Deploying Big Data Enablers to Strengthen Supply Chain Agility to Mitigate Bullwhip Effect: An Empirical Study of China’s Electronic Manufacturers
Abstract
:1. Introduction
- 1.
- What are the key BEFs, SCAIs, and BDEs in the electronic equipment manufacturing enterprises’ supply chains?
- 2.
- How could QFD and MCDM be integrated to link the relationships among three groups of variables?
- 3.
- Can these findings help industry decision makers formulate strategies for implementing big data analytics?
- a.
- To determine the key BEFs, SCAIs, and BDEs in the supply chain of electronic equipment manufacturing enterprises.
- b.
- To establish the relationship between the three groups of variables using the QFD method based on the integrated MCDM framework.
- c.
- To suggest some managerial implications for the use of the proposed BDA in the supply chain of electronic equipment manufacturing enterprises.
2. Literature Review
2.1. Bullwhip Effect
2.2. Supply Chain Agility
2.3. Big Data
2.4. Bullwhip Effect and Supply Chain Agility
2.5. Supply Chain Agility and Big Data
3. Methodology
3.1. Introduction to the MCDM-QFD Method
3.2. HoQ1: Linking BEFs and SCAIs
3.3. HoQ2: Linking SCAIs and BDEs
3.4. The Fuzzy Delphi Method (FDM)
- (1)
- If the fuzzy triangular numbers show no overlap, then signifies that the opinion intervals of the experts possess a consensus section. If so, then the evaluation item i “value importance level that has reached a consensus”, , equals the average of and , which is expressed as
- (2)
- If two fuzzy triangular numbers overlap, then and , where .Then, the consensus importance degree of the evaluation item is equal to the fuzzy set obtained from the intersection operation of the fuzzy relation of the two trigonometric fuzzy functions. The quantized score with the maximum membership degree of the modified fuzzy set is then obtained using the following formula:
- (3)
- If the triangle fuzzy functions overlap, and , there is no consensus segment in the opinion interval value of each questionnaire object, and the two objects, given the extreme value, have greatly different opinions from other questionnaire objects, resulting in diverging opinions. Therefore, the evaluation items whose opinions do not converge are provided to the respondents for reference, and the steps from A to D are repeated for another round of questionnaires until all the evaluation items can converge and the value of consensus importance is calculated.
3.5. Fuzzy Interpretative Structural Modeling (FISM)
3.6. Analysis Network Procedure (ANP)
3.7. Grey Relational Analysis (GRA)
4. Empirical Study
4.1. First HoQ Linking BEFs and SCAIs
4.1.1. Stage Ⅰ: Confirmation of Important BEFs and SCAIs, Using FDM
4.1.2. Stage Ⅱ: Verification of the Interaction between Key BEFs
4.1.3. Stage III: Obtain the Key BEFs Interaction Coefficients and Weight Values, Using ANP
4.1.4. Stage Ⅳ: Identify the Association Matrix of Key SCAIs
4.1.5. Stage Ⅴ: Evaluate the Correlation Matrix between Key BEFs and SCAIs
4.1.6. Stage Ⅵ: Arrange the Priority of Key SCAIs
- A.
- B.
- C.
- D.
- E.
4.2. Second HoQ Linking SCAIs and BDEs
4.2.1. Stage I: Selection of Key BDEs Using FDM
4.2.2. Stage Ⅱ: Calculate the Weight Value of the Key SCAIs
4.3. Discussion of Results
5. Managerial Implications
6. Conclusions
- The top three BEFs are “Information asymmetry,” “Batch ordering strategy,” and “Demand forecasting.”
- The top three SCAIs are “Actively build a shared information platform with partners,” “Timely detecting of threats in the environment,” and “Improve data accuracy.”
- The top five BDEs are “Get financial support,” “Developing the Internet of Things,” “Data visualization capability,” “Developing cloud computing technology,” and “Develop IT infrastructure.”
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
(BEFs) | Bullwhip effect factors |
(SCAIs) | Supply chain agility indicators |
(BDEs) | Big data enablers |
(BDA) | Big data analytics |
(QFD) | Quality function deployment |
(MCDM) | Multicriteria decision-making |
(HoQ) | House of quality |
(FDM) | Fuzzy Delphi method |
(FISM) | Fuzzy interpretative structural modeling |
(ANP) | Analysis network procedure |
(GRA) | Grey relational analysis |
Appendix A
NO. | Factors | A Brief Explanation of Each Factor | Relevant Literature |
---|---|---|---|
BEF 1 | Information asymmetry | Information asymmetry occurring in the upstream of the supply chain | Dahlin and Safstrom (2021) [62]. |
BEF 2 | Batch ordering strategy | Refers to the phenomenon involved in the placement of orders to upstream echelons in batches | Hussain and Saber (2012) [55]. |
BEF 3 | Demand forecasting | Adjustment of the supply chain order and demand changes using a demand forecasting model | Dahlin and Safstrom (2021) [62]. |
BEF 4 | Batch size | This refers to the quantity of a product, which is identical in quality, construction, and method of manufacture, produced at one time | Lee, Padmanabhan and Whang (1997) [47]. |
BEF 5 | The multiplier effect | Generally refers to a case of direct multiplication of orders with a knock-on effect between product manufacturers and their capital equipment suppliers | Bhattacharya and Bandyopadhyay (2011) [63]. |
BEF 6 | Price fluctuation | Price changes caused by price discounts, coupons, and other special promotions in the market | Dahlin and Safstrom (2021) [62]. |
BEF 7 | Lack of coordination in supply chain | Inadequate communication between suppliers and supply chain partners | Dahlin and Safstrom (2021) [62]. |
BEF 8 | The company process | Includes the “variability of machine reliability and output” and “variability in process capability and subsequent product quality” | Bhattacharya and Bandyopadhyay (2011) [63]. |
BEF 9 | A shortage of game | This refers to the approach of Buyer in managing supply shortages in the event of a shortage event | Dahlin and Safstrom (2021) [62]. |
BEF 10 | Factory capacity constraints | Capacity limits on merchandise in the warehouse of a dealer | Pastore, Alfieri and Zotteri (2019) [60]. |
BEF 11 | Inventory policy | Inventory policies specify decision rules with respect to the point in time when a replenishment of the inventory should be initiated, as well as to the replenishment quantity that should be ordered from the supplying node in the supply network. | Dahlin and Safstrom (2021) [62]; Bhattacharya and Bandyopadhyay (2011) [63]. |
BEF 12 | Lead time | Refers to the order to delivery time | Bhattacharya and Bandyopadhyay (2011) [63]. |
BEF 13 | Local optimization without Global vision | This focuses only on the optimization of its own echelon without considering its impact on other echelons | Bhattacharya and Bandyopadhyay (2011) [63]. |
BEF 14 | Lack of synchronization | This includes the lack of synchronization in the delivery, receipt, ordering, transportation, and other aspects of supply chain members | Bhattacharya and Bandyopadhyay (2011) [63]. |
NO. | Indicators | A Brief Explanation of Each Indicator | Relevant Literature |
---|---|---|---|
SCAI 1 | Improve data accuracy | This determines the accuracy of the data source or data source logic | Chan, Ngai and Moon(2017) [84]. |
SCAI 2 | Improve information transparency in the upstream and downstream of the supply chain | Indicates adequate information sharing between an enterprise and a supplier | Yang (2014) [85]. |
SCAI 3 | Actively build a shared information platform with partners | For establishing a shared information system between the company and suppliers, and sharing information between different business units | Rasi, Abbasi, Hatami (2019) [82]; Jermsittiparsert and Srisawat (2019) [83]. |
SCAI 4 | Improve market sensitivity | Companies must be aware of any in-demand changes relating to consumer tastes and preferences | Jermsittiparsert and Srisawat (2019) [83]. |
SCAI 5 | Jointly manage inventory with suppliers | Integrate and synchronize information to eliminate excess inventory and improve inventory | Pandeyand Garg (2009) [86]. |
SCAI 6 | Improve logistics capability | Improve logistics planning and management ability | Pandeyand Garg (2009) [86]. |
SCAI 7 | Supplier innovation | This is a process that creates opportunities for organizations to capture new markets and eliminate stagnation and downturns that threaten existing businesses | Rasi, Abbasi and Hatami (2019) [82]. |
SCAI 8 | Strategic flexibility | Superior knowledge and ability to adjust objectives and improve the ability of a company to respond to the market environment | Chan, Ngai and Moon (2019) [84] |
SCAI 9 | Using information technology | This includes a variety of tools for supply chain software solutions to meet the requirements of all stages of the supply chain | Pandey and Garg (2009) [86]. |
SCAI 10 | Automation | This involves the replacement of manual operations with computerized methods, or the implementation of decisions with minimal human intervention | Pandey and Garg (2009) [86]. |
SCAI 11 | Improve service quality | The result of providing products or services that meet customer requirements | Pandey and Garg (2009) [86]. |
SCAI 12 | Timely detection of threats in the environment | The rapid response of an organization to various forces with which it must interact | Rasi, Abbasiand Hatami (2019) [82]. |
SCAI 13 | Integrate supply chain partners | This refers to a shared mental framework between customers and suppliers regarding inter-enterprise dependency and principles of collaboration | Haq, Hameed and Raheem (2020) [81]. |
SCAI 14 | Plan and form long-term cooperative partners with suppliers | Becoming a partner in operational cooperation | Yang (2014) [85]. |
NO. | Enablers | A Brief Explanation of Each Enabler | Relevant Literature |
---|---|---|---|
BDE1 | Data integration and management capability | The ability of an organization to collect, integrate, transform, and store data from heterogeneous data sources using tools and technologies | Lamba and Singh (2018) [110]. |
BDE2 | Get financial support | A large amount of capital needs to be invested in various processes related to big data, such as data collection, storage, and processing | Lamba and Singh (2018) [110]. |
BDE3 | Big data storage maintenance | This is one of the essential aspects, which involve hardware devices and storage systems or mechanisms | Zhong et al. (2016) [109]. |
BDE4 | Advanced analytical skills | Defined as the ability of an organization to analyze supply chain data using tools and technologies in bulk, real-time, near-term, or as supply chain data flows and extracts meaningful decision insights | Arunachalam, Kumar and Kawalek (2018) [104]. |
BDE5 | Data-driven culture | As an intangible resource, this enabler represents the beliefs, attitudes, and opinions of the people on data segmentation decisions Ensuring data privacy at different stages of the collection, storage, and processing of big data | Arunachalam, Kumar and Kawalek(2018) [104]. |
BDE6 | Value data security and privacy | Ensuring data privacy at different stages of the collection, storage, and processing of big data | Lamba and Singh (2018) [110]. |
BDE7 | Develop IT infrastructure | This refers to the physical resources available for implementing IT innovations | Lai, Sunand Ren (2018) [111]. |
BDE8 | Developing cloud computing technology | Consideration of leveraging cloud computing infrastructure for data integration, storage, and analytics as a complementary resource | Arunachalam, Kumar and Kawalek(2018) [104]. |
BDE9 | Developing the Internet of Things | Development enables the formation of interconnected networks by common physical objects that can be individually addressed | Raman et al. (2018) [108]. |
BDE10 | Data visualization capability | This refers to the ability of an organization to leverage tools and technologies to present information visuals and visually deliver data-driven insights to decision makers in a timely manner | Arunachalam, Kumar and Kawalek(2018) [104]. |
Appendix B
NO. | The Most Conservative Value | The Most Optimistic Value |
---|---|---|
BEF 1 | ||
BEF 2 | ||
BEF 2 | ||
... |
BEF 1 | BEF 2 | BEF 3 | ... | |
---|---|---|---|---|
BEF 1 | ||||
BEF 2 | ||||
BEF 3 | ||||
... |
BEF 1 | BEF 6 | |
---|---|---|
BEF 1 | ||
BEF 6 |
BEF 2 | BEF 10 | |
---|---|---|
BEF 2 | ||
BEF 10 |
BEF 3 | BEF 6 | |
---|---|---|
BEF 3 | ||
BEF 6 |
BEF 4 | BEF 2 | BEF 5 | BEF 9 | BEF 10 | BEF 11 | |
---|---|---|---|---|---|---|
BEF 4 | ||||||
BEF 2 | ||||||
BEF 5 | ||||||
BEF 9 | ||||||
BEF 10 | ||||||
BEF 11 |
BEF 6 | BEF 7 | |
---|---|---|
BEF 6 | ||
BEF 7 |
BEF 7 | BEF 1 | BEF 4 | |
---|---|---|---|
BEF 7 | |||
BEF 1 | |||
BEF 4 |
BEF 11 | BEF 6 | BEF 9 | |
---|---|---|---|
BEF 11 | |||
BEF 6 | |||
BEF 9 |
BEF 12 | BEF 7 | |
---|---|---|
BEF 12 | ||
BEF 7 |
BEF 3 | BEF 9 | |
---|---|---|
BEF 3 | ||
BEF 9 |
SCAI 1 | SCAI 2 | SCAI 3 | ... | SCAI 14 | |
---|---|---|---|---|---|
SCAI 1 | |||||
SCAI 2 | |||||
SCAI 3 | |||||
... | |||||
SCAI 14 |
SCAI 1 | SCAI 2 | SCAI 3 | ... | SCAI 14 | |
---|---|---|---|---|---|
BEF 1 | |||||
BEF 2 | |||||
BEF 3 | |||||
... | |||||
BEF 14 |
NO. | The Most Conservative Value | The Most Optimistic Value |
---|---|---|
BDE 1 | ||
BDE 2 | ||
BDE 3 | ||
... |
BDE 1 | BDE 2 | BDE 3 | ... | BDE 10 | |
---|---|---|---|---|---|
BDE 1 | |||||
BDE 2 | |||||
BDE 3 | |||||
... | |||||
BDE 10 |
BDE 1 | BDE 2 | BDE 3 | ... | BDE 10 | |
---|---|---|---|---|---|
SCAI 1 | |||||
SCAI 2 | |||||
SCAI 3 | |||||
... | |||||
SCAI 14 |
References
- Zhang, F.; Gong, Z. Supply Chain Inventory Collaborative Management and Information Sharing Mechanism Based on Cloud Computing and 5G Internet of Things. Math. Probl. Eng. 2021, 2021, 6670718. [Google Scholar] [CrossRef]
- Kannisio, R.R. Factors Affecting Logistics and Supply Chain Management; Galgotias University: New Delhi, India, 2021. [Google Scholar]
- Faludi, T. Measurement and reduction of the bullwhip effect. In Proceedings of the 11th International Conference on Modern Research in Management, Economics and Accounting, Oxford, UK, 18–20 December 2020. [Google Scholar]
- Goodarzi, M.; Makvandi, P.; Saen, R.F.; Mohammad, D. What are causes of cash flow bullwhip effect in centralized and decentralized supply chains? Appl. Math. Model. 2017, 44, 640–654. [Google Scholar] [CrossRef]
- Lu, C. Research on bullwhip effect management in supply chain based on system dynamics. Journal of Physics: Conference Series, Proceedings of the 2021 International Conference on Computer Application in Transportation Engineering, Ningbo, China, 5–6 June 2021; IOP Publishing Ltd.: Bristol, UK, 2021. [Google Scholar]
- Lee, H.L.; Padmanabhan, V.; Whang, S. Comments on “Information distortion in a supply chain: The bullwhip effect”. Manag. Sci. 2004, 50, 1887–1893. [Google Scholar] [CrossRef] [Green Version]
- Plebner, M.; Buhren, C.; Frank, B. Market research with the aid of a smartphone application–a case study. Prod. Plan. Control 2018, 29, 117–130. [Google Scholar] [CrossRef]
- Akbal, H. COVID-19 Pandemisinin sağlık tedarik zincirine kamçı etkisi. Kesit Akademi Dergisi 2020, 6, 181–192. [Google Scholar] [CrossRef]
- Mackelprang, A.W.; Malhotra, M.K. The impact of bullwhip on supply chains: Performance pathways, control mechanisms, and managerial levers. J. Oper. Manag. 2015, 36, 15–32. [Google Scholar] [CrossRef]
- Vance, A.; Lowry, P.B.; Ogden, J.A. Testing the Potential of RFID to Increase Supply-Chain Agility and to Mitigate the Bullwhip. In Innovations in Logistics and Supply Chain Management Technologies for Dynamic Economies; IGI Global: Derry Township, PA, USA; Commonwealth of Pennsylvania: Harrisburg, PA, USA, 2012; Volume 4, pp. 49–68. [Google Scholar]
- Lee, J.; Cho, H.; Kim, Y.S. Assessing business impacts of agility criterion and order allocation strategy in multi-criteria supplier selection. Expert Syst. Appl. 2015, 42, 1136–1148. [Google Scholar] [CrossRef]
- Tarigan, Z.J.H.; Siagian, H.; Jie, F. Impact of Internal Integration, Supply Chain Partnership, Supply Chain Agility, and Supply Chain Resilience on Sustainable Advantage. Sustainability 2021, 13, 5460. [Google Scholar] [CrossRef]
- Ozkanlisoy, O. The covid-19 outbreaks effects and new inclinations in terms of logistics and supply chain activities: A conceptual framework. J. Manag. Mark. Logist. 2021, 8, 76–88. [Google Scholar] [CrossRef]
- Gligor, D.M.; Holcomb, M.C.; Stank, T.P. A multidisciplinary approach to supply chain agility: Conceptualization and scale development. J. Bus. Logist. 2013, 34, 94–108. [Google Scholar] [CrossRef]
- Gligor, D.; Gligor, N.; Holcomb, M.; Bozkurt, S. Distinguishing between the concepts of supply chain agility and resilience: A multidisciplinary literature review. Int. J. Logist. Manag. 2019, 30, 467–487. [Google Scholar] [CrossRef]
- Mukhsin, M.; Suryanto, T. The effect of supply agility mediation through the relationship between trust and commitment on supply chain performance. Uncertain Supply Chain. Manag. 2021, 9, 555–562. [Google Scholar] [CrossRef]
- Gligor, D.M.; Holcomb, M.C.; Feizabadi, J. An exploration of the strategic antecedents of firm supply chain agility: The role of a firm’s orientations. Int. J. Prod. Econ. 2016, 179, 24–34. [Google Scholar] [CrossRef]
- Shamout, M.D. Supply chain data analytics and supply chain agility: A fuzzy sets (fsQCA) approach. Int. J. Organ. Anal. 2020, 28, 1055–1067. [Google Scholar] [CrossRef]
- Muhtaroglu, F.C.P.; Demir, S.; Obali, M.; Girgin, C. Business model canvas perspective on big data applications. In Proceedings of the 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA, 6–9 October 2013. [Google Scholar]
- Wang, G.; Gunasekaran, A.; Ngai, E.W.; Papadopoulos, T. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. Int. J. Prod. Econ. 2016, 176, 98–110. [Google Scholar] [CrossRef]
- Maheshwari, S.; Gautam, P.; Jaggi, C.K. Role of Big Data Analytics in supply chain management: Current trends and future perspectives. Int. J. Prod. Res. 2021, 59, 1875–1900. [Google Scholar] [CrossRef]
- Giannakis, M.; Louis, M. A multi-agent based system with big data processing for enhanced supply chain agility. J. Enterp. Inf. Manag. 2016, 29, 706–727. [Google Scholar] [CrossRef]
- Dubey, R.; Gunasekaran, A.; Childe, S.J. Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility. Manag. Decis. 2018, 57, 2092–2112. [Google Scholar] [CrossRef] [Green Version]
- Mandal, S. An examination of the importance of big data analytics in supply chain agility development: A dynamic capability perspective. Manag. Res. Rev. 2018, 41, 1201–1219. [Google Scholar] [CrossRef]
- Mandal, S. The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility: An empirical investigation. Inf. Technol. People 2019, 32, 297–318. [Google Scholar] [CrossRef]
- Mikalef, P.; Pappas, I.O.; Krogstie, J.; Giannakos, M. Big data analytics capabilities: A systematic literature review and research agenda. Inf. Syst. e-Bus. Manag. 2018, 16, 547–578. [Google Scholar] [CrossRef]
- Zarei, M.; Fakhrzad, M.B.; Paghaleh, M.J. Food supply chain leanness using a developed QFD model. J. Food Eng. 2011, 102, 25–33. [Google Scholar] [CrossRef]
- Haq, A.N.; Boddu, V. Analysis of enablers for the implementation of leagile supply chain management using an integrated fuzzy QFD approach. J. Intell. Manuf. 2017, 28, 1–12. [Google Scholar] [CrossRef]
- Ayag, Z.; Samanlioglu, F.; Buyukozkan, G. A fuzzy QFD approach to determine supply chain management strategies in the dairy industry. J. Intell. Manuf. 2013, 24, 1111–1122. [Google Scholar] [CrossRef]
- Buyukozkan, G.; Cifci, G. An integrated QFD framework with multiple formatted and incomplete preferences: A sustainable supply chain application. Appl. Soft Comput. 2013, 13, 3931–3941. [Google Scholar] [CrossRef]
- He, L.; Wu, Z.; Xiang, W.; Goh, M.; Xu, Z.; Song, W.; Ming, X.; Wu, X. A novel Kano-QFD-DEMATEL approach to optimise the risk resilience solution for sustainable supply chain. Int. J. Prod. Res. 2021, 59, 1714–1735. [Google Scholar] [CrossRef]
- Hsu, C.H.; Chang, A.Y.; Zhang, T.Y.; Lin, W.D.; Liu, W.L. Deploying Resilience Enablers to Mitigate Risks in Sustainable Fashion Supply Chains. Sustainability 2021, 13, 2943. [Google Scholar] [CrossRef]
- Chowdhury, M.M.H.; Quaddus, M.A. A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of garment industry of Bangladesh. Omega 2015, 57, 5–21. [Google Scholar] [CrossRef]
- Lam, J.S.L.; Bai, X. A quality function deployment approach to improve maritime supply chain resilience. Transp. Res. Part. E Logist. Transp. Rev. 2016, 92, 16–27. [Google Scholar] [CrossRef]
- Lam, J.S.L. Designing a sustainable maritime supply chain: A hybrid QFD–ANP approach. Transp. Res. Part. E Logist. Transp. Rev. 2015, 78, 70–81. [Google Scholar] [CrossRef]
- Cui, H.Y.; Huang, Z.X.; Serhat, Y.K.; Hasan, H. Analysis of the innovation strategies for green supply chain management in the energy industry using the QFD-based hybrid interval valued intuitionistic fuzzy decision approach. Renew. Sustain. Energy Rev. 2021, 143, 110844. [Google Scholar]
- Mahmood, W.H.W.; Azlan, U.A.A. QFD Approach in Determining the Best Practices for Green Supply Chain Management in Composite Technology Manufacturing Industries. Malays. J. Compos. Sci. Manuf. 2020, 1, 45–56. [Google Scholar] [CrossRef]
- Deepu, T.S.; Ravi, V. An integrated ANP–QFD approach for prioritization of customer and design requirements for digitalization in an electronic supply chain. Benchmarking Int. J. 2020, 28, 1213–1246. [Google Scholar]
- Hsu, C.H.; Yu, R.Y.; Chang, A.Y.; Chung, W.H.; Liu, W.L. Resilience-Enhancing Solution to Mitigate Risk for Sustainable Supply Chain–An Empirical Study of Elevator Manufacturing. Processes 2021, 9, 596. [Google Scholar] [CrossRef]
- Shahin, A.; Rabbanimehr, M. Prioritising enablers of EFQM based on manager performance: An integration of 360 evaluation and house of quality (HoQ). Int. J. Procure. Manag. 2013, 6, 329–349. [Google Scholar] [CrossRef]
- Duckstein, L.; Opricovic, S. Multiobjective optimization in river basin development. Water Resour. Res. 1980, 16, 14–20. [Google Scholar] [CrossRef]
- Liu, H.T. Product design and selection using fuzzy QFD and fuzzy MCDM approaches. Appl. Math. Model. 2011, 35, 482–496. [Google Scholar] [CrossRef]
- Dursun, M.; Karsak, E.E. A QFD-based fuzzy MCDM approach for supplier selection. Appl. Math. Model. 2013, 37, 5864–5875. [Google Scholar] [CrossRef]
- Yazdani, M.; Zolfani, S.H.; Zavadskas, E.K. New integration of MCDM methods and QFD in the selection of green suppliers. J.Bus. Econ. Manag. 2016, 17, 1097–1113. [Google Scholar] [CrossRef] [Green Version]
- Yazdani, M.; Chatterjee, P.; Zavadskas, E.K.; Zolfani, S.H. Integrated QFD-MCDM framework for green supplier selection. J. Clean. Prod. 2017, 142, 3728–3740. [Google Scholar] [CrossRef]
- Forrester, J.W. Industrial dynamics: A major breakthrough for decision makers. Harv. Bus. Rev. 1958, 36, 37–66. [Google Scholar]
- Lee, H.L.; Padmanabhan, V.; Whang, S.J. Information distortion in a supply chain: The bullwhip effect. Manag. Sci. 1997, 43, 546–558. [Google Scholar]
- Derbel, M.; Chabchoub, H.; Hachicha, W.; Masmoudi, F. Measuring the impact of (s, S) ordering policy on the bullwhip effect by means of simulation optimization. In Proceedings of the 2013 International Conference on Advanced Logistics and Transport, Sousse, Tunisia, 29–31 May 2013. [Google Scholar]
- Braz, A.C.; Mello, A.M.; Gomes, L.A.V.; Nascimento, P.T. The bullwhip effect in closed-loop supply chains: A systematic literature review. J. Clean. Prod. 2018, 202, 376–389. [Google Scholar] [CrossRef]
- Wang, X.; Disney, S.M. The bullwhip effect: Progress, trends and directions. Eur. J. Oper. Res. 2016, 250, 691–701. [Google Scholar] [CrossRef] [Green Version]
- Cannella, S.; Lopez-Campos, M.; Dominguez, R.; Ashayeri, J.; Miranda, P.A. A simulation model of a coordinated decentralized supply chain. Int. Trans. Oper. Res. 2015, 22, 735–756. [Google Scholar] [CrossRef]
- Goodarzi, M.; Saen, R.F. How to measure bullwhip effect by network data envelopment analysis? Comput. Ind. Eng. 2020, 139, 105431. [Google Scholar] [CrossRef]
- Fu, D.; Ionescu, C.M.; Aghezzaf, E.H.; De Keyser, R. Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management. Comput. Ind. Eng. 2014, 73, 21–31. [Google Scholar] [CrossRef]
- Bolarin, F.C.; Frutos, A.G.; Lise, A. Assessing the impact of prices fluctuation on demand distortion whitin a multi-echelon supply chain. Traffic Transp. 2011, 23, 131–140. [Google Scholar]
- Hussain, M.; Saber, H. Exploring the bullwhip effect using simulation and Taguchi experimental design. Int. J. Logist. Res. Appl. 2012, 15, 231–249. [Google Scholar] [CrossRef]
- Dominguez, R.; Framinan, J.M.; Cannella, S. Serial vs. divergent supply chain networks: A comparative analysis of the bullwhip effect. Int. J. Prod. Res. 2014, 52, 2194–2210. [Google Scholar] [CrossRef]
- Hofmann, E. Big data and supply chain decisions: The impact of volume, variety and velocity properties on the bullwhip effect. Int. J. Prod. Res. 2017, 55, 5108–5126. [Google Scholar] [CrossRef]
- Khan, M.H.; Ahmad, S. Ranking operational causes of bullwhip effect in supply chain using AHP: Perception of managers in FMCG sector. Metamorphosis 2016, 15, 79–90. [Google Scholar] [CrossRef]
- Dai, J.; Peng, S.; Li, S. Mitigation of bullwhip effect in supply chain inventory management model. Procedia Eng. 2017, 174, 1229–1234. [Google Scholar] [CrossRef]
- Pastore, E.; Alfieri, A.; Zotteri, G. An empirical investigation on the antecedents of the bullwhip effect: Evidence from the spare parts industry. Int. J. Prod. Econ. 2019, 209, 121–133. [Google Scholar] [CrossRef]
- Michna, Z.; Disney, S.M.; Nielsen, P. The impact of stochastic lead times on the bullwhip effect under correlated demand and moving average forecasts. Omega 2020, 93, 102033. [Google Scholar] [CrossRef] [Green Version]
- Dahlin, K.; Safstrom, O. Causes of the Bullwhip Effect: A Study of the Bullwhip Effect in the Volvo Group Service Market Logistics’ Supply Chain; Linköping University: Linköping, Sweden, 2021. [Google Scholar]
- Bhattacharya, R.; Bandyopadhyay, S. A review of the causes of bullwhip effect in a supply chain. Int. J. Adv. Manuf. Technol. 2011, 54, 1245–1261. [Google Scholar] [CrossRef]
- Nagel, R.N.; Dove, R. 21st Century Manufacturing Enterprise Strategy: An Industry-Led View; Diane Publishing: Collingdale, PA, USA, 1991. [Google Scholar]
- Christopher, M. The agile supply chain: Competing in volatile markets. Ind. Mark. Manag. 2000, 29, 37–44. [Google Scholar] [CrossRef] [Green Version]
- Balaji, M.; Velmurugan, V.; Subashree, C. TADS: An assessment methodology for agile supply chains. J. Appl. Res. Technol. 2015, 13, 504–509. [Google Scholar] [CrossRef] [Green Version]
- Gligor, D.M.; Holcomb, M.C. The role of logistics alliance orientation on forming the alliance structure: A conceptual framework. J. Transp. Manag. 2014, 17, 438–453. [Google Scholar]
- Meyer, S.; Newsome, D.; Fuller, T.; Newsome, K.; Ghezzi, P.M. Agility: What It Is, How to Measure It, and How to Use It. Behav. Anal. Pract. 2021, 14, 598–607. [Google Scholar] [CrossRef] [PubMed]
- Perera, S.; Soosay, C.; Sandhu, S. Does agility foster sustainability: Development of a framework from a supply chain perspective. In Proceedings of the 12th ANZAM Operations, Supply Chain and Services Management Symposium, Auckland, New Zealand, 3–4 July 2014. [Google Scholar]
- Bargshady, G.; Chegeni, A.; Kamranvand, S.; Zahraee, S.M. A Relational Study of Supply Chain Agility and Firms’ Performance in the Services Providers. Int. Rev. Manag. Mark. 2016, 2016, 38–42. [Google Scholar]
- Sangari, M.S.; Razmi, J.; Gunasekaran, A. Critical factors for achieving supply chain agility: Towards a comprehensive taxonomy. Int. J. Ind. Syst. Eng. 2016, 23, 290–310. [Google Scholar] [CrossRef]
- Wu, K.J.; Tseng, M.L.; Chiu, A.S.; Lim, M.K. Achieving competitive advantage through supply chain agility under uncertainty: A novel multi-criteria decision-making structure. Int. J. Prod. Econ. 2017, 190, 96–107. [Google Scholar] [CrossRef]
- Martinez-Sanchez, A.; Lahoz-Leo, F. Supply chain agility: A mediator for absorptive capacity. Balt. J. Manag. 2018, 13, 264–278. [Google Scholar] [CrossRef]
- Irfan, M.; Wang, M.; Akhtar, N. Enabling supply chain agility through process integration and supply flexibility: Evidence from the fashion industry. Asia Pac. J. Mark. Logist. 2020, 32, 519–547. [Google Scholar] [CrossRef]
- Rasyidi, R.A.; Kusumastuti, R.D. Supply chain agility assessment of an Indonesian humanitarian organization. J. Humanit. Logist. Supply Chain. Manag. 2020, 10, 629–652. [Google Scholar] [CrossRef]
- Shukor, A.A.A.; Newaz, M.S.; Rahman, M.K.; Taha, A.Z. Supply chain integration and its impact on supply chain agility and organizational flexibility in manufacturing firms. Int. J. Emerg. Mark. 2020, 16, 1721–1744. [Google Scholar] [CrossRef]
- Rehman, A.U.; Al-Zabidi, A.; AlKahtani, M.; Umer, U.; Usmani, Y.S. Assessment of supply chain agility to foster sustainability: Fuzzy-DSS for a Saudi manufacturing organization. Processes 2020, 8, 577. [Google Scholar] [CrossRef]
- Al-Zabidi, A.; Rehman, A.U.; Alkahtani, M. An approach to assess sustainable supply chain agility for a manufacturing organization. Sustainability 2021, 13, 1752. [Google Scholar] [CrossRef]
- Jindal, A.; Sharma, S.K.; Sangwan, K.S.; Gupta, G. Modelling supply chain agility antecedents using fuzzy dematel. Procedia CIRP 2021, 98, 436–441. [Google Scholar] [CrossRef]
- Aprilia, A.; Laili, F.; Setyowati, P.B.; Waringga, K.F. The effect of supplier innovation on supply chain agility: Evidence from coffee shops in Malang area. IOP Conf. Ser. Earth Environ. Sci. 2021, 733, 012059. [Google Scholar] [CrossRef]
- Haq, M.A.; Hameed, I.; Raheem, A. An empirical analysis of behavioral flexibility, relationship integration and strategic flexibility in supply chain agility: Insights from smes sector of pakistan. South Asian J. Manag. 2020, 14, 104–121. [Google Scholar] [CrossRef] [Green Version]
- Rasi, R.E.; Abbasi, R.; Hatami, D. The effect of supply chain agility based on supplier innovation and environmental uncertainty. Int. J. Supply Oper. Manag. 2019, 6, 94–109. [Google Scholar]
- Jermsittiparsert, K.; Srisawat, S. The Role of Supply Chain Visibility in Enhancing Supply Chain Agility. Int. J. Innov. Creat. Chang. 2019, 5, 485–501. [Google Scholar]
- Chan, A.T.; Ngai, E.W.; Moon, K.K. The effects of strategic and manufacturing flexibilities and supply chain agility on firm performance in the fashion industry. Eur. J. Oper. Res. 2017, 259, 486–499. [Google Scholar] [CrossRef]
- Yang, J. Supply chain agility: Securing performance for Chinese manufacturers. Int. J. Prod. Econ. 2014, 150, 104–113. [Google Scholar] [CrossRef]
- Pandey, V.; Garg, S. Analysis of interaction among the enablers of agility in supply chain. J. Adv. Manag. Res. 2009, 6, 99–114. [Google Scholar] [CrossRef]
- Russom, P. Big data analytics. TDWI Best Pract. Rep. Fourth Quart. 2011, 19, 1–34. [Google Scholar]
- Wang, G.; Gunasekaran, A.; Ngai, E.W. Distribution network design with big data: Model and analysis. Ann. Oper. Res. 2018, 270, 539–551. [Google Scholar] [CrossRef]
- Tan, K.H.; Zhan, Y.; Ji, G.; Ye, F.; Chang, C. Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. Int. J. Prod. Econ. 2015, 165, 223–233. [Google Scholar] [CrossRef]
- Sheffi, Y. Preparing for disruptions through early detection. MIT Sloan Manag. Rev. 2015, 57, 31–42. [Google Scholar]
- Akter, S.; Wamba, S.F.; Gunasekaran, A.; Dubey, R.; Childe, S.J. How to improve firm performance using big data analytics capability and business strategy alignment? Int. J. Prod. Econ. 2016, 182, 113–131. [Google Scholar] [CrossRef] [Green Version]
- Ramanathan, U.; Subramanian, N.; Parrott, G. Role of social media in retail network operations and marketing to enhance customer satisfaction. Int. J. Oper. Prod. Manag. 2017, 37, 105–123. [Google Scholar] [CrossRef] [Green Version]
- Cakici, O.E.; Groenevelt, H.; Seidmann, A. Using RFID for the management of pharmaceutical inventory—system optimization and shrinkage control. Decis. Support Syst. 2011, 51, 842–852. [Google Scholar] [CrossRef]
- Zhong, R.Y.; Huang, G.Q.; Lan, S.; Dai, Q.Y.; Chen, X.; Zhang, T. A big data approach for logistics trajectory discovery from RFID-enabled production data. Int. J. Prod. Econ. 2015, 165, 260–272. [Google Scholar] [CrossRef]
- Mishra, N.; Singh, A. Use of twitter data for waste minimisation in beef supply chain. Ann. Oper. Res. 2018, 270, 337–359. [Google Scholar] [CrossRef] [Green Version]
- Govindan, K.; Khodaverdi, R.; Jafarian, A. A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J. Clean. Prod. 2013, 47, 345–354. [Google Scholar] [CrossRef]
- Chan, F.T.; Prakash, A.; Mishra, N. Priority-based scheduling in flexible system using AIS with FLC approach. Int. J. Prod. Res. 2013, 51, 4880–4895. [Google Scholar] [CrossRef]
- Govindan, K.; Soleimani, H.; Kannan, D. Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. Eur. J. Operat. Res. 2015, 240, 603–626. [Google Scholar] [CrossRef] [Green Version]
- Schoenherr, T.; Speier-Pero, C. Data science, predictive analytics, and big data in supply chain management: Current state and future potential. J. Bus. Logist. 2015, 36, 120–132. [Google Scholar] [CrossRef]
- Govindan, K.; Kadziński, M.; Sivakumar, R. Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. Omega 2017, 71, 129–145. [Google Scholar] [CrossRef]
- Kannan, D. Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process. Int. J. Prod. Econ. 2018, 195, 391–418. [Google Scholar] [CrossRef]
- Chen, D.Q.; Preston, D.S.; Swink, M. How the use of big data analytics affects value creation in supply chain management. J. Manag. Inf. Syst. 2015, 32, 4–39. [Google Scholar] [CrossRef]
- Queiroz, M.M.; Telles, R. Big data analytics in supply chain and logistics: An empirical approach. Int. J. Logist. Manag. 2018, 29, 767–783. [Google Scholar] [CrossRef]
- Arunachalam, D.; Kumar, N.; Kawalek, J.P. Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transp. Res. Part. E Logist. Transp. Rev. 2018, 114, 416–436. [Google Scholar] [CrossRef]
- Moktadir, M.A.; Ali, S.M.; Paul, S.K.; Shukla, N. Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh. Comput. Ind. Eng. 2019, 128, 1063–1075. [Google Scholar] [CrossRef]
- Ali, S.; Poulova, P.; Yasmin, F.; Danish, M.; Akhtar, W.; Usama Javed, H.M. How Big Data Analytics Boosts Organizational Performance: The Mediating Role of the Sustainable Product Development. J. Open Innov. Technol. Market. Complex. 2020, 6, 190. [Google Scholar]
- Hassen, A.; Chen, B. Big Data Analytics for Agriculture Input Supply Chain in Ethiopia: Supply Chain Management Professionals Perspective; Linnaeus University: Växjö, Sweden, 2020. [Google Scholar]
- Raman, S.; Patwa, N.; Niranjan, I.; Ranjan, U.; Moorthy, K.; Mehta, A. Impact of big data on supply chain management. Int. J. Logist. Res. Appl. 2018, 21, 579–596. [Google Scholar] [CrossRef]
- Zhong, R.Y.; Newman, S.T.; Huang, G.Q.; Lan, S. Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Comput. Ind. Eng. 2016, 101, 572–591. [Google Scholar] [CrossRef]
- Lamba, K.; Singh, S.P. Modeling big data enablers for operations and supply chain management. Int. J. Logist. Manag. 2018, 29, 629–658. [Google Scholar] [CrossRef]
- Lai, Y.; Sun, H.; Ren, J. Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. Int. J. Logist. Manag. 2018, 29, 676–703. [Google Scholar] [CrossRef]
- Kim, D.; Lee, R.P. Systems collaboration and strategic collaboration: Their impacts on supply chain responsiveness and market performance. Decis. Sci. 2010, 41, 955–981. [Google Scholar] [CrossRef]
- Chen, J.V.; Yen, D.C.; Rajkumar, T.M.; Tomochko, N.A. The antecedent factors on trust and commitment in supply chain relationships. Comput. Stand. Interfaces 2011, 33, 262–270. [Google Scholar] [CrossRef]
- Zhang, Y.; Wang, Y.; Wu, L. Research on demand-driven leagile supply chain operation model: A simulation based on anylogic in system engineering. Syst. Eng. Procedia 2012, 3, 249–258. [Google Scholar] [CrossRef] [Green Version]
- Lin, W.J.; Jiang, Z.B.; Liu, R.; Wang, L. The bullwhip effect in hybrid supply chain. Int. J. Prod. Res. 2014, 52, 2062–2084. [Google Scholar] [CrossRef]
- Lee, S.Y.; Klassen, R.D.; Furlan, A.; Vinelli, A. The green bullwhip effect: Transferring environmental requirements along a supply chain. Int. J. Prod. Econ. 2014, 156, 39–51. [Google Scholar] [CrossRef]
- Seles, B.M.R.P.; de Sousa Jabbour, A.B.L.; Jabbour, C.J.C.; Dangelico, R.M. The green bullwhip effect, the diffusion of green supply chain practices, and institutional pressures: Evidence from the automotive sector. Int. J. Prod. Econ. 2016, 182, 342–355. [Google Scholar] [CrossRef]
- Sabbaghnia, A.; Razmi, J.; Babazadeh, R.; Moshiri, B. Reducing the Bullwhip effect in a supply chain network by application of optimal control theory. RAIRO-Oper. Res. 2018, 52, 1377–1396. [Google Scholar] [CrossRef] [Green Version]
- Ojha, D.; Sahin, F.; Shockley, J.; Sridharan, S.V. Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type. Int. J. Prod. Econ. 2019, 208, 529–543. [Google Scholar] [CrossRef]
- Ran, W.; Wang, Y.; Yang, L.; Liu, S. Coordination Mechanism of Supply Chain considering the Bullwhip Effect under Digital Technologies. Math. Probl. Eng. 2020, 2020, 3217927. [Google Scholar] [CrossRef]
- Saffari Darberazi, A.; Malekinejad, P.; Ziaeian, M. Design a conceptual model of bullwhip effect reduction strategies in closed loop supply chains (Case study: Automotive oil production industries). J. Strateg. Manag. Stud. 2021, 12. [Google Scholar]
- Dubey, R.; Altay, N.; Gunasekaran, A.; Blome, C.; Papadopoulos, T.; Childe, S.J. Supply chain agility, adaptability and alignment: Empirical evidence from the Indian auto components industry. Int. J. Oper. Prod. Manag. 2018, 38, 129–148. [Google Scholar] [CrossRef]
- Srimarut, T.; Mekhum, W. From Supply Chain Connectivity (SCC) to Supply Chain Agility (SCA), Adaptability and Alignment: Mediating Role of Big Data Analytics Capability. Int. J. Supply Chain. Manag. 2020, 9, 183–189. [Google Scholar]
- Haber, N.; Fargnoli, M.; Sakao, T. Integrating QFD for product-service systems with the Kano model and fuzzy AHP. Total. Qual. Manag. Bus. Excell. 2020, 31, 929–954. [Google Scholar] [CrossRef]
- Lin, Y.; Pekkarinen, S. QFD-based modular logistics service design. J. Bus. Ind. Mark. 2011, 26, 344–356. [Google Scholar] [CrossRef]
- Wang, X.; Fang, H.; Song, W.Y. Technical attribute prioritisation in QFD based on cloud model and grey relational analysis. Int. J. Prod. Res. 2019, 58, 5751–5768. [Google Scholar] [CrossRef]
- Zhang, Z.; Liao, H.; Chang, J.; Al-barakati, A. Green-duilding-material supplier selection with a rough-set-enhanced quality function deployment. Sustainability 2019, 11, 7153. [Google Scholar] [CrossRef] [Green Version]
- Murray, T.J.; Pipino, L.L.; van Gigch, J.P. A pilot study of fuzzy set modification of Delphi. Hum. Syst. Manag. 1985, 5, 76–80. [Google Scholar] [CrossRef]
- Rejab, M.M.; Azmi, N.F.M.; Chuprat, S. Fuzzy Delphi Method for evaluating HyTEE model. Int. J. Adv. Comput. Sci. Appl. 2019, 10, 529–535. [Google Scholar]
- Warfield, J.W. Developing interconnected matrices in structural modeling. IEEE Transcr. Syst. Men Cybern. 1974, 4, 51–81. [Google Scholar]
- Saxena, J.P.; Vrat, P. Scenario building: A critical study of energy conservation in the Indian cement industry. Technol. Forecast. Soc. Chang. 1992, 41, 121–146. [Google Scholar] [CrossRef]
- Ragade, R.K. Fuzzy interpretive structural modeling. Cybern. Syst. 1976, 6, 189–211. [Google Scholar] [CrossRef]
- Saxena, J.P.; Vrat, P. Policy and Strategy Formulation: An Application of Flexible Systems Methodology; GIFT Pub: New Delhi, India, 2006. [Google Scholar]
- Saaty, T. Decision Making with Dependence and Feedback: The Analytic Network Process: The Organization and Prioritization of Complexity, 1st ed.; RWS Publications: Pittsburgh, PA, USA, 1996. [Google Scholar]
- Yang, Y.P.O.; Shieh, H.M.; Leu, J.D.; Tzeng, G.H. A novel hybrid MCDM model combined with DEMATEL and ANP with applications. Int. J. Oper. Res. 2008, 5, 160–168. [Google Scholar]
- Valmohammadi, C. Using the analytic network process in business strategy selection: A Case Study. Aust. J. Basic Appl. Sci. 2010, 4, 5205–5213. [Google Scholar]
- Deng, J. Control problems of grey system. Syst. Control. Lett. 1982, 1, 288–294. [Google Scholar]
- Deng, J.L. Introduction to grey system theory. J. Grey Syst. 1989, 1, 1–24. [Google Scholar]
- Kuo, Y.; Yang, T.; Huang, G.W. The use of grey relational analysis in solving multiple attribute decision-making problems. Comput. Ind. Eng. 2008, 55, 80–93. [Google Scholar] [CrossRef]
- Chang, A.Y.; Cheng, Y.T. Analysis model of the sustainability development of manufacturing small and medium-sized enterprises in Taiwan. J. Clean. Prod. 2019, 207, 458–473. [Google Scholar] [CrossRef]
Assessment Scale | Definition | Instruction |
---|---|---|
1 | Equal important | Both are of equal importance. |
3 | A little important | As a rule of thumb, one indicator is slightly more important. |
5 | quite important | As a rule of thumb, one indicator matters. |
7 | Very important | As it turns out, a certain indicator is very important. |
9 | Absolutely important | There is ample evidence that one metric is absolutely important. |
2, 4, 6, 8 | The median of adjacent scales | A compromise option |
(n) | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
R.I. | 0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 | 1.48 |
Respondent | Years of Experience |
---|---|
R & D manager | 12 years |
IT specialist | 12 years |
Supply chain manger | 10 years |
Production manager | 12 years |
Quality manager | 15 years |
Supply chain manger | 15 years |
NO. | Factors | Gi |
---|---|---|
BEF 1 | Information asymmetry | 7.56 |
BEF 2 | Batch ordering strategy | 7.22 |
BEF 3 | Demand forecasting | 6.32 |
BEF 4 | Batch size | 6.20 |
BEF 5 | The multiplier effect | 5.87 |
BEF 6 | Price fluctuation | 5.77 |
BEF 7 | Lack of coordination in supply chain | 5.76 |
BEF 8 | The company process | 5.71 |
BEF 9 | A shortage of game | 5.64 |
BEF 10 | Factory capacity constraints | 5.58 |
BEF 11 | Inventory policy | 5.56 |
BEF 12 | lead time | 5.56 |
BEF 13 | Local optimization without Global vision | 5.42 |
BEF 14 | Lack of synchronization | 5.34 |
NO. | Indicators | Gi |
---|---|---|
SCAI 1 | Improve data accuracy | 8.12 |
SCAI 2 | Improve information transparency in the upstream and downstream of the supply chain | 7.78 |
SCAI 3 | Actively build a shared information platform with partners | 7.47 |
SCAI 4 | Improve market sensitivity | 7.41 |
SCAI 5 | Jointly manage inventory with suppliers | 7.36 |
SCAI 6 | Improve logistics capability | 7.22 |
SCAI 7 | Supplier innovation | 7.17 |
SCAI 8 | Strategic flexibility | 6.91 |
SCAI 9 | Using information technology | 6.79 |
SCAI 10 | Automation | 6.75 |
SCAI 11 | Improve service quality | 6.69 |
SCAI 12 | Timely detecting of threats in the environment | 6.48 |
SCAI 13 | Integrate supply chain partners | 6.42 |
SCAI 14 | Plan and form long-term cooperative partners with suppliers | 6.41 |
BEF1 | BEF2 | BEF3 | BEF4 | BEF5 | BEF6 | BEF7 | BEF8 | BEF9 | BEF10 | BEF11 | BEF12 | BEF13 | BEF14 | Weight Value | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BEF1 | 0.53 | 0 | 0 | 0 | 0 | 0 | 0.27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.013 |
BEF2 | 0 | 0.40 | 0 | 0.19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006 |
BEF3 | 0 | 0 | 0.51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007 |
BEF4 | 0 | 0 | 0 | 0.13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.001 |
BEF5 | 0 | 0 | 0 | 0.14 | 1.00 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.012 |
BEF6 | 0.47 | 0 | 0.49 | 0 | 0 | 0.63 | 0 | 0 | 0 | 0 | 0.33 | 0 | 0 | 0 | 0.027 |
BEF7 | 0 | 0 | 0 | 0 | 0 | 0.79 | 0.41 | 0 | 0 | 0 | 0 | 0.68 | 0 | 0 | 0.024 |
BEF8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.00 | 0 | 0 | 0 | 0 | 0 | 0.77 | 0.018 |
BEF9 | 0 | 0 | 0 | 0.15 | 0 | 0 | 0 | 0 | 1.00 | 0 | 0.61 | 0 | 0 | 0 | 0.020 |
BEF10 | 0 | 0.60 | 0 | 0.19 | 0 | 0 | 0 | 0 | 0 | 1.00 | 0 | 0 | 0 | 0 | 0.018 |
BEF11 | 0 | 0 | 0 | 0.20 | 0 | 0 | 0 | 0 | 0 | 0 | 0.45 | 0 | 0 | 0 | 0.007 |
BEF12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.58 | 0 | 0 | 0.005 |
BEF13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1.00 | 0 | 0.013 |
BEF14 | 0 | 0 | 0 | 0 | 0 | 0 | 0.67 | 0 | 0 | 0 | 0 | 0 | 0 | 0.38 | 0.016 |
SCAI1 | SCAI2 | SCAI3 | SCAI4 | SCAI5 | SCAI6 | SCAI7 | SCAI8 | SCAI9 | SCAI10 | SCAI11 | SCAI12 | SCAI13 | SCAI14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SCAI1 | 1.00 | 6.33 | 5.50 | 6.17 | 5.00 | 5.17 | 4.50 | 4.50 | 3.00 | 4.00 | 7.00 | 5.83 | 5.00 | 5.00 |
SCAI2 | 6.33 | 1.00 | 6.17 | 6.00 | 5.33 | 5.00 | 5.00 | 3.67 | 6.33 | 5.00 | 3.67 | 4.83 | 5.83 | 5.50 |
SCAI3 | 5.50 | 6.17 | 1.00 | 5.17 | 5.00 | 5.50 | 6.17 | 6.50 | 5.83 | 4.00 | 5.50 | 5.00 | 5.83 | 5.67 |
SCAI4 | 6.17 | 6.00 | 5.17 | 1.00 | 4.83 | 6.50 | 5.17 | 5.33 | 5.00 | 3.50 | 5.83 | 7.33 | 5.17 | 4.33 |
SCAI5 | 5.00 | 5.33 | 5.00 | 4.83 | 1.00 | 4.17 | 6.00 | 4.67 | 5.50 | 5.83 | 5.50 | 4.50 | 4.00 | 5.00 |
SCAI6 | 5.17 | 5.00 | 5.50 | 6.50 | 4.17 | 1.00 | 4.67 | 4.50 | 4.17 | 5.17 | 3.50 | 4.17 | 4.00 | 5.50 |
SCAI7 | 4.50 | 5.00 | 6.17 | 5.17 | 6.00 | 4.67 | 1.00 | 4.17 | 3.17 | 3.67 | 3.17 | 3.50 | 3.83 | 5.00 |
SCAI8 | 4.50 | 3.67 | 6.50 | 5.33 | 4.67 | 4.50 | 4.17 | 1.00 | 4.83 | 3.33 | 5.50 | 7.17 | 4.17 | 4.17 |
SCAI9 | 3.00 | 6.33 | 5.83 | 5.00 | 5.50 | 4.17 | 3.17 | 4.83 | 1.00 | 7.83 | 5.50 | 6.33 | 5.83 | 5.17 |
SCAI10 | 4.00 | 5.00 | 4.00 | 3.50 | 5.83 | 5.17 | 3.67 | 3.33 | 7.83 | 1.00 | 4.17 | 5.50 | 3.83 | 4.00 |
SCAI11 | 7.00 | 3.67 | 5.50 | 5.83 | 5.50 | 3.50 | 3.17 | 5.50 | 5.50 | 4.17 | 1.00 | 5.50 | 3.83 | 5.50 |
SCAI12 | 5.83 | 4.83 | 5.00 | 7.33 | 4.50 | 4.17 | 3.50 | 7.17 | 6.33 | 5.50 | 5.50 | 1.00 | 6.50 | 5.17 |
SCAI13 | 5.00 | 5.83 | 5.83 | 5.17 | 4.00 | 4.00 | 3.83 | 4.17 | 5.83 | 3.83 | 3.83 | 6.50 | 1.00 | 5.83 |
SCAI14 | 5.00 | 5.50 | 5.67 | 4.33 | 5.00 | 5.50 | 5.00 | 4.17 | 5.17 | 4.00 | 5.50 | 5.17 | 5.83 | 1.00 |
SCAI1 | SCAI2 | SCAI3 | SCAI4 | SCAI5 | SCAI6 | SCAI7 | SCAI8 | SCAI9 | SCAI10 | SCAI11 | SCAI12 | SCAI13 | SCAI14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BEF1 | 7.33 | 5.33 | 6.17 | 3.33 | 3.17 | 4.50 | 4.83 | 3.50 | 5.50 | 4.50 | 2.50 | 4.00 | 4.67 | 4.67 |
BEF2 | 4.33 | 5.83 | 5.33 | 4.83 | 7.33 | 6.33 | 5.33 | 5.50 | 4.00 | 4.33 | 4.67 | 4.83 | 3.83 | 4.83 |
BEF3 | 5.17 | 4.33 | 4.17 | 6.50 | 5.67 | 5.67 | 5.33 | 5.00 | 4.67 | 5.17 | 3.17 | 5.50 | 5.33 | 3.50 |
BEF4 | 4.00 | 4.67 | 4.50 | 6.00 | 6.33 | 6.00 | 4.83 | 5.67 | 4.50 | 3.67 | 6.33 | 4.83 | 3.50 | 4.17 |
BEF5 | 4.67 | 4.00 | 3.83 | 4.83 | 4.67 | 4.83 | 4.17 | 3.33 | 2.67 | 4.17 | 2.33 | 5.17 | 4.67 | 3.33 |
BEF6 | 4.83 | 7.00 | 6.83 | 7.17 | 3.50 | 6.33 | 6.33 | 6.67 | 4.67 | 5.83 | 5.17 | 7.17 | 5.50 | 5.83 |
BEF7 | 6.00 | 7.67 | 4.67 | 5.83 | 4.83 | 6.33 | 5.00 | 7.17 | 5.17 | 4.67 | 4.83 | 6.50 | 6.00 | 4.17 |
BEF8 | 6.00 | 4.17 | 6.00 | 5.33 | 4.17 | 4.83 | 4.50 | 3.17 | 3.67 | 3.67 | 3.50 | 4.00 | 6.00 | 5.67 |
BEF9 | 3.33 | 4.50 | 3.50 | 7.17 | 4.50 | 5.83 | 6.17 | 5.17 | 4.33 | 4.67 | 3.67 | 4.50 | 4.17 | 5.50 |
BEF10 | 4.33 | 4.00 | 3.83 | 6.00 | 5.17 | 5.67 | 7.00 | 7.17 | 5.00 | 3.67 | 3.33 | 5.33 | 3.83 | 5.17 |
BEF11 | 5.50 | 5.17 | 4.33 | 4.67 | 5.83 | 4.50 | 6.67 | 3.50 | 4.83 | 3.33 | 3.00 | 3.00 | 3.00 | 4.67 |
BEF12 | 5.17 | 5.17 | 6.33 | 4.33 | 4.33 | 4.83 | 4.50 | 4.00 | 3.33 | 3.00 | 4.83 | 4.50 | 5.33 | 4.17 |
BEF13 | 4.17 | 6.83 | 3.50 | 6.00 | 4.50 | 4.17 | 4.67 | 4.83 | 5.50 | 3.83 | 4.67 | 5.33 | 4.67 | 4.67 |
BEF14 | 7.33 | 7.50 | 5.83 | 5.17 | 5.50 | 5.67 | 5.17 | 5.33 | 4.67 | 5.33 | 6.00 | 5.00 | 7.67 | 6.67 |
SCAI1 | SCAI2 | SCAI3 | SCAI4 | SCAI5 | SCAI6 | SCAI7 | SCAI8 | SCAI9 | SCAI10 | SCAI11 | SCAI12 | SCAI13 | SCAI14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BEF1 | 463.36 | 541.25 | 572.61 | 533.61 | 501.17 | 470.25 | 451.97 | 509.78 | 502.86 | 486.19 | 513.11 | 552.17 | 512.56 | 500.03 |
BEF2 | 558.50 | 597.33 | 653.50 | 604.11 | 547.22 | 531.92 | 531.14 | 576.28 | 597.42 | 558.17 | 578.42 | 616.17 | 583.33 | 569.64 |
BEF3 | 553.92 | 621.28 | 668.94 | 613.39 | 569.17 | 545.67 | 528.36 | 593.42 | 589.92 | 556.89 | 585.25 | 640.83 | 582.50 | 581.33 |
BEF4 | 541.39 | 574.03 | 624.08 | 577.75 | 526.56 | 498.44 | 504.44 | 555.47 | 559.08 | 530.03 | 538.97 | 608.19 | 549.25 | 546.75 |
BEF5 | 473.94 | 533.89 | 567.97 | 526.03 | 481.67 | 462.92 | 463.64 | 518.81 | 516.97 | 481.06 | 508.92 | 540.89 | 506.72 | 495.08 |
BEF6 | 628.00 | 685.28 | 738.17 | 685.67 | 648.42 | 610.31 | 582.56 | 666.03 | 672.39 | 618.00 | 646.75 | 701.47 | 665.44 | 651.86 |
BEF7 | 579.33 | 635.39 | 702.56 | 642.42 | 598.39 | 563.36 | 545.22 | 604.44 | 623.22 | 581.28 | 609.33 | 665.89 | 613.94 | 610.56 |
BEF8 | 502.64 | 562.28 | 591.53 | 551.25 | 508.56 | 486.64 | 484.03 | 539.11 | 536.78 | 504.19 | 529.64 | 581.47 | 525.64 | 520.25 |
BEF9 | 518.75 | 570.53 | 618.00 | 558.19 | 531.44 | 503.42 | 485.50 | 544.92 | 551.86 | 512.33 | 538.69 | 590.67 | 542.75 | 526.39 |
BEF10 | 527.47 | 574.22 | 630.06 | 573.78 | 527.92 | 508.19 | 492.06 | 542.53 | 550.81 | 524.97 | 552.75 | 591.42 | 552.56 | 542.31 |
BEF11 | 497.92 | 565.03 | 617.03 | 565.67 | 521.94 | 500.53 | 480.56 | 548.25 | 525.33 | 520.89 | 536.92 | 584.22 | 543.28 | 529.33 |
BEF12 | 504.42 | 556.22 | 596.50 | 558.75 | 512.58 | 490.36 | 479.22 | 543.86 | 542.28 | 513.06 | 521.08 | 566.64 | 532.06 | 529.75 |
BEF13 | 533.69 | 580.14 | 639.08 | 576.25 | 542.17 | 516.81 | 502.19 | 560.31 | 572.25 | 539.97 | 557.83 | 605.47 | 568.28 | 552.06 |
BEF14 | 614.69 | 679.86 | 746.03 | 681.92 | 630.00 | 599.89 | 580.75 | 651.72 | 658.78 | 620.69 | 645.08 | 706.67 | 650.00 | 646.31 |
SCAI1 | SCAI2 | SCAI3 | SCAI4 | SCAI5 | SCAI6 | SCAI7 | SCAI8 | SCAI9 | SCAI10 | SCAI11 | SCAI12 | SCAI13 | SCAI14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BEF1 | 0.000 | 0.049 | 0.027 | 0.048 | 0.117 | 0.050 | 0.000 | 0.000 | 0.000 | 0.038 | 0.030 | 0.070 | 0.037 | 0.032 |
BEF2 | 0.578 | 0.419 | 0.503 | 0.489 | 0.393 | 0.468 | 0.606 | 0.426 | 0.558 | 0.563 | 0.504 | 0.469 | 0.483 | 0.476 |
BEF3 | 0.550 | 0.577 | 0.593 | 0.547 | 0.525 | 0.561 | 0.585 | 0.535 | 0.514 | 0.554 | 0.554 | 0.622 | 0.477 | 0.550 |
BEF4 | 0.474 | 0.265 | 0.330 | 0.324 | 0.269 | 0.241 | 0.402 | 0.292 | 0.332 | 0.358 | 0.218 | 0.419 | 0.268 | 0.330 |
BEF5 | 0.064 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.089 | 0.058 | 0.083 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
BEF6 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
BEF7 | 0.704 | 0.670 | 0.791 | 0.729 | 0.700 | 0.681 | 0.714 | 0.606 | 0.710 | 0.732 | 0.729 | 0.778 | 0.676 | 0.737 |
BEF8 | 0.239 | 0.188 | 0.138 | 0.158 | 0.161 | 0.161 | 0.245 | 0.188 | 0.200 | 0.169 | 0.150 | 0.253 | 0.119 | 0.161 |
BEF9 | 0.336 | 0.242 | 0.294 | 0.201 | 0.299 | 0.275 | 0.257 | 0.225 | 0.289 | 0.228 | 0.216 | 0.310 | 0.227 | 0.200 |
BEF10 | 0.389 | 0.266 | 0.365 | 0.299 | 0.277 | 0.307 | 0.307 | 0.210 | 0.283 | 0.321 | 0.318 | 0.315 | 0.289 | 0.301 |
BEF11 | 0.210 | 0.206 | 0.288 | 0.248 | 0.242 | 0.255 | 0.219 | 0.246 | 0.133 | 0.291 | 0.203 | 0.270 | 0.230 | 0.218 |
BEF12 | 0.249 | 0.148 | 0.168 | 0.205 | 0.185 | 0.186 | 0.209 | 0.218 | 0.233 | 0.234 | 0.088 | 0.160 | 0.160 | 0.221 |
BEF13 | 0.427 | 0.306 | 0.418 | 0.315 | 0.363 | 0.366 | 0.385 | 0.323 | 0.409 | 0.430 | 0.355 | 0.402 | 0.388 | 0.363 |
BEF14 | 0.919 | 0.964 | 1.046 | 0.977 | 0.890 | 0.929 | 0.986 | 0.908 | 0.920 | 1.020 | 0.988 | 1.032 | 0.903 | 0.965 |
SCAI1 | SCAI2 | SCAI3 | SCAI4 | SCAI5 | SCAI6 | SCAI7 | SCAI8 | SCAI9 | SCAI10 | SCAI11 | SCAI12 | SCAI13 | SCAI14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BEF1 | 1.000 | 0.951 | 0.973 | 0.952 | 0.883 | 0.950 | 1.000 | 1.000 | 1.000 | 0.962 | 0.970 | 0.930 | 0.963 | 0.968 |
BEF2 | 0.422 | 0.581 | 0.497 | 0.511 | 0.607 | 0.532 | 0.394 | 0.574 | 0.442 | 0.437 | 0.496 | 0.531 | 0.517 | 0.524 |
BEF3 | 0.450 | 0.423 | 0.407 | 0.453 | 0.475 | 0.439 | 0.415 | 0.465 | 0.486 | 0.446 | 0.446 | 0.378 | 0.523 | 0.450 |
BEF4 | 0.526 | 0.735 | 0.670 | 0.676 | 0.731 | 0.759 | 0.598 | 0.708 | 0.668 | 0.642 | 0.782 | 0.581 | 0.732 | 0.670 |
BEF5 | 0.936 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.911 | 0.942 | 0.917 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
BEF6 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
BEF7 | 0.296 | 0.330 | 0.209 | 0.271 | 0.300 | 0.319 | 0.286 | 0.394 | 0.290 | 0.268 | 0.271 | 0.222 | 0.324 | 0.263 |
BEF8 | 0.761 | 0.812 | 0.862 | 0.842 | 0.839 | 0.839 | 0.755 | 0.812 | 0.800 | 0.831 | 0.850 | 0.747 | 0.881 | 0.839 |
BEF9 | 0.664 | 0.758 | 0.706 | 0.799 | 0.701 | 0.725 | 0.743 | 0.775 | 0.711 | 0.772 | 0.784 | 0.690 | 0.773 | 0.800 |
BEF10 | 0.611 | 0.734 | 0.635 | 0.701 | 0.723 | 0.693 | 0.693 | 0.790 | 0.717 | 0.679 | 0.682 | 0.685 | 0.711 | 0.699 |
BEF11 | 0.790 | 0.794 | 0.712 | 0.752 | 0.758 | 0.745 | 0.781 | 0.754 | 0.867 | 0.709 | 0.797 | 0.730 | 0.770 | 0.782 |
BEF12 | 0.751 | 0.852 | 0.832 | 0.795 | 0.815 | 0.814 | 0.791 | 0.782 | 0.767 | 0.766 | 0.912 | 0.840 | 0.840 | 0.779 |
BEF13 | 0.573 | 0.694 | 0.582 | 0.685 | 0.637 | 0.634 | 0.615 | 0.677 | 0.591 | 0.570 | 0.645 | 0.598 | 0.612 | 0.637 |
BEF14 | 0.081 | 0.036 | 0.046 | 0.023 | 0.110 | 0.071 | 0.014 | 0.092 | 0.080 | 0.020 | 0.012 | 0.032 | 0.097 | 0.035 |
SCAI1 | SCAI2 | SCAI3 | SCAI4 | SCAI5 | SCAI6 | SCAI7 | SCAI8 | SCAI9 | SCAI10 | SCAI11 | SCAI12 | SCAI13 | SCAI14 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BEF1 | 0.333 | 0.345 | 0.340 | 0.344 | 0.362 | 0.345 | 0.333 | 0.333 | 0.333 | 0.342 | 0.340 | 0.350 | 0.342 | 0.340 |
BEF2 | 0.542 | 0.463 | 0.501 | 0.495 | 0.452 | 0.485 | 0.559 | 0.465 | 0.531 | 0.534 | 0.502 | 0.485 | 0.491 | 0.488 |
BEF3 | 0.526 | 0.542 | 0.551 | 0.525 | 0.513 | 0.533 | 0.546 | 0.518 | 0.507 | 0.528 | 0.528 | 0.570 | 0.489 | 0.526 |
BEF4 | 0.487 | 0.405 | 0.427 | 0.425 | 0.406 | 0.397 | 0.455 | 0.414 | 0.428 | 0.438 | 0.390 | 0.463 | 0.406 | 0.427 |
BEF5 | 0.348 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.354 | 0.347 | 0.353 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 |
BEF6 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
BEF7 | 0.628 | 0.603 | 0.705 | 0.649 | 0.625 | 0.611 | 0.636 | 0.559 | 0.633 | 0.651 | 0.648 | 0.693 | 0.606 | 0.655 |
BEF8 | 0.396 | 0.381 | 0.367 | 0.373 | 0.373 | 0.373 | 0.399 | 0.381 | 0.385 | 0.376 | 0.370 | 0.401 | 0.362 | 0.373 |
BEF9 | 0.430 | 0.397 | 0.415 | 0.385 | 0.416 | 0.408 | 0.402 | 0.392 | 0.413 | 0.393 | 0.389 | 0.420 | 0.393 | 0.385 |
BEF10 | 0.450 | 0.405 | 0.440 | 0.416 | 0.409 | 0.419 | 0.419 | 0.387 | 0.411 | 0.424 | 0.423 | 0.422 | 0.413 | 0.417 |
BEF11 | 0.388 | 0.386 | 0.413 | 0.399 | 0.397 | 0.402 | 0.390 | 0.399 | 0.366 | 0.414 | 0.386 | 0.406 | 0.394 | 0.390 |
BEF12 | 0.400 | 0.370 | 0.375 | 0.386 | 0.380 | 0.381 | 0.387 | 0.390 | 0.394 | 0.395 | 0.354 | 0.373 | 0.373 | 0.391 |
BEF13 | 0.466 | 0.419 | 0.462 | 0.422 | 0.440 | 0.441 | 0.448 | 0.425 | 0.458 | 0.467 | 0.437 | 0.455 | 0.450 | 0.440 |
BEF14 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 | 0.333 |
NO. | Factors | Correlation Degree | Ranking |
---|---|---|---|
SCAI 1 | Improve data accuracy | 0.0981 | 3 |
SCAI 2 | Improve information transparency in the upstream and downstream of the supply chain | 0.0944 | 13 |
SCAI 3 | Actively build a shared information platform with partners | 0.0988 | 1 |
SCAI 4 | Improve market sensitivity | 0.0957 | 9 |
SCAI 5 | Jointly manage inventory with suppliers | 0.0956 | 10 |
SCAI 6 | Improve logistics capability | 0.0955 | 11 |
SCAI 7 | Supplier innovation | 0.0972 | 4 |
SCAI 8 | Strategic flexibility | 0.0931 | 14 |
SCAI 9 | Using information technology | 0.0964 | 6 |
SCAI 10 | Automation | 0.0971 | 5 |
SCAI 11 | Improve service quality | 0.0957 | 8 |
SCAI 12 | Timely detecting of threats in the environment | 0.0987 | 2 |
SCAI 13 | Integrate supply chain partners | 0.0945 | 12 |
SCAI 14 | Plan and form long-term cooperative partners with suppliers | 0.0960 | 7 |
NO. | Enablers | Gi |
---|---|---|
BDE 1 | Data integration and management capability | 7.70 |
BDE 2 | Get financial support | 7.55 |
BDE 3 | Big data storage maintenance | 7.33 |
BDE 4 | Advanced analytical skills | 7.00 |
BDE 5 | Data-driven culture | 6.91 |
BDE 6 | Value data security and privacy | 6.86 |
BDE 7 | Develop IT infrastructure | 6.86 |
BDE 8 | Developing cloud computing technology | 6.79 |
BDE 9 | Developing the Internet of Things | 6.78 |
BDE 10 | Data visualization capability | 6.77 |
Indicators | SCAI1 | SCAI2 | SCAI3 | SCAI4 | SCAI5 | SCAI6 | SCAI7 | SCAI8 | SCAI9 | SCAI10 | SCAI11 | SCAI12 | SCAI13 | SCAI14 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Weight Value | 0.0728 | 0.0701 | 0.0733 | 0.0710 | 0.0710 | 0.0709 | 0.0722 | 0.0691 | 0.0716 | 0.0721 | 0.0711 | 0.0734 | 0.0702 | 0.0712 |
Enablers | BDE1 | BDE2 | BDE3 | BDE4 | BDE5 | BDE6 | BDE7 | BDE8 | BDE9 | BDE10 |
---|---|---|---|---|---|---|---|---|---|---|
Correlation Degree | 0.5663 | 0.5842 | 0.5718 | 0.5597 | 0.5612 | 0.5634 | 0.5721 | 0.5749 | 0.5819 | 0.5767 |
Ranking | 7 | 1 | 6 | 10 | 9 | 8 | 5 | 4 | 2 | 3 |
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Hsu, C.-H.; Yang, X.-H.; Zhang, T.-Y.; Chang, A.-Y.; Zheng, Q.-W. Deploying Big Data Enablers to Strengthen Supply Chain Agility to Mitigate Bullwhip Effect: An Empirical Study of China’s Electronic Manufacturers. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 3375-3405. https://doi.org/10.3390/jtaer16070183
Hsu C-H, Yang X-H, Zhang T-Y, Chang A-Y, Zheng Q-W. Deploying Big Data Enablers to Strengthen Supply Chain Agility to Mitigate Bullwhip Effect: An Empirical Study of China’s Electronic Manufacturers. Journal of Theoretical and Applied Electronic Commerce Research. 2021; 16(7):3375-3405. https://doi.org/10.3390/jtaer16070183
Chicago/Turabian StyleHsu, Chih-Hung, Xue-Hua Yang, Ting-Yi Zhang, An-Yuan Chang, and Qing-Wen Zheng. 2021. "Deploying Big Data Enablers to Strengthen Supply Chain Agility to Mitigate Bullwhip Effect: An Empirical Study of China’s Electronic Manufacturers" Journal of Theoretical and Applied Electronic Commerce Research 16, no. 7: 3375-3405. https://doi.org/10.3390/jtaer16070183
APA StyleHsu, C. -H., Yang, X. -H., Zhang, T. -Y., Chang, A. -Y., & Zheng, Q. -W. (2021). Deploying Big Data Enablers to Strengthen Supply Chain Agility to Mitigate Bullwhip Effect: An Empirical Study of China’s Electronic Manufacturers. Journal of Theoretical and Applied Electronic Commerce Research, 16(7), 3375-3405. https://doi.org/10.3390/jtaer16070183