EXPLORING THE EFFECTS OF ADVANCED MANU- FACTURING TECHNOLOGY AND E-COMMERCE IN
THE ALIGNMENT OF SUPPLY CHAIN COORDINA- TION AND COMPETITIVENESS PERFORMANCE
D. Y. Sha
Department of Industrial Engineering and System Management Chung Hua University
P. K. Chen
Department of Industrial Engineering and Management National Chiao Tung University
Yung-Hsin Chen
*Department of Business Administration Asia University
ABSTRACT
The objective of this study is to empirically identify various alignments strategies through various adoption levels between advanced manufacturing technology (AMT) applications and e-commerce settings in supply chain context, and then test what alignment strategies that will have significant influence on supply-chain coordination outcomes. Based on the test result, we can justify the optimal align- ment strategy for the improvement of coordination activities. Using the data from the International Manufacturing Strategy Survey (IMSS) database, we analyzed 497 samples and classified seven types of alignment strategies by different adop- tion level of AMT and e-commerce. The test results indicated that alignment of broad adoption level of AMT and the equivalent broad adoption level of e-commerce can certainly influence the coordination efforts among partner firms and make improvement in supply chain efficiency. We also found that, e-commerce plays a more important key role in the alignment process.
Keywords: Supply chain coordination, advanced manufacturing technology (AMT), e-commerce, strategy alignment
1. INTRODUCTION
In the domain of manufacturing, supply chain management (SCM) is a contemporary and
1critical tool for a firm’s competitiveness and performance. Through close integration efforts between partner firms of upstream and down- stream in the areas such as production, delivery, purchasing, inventory management, and sales, SCM empowers a company to enhance its own competitive capability.
However, partner firms as the owner of various operations stages in a supply chain may have conflicting goals, and this conflict may
*Corresponding author:
thomaschen@asia.edu.tw thomaschen.iem90g@nctu.edu.tw
adversely undermine the integration of opera- tions. Therefore, if all firms in the supply chain determine to cooperate in the pursuit of global optimization, the efforts for coordination must be made [3].
Supply chain coordination refers to the ability of a firm to coordinate transactional ac- tivities with supply chain partners in each related internal action [4]. This also means that partner firms in the supply chain must communicate one another in the events pertaining to the opera- tional activities what take place in the supply chain. Doing so leads to better synchronization of pertinent internal operations and decision making, therefore, enhances the firm’s ability to satisfy market requirements, win customer satis- faction, and further gain competitive advantage.
Ghiassi [7] argued that the synchronization of
supply chain internal activities can achieve the benefit of time-to-market, quick response, and better communication and flexibility/reliability in component supply.
Business practices underpin the presump- tion that better coordination among supply chain partners results in greater customer satisfaction in the market. For example, Hewlett-Packard and Dell Computers gain ground owing to their dexterous manipulation of supply chain coordi- nation and mass customization. To achieve the better performance, a firm must make every ef- fort to improve the efficiency and effectiveness of coordination tasks within their supply chain.
In order to obtain better supply chain co- ordination results, in the recent years researchers argued that the adopting advanced manufactur- ing technology (AMT) can make coordination improvement between partner firms in the sup- ply chain [5] [9] [13] [20]. AMT encompasses mainly technologies related to com- puter-aided-manufacturing (CAM) and computer integrated manufacturing (CIM) systems, which depend upon information flow along communi- cation lines within a computer local area net- work (LAN). LAN connects the production equipment and other facilities in the manufac- turing site. The actions taken by manufacturers to apply some components of AMT enables op- erational departments to readily coordinate each other thanks to the connectivity furnished by LAN, and further build an optimal internal op- erations mode in terms of cost, quality and de- livery. Therefore, in the earlier period, AMT applications did influence and drive the coordi- nation and integration on the operational level in the manufacturing context. In the era of e-business bolstered by the Internet, literature in research indicated that AMT also benefits the coordination among supply chain partners’ in- ternal operations given AMT facilities have been installed at their site.
E-business is defined as “…not just the buying and selling of goods but also servicing customers (B2C model), collaborating with business partners (B2B model), and conducting electronic transactions within an organization”
[25]. To successfully coordinate each partner’s internal operations, information technology plays a critical role to direct the information flow in-between to augment the scope of coop- eration. However, in the supply chain environ- ment partners are legitimately independent enti- ties, without the common communication pro- tocol they can get connected with intranet or LAN in each partner’s site to share information and data. In recent years, many firms have in- troduced e-commerce settings for connectivity
and accomplishment has been so encouraging that more and more firms decided to follow the suit. They trusted that firms adopting e-commerce setting to work with AMT in con- gruence enables better coordination associated with internal operations [20] [21].
However, an empirical study [21] demon- strated that firms always consider their incum- bent resources to decide the scale and scope of AMT and e-commerce set-up, and this act of differentiated investment incurs different level of influence upon coordination outcomes. Based on the above discussion, the objective of this em- pirical study is to identify various alignments strategies encompassing the combination of dif- ferent adoption levels of AMT and adoption lev- els of e-commerce, and then test whether these alignments strategies exert different extent of influence upon supply chain coordination out- comes. By the test result we can deduce which alignments strategy can significantly improve the coordination of related internal operations among supply chain partner firms. This study is based on large samples from the International Manufacturing Strategy Survey (IMSS) database, a global research network initiated by London Business School.
The remaining part of this paper is struc- tured as follows. Section 2 is the literature re- view and the hypothesis. In section 3 we de- scribe the methodology. Section 4 is the empiri- cal test results and discussion. Finally, we draw our conclusion and indicate directions for further research.
2. LITERATURE AND HY- POTHESIS
2.1 Supply chain coordination and AMT
Globalization aggravate competition be- yond the boundary of domestics market, firms continuously exploit supply chain management to build capabilities to cope with the volatile market situation and tough competition. How- ever, the formation of a supply chain is out of different legitimately independent firms, com- pany aiming to enhance competitive capability by driving the cooperation of partners must fo- cus on the resolution how to improve coordina- tion efforts in the internal operations of partners firms [3]. In fact, the coordination means the mutual communication for internal transac- tion-related activities within supply chain opera- tional environment [4]. These related transac- tion-related activities between partner firms in-
clude buyer-vendor, production-distribution, and inventory management [1] [15] [22]. Therefore, the successful coordination of activities deter- mines the effective and efficient supply chain collaboration [11].
To improve coordination outcomes in terms of partner firms’ input, researchers and practitioners tried very hard to develop the con- cept, technologies, tools and whatever can make progress. Recently, researchers have identified that the adoption of advanced manufacturing technology (AMT) can effectively lead to the cohesion of partner firms and support better co- ordination for internal transaction-related activi- ties in the supply chain operational environment.
Sun [24] regarded ATM as the computer-aided technologies used in a firm where manufacturing is the major activities, where information flow along communication network embedded in firms’ information technology infrastructure connects the activities in different department, production process, or parts material flow. Chase [2] and Sun [24] indicated that, the different sorts of AMT applications will construct differ- ent internal operations context. Therefore, dif- ferent AMT applications spectrum will render different pattern of involving compatible internal operations. For example, when firms adopt ERP, internal department, process, or parts will coor- dinate and improve related production operations;
when CAD/CAE/CAPP are adopted, product design operations will be coordinated and im- proved; when NC/CNC or FMS are adopted, the fabrication/assembly operations will be coordi- nate and improved [24].
Based on the finding of influence of AMT upon internal operations coordination, most re- searchers [6] [9] [19] [28] also found the AMT applications in the supply chain operational en- vironment will further promote the level of co- ordination. [9] took flexible manufacturing sys- tem (FMS) as the example to describe how the flexibility and responsiveness can be developed and influence the relationship between manu- facturers and downstream firms, and further lead to a more harmonized coordination results. Lit- erature in research presented much more varie- ties AMT adoption patterns in supply chain op- erational environment, they also evidenced that the adoption of AMT in one company may en- courage others to install AMT facilities. A good example is the application of RFID. Porter. [19]
and Zhang [28] stated that the introduction of RFID can influence incurs closer coordination and integration of product flow between supply chain partner firms to ensure the performance of inventory management in the supply chain con- text. On the other hand, Dyer [6] indicated that
computer numerically controlled (CNC) ma- chine tools and CAD/CAM systems can influ- ence collaborative manufacturing relationships between the supplier and manufacturer, or the buyer and manufacturer, and further achieve the related performance such as delivery and flexi- bility.
2.2 Alignment between AMT and e-commerce for coordination im- provement
Although AMT applications coheres part- ner firms together to influence and effectively coordinate the related internal operations, how- ever, the contribution of AMT in the influence on coordination between partner firms can be extended via the intervention of information technology. In the beginning, AMT can influence manufacturing process and lead to better coor- dination results in the areas of information flow within a computer local area network (LAN) or Intranet to connect the production equipments and other facilities in the manufacturing site.
However, to make progress in coordination, AMT applications must work closely with the latest network infrastructure and new technology within partner firms’ plant.
For the past decade, e-commerce plays setting an important role for information flow and further leads to effective coordination be- tween partner firms. E-commerce originated from conventional electronic data interchange (EDI), and was then supported by the modern Internet or World Wide Web to develop Busi- ness-to-Business (B2B) or Business-to-Customer (B2C) connectivity. Using the B2B model of e-commerce, firms within a supply chain can share information with each other using the Internet or dedicated electronic communication lines. Therefore, the e-commerce approach is supposed to be a powerful means to improve supply-chain coordination. Many researchers [7]
[8] [10] [12] [14] [16] [17] [18] [23] [26] [27]
found that most firms adopt e-commerce tech- nology to connect partner firms for improved information flow between partner firms in the supply chain operational environment. Research works such as [5] [20] [21] also found by em- pirical test that, in the real world, when firms adopt AMT to guide partner firms for coordina- tion in the supply chain operational environment, firms usually align e-commerce activities with AMT applications. They also found that the syn- ergy is available when firms adopt e-commerce in congruence with AMT.
2.3 Effect of different alignment be- tween AMT and e-commerce for improved coordination
Based on above discussion, we understand that aligning e-commerce with AMT enables improved coordination outcomes for partner firms in a supply chain. However, considering the constraint of incumbent resources, firms in the real world always employ different adoption level of AMT application and e-commerce set- ting [20] [21]. They are: narrow, middle, and broad level of adoption (Figure 1). Narrow adoption level implies that the deployment of AMT and e-commerce applications is still un- able to influence the coordination of partner firms upstream and downstream; broad level of AMT and e-commerce adoption is just the an- tipodes, means that the deployment of AMT and e-commerce applications to be able to influence completely the coordination of partner firms upstream and downstream; Middle adoption level means the deployment of AMT and e-commerce applications is able to influence but in-complete for the coordination of partner firms upstream and downstream. In fact, different alignment strategies of using different adoption level for AMT and e-commerce in each partner firms in the supply chain will bring out quite different extent of contribution in coordination outcomes.
There also exists a possibility that in case supply chain partners employ incompatible adoption level for AMT and e-commerce, the global optimization could be jeopardized. Since coordination involves all of partner firms, if a company makes determination to influence and drive all partner firms to attain coordination by AMT, then adoption level of AMT must be broad throughout the supply chain operational envi- ronment. By the same token, to take advantage of synergy the adoption level of AMT should be in congruence with that of e-business setting to complement each other and the adoption level of e-commerce must be also broad in supply chain operational environment. We make presume that
Figure 1. Adoption level of AMT and e-commerce in the supply chain context
if a company wants to reach the optimal coordi- nation result with partner firms, both the adop- tion level of AMT and e-commerce must be broad and compatible. Hereby, this study will test following the hypothesis:
H1: The alignment of broad adoption level for AMT and broad adoption level of e-commerce can significantly improve the coordination out- comes between partner firms in supply chain.
3. RESEARCH DESIGN
3.1 Survey database and test samples profiles
This study is based on the International Manufacturing Strategy Survey (IMSS-IV) da- tabase. The IMSS is an international cooperative research network focusing on manufacturing strategy (MS) research. It gathered data about firms’ practice and operational performance re- lated to manufacturing strategy in a global set- ting, and data pertaining to supply chain man- agement practice are also collected. As for the survey methodology, the IMSS project employed questionnaire of five-point Likert scale as the means of measurement.
There are four phases in the evolution of IMSS. The first iteration (IMSS-I) was carried out and completed from 1992–1994, with the participation of 600 firms in 20 different coun- tries. The second iteration (IMSS-II) was carried out from 1996–1998, with the participation of 703 firms in 23 different countries. The third iteration (IMSS-III) was carried out from 2000–2002, with the participation of 585 firms in 17 different countries.
The fourth iteration (IMSS-IV) was pub- lished in 2005. IMSS-IV was designed to in- volve researchers from around the world, in- cluding Western and Eastern Europe, the Ameri- cas, and various regions of Asia and Africa. The primary method of gathering data is by ques- tionnaire, and survey of this iteration focuses upon ISIC 28–35: ISIC 28 – manufacturing of fabricated metal products (271 responses); 29 – machinery and equipment (147); 30 – office, accounting and computing machinery (16); 31 – electrical machinery and apparatus (92); 32 – radio, television and communication equipment and apparatus (39); 33 – medical, precision and optical instruments, watcher and clocks (29);
34 – motor vehicles, trailers and semi-trailers (68); 35 – other transport equipment (41). The total responses number 711 firms from 23 dif- ferent countries. In the 2006, Taiwan start to
AMT E-commerce
Narrow adoption level Broad adoption level
joint IMSS and replenish 50 samples for IMSS, therefore, all of samples in IMSS total number 761 firms. These data were used in this study.
We eliminated 264 samples whose re- sponses were not complete or with missed values for variables of e-commerce, AMT, and sup- ply-chain coordination. To sum up, only 497 of the 761 total responses are included in the analy- sis in this paper. In the sample profiles, ap- proximately 38.66% (191) of samples were manufacture of fabricated metal products;
20.65% (102) of samples were manufacture of machinery and equipment; 3.24% (16) of sam- ples were manufacture of office, accounting and computing machinery; 12.55% (62) of samples were manufacture of electrical machinery and apparatus; 5.87% (29) of samples were manu- facture of radio, television and communication equipment and apparatus; 3.85% (19) of samples were manufacture of medical, precision and op- tical instruments, watches and clocks; 9.11% (45) of samples were manufacture of motor vehicles, trailers and semi-trailers; 6.07% (30) of samples were manufacture of other transport equipment.
Otherwise, the average employment of sample firms was 17832 employees.
3.2 Construct measurement
In terms of research purpose and objective, this study involves the testing of three variables:
supply-chain coordination outcomes, AMT adoption level, and e-commerce adoption level.
For supply-chain coordination, IMSS in- cludes eight kinds of coordination activities with suppliers upstream and customers downstream, including: (1) share inventory-level knowledge, (2) share production planning decisions and de- mand forecast knowledge, (3) order track- ing/tracing, (4) agreements on delivery fre- quency, (5) dedicated capacity, (6) require sup- plier/customer(s) part to manage or hold inven- tories of materials at manufacturing site (e.g., vendor-managed inventory, consignment stock), (7) collaborative planning, forecasting and re- plenishment, and (8) physical integration of the supplier into the plant, to measure the effective- ness of coordination and integration operation.
Based on IMSS, this study will test these eight kinds of coordination activities. For these eight kinds of coordination activities, we first test data distribution. Test results indicated that “physical integration of the supplier into the plant” data of supplier and customer are non-normal distribu- tion; therefore, we drop “physical integration of the supplier into the plant” from sup- plier/customer coordination activities. The other seven kinds of coordination activities, we further
test content and construct validity. Content va- lidity means the sufficiency with which a spe- cific domain of content was sampled. In the content validity, we use t-test to test. Test results indicated that all of coordination activities of supplier and customer show significant results.
In addition to content validity, all coordi- nation activities of supplier/customer will be developed for each construct in this study.
However, before developed, we must check for seven coordination activities data construct. For checking the data, we first adopt exploratory factor analysis (EFA) to test. EFA result indi- cated that the Kaiser-Meyer-Olkin measures of sampling were 0.804(supplier) and 0.874(customer), KMO results excess 0.50, means result very good. In addition to KMO, the Bartlett test of sphericity were 903.654 (supplier) and 1266.586 (customer) with significance lev- els of p <0.01, means test result can be accepted.
For ensured EFA results, we further test reliabil- ity. Test results indicated that reliability of sup- plier and customer excess 0.7, reliability results can be accepted. Second, we adopt confirmatory factor analysis to test construct. Test results in- dicated that single factor loading for the seven supplier and customer coordination activities integrate with supplier and customer coordina- tion. Results of content validity, EFA, reliability, and CFA have shown in Table 1.
In the AMT aspect, IMSS includes ten kinds of AMT applications: (1) stand-alone CNC machines, (2) machining centers, (3) automated parts loading/unloading, (4) automated parts loading/unloading, (5) automated stor- age-retrieval systems (AS/RS), (6) flexible manufacturing/assembly systems/cells (FMS, FAS, FMC), (7) computer-aided inspec- tion/testing, (8) product/part tracking and tracing (bar codes, RFID), (9) integrated de- sign-processing systems (CAD, CAE, CAM, CAPP), and (10) engineering databases and product data management systems to measure the extent of adoption and diffusion level of AMT. For these ten kinds of AMT applications, we first test data distribution. Test results indi- cated that “automated parts loading/unloading”
and “automated storage-retrieval systems (AS/RS)” data are non-normal distribution;
therefore, we drop “automated parts load- ing/unloading” and “automated storage-retrieval systems (AS/RS)” from AMT. Other eight AMTs, we further test content and construct validity. In the content validity, we use t-test to test. Test results indicated that all of AMTs show signifi- cant results. In addition to content validity, all AMT applications will work together with each alignment construct with e-commerce setting in
Table 1. Result of EFA and CFA for coordination activities
t-test Supplier Coordination items Customer t-test
0.000** 0.561 CA1. Share inventory level knowledge 0.665 0.000**
0.000** 0.652 CA2. Share production planning decisions and
demand forecast knowledge 0.615 0.000**
0.000** 0.770 CA3. Order tracking/tracing 0.786 0.000**
0.000** 0.586 CA4. Agreements on delivery frequency 0.528 0.000**
0.000** 0.741 CA5. Dedicated capacity 0.753 0.000**
0.000** 0.806
CA6. Require supplier/customer (s) part to manage or hold inventories of materials at manufac- turing site (e.g. Vendor Managed Inventory, Consignment Stock)
0.855 0.000**
0.000** 0.811 CA7. Collaborative Planning, Forecasting and Re-
plenishment 0.827 0.000**
0.793 Cronbanch’s α 0.854
3.139 Eigen value 3.746
44.840 Percent of variation 53.517
** Significant at the p < 0.01 level
* Significant at the p < 0.05 level
Table 2. Result of EFA and CFA for AMT
AMT Factor loading t-test
A1. Stand-alone/NC machines 0.315 0.000**
A2. Machining centers 0.508 0.000**
A3. Automated parts loading/unloading 0.548 0.000**
A4. Flexible manufacturing/assembly systems – cells
(FMS/FAS/FMC) 0.480 0.000**
A5. Computer-aided inspection/testing 0.615 0.000**
A6. Product/part tracking and tracing (bar codes, RFID) 0.570 0.000**
A7. Integrated design-processing systems
(CAD-CAE-CAM-CAPP) 0.621 0.000**
A8. Engineering databases, Product Data Management systems 0.563 0.000**
Cronbanch’s α 0.754
Eigenvalue 2.987
Percent of variation 37.342
** Significant at the p < 0.01 level
* Significant at the p < 0.05 level
Table 3. Result of EFA and CFA for e-commerce
t-test Supplier E-commerce Customer t-test
0.000** 0.554 E1. Scouting/ pre-qualify 0.649 0.000**
0.000** 0.627 E2. RFx (request for quotation, proposal, informa-
tion) 0.615 0.000**
0.000** 0.748 E3. Data analysis (audit and reporting) 0.798 0.000**
0.000** 0.587 E4. Access to catalogues 0.556 0.000**
0.000** 0.751 E5. Order management and tracking 0.765 0.000**
0.000** 0.814 E6. Content and knowledge management 0.847 0.000**
0.000** 0.819 E7. Collaboration support services 0.836 0.000**
0.874 Cronbanch’s α 0.881
4.021 Eigen value 4.145
57.439 Percent of variation 59.212
** Significant at the p < 0.01 level
* Significant at the p < 0.05 level
this study; therefore, we must ensure the all AMTs can be integrated with single construct. In the data construct, we first adopt exploratory
factor analysis (EFA) to test. EFA result indi- cated that the Kaiser-Meyer-Olkin measures of sampling was 0.760, KMO result excesses 0.50,
means result very good. In addition to KMO, the Bartlett test of shyericity was 952.364 with sig- nificance levels of p <0.01, means test result can be accepted. For ensured EFA results, we further test reliability. Test result indicated that reliabil- ity of AMT excesses 0.7, reliability results can be accepted. Second, we adopt confirmatory factor analysis to test construct. Test results in- dicated that single factor loading for the eight AMTs integrate with AMT. Results of content validity, EFA, reliability, and CFA have shown in Table 2.
In the e-commerce, IMSS provides eight kinds of e-commerce technology to investigate the adoption level of e-commerce in suppliers and customers: (1) scouting/pre-qualify, (2) auc- tions, (3) RFx (request for quotation, proposal, information), (4) data analysis (audit and report- ing), (5) access to catalogues, (6) order man- agement and tracking, (7) content and knowl- edge management, and (8) collaboration support services. As eight kinds of e-commerce technol- ogy of supplier and customer, we first test data distribution. Test results indicated that “auc- tions” data of supplier and customer are non-normal distribution; therefore, we drop
“auctions” of supplier and customer from e-commerce. Other seven e-commerce technolo- gies of supplier and customer, we further test content and construct validity. In the content validity, we use t-test to test. Test results indi- cated that all of e-commerce technologies of supplier and customer show significant results.
In addition to content validity, e-commerce technology will are combined and developed for each alignment construct with AMT in this study; therefore, we must ensure the all e-commerce technologies of supplier and customer can be integrated with single construct.
In the data construct, we first adopt exploratory factor analysis (EFA) to test. EFA result indi- cated that the Kaiser-Meyer-Olkin measures of sampling were 0.882 (supplier) and 0.895 (cus- tomer), KMO results excess 0.50, means result very good. In addition to KMO, the Bartlett test of shyericity were 1583.197 (supplier) and 1722.908 (customer) with significance levels of p <0.01, means test result can be accepted. For ensured EFA results, we further test reliability.
Test results indicated that reliability of supplier and customer excess 0.7, reliability results can be accepted. Second, we adopt confirmatory factor analysis to test construct. Test results in- dicated that single factor loading for the seven e-commerce technologies of supplier and cus- tomer integrate with supplier and customer. Re- sults of content validity, EFA, reliability, and CFA have shown in Table 3.
3.3 Operationalization variables – alignment strategies of different adoption level
We further tried to define different align- ments strategies from test samples. Through the factor score for e-commerce technology adop- tion level and AMT adoption level, we establish a measurement model to classify different alignments between AMT and e-commerce tech- nology. The measurement model has shown in Figure 2. In the Figure 2, the left is to measure AMT adoption level, and the right is to measure e-commerce technology adoption level. Adop- tion level can be classified three quartiles in- cluding upper quartile, middle quartile, and lower quartile. As figure 2, we classified each sample into either the upper, middle, or lower quartiles. Quartiles were then used to sort the 467 samples into the seven different alignments.
Classified results indicated that 36 (7.24%) sam- ples are sorted into first type alignment strategy.
In the first type of alignment strategy, adoption level of both AMT and e-commerce technology are in the lower quartiles with 266 (53.52%) samples sorted into the second type alignment.
The second type alignment strategy was in the middle quartiles with both adoption level of AMT and e-commerce technology. 9 (1.81%) samples are sorted into the third type of align- ment. The third type of alignment is in the upper quartiles with both adoption level of AMT and e-commerce technology. 58 (11.67%) samples are sorted into forth type alignment. The forth type of alignment was in the middle quartiles with adoption level of AMT and lower quartile adoption level with e-commerce technology. 59 (11.87%) samples are sorted into the fifth type alignment. Fifth type alignment was in the lower quartiles with adoption level of AMT and middle quartile adoption level with e-commerce tech- nology. 45 (9.05%) samples are sorted into sixth type of alignment.
Figure 2. Classification of alignment type based on the survey measurement for AMT and
e-commerce
AMT E-commerce
4 2
2
4 3 1 3 5
5
Lower quartile Middle quartile Upper quar-
tile
Table 4. Results of discriminant analysis (before)
Cases Alignment 1 Alignment 2 Alignment 3 Alignment 4 Alignment 5 Alignment 6 Alignment 7
Alignment 1 36 36 (100%) 0 0 0 0 0 0
Alignment 2 266 0 188 (70.7%) 0 17 (6.4%) 23 (8.6%) 16 (6.0%) 22 (8.3%)
Alignment 3 9 0 0 9 (100%) 0 0 0 0
Alignment 4 58 10 (17.2%) 0 0 48 (82.8%) 0 0 0
Alignment 5 59 4 (6.8%) 0 0 0 55 (93.2%) 0 0
Alignment 6 45 0 0 6 (13.3%) 0 0 39 (86.7%) 0
Alignment 7 24 0 0 1 (4.2%) 0 0 0 23 (95.8%)
80.1% of original grouped cases correctly classified 79.5% of cross-validated grouped cases correctly classified
Table 5. Results of discriminant analysis (after)
Cases Alignment 1 Alignment 2 Alignment 3 Alignment 4 Alignment 5 Alignment 6 Alignment 7
Alignment 1 69 69 (100%) 0 0 0 0 0 0
Alignment 2 97 0 95 (97.9%) 0 0 0 0 2 (2.1%)
Alignment 3 43 0 0 43 (100%) 0 0 0 0
Alignment 4 48 0 0 0 47 (97.9%) 0 0 1 (2.1%)
Alignment 5 92 0 2 (2.2%) 0 0 90 (97.8%) 0 0
Alignment 6 58 0 0 0 0 0 58 (100%) 0
Alignment 7 90 0 2 (2.2%) 0 0 0 0 88 (97.8%)
98.6% of original grouped cases correctly classified 98.2% of cross-validated grouped cases correctly classified
The sixth type alignment was in the upper quar- tiles with adoption level of AMT and the middle quartile adoption level with e-commerce tech- nology. 24 (4.83%) samples are sorted into the seventh type alignment. The seventh type align- ment was in the middle quartiles with adoption level of AMT and upper quartile adoption level with e-commerce technology.
To ensure the classified results, this study also used the discriminant analysis as a check.
As seen in Table 4, the results of discriminant analysis confirmed that strategy No.1 (100%), strategy No.2 (70.7%), strategy No.3 (100%), strategy No.4 (82.8%), strategy No.5 (93.2%), strategy No.6 (86.7%) and strategy No.7 (95.8%). However, the constituent of strategy No.2 result only is 70.7% of the samples, the results of original grouped cases correctly classi- fied is 80.1, and cross-validated grouped cases correctly classified is 79.5%, these results means the classified results should be adjusted and re- vised. For adjusted and revised classified results, we further adopt k-mean to adjust and revise.
Revision results has shown in the table 5, results indicated that 69 (13.88%) samples are into strategy 1, correct rate of classified is 100%; 97 (19.52%) samples are into strategy 2, correct rate of classified is 97.9%; 43 (8.65%) samples are into strategy 3, correct rate of classified is 100%;
48 (9.66%) samples are into strategy 4, correct rate of classified is 97.9%; 92 (18.51%) samples are into strategy 5, correct rate of classified is 97.8%; 58 (11.67%) samples are into strategy 6, correct rate of classified is 100%; 90 (18.11%) samples are into strategy 7, correct rate of classi- fied is 97.8%. The result of original grouped cases correctly classified is 98.6%, and cross-validated of grouped cases correctly clas- sified is 98.2%. Revision results can be accepted.
After revision, all new classified results will be further test.
Based on the above, we define seven alignment strategies. In the next section, we fur- ther test the research objective through sup- ply-chain coordination activities and seven alignment strategies.
3.4 Methodology and test framework
As for methodology, this study uses Struc- tural Equation Modeling (SEM) to test influence of seven alignments strategies between AMT and e-commerce for seven kinds of coordination activities. The SEM is a very general, chiefly linear, chiefly cross-sectional statistical model- ing technique. It is a largely confirmatory, rather than exploratory, technique. Through SEM, we can ensure whether a certain model is valid, and E-commerceAMT Seven alignment
types of applied ex- tent between AMT and e-commerce
CA1. Share inventory level knowledge
CA2. Share production plan- ning decisions and demand forecast knowledge CA3. Order tracking/tracing
CA4. Agreements on delivery frequency
CA5. Dedicated capacity CA6. Require sup-
plier/customer (s) part to man- age or hold inventories of ma- terials at manufacturing site (e.g. Vendor Managed Inven- tory, Consignment Stock) CA7. Collaborative Planning, Forecasting and Replenishment Figure 3. Research model
further explore and test for research question.
In the test framework, as research objec- tive, definition of coordination activities, and operationalization variables, this study will de- velop seven types of test frameworks to explore research hypothesis. In next section, we will use SEM to test seven frameworks. Test framework has shown in Figure 3.
4. RESULTS AND DISCUS- SION
In this section, we test seven kinds of alignment strategies by SEM, the results are presented in Figure 4. In the fit indices, all re- sults of indices include Incremental Fit Index (IFI), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA) they all can be ac- cepted. Results of fit indices indicated that IFI of model 1 is 0.944, model 2 is 0.940, model 3 is 0.997, model 4 is 0.965, model 5 is 0.964, model 6 is 0.955, and model 7 is 0.952. TLI of model 1 is 0.908, model 2 is 0.909, model 3 is 0.995, model 4 is 0.943, model 5 is 0.942, model 6 is 0.928, and model 7 is 0.919. CFI of model 1 is 0.938, model 2 is 0.937, model 3 is 0.996, model 4 is 0.962, model 5 is 0.962, model 6 is 0.948, and model 7 is 0.948. RMSEA of model 1 is 0.067, model 2 is 0.071, model 3 is 0.022, model 4 is 0.065, model 5 is 0.057, model 6 is 0.047, and model 7 is 0.068.These results confirm the evidence of convergent validity in all models.
Regarding the test results, we found that alignment strategy 3 has a direct influence on all the coordination activities. Test result of align- ment strategy 3 also supports the hypothesis in this study. However, in addition to alignment strategy 3, we also found the alignment strategy 2 and 7 can influence on all the coordination activities. Otherwise, we also found the align- ment strategy 5 can influence few coordination activities. Finally, test results indicated that alignment strategy 1, 4, and 6 cannot influence any coordination activity.
This study first explores the effect of alignment strategies of different adoption level between AMT and e-commerce for supply chain coordination. Based on the test result of align- ment strategy 3 against the hypothesis, we can understand when firms pursues the improvement of coordination between partner firms, taking the alignment of broad adoption level between AMT and e-commerce does influence partner firms to readily carry out the necessary coordination ac- tivities, thus significantly improve the supply chain coordination outcomes. On the other hand,
supply chain operational environment also in- volves partner firms of upstream and down- stream. That’s the reason why in case that a company wants to influence and further guide partner firms to do coordination via AMT appli- cations, the adoption level of AMT must be equivalently throughout partner firms of up- stream and downstream in the entire supply chain. However, information flow also plays an important role to reinforce the influence on co- ordination outcomes that AMT has contributed.
Given the equivalent adoption level of AMT has already diffused through partner firms of up- stream and downstream, but not so for the diffu- sion of information technology/e-business, then the globalization for overall coordination could be impeded. Test results of alignment strategy 3, 4, and 6 can evidence the above inference. In the alignment strategy 3, it showed the adoption level of AMT and e-commerce complements each other in the diffusion process, therefore, makes effective improvement for mutual coor- dination. In the alignment strategy 4 and 6, test results of alignment strategy 4 and 6 showed the adoption level of e-commerce is lower than that of AMT, test results indicated that alignment 4 and 6 are difficult to improve coordination be- tween partner firms.
Although equivalent adoption level for both AMT and e-commerce that completely dif- fuses in the supply chain partners can effectively influence coordination, however, by the test re- sult of alignment strategy 2, incomplete diffused but equivalent adoption level of AMT and e-commerce can still influence the coordination between partner firms. Definition of alignment strategy 2 is “in the middle quartiles with both adoption level of AMT and e-commerce tech- nology”. The definition of alignment strategy 2 means although the diffusion of AMT and e-commerce is not complete in the chain, but alignment strategy 2 still can influence partner firms for coordination. As this result, we pre- sume that it is the information flow under e-business setting that makes the contribution, and this implies the crucial position e-business lies in the coordination. Test results of alignment strategy 5 and 7 can support this statement.
Alignment strategy 5 means the adoption level of e-commerce has diffused to partner firms of upstream and downstream but is not complete, and adoption level AMT is only to focus on manufacturing. However, alignment strategy 5 still imposes a little influence for coordination.
Alignment strategy 7 means the adoption level of e-commerce has completely diffused within partner firms, but adoption level of AMT is in- complete in diffusion. However, test result of
Figure 4. Results of SEM
Alignment 1 AMT
E-commerce
CA1
CA2
CA3
CA4
CA5
CA6 CA7
0.21
0.16 0.69
0.76 0.42
0.68 0.42 0.52 0.53
CMIN=31.399, df=24, CFI=0.938 IFI=0.944, TLI=0.908, RMSEA=0.067
Alignment 2 AMT
E-commerce
CA1
CA2
CA3
CA4
CA5
CA6 CA7
0.16
0.28 0.74*
0.58*
0.40*
0.38*
0.64*
0.62*
0.77*
CMIN=37.203, df=25, CFI=0.937 IFI=0.940, TLI=0.909, RMSEA=0.071
Alignment 3 AMT
E-commerce
CA1
CA2
CA3
CA4
CA5
CA6 CA7
0.57
0.55 0.73*
0.64**
0.59**
0.65**
0.74**
0.76**
0.74**
CMIN=26.505, df=26, CFI=0.996 IFI=0.997, TLI=0.995, RMSEA=0.022
Alignment 4 AMT
E-commerce
CA1
CA2
CA3
CA4
CA5
CA6 CA7
0.23
0.17 0.70
0.70 0.48
0.61 0.59 0.84 0.81
CMIN=28.761, df=24, CFI=0.962 IFI=0.965, TLI=0.943, RMSEA=0.065
Alignment 5 AMT
E-commerce
CA1
CA2
CA3
CA4
CA5
CA6 CA7
0.13
0.42 0.75**
0.84**
0.75**
0.41 0.31 0.16 0.48**
CMIN=30.990, df=24, CFI=0.962 IFI=0.964, TLI=0.942, RMSEA=0.057
Alignment 6 AMT
E-commerce
CA1
CA2
CA3
CA4
CA5
CA6 CA7
0.17
0.01 0.58
0.62 0.59
0.41 0.68 0.25 0.56
CMIN=29.237, df=26, CFI=0.948 IFI=0.955, TLI=0.928, RMSEA=0.047
Alignment 7 AMT
E-commerce
CA1
CA2
CA3
CA4
CA5
CA6 CA7
-0.1
0.30 0.71**
0.79**
0.56*
0.57*
0.53*
0.49*
0.58*
CMIN=32.458, df=23, CFI=0.948 IFI=0.952, TLI=0.919, RMSEA=0.068
alignment strategy 7 tells us that the alignment strat- egy 7 can influence significantly coordination be- tween partner firms. These test results evidence that although AMT adoption diffusion is not so complete, however, underpinned by information via e-business setting the coordination still can be improved.
However, if adoption level of AMT and e-commerce has not completely diffused, or that of e-commerce is higher than AMT, the influence on coordination will be lower than that of complete dif- fusion cases. Comparison between the result align- ment strategy 2 and 3, or between alignment strategy 7 and 3 can justify the above viewpoint. According to comparison result between alignment 2 and 3, we found the significance of coordination improvement of alignment strategy 2 is lower than that of align- ment strategy 3; otherwise, the significance of coor- dination of alignment strategy 7 is lower than that of alignment strategy 3. Therefore, when firms want to improve coordination between partner firms, taking alignment strategy of broad adoption level between AMT and e-commerce to influence coordination of partner firms, enables a significantly improved coor- dination outcomes.
5. CONCLUSION
The objective of this study is to empirically identify various alignments in the context of different applied of AMT and e-commerce, and test whether these alignments strategy can impose influence upon supply-chain coordination outcomes. Through the test result we can infer which alignments strategy makes more significant contribution. Using the data set based on large samples from the International Manu- facturing Strategy Survey (IMSS) database, we have justified the proposition.
In this study, we classified seven types of alignments strategies of utilizing AMT and e-commerce for supply chain coordination and then using SEM to test how influential each strategy can be. The conclusion is made here:
First, Applied of AMT must completely diffuse to partner firms of upstream and downstream if a company wants to influence and further guide supply chain partner firms for effective coordination by AMT. By the test results, complete diffusion of AMT under the constraint of incomplete diffusion of e-business adoption, however, the premise is that that adoption level of e-business should be compatible among partner firms. To pursue the optimal results, company must develop and build competent informa- tion technology infrastructure to empower the advan- tage of AMT in the supply chain operational envi- ronment as firms can use e-commerce setting to con- nect partners firms to streamline the information flow.
Therefore, it is evident that the adoption level of AMT must be equivalent to the adoption level of
e-commerce to support the significant influence and improvement in supply chain coordination outcomes.
Second, information flow plays an important role when firms adopt AMT to influence and guide partner firms for coordination. Test results indicated that the adoption level of e-commerce must equate that of AMT if AMT applications are expected to effectively influence partner firms for coordination, or else it could be difficult to significant improve the coordination outcomes between partner firms by AMT. Test results also indicated, if applied extent of e-commerce is higher than applied extent of AMT, coordination between partner firms still can be influ- enced and improved by AMT; however, if adoption level of e-commerce is lower than that of AMT, ef- fective coordination between partner firms will be hardly to be achieved. Therefore, this study has de- liberately identified the critical role of e-commerce setting plays in the coordination of supply chain ac- tivities.
Based on the findings in this study, we recog- nize the fact that e-business/information technology indeed plays a critical role in the supply chain inte- gration. Therefore, we suggest that an in-depth ex- ploration into the relationship between e-business/information flow and coordination out- comes, and the direct or interaction effect of AMT and e-business on coordination outcomes as the di- rection for further research.
REFERENCES
1. Bramel, J., S. Goyal and P. Zipkin, “Coordination of production/distribution networks with unbalanced leadtimes,” Operations Research, 48, 570-577 (2000).
2. Chase, R.B. and N. Aquilano, Production and Opera- tions Management, Irwin, Chicago (1997).
3. Chopra, S. and P. Meindl, Supply Chain Manage- ment: Strategy, Planning, and Operation, Prentice Hall (2003).
4. Clemons, E.K. and M.C. Row, “Limits to interfirm coordination through information technology: results of a field study in consumer packaged goods distri- bution,” Journal of Management Information Sys- tems, 10, 73-95 (1993).
5. Das, A. and R. Narasimhan, “Process-technology fit and its implications for manufacturing performance,”
Journal of Operations Management, 19, 521-540.
(2001).
6. Dyer, J.H. and W.G. Ouchi, “Japanese-style partner- ships: giving companies a competitive edge,” Sloan Management Review, Fall, 51-63 (1993).
7. Ghiassi, M. and C. Spera, “Defining the inter- net-based supply chain system for mass customized markets,” Computers and Industrial Engineering, 45, 17-41 (2003).
8. Goutsos, S. and N. Karacapilidis, “Enhanced supply
chain management for e-business transactions,” In- ternational Journal of Production Economics, 89, 141-152 (2004).
9. Gules, H.K. and T.F. Burgess, “Manufacturing tech- nology and the supply chain,” European Journal of Purchasing and Supply Management, 2, 31-38 (1996).
10. Kalakota, R. and M. Robinson, E-business: Road- map for Success, Addison Wesley, Reading, Massa- chusetts, MA (1999).
11. Khouja, M., “Synchronization in supply chains: im- plications for design and management,” Journal of the Operational Research Society, 54, 984-994 (2003).
12. Lancioni, R., H.J. Schau and M.F. Smith, “Internet impacts on supply chain management,” Industrial Marketing Management, 32, 173-175 (2003).
13. Lewis, M.W. and K.K. Boyer, “Factors impacting AMT implementation: an integrative and controlled study,” Journal of Engineering and Technology Management, 19, 111-130 (2002).
14. Manthou, V., M. Vlachopoulou and D. Folinas,
“Virtual e-Chain (VeC) model for supply chain col- laboration,” International Journal of Production Economics, 87, 241-250 (2004).
15. Munson, C.L., J. Hu and M.J. Rosenblatt, “Teaching the costs of uncoordinated supply chains,” Interfaces, 33, 24-39 (2003).
16. Naim, M.M., “The impact of the net present value on the assessment of the dynamic performance of e-commerce enabled supply chains,” International Journal of Production Economics, 104, 382-393 (2006).
17. Olhager, J. and M. Rudberg, “Manufacturing strat- egy and e-business: an exploratory study,” Inte- grated Manufacturing Systems, 14, 334-345 (2003).
18. Phan, D.D., “E-business development for competi- tive advantages: a case study,” Information Man- agement, 40, 581-590 (2003).
19. Porter, J.D., R.E. Billo and M.H. Mickle, “A Stan- dard test protocol for evaluation of radio frequency identification systems for supply chain applications,”
Journal of Manufacturing Systems, 23, 46-55 (2004).
20. Sha, D. Y., P. K. Chen and Y. H, Chen, “Exploring the effects of advanced manufacturing technologies and e-commerce in supply-chain coordination – an empirical study,” Proceedings of the 2007 APIEMS
&CIIE Conference, Dec. 9-13, Kaohsiung, Taiwan R.O.C., Paper ID: 537 (2007).
21. Sha, D. Y., P. K. Chen and Y. H. Chen, “Alignment strategies of AMT and e-commerce setting to im- prove business strategy in the supply chain opera- tions environment-An empirical study,” Proceedings of the 2007 IEEE International Conference on In- dustrial Engineering and Engineering Management, Dec. 2-5, Singapore, 1487-1491 (2007).
22. Starbird, S.A., “Effect of coordinated replenishment policies on quality,” Journal of the Operational Re-
search Society, 54, 32-39 (2003).
23. Strader, T.J., F.R. Lin and M.J. Shaw, “Business-to business electronic commerce and convergent as- sembly supply chain management,” Journal of In- formation Technology, 14, 361-373 (1999).
24. Sun, H., “Current and future patterns of using ad- vanced manufacturing technologies,” Technovation, 20, 631-641 (2000).
25. Turban, E., D. King, J. Mckay and J. Lee, Electronic Commerce: A Managerial perspective, Pearson Edu- cation Inc., New Jersey, NJ (2008).
26. Wu, F., S. Yeniyurt, D. Kim and S.T. Cavusgil, “The impact of information technology on supply chain capabilities and firm performance: A resource-based view,” Industrial Marketing Management, 35, 493-504 (2006).
27. Xue, X., Y. Wang, Q. Shen and X. Yu, “Coordination mechanisms for construction supply chain manage- ment in the internet environment,” International Journal of Project Management, 25, 150-157 (2007).
28. Zhang, C. and S. Li, “Secure information sharing in internet-based supply chain management systems,”
The Journal of Computer Information Systems, 46, 18-24 (2006).
ABOUT THE AUTHORS
D. Y. Sha received his master and Ph.D. degrees in Industrial Engineering from the University of Texas at Arlington, U. S. A. He is currently the Chair Pro- fessor at the Department of Industrial Engineering and System Management of Chung Hua University.
Professor Sha is the President of Taiwan TRIZ Asso- ciation. He has also garnered several awards such as an industrial engineering medal from the Chinese Institute of Industrial Engineering, two silver medals of the National Invention and Creation Award from the Ministry of Economic Affairs of Taiwan R. O. C., and a silver medal of Taipei International Invention Show. Professor Sha’s research interests include manufacturing strategy, production and operations management, and TRIZ systematic innovation meth- odology.
P. K. Chen received the MBA degree in business and operations management from Chang Jung Christian University, Tainan, Taiwan in 2003. Mr. Chen has been a Ph.D. candidate in the Department of Indus- trial and Engineering Management, National Chiao Tung University in Hsinchu, Taiwan since September 2003. He will receive the Ph.D. degree in 2008, and be going to become an assistant professor in the De- partment of Technology Management, Aletheia Uni- versity in august 2008. His current research interests include manufacturing strategy, supply chain man- agement, e-commerce, and TRIZ systematic innova- tion methodology.
Yung-Hsin Chen received a Ph.D. degree in Indus- trial Engineering and Management form National Chiao Tung University, Taiwan. He has a B.S. degree in Mechanical Engineering and MBA at National Cheng Kung University, Taiwan and M.S. in Indus- trial Engineering at Purdue University, West Lafay- ette, Indiana, U.S.A. Mr. Chen is currently an assis- tant professor in the Department of Business Admini- stration, Asia University. His research area includes customer equity management (or CRM), KM, SCM, strategic management of technology and innovation, neural network applications in data mining. He re- ceived the Award of “Young Distinguished Engineer”
from the Society of Mechanical Engineering, Taiwan and have been listed in the “Marquis’ Who is Who in science and engineering 2006-2007” and “Marquis’
Who is Who in the World, 2008”.
(Received February 2008; accepted April 2008)
探討 探討 探討
探討先進製造技術與電子商務 先進製造技術與電子商務 先進製造技術與電子商務 先進製造技術與電子商務對供應鏈協調與競爭績效連 對供應鏈協調與競爭績效連 對供應鏈協調與競爭績效連 對供應鏈協調與競爭績效連 結關係之影響
結關係之影響 結關係之影響 結關係之影響
沙永傑
中華大學工業工程與系統管理學系 陳屏國
國立交通大學工業工程與管理學系 陳永信*
亞洲大學經營管理學系
摘要 摘要 摘要 摘要
本研究之目的在於根據不同使用程度的先進製造技術與電子商務，定義在供 應鏈作業環境中的形成各的種連結策略，並驗證各種連結策略對供應鏈協調 的影響結果。基於驗證結果，我們可以證明能夠增進供應鏈協調的最佳連結 策略。樣本方面，本研究使用國際製造策略調查資料庫 (International Manu- facturing Strategy Survey, IMSS)之資料進行驗證。我們由 IMSS 中選出 497 個 樣本來進行分析，並根據不同使用程度的先進製造技術與電子商務去歸類出 七種連結策略。驗證結果指出若先進製造技術與電子商務的使用程度高且完 全擴散於供應鏈作業環境中其能有效影響夥伴廠商之間的協調，並增進供應 鏈作業績效。此外，我們也發現電子商務在連結策略中扮演相當重要的角色。
關鍵詞: 供應鏈協調，先進製造技術(AMT)，電子商務，策略連結 (*聯絡人: thomaschen@asia.edu.tw, thomaschen.iem90g@nctu.edu.tw)
