcs servce provder and the penalty cost of the delvery tme. st.. y n m n mn Z y p c -qc under the U =1, n =1, m 0 else A 1 p p / p Q / Q (3) 0 t T c t T p t>t q (1) (2) (4) m y,m=at( A B) 1 (5)

4 n m H y p c S 1 c e 1 1 Q Q max Order allocaton for servce supply chan 87 q m, q x, x m, p p 1 1 n Qmax Q 0,1, 0,1 0,1, 5, 1,5, (9) The obectve functon (1) represents that mnmzes the total servce cost of logstcs servce ntegrator, and the cost s composed as descrbed above. The functon (2) s the decson varables that select the th functonal logstcs servce provder whch has hgher attracton to the th customer. (3)s the functon of attracton whch functonal logstcs servce provder appeal to the customer. Wthout consderng the dfference n product qualty, the hgher level of logstcs servce that the provders offered, the hgher attractveness to customers and the same to hgher logstcs costs. But the hgher logstcs costs, the hgher quote of the functonal logstcs servce provder, whch wll reduce the attractveness to customers. So we defne attractveness as functon (3). The functon (4) represents penalty cost of the delvery tme and T s the best delvery tme of the customer that s predcted by analyss of the bg data. (5) represents the number of provders who assgned to offer servce. (6) represents the proft constrant of functonal logstcs servce provder whch can partcpate n order allocaton. (7) s the servce costs of logstcs, and s the servce cost coeffcent.(8) represents respectvely the delvery quantty that must meet the customer demand and the delvery quantty that s no more than the maxmum servce capacty of functonal logstcs servce provder. And the quote of the provder who gets the order must no more than the hghest acceptable prce of the customer. Functon (9) represents the scope of the varables. 3. Data preparaton and processng (6) (7) (8) 3.1. Demand forecastng In the context of bg data, t can predct the sales of goods accordng to the customers hts, browsng tme, shoppng cart, revews and all sales-related data. The product was known as an hot goods n near future, f the product hts soared recently. So there must be a correlaton between clcks and sales. Then the browsng tme guarantee the effectveness of the hts whch are not delayed clcks or malcous clcks. So ths paper consders the correlaton among the product hts, browsng tme and sales to predct sales. Assume that the number of customer s demand s m, and product hts s A, the browsng tme s B. We defne one customer clck on the web page of the product s 1, else s 0. The browsng tme s more than 30 mnutes s 1, else s 0. Whle the customer buy ths product s 1,else 0. The data form s shown n table 1.

5 88 Lu & Wang Table 1: network data of customer Product hts A Browsng tme B Sales m Based on the confdence and support of the assocaton rules to be udged: T X Y S XY T C XY T X Y T X (2) Functon (1) ndcates that the support of the rules. It represents the unversalty of smple assocaton rules, f the support s low then the rules can t be use to udge. T X Y represents the number of event X and event Y whch occur smultaneously. T represents the total number of events. Functon (2) ndcates that the confdence of the rules that represents the accuracy of smple assocaton rules. If the confdence s hgh, then the probablty of event Y s hgh under the condtons of event X appearng. TX ndcates the number of event X. Only when the confdence and support rules acheve the set mnmum support, the rules was set up. So ths paper mnes the correlaton through usng Apror algorthm. Then the number of customer demand can get by the formula: m=ata B. a ndcates the correlaton among the product hts, browsng tme and sales Delvery tme predcton Assumng that poston coordnates of customers s xn, yn, t n,( xn, yn) ndcates horzontal and vertcal coordnates of the locaton of customers, t n s tme data to the customers locaton correspondngly. Frst of all, accordng to the customer s locaton data, the day of customer s dvded nto several sectons by usng segmentaton technques. And then all dstances that are less than the mnmum dstance are clustered, changed that poston coordnates the same as the poston coordnates of cluster centers. At last the assocaton rule s used to analyze user behavor Data mnng Algorthm Step 1. Enter the tme seresp Defne dstance= (1) assume that =1, deal wth the frst column of data. A,dstance(,1)= A A A A ( 1,1,1 ) ( 1,2,2 ) 2 2,

6 Order allocaton for servce supply chan 89 ndex=dstance 0.5, and select out all data whch meet the condton A(ndex1: 2 )( = A 1,1:), 2 Output data and merge same data as Q 11 = ( x 11, y 11, t 11, t 1 ), crculaton processng all the data and then get the segment matrxq. Step2.Inpu Q t, fnd out Q whch meet the condtons accordng to the formula x x11, y y11, and named t asq 11. Cycle to fnd coordnates of all the same poston and clusterng. Step3. Calculate the number of eachq, calculate correlaton between all adacent Q accordng the functon S T Q Q T, C T Q Q T Q Q k Q k k k Q k, select out Q k k k k all Q whch meet the condtons k k. 4. Data Smulaton 4.1. Demand Forecastng The selected data must be authortatve and obectvty to ensure the qualty of data. The experments selected data s from the network of easy car n January The number of customer hts s , the number of browsng tme s , the sales of the car s 76663, then these data are organzed nto the form as Table 1. The number of meetng the condton n clck and browsng tme s So the correlaton among the clck, browsng tme and sales s 91%, the clck and the browsng tme has strong ablty to explan to sales. Thus, t can predct the future sales based on the clck and browsng tme n the next perod The best delvery tme predcton of customers Randomly selectng locaton data of 10 users wthn a week to analyze, we only consder the tme that from 10:00 am to 19:00 pm because of the actual delvery tme. Data as table 2 Table2: user s locaton data n a week Monday Tuesday Wednesday Thursday (10,9.5,10:00) (10.1,9.55,10:00) (10,9.6,10:00) (10.01,9.68,10:00) (10.01,9.5,10:59) (10.2,9.58,10:30) (10.1,9.6,10:41) (10.08,9.67,11:00) (10.09,9.82,11:38) (10.09,9.80,11:40) (10.54,10,12:00) (10.5,10.2,12:37) (10.1,9.95,12:35) (10.06,9.8,12:50) (10.36,10.45,13:30) (10.2,9.8,14:35) (10.3,10,13:30) (10.2,10.1,13:50) (10,9.55,14:17) (9.8,9.9,15:30) (10,9.8,14:30) (10,9.85,14:45) (10,9.86,15.37) (9.9,10,17:00) (9.9,10,16:30) (9.9,10,16:16) (9.96,10,16:59) (4.31,4,18:00) (5,6.3,17:29) (5.01,6.21,17:40) (5,6.3,17:20) (4.3,4,19:00) (5.1,6.2,18:30) (4.29,4,18:45) (4.35,4.2,18:30)

7 90 Lu & Wang Frday Saturday Sunday (10,9.6,10:01) (4.32,4.01,10:00) (4.3,4.1,10:00) (10.1,9.61,10:50) (4.3,4.02,10:50) (4.35,4.05,11:30) (10.32,10.1,11:50) (4.29,4,11:30) (4.3, 4.1,12:59) (11,12,12:30) (15,15.5,12:10) (11,10,14:45) (9.9,9.9,14:20) (15.5,16,13:40) (11.02,10.10,17:19) (10,10,17:10) (15.2,15.7,14:45) (11.1,10.15,18:00) (4.31,4.1,18:20) (15.6,16,17:30) (4.3,4.1,19:00) (4.31,4,19:00) (15.3,16.1,18:35) The result of data segmentaton as table 3 Table3: customer s segmentaton data d d d d4 Q Q 11 =(10,9.5,10:00,16:30) 11 Q =(10.1,9.55,10:00,16:16) 11 =(10,9.6,10:00,16:59) Q11 ( 10.01,9.68,10 : 00,11:30) Q 12 =(5,6.3,17:29,18:30) 12 Q =(5.01,6.21,17:40) ,3,17: 20 Q13 4.3, 4.19,19 : 00 Q 13 = 4.29, 4.18,18 : 45,19 : 00 Q = 4.35,4.2,18:30,19 : 00 Q Q 13 =(4.31,4.18,18:00,19:00) 13 d5 d d 6 7 Q Q 11 =(10,9.6,10:01,17:10) 13 =(4.32,4.01,10:00,11:30) Q 13 =(4.3,4.1,10:00,19:00) Q 13 = 4.31,4.1,18: 2 0, 19 : 00 Q =15,15.5,12:10 Q ,10,14 : 45,18 : 00 Q 63 = 15.5,16,13: 40,18:35 Q = 16,17,19 : 00 We can get the correlaton between Q 11 and Q 12, Q 12 and Q 13 hgher than other data base on the result. So the users convenence can be mproved,f we chose these 2- tme-perods to delvery. Because of the order delvery address of customer s known, so we chose the delvery tme whch closer to the order delvery address. Q 11 ndcates the address of customer s company, Q12 ndcates the address of supermarket whch near to the customer s home, Q13 ndcates the address of home. If the customer fll the home address nto the order form address, then output the tme Q 12, so we get the delvery tme of the customer s 17:20pm. Compare the predcted results wth expectaton of customer; t can meet customer acceptable tme Logstcs Servce Supply Chan Smulaton 64 There are 8 functonal logstcs servce provders n ths example. a s 90%, T( A B) s 12, Assume that the senstvty coeffcent of all customers to the prce s 1, The senstvty coeffcent of customer to the level of servce s 0.9,0.9,0.7,0.75, 1,0.7,0.75,0.85,0.8,0.7, servce cost coeffcent s 0.8, S s , x s , penalty

8 Order allocaton for servce supply chan 91 coeffcent s 0.002, the prce of the product c s all 100, Dfferent functonal logstcs servce provder for dfferent tasks has dfferent levels of servce, and the data as table 4 to table 6 Table 4: logstcs servce level of functonal logstcs servce provder user provder Table 5: e quote of functonal logstcs servce provder user provder Q Table 6: hghest acceptable prce and the desred servce level of customer user p The result of the model that s the 3th provder offer servce to customer1,2, the 6th functonal logstcs servce provder offer servce to customer , the 8th functonal logstcs servce provder offer servce to customer 5,9. The delvery tme of customer 3,4,6 whch the 6th functonal logstcs servce provder to offer s 20mn late, 10mn late, 30mn late and the others are not late. The result of the logstcs servce costs s Yuan. 5. Concluson Ths paper antcpates customers demand on the bass of analyss data of customers hts and browsng tme, then change uncertan demand nto certan demand, whch s

9 92 Lu & Wang convenent to solve. The behavor of clents s analyzed accordng to the customers locaton data, then the best delvery tme of customers can be gotten out. On the bass of the result as above a three-echelon order allocaton model s bult, ths model consders the attracton, servce levels, logstcs cost, delvery tme penalty cost. The results showed that analyzng demand and behavor of customers n the context of bg data can better help companes understandng customer s nterest, so that t can provde better servce for customer. Meanwhle, t can reduce the cost of logstcs servces under the premse of mprovng the level of customer servce, thereby, enhancng the credblty of the enterprse, whch s conducve to long-term development of enterprses to get more benefts. There are stll many lmtatons n ths paper. For example, we defne the attracton only from two respect of servce level and quote. And we wll consder more factors n future research, so that t can mprove the accuracy of customers ndvdual needs. Because that the analyss of bg data has ust started, ths paper consder less factors n assocaton of data analyss. In order to mprove the accuracy of the data mnng, we wll consder more factors n future research. At the same tme, we not only ntroduce bg data nto logstcs servce supply chan, but also ntroduce t nto other areas of the logstcs. References Shan-shan L.(2014):Genetc Algorthm to Logstcs Servce Supply Chan Order Allocaton Problem, Operatons Research And Management Scence, 23(05),pp Zh-un G, We L, Zh-qang F, We-png Z,(2013): Mult-obectve Optmzaton Model of Order Allocaton of LSSC Consderng Transacton Costs, Systems Engneerng, 30(7), pp We-hua L, S-Yuan Q, Sh-quan Z.(2012):Order allocaton n three-echelon logstcs servce supply chan under stochastc envronments, Computer Internet Manufacturng Systems, 20(7),pp Wehua L Meyng G, Wenchen X, (2014): An order allocaton model n logstcs servce supply chan based on the pre-estmate behavor and compettve-bddng strategy. Internatonal Journal of Producton Research 52(8): Chen F, Xaol W, Jn C, (2014):Servce Order Allocaton Based on Mult-obectve Dynamc Programmng, JOURNAL OF TONGJI UNIVERSITY NATURAL SCIENCE, 42(9),pp Jan-feng L, Sh-png C, Rong-hua Y.(2013): Research on Prcng Decsons and Effcency n a Two-Level Logstcs Servce Supply Chan, Chnese Journal of Management Scence,,21(2),pp Zhqang F (2012): Smulated annealng algorthm to supply chan order allocaton problem. Computer Engneerng and Applcatons, 48(25),pp Tan-yang L, Tng H, Han-chuan X,(2015): Value-orented operaton process model for servce supply chan, Computer Integrated Manufacturng Systems, 20(1),pp Amn,S.H.,Razm J, Zhang G.(2011): Suppler Selecton and Order Allocaton Based on Fuzzy SWOT Analyss and Fuzzy Lnear Programmng. Expert Systems wth Applcaton 38, pp Yang-zhen L. (2014):Relablty and optmzaton of logstcs servce supply chan. Statstcs and Decson,05, pp