Enterprise service computing from concept_13 ppt

342 Dotoli, Fanti, Meloni, & Zhou Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permis- sion of Idea Group Inc. is prohibited. The Case Study To illustrate the network design optimization procedure, we consider a case study inspired by an example proposed in Luo et al. (2001). The target product is a typi- cal desktop computer system consisting of a computer, hard-disk driver, monitor, keyboard, and mouse. The SC is composed of N S =6 stages: suppliers, manufactur- ers, distributors, retailers, consumers, and recyclers. In the following, we apply and discuss the different steps of the proposed design procedure for this case study. Candidate Selection The rst module of the DSS structure in Figure 1 has to perform the candidate selec- tion for each stage of the IESC case study. As an example, we focus on the partner selection for the second stage, that is, the manufacturers. Obviously, the methodol- ogy proposed for manufacturer selection is applicable to each stage of the IESC. Table 1. Performance matrix of manufacturers with scores for each criterion n 1 c n 2 c n 3 c n 4 c n 5 c n 6 c n 7 c n 8 c n 9 c n 10 c n 11 c n 12 c n 13 c n 14 c n 15 c E 80 70 20 10 90 35 35 60 30 30 30 65 70 40 80 RM 30 15 60 80 70 40 90 30 75 10 60 20 30 85 40 F 10 90 85 55 30 60 40 15 20 10 35 45 25 40 20 FL 40 10 30 55 75 80 15 90 45 40 20 40 20 30 20 T 65 30 80 30 80 10 45 45 20 30 90 35 40 15 35 Q 85 80 60 50 65 50 60 15 10 40 15 60 45 60 30 E RM F FL T Q Indifference threshold 10 25 20 5 10 20 Preference threshold 20 45 30 10 20 30 Veto threshold 35 85 60 30 60 60 Weights 1.0 0.6 0.8 0.6 0.6 0.6 Table 2. Thresholds and weights for the Electre method Position 1 2 3 5 6 7 8 9 10 12 13 Manufacturer n 5 c n 1 c n 3 c , n 12 c n 8 c n 6 c n 2 c n 13 c n 15 c n 7 c ,n 11 c n 10 c n 4 c , n 9 c , n 14 c Table 3. Manufacturers’ rankings according to the Electre method for the case study Service Computing for Design and Reconguration of Integrated E-Supply Chains 343 Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. The rst step of the procedure determines the candidate set of the second stage, for example, P 2 c ={n 1 c ,n 2 c , …,n 15 c }, where we assume that 15 candidates are competing to join the second stage of the IESC. In the second step of the procedure, the decision makers dene the most relevant criteria for the selection: F, RM, E, FL, T, and Q. Obviously, such a choice can only be based on experience and expert knowledge of the IESC processes, products, and actors. Then, in the third step, a data-analysis system assigns the scores to each candidate. Table 1 reports the performance matrix assigned to each alternative manufacturer. Subsequently, since the Electre method is employed, the decision makers assign the thresholds and weights for the case study as shown in Table 2. Using the thresholds of Table 2, the Electre method seeks for an outranking relation. Table 3 shows the nal ranking of the candidates, obtained with a Matlab implementation of the method that employs the intrinsic characteristic of the Matlab programming environment to operate with matrices (MathWorks Inc., 2002). The reader is referred to Mousseau et al. (2000) for a discussion on the denition of the decision parameters required by the Electre method, and to a previous work by the authors (Dotoli et al., 2005) for further insights on the provided example. According to the results in Table 3, the decision maker selects P 2 ={n 5 c } if one manufacturer only is to be included in the network. On the contrary, if several manufacturers have to be incorporated in the IESC, a corresponding number of candidates are selected from Table 3 starting from the one with the highest posi- Figure 4. The stages of the IESC network for the case study Figure 5. The digraph associated with the IESC of the case study Stage P 1 1 n 2 n 3 n 4 n Stage P 2 5 n Stage P 3 6 n 7 n Stage P 6 11 n 12 n 13 n 14 n Stage P 4 8 n 9 n Stage P 5 10 n 18 18 m,e 17 17 m,e 15 m 25 m 35 m 45 m 4,10 4,10 m,e 56 m 5,10 m 57 m 59 m 68 m 78 78 m,e 79 79 m,e 7,10 7,10 m,e 8,10 8,10 m,e 9,10 9,10 m,e 10,11 m 10,12 m 10,13 m 10,14 m 11,5 m 14,3 m Supplier Manufacturer Distributor RecyclerRetailer Consumer 1 n 2 n 3 n 4 n 5 n 6 n 7 n 11 n 12 n 13 n 14 n 8 n 9 n 10 n 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 x 10 x 11 x 12 x 13 x 15 x 14 x 16 x 17 x 18 x 19 x 20 x 21 x 22 x 23 x 344 Dotoli, Fanti, Meloni, & Zhou Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permis- sion of Idea Group Inc. is prohibited. tion. For instance, if two manufacturers are to be included in the IESC, the decision maker selects P 2 ={n 1 c ,n 5 c }. Note that the former choice is made in the following so that one manufacturer only is selected. The IESC network obtained after the iteration of the candidate-selection technique for each stage is depicted in Figure 4, while its digraph is shown in Figure 5, com- posed as follows: four suppliers, one manufacturer, two distributors, two retailers, one consumer, and four recyclers, for a total of N=14 partners. The data for the IESC are reported in Table 4 (Luo et al., 2001), showing the value of each performance index M q with q=1, …, 4 associated with the links of the considered IESC. More precisely, the adopted performance indices are total cost (M 1 ), energy (M 2 ), CO 2 emission (M 3 ), and cycle time (M 4 ). We indicate generically by cycle time Links Edges Variables Cost (M 1 ) in $ Energy (M 2 ) in MJ CO 2 emission (M 3 ) in KgCE Cycle time (M 4 ) in hours m 18 , e 18 y 18 x 1 41.80 359.00 0.87 19.30 m 17 , e 1,7 y 17 x 2 46.70 332.00 0.74 16.80 m 15 y 15 x 3 319.00 1479.00 2.21 12.50 m 25 y 25 x 4 308.00 1776.00 2.19 12.80 m 35 y 35 x 5 238.00 1540.00 3.10 16.20 m 45 y 45 x 6 246.00 1409.00 1.47 10.20 m 4,10 , e 4,10 y 4,10 x 7 53.90 369.00 30.20 5.30 m 56 y 56 x 8 448.00 3618.00 8.74 19.20 m 5,10 y 5,10 x 9 379.00 3542.00 296.00 4.20 m 57 y 57 x 10 358.00 2885.00 6.26 16.20 m 59 y 59 x 11 358.00 3259.00 223.00 3.90 m 68 y 68 x 12 20.89 13.40 0.87 121.70 m 78 , e 78 y 78 x 13 25.20 16.40 1.10 123.00 m 7,10 , e 7,10 y 7,10 x 14 22.90 35.10 2.58 65.80 m 79 , e 79 y 79 x 15 20.70 9.18 0.59 61.30 m 8,10 , e 8,10 y 8,10 x 16 64.00 90.40 0.56 120.30 m 9,10 , e 9,10 y 9,10 x 17 58.10 4.68 0.13 100.00 m 10,11 y 10,11 x 18 0.42 4.80 0.37 0.80 m 10,12 y 10,12 x 19 0.42 4.80 0.37 0.80 m 10,13 y 10,13 x 20 0.42 4.80 0.37 0.80 m 10,14 y 10,14 x 21 0.42 4.80 0.37 0.80 m 11,5 y 11,5 x 22 -18.00 -11.00 0.74 4.80 m 14,3 y 14,3 x 23 -28.00 -6.60 1.10 6.50 Table 4. Data sheet for the network links in the case study Service Computing for Design and Reconguration of Integrated E-Supply Chains 345 Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. associated with an m-link the related time required by the transportation and/or the production process. The considered performance index values are reported in Table 4 and depend on the type of link (m- and e-link, or m-link only), the distance between the connected SC partners, the transportation mode (truck, car, airplane, etc.), and the type of material to be transported. In particular, the cost and energy performance indices reported in the last two rows of Table 4, respectively associ- ated with links m 11,5 and m 14,3 , are negative. In fact, in the recycler stage P 6 , partner n 11 is a demanufacturer with an output link m 11,5 connecting to manufacturer n 5 , and partner n 14 is a material recoverer with an output link m 14,3 connecting to supplier n 3 (see Figure 4). Hence, the total cost and energy associated with links m 11,5 and m 14,3 are negative; that is, they correspond to recycling materials and parts. According to the data in Table 4, the IESC in Figure 4 exhibits the m- and e-links m 18 , e 18 , m 17 , e 17 , m 4,10 , e 4,10 , m 78 , e 78 , m 79 , e 79 , m 7,10 , e 7,10 , m 9,10 , e 9,10 , m 8,10 , and e 8,10 , while the remaining links are m-links. Moreover, the associated digraph D=(N,E) depicted in Figure 5 has N=14 nodes and E=23 edges. Obviously, edges y 18 , y 17 , y 4,10 , y 78 , y 79 , y 7,10 , y 9,10 , and y 8,10 are associated both with m- and e-links, and the remaining edges of the digraphs are associated with m-links only. Moreover, each edge in E is labeled by its corresponding variable x h with h=1, …, E used in the optimization procedure and dened in the previous section. Optimization Model Various computational experiments are performed to minimize cost, energy con- sumption, CO 2 emission, and total lead time (TLT). In particular, the TLT is dened as the total time elapsed from the instant at which the raw material begins its travel until the instant the nished product is delivered to consumers. Furthermore, a multiobjective function for Problem 2 is chosen. The solutions are obtained via the well-known two-phase simplex method in the Matlab framework (MathWorks Inc., 2002; Venkataraman, 2001). The rst step of the optimization is to dene the model constraints. Then Problem 1 or Problem 2 is solved. BOM constraints. The component supplier constraints are obtained assuming that the BOM of the second stage in Figure 4, representing the manufacturer, is the fol- lowing: computer (C), hard-disk driver (H), monitor (M), and keyboard and mouse (K). We suppose that C is produced by n 1 and n 2 ; H is produced by n 1 , n 2 , and n 3 ; M is produced by n 2 , n 3 , and n 4 ; and K is produced by n 3 and n 4 (Luo et al., 2001). Hence, with reference to Figure 5, the constraints imposed on the variables labeling the edges are as follows: 346 Dotoli, Fanti, Meloni, & Zhou Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permis- sion of Idea Group Inc. is prohibited. 4 3 4 5 4 5 6 5 6 1 1 1 + 1 + ≥ + + ≥ + + ≥ ≥ 3 x x x x x x x x x x . (18) Path constraints. The case study includes only one manufacturer and only one consumer (node n 5 of stage P 2 and n 10 of P 5 , respectively, in Figure 4). Hence, a path between nodes n 5 and n 10 is needed. Consequently, we build the N×E incidence matrix I M associated with digraph D. Moreover, we dene the 23-vector b 5,10 =[b 1 b 2 … b 23 ] with b 5 =-1 b 10 =1 and b p =0 for p≠5, 10 and p=1, …, 23. The constraint that imposes the presence of a path starting from node n 5 and ending in node n 10 is written as follows: I M x≥b 5,10 . (19) Mutual-exclusion constraints. It is assumed that one and only one partner is to be included in the IESC recycler stage (stage P 6 in Figure 4). Furthermore, only one type of commerce has to be present between the second and third stages, and one and only one m and e-link has to be present among the rst stage and the others. Hence, with reference to Figure 5, the mutual-exclusion constraints are the following: 18 19 20 21 13 14 15 1 2 7 1 1 1 + + + ≤ + + ≤ + + = x x x x x x x x x x . (20) f 1 ($) f 2 (MJ) f 3 (KgCE) f 4 (hours) min f 1 946.02 6566.30 16.34 98.20 min f 2 1030.92 6112.66 12.51 190.00 min f 3 1037.50 6415.86 11.38 190.30 min f 4 997.90 6799.00 329.88 16.70 Table 5. The values of objective functions f 1 , f 2 , f 3 , and f 4 for Problem 1 Figure 6. Solution digraph of min(f 1 ) 1 n 2 n 3 n 5 n 14 n 10 n 4 x 21 x 23 x 5 x 7 n 10 x 14 x Service Computing for Design and Reconguration of Integrated E-Supply Chains 347 Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Structural constraints. The constraints derived from the digraph in Figure 5 are as follows: 22 18 23 21 5 23 16 1 13 14 15 2 16 13 17 15 - 0 - 0 0 0 0 0 0 = = − ≥ − ≥ + + − ≥ − ≥ − ≥ x x x x x x x x x x x x x x x x . (21) For example, the rst constraint of Equation 21 means that the edge corresponding to x 22 is selected if and only if the edge labeled by x 18 is selected. In addition, the third constraint of Equation 21 means that if the edge labeled by x 23 is selected, then the edge corresponding to x 5 is selected. Figure 7. Solution digraph of min(f 2 ) Figure 8. Solution digraph of min(f 3 ) Figure 9. Solution digraph of min(f 4 ) 1 n 4 n 5 n 7 n 11 n 9 n 10 n 2 x 3 x 6 x 10 x 15 x 17 x 18 x 22 x 1 n 2 n 5 n 9 n 2 x 4 x 10 x 15 x 17 x 7 n 10 n 4 n 6 x 1 n 4 n 5 n 3 x 6 x 9 x 7 x 10 n 348 Dotoli, Fanti, Meloni, & Zhou Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permis- sion of Idea Group Inc. is prohibited. Solution of Problem 1 Problem 1 is solved with respect to four objectives: cost, energy, CO 2 emission, and TLT. The corresponding objective functions are denoted by f 1 to f 4 , respectively. The obtained subdigraphs are presented in Figures 6 to 9, and the corresponding objective functions are given in Table 5. Comparing our results with the solutions obtained by the fuzzy optimization and reported in Luo et al. (2001), we note the following two aspects. First, while the results of the optimization problems min(f 2 ) and min(f 4 ) provide the same results as the fuzzy optimization, the minimization of the objective functions f 1 and f 3 does not provide the same digraphs obtained by the fuzzy optimization. Indeed, the fuzzy optimization can lead to suboptimal solutions: The optimal value of cost and CO 2 emission obtained with ILP is f 1 =$946.02 and f 3 =11.38 KgCE re- spectively, while the fuzzy optimization performed in Luo et al. (2001) determines two solutions with f 1 =$951.00 and f 3 =14.10 KgCE. Consequently, the ILP approach with a single-criterion objective function guarantees optimal solutions. Second, in Luo et al. (2001), the authors use the same structure of BOM constraints for all the considered performance indices, but such a structure is not suited to the TLT performance measure. Indeed, the cycle time associated with BOM constraints is not the sum of the corresponding edge performance indices but the maximum among the performance indices. For example, if we choose edges y 25 and y 35 cor- responding to variables x 4 and x 5 as the BOM for P 2 , the corresponding cycle time cannot be computed as M 4 (m 25 )+M 4 (m 35 ), but as the maximum between M 4 (m 25 ) and M 4 (m 35 ): In such a case, the constraint becomes nonlinear. Hence, to obtain a more rigorous model but with linear constraints, we modify the constraints of Equation 18 Figure 10. The digraph structure of a traditional SC composed of m-links Figure 11. Solution digraph of min(f 1 ) imposing a xed structure of m- and e-links 2 n 3 n 5 n 4 x 5 x 9 x 19 x 10 n 12 n 2 n 3 n 5 n 4 x 5 x 9 x 19 x 10 n 12 n 4 n 7 x Service Computing for Design and Reconguration of Integrated E-Supply Chains 349 Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. to transform the nonlinear BOM constraints for the TLT in suited linear constraints (Mangini, 2003). However, since the cycle times assigned to links m ij ∈L m do not differ much, in this particular case the assumption used in Luo et al. is admissible, and we obtain the same solution digraph of problem min(f 4 ) (see Figure 9). Finally, the following example shows that the presented optimization method can improve the recongurability of the network. In particular, let us consider a tradi- tional SC that has a network composed of m-links only (see Figure 10). Moreover, the designer has to add the e-links in order to introduce e-commerce and e-busi- ness xtures in the network structure optimizing the cost. Consequently, Problem 1 is solved by selecting cost as a performance index subject to the constraints in Equations 18 to 21, and the following mutual-exclusion constraints that impose the initial structure of the SC: x 4 =x 5 =x 9 =x 19 =1. (22) The resulting IESC network is depicted in Figure 11 and exhibits the cost of $979.32. Solutions Cost ($) Energy (MJ) CO 2 (KgCE) TLT (hours) Variables indices x h =1 x A 946.02 6566.30 16.34 98.20 2,4,5,10,14,21,23 x B 957.02 6269.30 16.36 98.20 2,3,5,10,14,21,23 x C 964.02 6430.90 14.35 94.80 2,4,6,10,14,18,22 x D 975.02 6133.90 14.37 94.50 2,3,6,10,14,18,22 x E 981.60 6437.10 13.24 94.80 2,4,6,10,14 x F 992.60 6140.10 13.26 94.50 2,3,6,10,14 x G 1030.92 6112.66 12.51 190.00 2,3,6,10,15,17,18,22 x H 1037.50 6415.86 11.38 190.30 2,4,6,10,15,17 x I 1048.50 6118.86 11.40 190.00 2,3,6,10,15,17 Table 6. The values of the performance indices for Problem 2: Optimal solutions for multiobjective function of cost, energy, and CO 2 emission (min(f 5 )) Figure 12. Digraph representing solution x D of Problem 2 1 n 4 n 5 n 11 n 10 n 2 x 3 x 18 x 22 x 6 x 7 n 10 x 14 x 350 Dotoli, Fanti, Meloni, & Zhou Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permis- sion of Idea Group Inc. is prohibited. Solution of Problem 2 The multiobjective optimization problem is solved considering the following per- formance indices: cost, energy, and CO 2 emission (f 5 ). According to the previous remarks, we do not consider the cycle time in the multiobjective optimization, but we compute the TLT values of the problem solutions. Table 6 reports the ILP solutions and the corresponding performance indices. The results show the efciency of the proposed method, which is able to provide a set of optimal solutions. For example, solution x A , obtained by minimizing objective function f 5 , is equal to the solution obtained by minimizing f 1 (compare the rst row of Table 6 with Figure 6). Table 6 shows that solution x A exhibits a large value of CO 2 emission. On the other hand, minimizing objective function f 5 provides solution x D in Table 6, featuring satisfac- tory values of cost, energy, CO 2 emission, and TLT. In other words, the benets of using multicriteria optimization are due to the fact that the method enables us to choose among several near-optimal solutions. The digraph corresponding to solu- tion x D in Table 6 is depicted in Figure 12; the other solution digraphs may easily be obtained from the last column in Table 6. Comparing the results obtained solving the ILP problem with the fuzzy-optimization results, we note that the presented optimization method provides a set of near-opti- mal solutions instead of only one suboptimal solution. Hence, the designer can be guided by priorities and preferences to choose a satisfactory IESC network, improv- ing system exibility and agility. Note that solution x B of Table 6 is the solution obtained by fuzzy optimization (Luo et al., 2001). Solution-Evaluation and Validation Module The purpose of this DSS module is to evaluate alternative IESC network congu- rations obtained from the higher levels with respect to operational performances representing resources (cost, utilization, and inventory), output (quality and lead time), and exibility (lead time and its variability; Beamon, 1999; Persson & Ol- hager, 2002). At this level of the decision process, it is necessary to increase the understanding of the interrelationships among parameters, relevant for describing the IESC at the operational level, such as operation and transportation times, global capacities of manufacturing facilities, pull demand from retailers, and the push of raw material from suppliers. Similar to the previously described DSS modules, in this level the process of data collection and elaboration may be simplied by remote and collaborative evaluation using a Web-based platform. In order to capture these relationships, analytical models and simulation models can be alternatively used. In particular, analytical models include discrete event models Service Computing for Design and Reconguration of Integrated E-Supply Chains 351 Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. that are particularly suitable for the verication of distributed manufacturing systems. In such a modeling approach, the evolution of the system depends on the complex interaction of the timing of various discrete events such as arrivals of components at the suppliers, departures of trucks from the suppliers, the beginning of assembly operations at the manufacturers, arrivals of nished goods at the customers, payment approvals by the sellers, and so forth (Viswanadham & Raghavan, 2000). Despite the appropriateness of discrete event models to represent IESC, they cannot be detailed enough to handle all the relevant parameters of complex supply-chain systems. Hence, simulation can represent a more general and efcient instrument to evaluate alternative SC designs and to validate an IESC network conguration (Jansen, van Weert, Beulens, & Huime, 2001). Very attractive general-purpose simulation pack- ages are now available to model a manufacturing enterprise, for example, ARENA, SIMPROCESS, and Taylor II (Viswanadham & Raghavan). Summing up, comparing different IESC network design solutions and analyzing the system behavior in the presence of additional details or uncertainties allow us to determine the performance of a given solution at the operational level. Hence, the DSS to congure the IESC is closed by this module, which is able to evaluate and validate the optimal or near-optimal solution. As specied, if the third level results are not satisfying, it may be necessary to select different solutions among the network structures obtained at the previous levels in order to improve the IESC performance. The obtained DSS results in a closed-loop procedure. Moreover, if the proposed DSS is equipped with an agile data-acquisition and -elaboration tool, it may be constantly employed to conrm or modify the IESC conguration upon variations of the conjectured scenarios or changes in the context of the real market. Conclusion This chapter focuses on the application of enterprise service computing to determine and optimize the conguration of IESCs, that is, business strategies incorporating the power of e-commerce to streamline the manufacturing processes. An IESC system has a more complex structure than a traditional SC system since it embraces the e- business strategy to establish information links and integrates end-of-life processes into the entire SC structure. In particular, a hierarchical DSS is presented to design and recongure an IESC based on data and information that can be obtained via Internet and Web-based instruments. More specically, the proposed DSS is composed of three hierarchical levels differ- ing in data requirements, performance-index utilization, and output solutions, which are comprehensively reviewed and discussed with regard to the related literature. In [...]... Customer -Service Management Customer -service management usually starts through the signing up of a service agreement with the customer, which includes several contract items like discounts on replacement parts, guaranteed response time, technicians’ hourly rates, and support time (Curran & Keller, 2000) This process consists of service planning and scheduling, service contract management, service order... Copyright © 2007, Idea Group Inc Copying or distributing in print or electronic forms without written permission of Idea Group Inc is prohibited Service Computing for Design and Reconfiguration of Integrated E-Supply Chains 355 Section VI Enterprise Service Computing: Best Practices and Deployment Copyright © 2007, Idea Group Inc Copying or distributing in print or electronic forms without written permission... performance of the conducted service tasks, and settle service costs Some of the CSM-process pain points are the complexity of today’s products, which makes managing this process more difficult; customer dissatisfaction due to service- part shortage; inaccurate forecasting for service parts that are considered of high-dollar value, and slow-moving items; and the fact that customer service is becoming a competitive... performance indices The evaluation of the performance measures tests the design of the IESC as obtained from the candidate-selection and network-design levels It can provide some feedback for IESC modifications if the performance indices are not satisfactory at the third level A case study based on enterprise service computing via the presented decision structure is reported in detail In addition, the integer... logic SCM is defined as the process of optimizing the flow of goods, services, and information along the supply chain from supplier to customer It is also the process to strategize, plan, and execute business processes across facilities and business units It focuses on the internal supply chain, which is under the direct control of the enterprise Information at all points along the supply chain is captured... described in detail and will be the subject of future research Moreover, a further future perspective is a Web-based implementation of the proposed DSS for a shared and remote platform dedicated to enterprise service computing Acknowledgments This work was supported in part by the Italian Ministry for University and Research (MIUR) under Project No 2003090090, and NSFC under Grant No 60228004 and 60334020... channel for receiving the service order notification, Copyright © 2007, Idea Group Inc Copying or distributing in print or electronic forms without written permission of Idea Group Inc is prohibited 370 Sabri which represents a request for a customer -service activity that can be used to plan specific tasks related to the usage of spare parts and resources, allocate resources to the service task, monitor... technologies like Web services (XML [extensible markup language], SOAP [simple object access protocol], UDDI [universal description, discovery, and integration], and WSDL [Web service description language]), the wireless application protocol (WAP), the global positioning system (GPS), bar coding, and radio-frequency identification (RFID) to transmit the data into computer applications Web services are built... across the world Companies nowadays outsource assembly work, information-systems management, call centers, service- parts repair and management, and product engineering to contractors The bigger challenge is to decide what to outsource, and how to make sure that customer satisfaction, delivery service, or quality are not impacted • Expensive cost structure, especially when companies are facing intensified... increase profitability across their total global network? How can companies reduce their supplier base? 5 Risk management: This enabler addresses the new risks related to product quality and service delivery that arise from outsourcing, locating supply-chain activities across the world, and the acceleration of new product introductions It also addresses the security concerns after September 11, 2001 6 . 120.30 m 9,10 , e 9,10 y 9,10 x 17 58.10 4.68 0 .13 100.00 m 10,11 y 10,11 x 18 0.42 4.80 0.37 0.80 m 10,12 y 10,12 x 19 0.42 4.80 0.37 0.80 m 10 ,13 y 10 ,13 x 20 0.42 4.80 0.37 0.80 m 10,14 y 10,14 x 21 0.42. prohibited. Structural constraints. The constraints derived from the digraph in Figure 5 are as follows: 22 18 23 21 5 23 16 1 13 14 15 2 16 13 17 15 - 0 - 0 0 0 0 0 0 = = − ≥ − ≥ + + − ≥ − ≥ − ≥ x. 6430.90 14.35 94.80 2,4,6,10,14,18,22 x D 975.02 6133 .90 14.37 94.50 2,3,6,10,14,18,22 x E 981.60 6437.10 13. 24 94.80 2,4,6,10,14 x F 992.60 6140.10 13. 26 94.50 2,3,6,10,14 x G 1030.92 6112.66 12.51 190.00