Enterprise service computing from concept_13 ppt

Candidate.Selection The first module of the DSS structure in Figure 1 has to perform the candidate tion for each stage of the IESC case study.. The stages of the IESC network for the cas

 Dotol, Fant, Melon, & Zhou

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 NS=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 first module of the DSS structure in Figure 1 has to perform the candidate 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

selec-Table 1 Performance matrix of manufacturers with scores for each criterion

The first step of the procedure determines the candidate set of the second stage, for example, P2c={n1c,n2c

,…,n15c}, 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 define 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 final 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 definition 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 P2={n5c} 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

n

12 n 13

7 n

11 n 12 n 13 n 14 n

8 n

9 n

10 n

 Dotol, Fant, Melon, & Zhou

tion For instance, if two manufacturers are to be included in the IESC, the decision maker selects P2={n1c,n5c} 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 Mq with q=1, …, 4 associated with the links of the considered IESC

More precisely, the adopted performance indices are total cost (M1), energy (M2),

CO2 emission (M3), and cycle time (M4) We indicate generically by cycle time

(M1) in $ Energy(M2) in MJ CO(M32) in KgCE emission Cycle time(M4) in hours

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 m11,5 and m14,3, are negative In fact, in the recycler stage P6, partner

n11 is a demanufacturer with an output link m11,5 connecting to manufacturer n5, and partner n14 is a material recoverer with an output link m14,3 connecting to supplier n3(see Figure 4) Hence, the total cost and energy associated with links m11,5 and m14,3are 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

BOM constraints The component supplier constraints are obtained assuming that

the BOM of the second stage in Figure 4, representing the manufacturer, is the lowing: computer (C), hard-disk driver (H), monitor (M), and keyboard and mouse (K) We suppose that C is produced by n1 and n2; H is produced by n1, n2, and n3;

fol-M is produced by n2, n3, and n4; and K is produced by n3 and n4 (Luo et al., 2001) Hence, with reference to Figure 5, the constraints imposed on the variables labeling the edges are as follows:

 Dotol, Fant, Melon, & Zhou

Path constraints The case study includes only one manufacturer and only one

consumer (node n5 of stage P2 and n10 of P5, respectively, in Figure 4) Hence, a path between nodes n5 and n10 is needed Consequently, we build the N×E incidence matrix IM associated with digraph D Moreover, we define the 23-vector b 5,10 =[b 1

b 2 … b 23] with b5=-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 n5 and ending in node n10 is written as follows:

Mutual-exclusion constraints It is assumed that one and only one partner is to be

included in the IESC recycler stage (stage P6 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 first stage and the others Hence, with reference to Figure 5, the mutual-exclusion constraints are the following:

1 1

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

14

x

Structural constraints The constraints derived from the digraph in Figure 5 are as

For example, the first 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 )

7 n

11 n

9 n

10 n

 Dotol, Fant, Melon, & Zhou

Solution.of.Problem.1

Problem 1 is solved with respect to four objectives: cost, energy, CO2 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 CO2 emission obtained with ILP is f 1 =$946.02 and f 3=11.38 KgCE spectively, while the fuzzy optimization performed in Luo et al (2001) determines

re-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 y25 and y35 cor-

responding to variables x 4 and x 5 as the BOM for P2, the corresponding cycle time cannot be computed as M4(m25)+M4(m35), but as the maximum between M4(m25) and

M4(m35): 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 fixed structure of m- and e-links

3 n

5 n 4

4

to transform the nonlinear BOM constraints for the TLT in suited linear constraints (Mangini, 2003) However, since the cycle times assigned to links mij∈Lm 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 reconfigurability 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 fixtures 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:

0 Dotol, Fant, Melon, & Zhou

Solution.of.Problem.2

The multiobjective optimization problem is solved considering the following formance indices: cost, energy, and CO2 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 efficiency of the proposed method, which is able to provide a set of optimal solutions For example, solution xA, obtained by minimizing objective function f 5, is equal to the solution

per-obtained by minimizing f 1 (compare the first row of Table 6 with Figure 6) Table

6 shows that solution xA exhibits a large value of CO2 emission On the other hand,

minimizing objective function f 5 provides solution xD in Table 6, featuring tory values of cost, energy, CO2 emission, and TLT In other words, the benefits 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 xD in Table 6 is depicted in Figure 12; the other solution digraphs may easily

satisfac-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 flexibility and agility Note that solution xB 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 rations obtained from the higher levels with respect to operational performances representing resources (cost, utilization, and inventory), output (quality and lead time), and flexibility (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 simplified by remote and collaborative evaluation using a Web-based platform

configu-In order to capture these relationships, analytical models and simulation models can

be alternatively used In particular, analytical models include discrete event models

that are particularly suitable for the verification 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 finished 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 efficient instrument to evaluate alternative SC designs and to validate an IESC network configuration (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 configure the IESC is closed by this module, which is able to evaluate and validate the optimal or near-optimal solution As specified, 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 confirm or modify the IESC configuration 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 configuration 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 reconfigure an IESC based on data and information that can be obtained via Internet and Web-based instruments

More specifically, the proposed DSS is composed of three hierarchical levels ing in data requirements, performance-index utilization, and output solutions, which are comprehensively reviewed and discussed with regard to the related literature In

differ- Dotol, Fant, Melon, & Zhou

particular, the first level (candidate-selection module) uses aggregate performance

indices and optimization techniques to obtain a rank of possible candidates for

each stage of the IESC In the second level (network-design module), the structure

of the IESC is modeled by a digraph, describing the actors of the stages, and the material and information links connecting the stages An integer multicriteria op-timization model proposes different structures for the IESC on the basis of a set of chosen performance indices and cost Finally, the third level (solution-evaluation and -validation module) analyzes and evaluates the optimal or suboptimal solution networks obtained at the previous levels by comparing operational 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 satisfac-tory 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 multicriteria optimization methodology is applied to the case study and is compared with a fuzzy-optimization method presented in the related literature

The third module is not 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

References

Beamon, B M (1999) Measuring supply chain performance International Journal

of Operations and Production Management, 19(3), 257-292.

Bellman, R E., & Zadeh, L A (1970) Decision-making in a fuzzy environment

Management Science, 17(4), 141-164.

Biswas, S., & Narahari, Y (2004) Object oriented modelling and decision support for

supply chains European Journal of Operational Research, 153(3), 704-726.

Buchanan, J., Sheppard, P., & Vanderpooten, D (1999) Project ranking using

ELECTRE III (Tech Rep No 99-01) Waikato, New Zealand: University of

Waikato, Department of Management Systems

Chryssolouris, G., Makris, S., Xanthakis, V., & Mourtzis, D (2004) Towards the

Internet-based supply chain management for the ship repair industry

Interna-tional Journal of Computer Integrated Manufacturing, 17(1), 45-57.

Dotoli, M., Fanti, M P., Meloni, C., & Zhou, M C (2005) A multi-level approach

for network design of integrated supply chains International Journal of

Ehrgott, M (2000) Multicriteria optimization Berlin, Germany: Springer-Verlag.

Farina, M., & Amato, P (2004) A fuzzy definition of optimality for many-criteria

optimization problems IEEE Transactions on Systems, Man and Cybernetics,

Part A, 34(3), 315-326.

Gaonkar, R., & Viswanadham, N (2001) Collaboration and information sharing

in global contract manufacturing networks IEEE/ASME Transactions on

Mechatronics, 6(4), 366-376.

Hillier, F S., & Lieberman, G J (2005) Introduction to operations research Singapore:

McGraw Hill

Instutute of Electrical and Electronics Engineer (IEEE) (2004) IEEE Technical

Com-munity for Services Computing Newsletter, 1, 1.

Jang, Y.-J., Jang, S.-Y., Chang, B.-M., & Park, J (2002) A combined model of network design and production/distribution planning for a supply network

Computers & Industrial Engineering, 43(1-2), 263-281.

Jansen, D R., van Weert, A., Beulens, A J M., & Huirne, R B M (2001)

Simu-lation model of multi-compartment distribution in the catering supply chain

European Journal of Operational Research, 133(1), 210-224.

Keskinocak, P., Goodwin, R., Wu, F., Akkiraju, R., & Murthy, S (2001) Decision

support for managing an electronic supply chain Electronic Commerce

Re-search, 1(1-2), 15-31.

Luo, Y., Zhou, M C., & Caudill, R J (2001) An integrated e-supply chain model

for agile and environmentally conscious manufacturing IEEE/ASME

Transac-tions on Mechatronics, 6(4), 377-386.

Mangini, A (2003) Modelli e algoritmi per la configurazione di una catena

logis-tico-produttiva Unpublished master’s thesis, Politecnico di Bari, Bari, Italy.

MathWorks Inc (2002) Matlab release notes for release 13 Natick, MA: Author.

 Dotol, Fant, Melon, & Zhou

Mousseau, V., Slowinski, R., & Zielniewicz, P (2000) A user-oriented tion of the ELECTRE TRI method integrating preference elicitation support

implementa-Computers & Operations Research, 27(7-8), 757-777.

Mustajoki, J., & Hämäläinen, R P (2000) Web-Hipre: Global decision support by

value tree and AHP analysis Information Systems and Operational Research,

38(3), 208-220.

Persson, F., & Olhager, J (2002) Performance simulation of supply chain designs

International Journal of Production Economics, 77(3), 231-245.

Saaty, T L., Vargas, L G., & Dellmann, K (2003) The allocation of intangible

resource: The analytic hierarchy process and linear programming

Socio-Eco-nomic Planning Sciences, 37(3), 169-184.

Shapiro, J F (2001) Modeling the supply chain Pacific Grove, CA: Duxbury Press.

Shapiro, J F (2004) Challenges of strategic supply chain planning and modeling

Computers and Chemical Engineering, 28(6-7), 855-861.

Sprague, R H., Jr., & Carlson, E D (1982) Building effective decision support

systems Englewood Cliffs, NJ: Prentice-Hall.

Talluri, S., & Baker, R J (2002) A multi-phase mathematical programming

ap-proach for effective supply chain design European Journal of Operational

Research, 141(3), 544-558.

Tayur, S., Ganeshan, R., & Magazine, M (1999) Quantitative models for supply

chain management Norwell, MA: Kluwer Academic Publisher.

Thanassoulis, E (2001) Introduction to the theory and application of data

envelop-ment analysis Dordrecht, Netherlands: Kluwer Academic Publisher.

Venkataraman, P (2001) Applied optimization with MATLAB programming New

York: Wiley Interscience

Vidal, C J., & Goetschalckx, M (1997) Strategic production-distribution models:

A critical review with emphasis on global supply chain models European

Journal of Operational Research, 98(1), 1-18.

Viswanadham, N., & Gaonkar, R S (2003) Partner selection and synchronized

planning in dynamic manufacturing networks IEEE Transactions on Robotics

and Automation, 19(1), 117-130.

Viswanadham, N., & Raghavan, N R S (2000) Performance analysis and design

of supply chains: A petri net approach Journal of the Operations Research

Society, 51(10), 1158-1169.

Wu, N., Mao, N., & Qian, Y (1999) An approach to partner selection in agile

manufacturing Journal of Intelligent Manufacturing, 10(6), 519-529.

Yang, H., Yu, Z., & Edwin Cheng, T C (2003) A strategic model for supply chain

design with logical constraints: Formulation and solution Computers of

Op-erations Research, 30(14), 2135-2155.

 Sabr

Best.Practice.in.Leveraging E-Business.Technologies.to Achieve.Business.Agility

Ehap H Sabr, Unversty of Texas at Dallas, USA

Abstract

This chapter explains the best practice in implementing e-business Technologies

to achieve business cost reduction and business agility Many companies started to realize that gaining competitive advantage is no longer feasible by only managing their own organizations; it also requires getting involved in the management of all upstream supply organizations as well as the downstream network E-business technologies present huge opportunities that are already being tapped by several companies and supply chains Although the benefits of implementing e-business tech- nologies are clear, enterprises struggle in integrating e-business technologies into supply-chain operations The author illustrates the strategic and operational impact

of e-business technologies on supply chains and explains the performance benefits and challenges firms should expect in implementing these technologies Also, the author provides the best-practice framework in leveraging e-business applications to support process improvements in order to eliminate non-value-added activities and provide real-time visibility and velocity for the supply chain Finally, this chapter presents the future trends of using e-business in transformation programs.