Discovering Sustainable Business Partnerships through a Deep Learning Approach to Maximize Potential Value
Abstract
:1. Introduction
- The deep learning-based recommendation method [18] is used to extend business networks by discovering new business partnerships. This method successfully discovers new business partnerships compared to existing ML-based BP prediction models.
- We propose a method that calculates potential value using network measures and financial data. Using two methods, we evaluate potential gain and loss of companies from an extended business network.
- To verify the effectiveness of our method, we use real-world business transaction data collected from the Republic of Korea. The results demonstrate that companies can gain more potential value from extended networks when compared with previous ones. Furthermore, potential value results show clear distinctions between industries.
- When a small and medium-sized company either maintains existing business partners or establishes new business partnerships with other companies, is it possible to estimate the potential benefits a small and medium-sized company can achieve from these partnerships? If so, how can these benefits be estimated?
- Are there any distinctions in benefits from these business partnerships at the industry level?
2. Literature Review
3. Methodology
Computation of Potential Values
4. Experiments
4.1. Dataset
4.2. Data Analysis
5. Discussions and Implications
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Chowdhury, S.R. Impact of global crisis on small and medium enterprises. Glob. Bus. Rev. 2011, 12, 377–399. [Google Scholar] [CrossRef]
- Alberti, F.G.; Varon Garrido, M.A. Can profit and sustainability goals co-exist? New business models for hybrid firms. J. Bus. Strategy 2017, 38, 3–13. [Google Scholar] [CrossRef]
- Guo, X.; Yuan, Z.; Tian, B. Supplier selection based on hierarchical potential support vector machine. Expert Syst. Appl. 2009, 36, 6978–6985. [Google Scholar] [CrossRef]
- Markovic, S.; Jovanovic, M.; Bagherzadeh, M.; Sancha, C.; Sarafinovska, M.; Qiu, Y. Priorities when selecting business partners for service innovation: The contingency role of product innovation. Ind. Mark. Manag. 2020, 88, 378–388. [Google Scholar] [CrossRef]
- Chopra, S. Designing the distribution network in a supply chain. Transp. Res. Part E Logist. Transp. Rev. 2003, 39, 123–140. [Google Scholar] [CrossRef]
- Dyer, J.H.; Singh, H. The Relational View: Cooperative Strategy and Sources of Interorganizational Competitive Advantage. Acad. Manag. Rev. 1998, 23, 660–679. [Google Scholar] [CrossRef]
- Tuten, T.L.; Urban, D.J. An Expanded Model of Business-to-Business Partnership Formation and Success. Ind. Mark. Manag. 2001, 30, 149–164. [Google Scholar] [CrossRef]
- Rezaei, J.; Ortt, R.; Trott, P. How SMEs can benefit from supply chain partnerships. Int. J. Prod. Res. 2015, 53, 1527–1543. [Google Scholar] [CrossRef]
- McGuire, J.; Dow, S. The Japanese keiretsu system: An empirical analysis. J. Bus. Res. 2002, 55, 33–40. [Google Scholar] [CrossRef]
- Choy, K.L.; Lee, W.B.; Lo, V. Design of an intelligent supplier relationship management system: A hybrid case based neural network approach. Expert Syst. Appl. 2003, 24, 225–237. [Google Scholar] [CrossRef]
- Wu, D. Supplier selection: A hybrid model using DEA, decision tree and neural network. Expert Syst. Appl. 2009, 36, 9105–9112. [Google Scholar] [CrossRef]
- Mori, J.; Kajikawa, Y.; Kashima, H.; Sakata, I. Machine learning approach for finding business partners and building reciprocal relationships. Expert Syst. Appl. 2012, 39, 10402–10407. [Google Scholar] [CrossRef]
- Autry, C.W.; Golicic, S.L. Evaluating buyer-supplier relationship-performance spirals: A longitudinal study. J. Oper. Manag. 2010, 28, 87–100. [Google Scholar] [CrossRef]
- Carnovale, S.; Yeniyurt, S. The Role of Ego Networks in Manufacturing Joint Venture Formations. J. Supply Chain Manag. 2014, 50, 1–17. [Google Scholar] [CrossRef]
- Kao, T.W.D.; Simpson, N.C.; Shao, B.B.M.; Lin, W.T. Relating supply network structure to productive efficiency: A multi-stage empirical investigation. Eur. J. Oper. Res. 2017, 259, 469–485. [Google Scholar] [CrossRef]
- Shao, B.B.M.; Shi, Z.M.; Choi, T.Y.; Chae, S. A data-analytics approach to identifying hidden critical suppliers in supply networks: Development of nexus supplier index. Decis. Support Syst. 2018, 114, 37–48. [Google Scholar] [CrossRef]
- Sasaki, H.; Sakata, I. Business partner selection considering supply-chain centralities and causalities. Supply Chain Forum 2021, 22, 74–85. [Google Scholar] [CrossRef]
- Lee, D.; Kim, K. Business transaction recommendation for discovering potential business partners using deep learning. Expert Syst. Appl. 2022, 201, 117222. [Google Scholar] [CrossRef]
- Chan, F.T.S.; Kumar, N. Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega 2007, 35, 417–431. [Google Scholar] [CrossRef]
- Jain, V.; Sangaiah, A.K.; Sakhuja, S.; Thoduka, N.; Aggarwal, R. Supplier selection using fuzzy AHP and TOPSIS: A case study in the Indian automotive industry. Neural Comput. Appl. 2018, 29, 554–564. [Google Scholar] [CrossRef]
- Chen, C.H. A novel multi-criteria decision-making model for building material supplier selection based on entropy-AHP weighted TOPSIS. Entropy 2020, 22, 259. [Google Scholar] [CrossRef]
- Sarkis, J.; Dhavale, D.G. Supplier selection for sustainable operations: A triple-bottom-line approach using a Bayesian framework. Int. J. Prod. Econ. 2015, 166, 177–191. [Google Scholar] [CrossRef]
- Kaya, R.; Yet, B. Building Bayesian networks based on DEMATEL for multiple criteria decision problems: A supplier selection case study. Expert Syst. Appl. 2019, 134, 234–248. [Google Scholar] [CrossRef]
- Cui, L.; Wu, H.; Dai, J. Modelling flexible decisions about sustainable supplier selection in multitier sustainable supply chain management. Int. J. Prod. Res. 2021, 61, 4603–4624. [Google Scholar] [CrossRef]
- Borgatti, S.P.; Li, X. On social network analysis in a supply chain context. J. Supply Chain Manag. 2009, 45, 5–22. [Google Scholar] [CrossRef]
- Kim, Y.; Choi, T.Y.; Yan, T.; Dooley, K. Structural investigation of supply networks: A social network analysis approach. J. Oper. Manag. 2011, 29, 194–221. [Google Scholar] [CrossRef]
- Abdulkarim, R.; Abdallah, S. Using Social Network Analysis to Study Diversity in Business Partnerships. In Proceedings of the 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), Granada, Spain, 22–25 October 2019. [Google Scholar]
- Liu, L.; Ran, W. Research on supply chain partner selection method based on BP neural network. Neural Comput. Appl. 2020, 32, 1543–1553. [Google Scholar] [CrossRef]
- Basole, R.C.; Ghosh, S.; Hora, M.S. Supply Network Structure and Firm Performance: Evidence from the Electronics Industry. IEEE Trans. Eng. Manag. 2018, 65, 141–154. [Google Scholar] [CrossRef]
- Kim, D.Y.; Zhu, P.; Xiao, W.; Lin, Y.T. Customer degree centrality and supplier performance: The moderating role of resource dependence. Oper. Manag. Res. 2020, 13, 22–38. [Google Scholar] [CrossRef]
- Arora, S.; Brintrup, A. How does the position of firms in the supply chain affect their performance? An empirical study. Appl. Netw. Sci. 2021, 6, 19. [Google Scholar] [CrossRef]
- Orenstein, P.; Tang, H. Identifying the relation between a supply chain network’s structure and its overall financial performance. Oper. Supply Chain Manag. 2021, 14, 399–409. [Google Scholar] [CrossRef]
- Seiler, A.; Papanagnou, C.; Scarf, P. On the relationship between financial performance and position of businesses in supply chain networks. Int. J. Prod. Econ. 2020, 227, 107690. [Google Scholar] [CrossRef]
- Landherr, A.; Friedl, B.; Heidemann, J. A Critical Review of Centrality Measures in Social Networks. Bus. Inf. Syst. Eng. 2010, 2, 371–385. [Google Scholar] [CrossRef]
- Song, S.; Nerur, S.; Teng, J.T.C. Understanding the influence of network positions and knowledge processing styles. Commun. ACM 2008, 51, 123–126. [Google Scholar] [CrossRef]
References | Methods | Highlights |
---|---|---|
[12] | SVM | Utilizing reciprocal relationships between companies for seller selection |
[10] | CBR, NN | Combining CBR and NN to predict potential suppliers |
[3] | SVM | Hierarchical SVM combined with DT for supplier sections |
[11] | DEA, DT, NN | Combining DEA and DT or NN to predict new suppliers |
Applied Methods | Illustrative Examples | References |
---|---|---|
Analytic Hierarchy Process | Select a global supplier for manufacturing companies | [19] |
Select a supplier for automobile companies in India | [20] | |
Evaluate and select appropriate building material suppliers | [21] | |
Bayesian Network | Select a supplier using Monte Carlo Markov Chain simulation | [22] |
Select suppliers in a large-scale automobile manufacturer in Turkey | [23] | |
Select suppliers and develop multitier sustainable supply chain management | [24] | |
Social Network Analysis | Analyze business partnerships in supply chain networks | [25] |
Select suppliers in an automotive supply chain network | [26] | |
Identify hidden critical suppliers in a Honda supply chain network | [16] | |
Select suppliers in a supply chain network in Dubai city | [27] | |
Select business partners in a supply chain network in Tohoku city | [17] | |
Machine Learning | Select suppliers for manufacturing companies in Beijing | [3] |
Suggest sellers for manufacturing companies in Tokyo | [12] | |
Select business partners in the supply chain network | [28] | |
Recommend business partners for companies in the Republic of Korea | [18] |
Industry Average Sales No. of Companies | Number of Employees | |||||
---|---|---|---|---|---|---|
≤500 | ≤1000 | ≤1500 | ≤2000 | >2000 | ||
Manufacturing | $9073 84,334 | $413,229 226 | $938,159 53 | $1.424 M 26 | $7.586 M 57 | |
Wholesale and retail | $10,216 53,994 | $569,974 49 | $2391,317 13 | $2.816 M 7 | $3.833 M 14 | |
Construction | $6849 32,886 | $446,970 16 | $164,331 1 | $1.063 M 3 | $3.740 M 5 | |
Information and communication | $4439 14,644 | $245,179 37 | $335,593 12 | $0.933 M 4 | $3.125 M 18 | |
Total | $30.055 M 186,399 | $30,579 185,858 | $1.375 M 328 | $3.827 M 79 | $6.236 M 40 | $18.285 M 94 |
Buyers | Manufacturing | Wholesale and Retail | Construction | Information and Communication | |
---|---|---|---|---|---|
Sellers | |||||
Manufacturing | $3044 M | $2046 M | $15 M | $59 M | |
Wholesale and retail | $749 M | $2468 M | $159 M | $13,918 M | |
Construction | $1560 M | $6671 M | $79 M | $454 M | |
Information and communication | $279 M | $7396 M | $296 M | $15,134 M |
Industries | Estimated Loss | Estimated Gain | ||
---|---|---|---|---|
Mean | Std | Mean | Std | |
Manufacturing | ||||
Direct link | 3.108 | 2.042 | 1.996 | 1.368 |
Direct & indirect links | 1.198 | 1.981 | 2.718 | 1.979 |
Wholesale and Retail | ||||
Direct link | 3.050 | 2.367 | 2.329 | 1.476 |
Direct & indirect links | 1.126 | 1.912 | 3.186 | 1.969 |
Construction | ||||
Direct link | 3.253 | 2.034 | 1.624 | 1.563 |
Direct & indirect links | 2.098 | 2.283 | 3.269 | 1.733 |
Information and communication | ||||
Direct link | 2.846 | 2.210 | 2.851 | 1.593 |
Direct & Indirect links | 0.544 | 1.304 | 4.474 | 1.979 |
Sum of Squares | DF | Mean Squares | F-Scores | |
---|---|---|---|---|
Direct link Between groups Within groups Total | 13,593.93 1,398,982.54 1,412,576.47 | 3 2585 2588 | 4531.31 541.19 | 8.37 * |
Direct & indirect links Between groups Within groups Total | 16,285.06 902,007.28 918,292.34 | 3 2585 2588 | 5428.35 348.94 | 15.56 * |
Networks | Industries | Target Industries | Mean Differences | Std. Errors | Sig. Values |
---|---|---|---|---|---|
Direct link | Manufacturing | Wholesale and retail | 2.96 | 1.50 | 0.201 |
Construction | −3.39 | −1.58 | 0.142 | ||
Information and communication | −7.11 * | 2.20 | 0.008 | ||
Construction | Wholesale and retail | 6.35 * | 2.05 | 0.011 | |
Information and communication | −3.72 | 2.61 | 0.484 | ||
Wholesale and retail | Information and communication | −10.07 * | 2.56 | <0.001 | |
Direct & Indirect links | Manufacturing | Wholesale and retail | −2.25 | 1.10 | 0.171 |
construction | −3.62 * | 1.34 | 0.036 | ||
Information and communication | −8.38 * | 1.92 | <0.001 | ||
Construction | Wholesale and retail | 5.87 * | 1.63 | 0.002 | |
Information and communication | −4.76 | 2.27 | 0.157 | ||
Wholesale and retail | Information and communication | −10.63 * | 2.13 | <0.001 |
Industries | No Change (−Margin) | − to +Margin | + to −Margin | No Change (+Margin) |
---|---|---|---|---|
Manufacturing | 46.06% | 27.47% | 2.85% | 23.61% |
Wholesale and retail | 38.31% | 28.14% | 2.37% | 31.19% |
Construction | 56.08% | 23.37% | 2.34% | 18.22% |
Information and communication | 21.93% | 33.16% | 1.07% | 43.89% |
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Lee, D.; Kim, J.; Song, S.; Kim, K. Discovering Sustainable Business Partnerships through a Deep Learning Approach to Maximize Potential Value. Sustainability 2023, 15, 15885. https://doi.org/10.3390/su152215885
Lee D, Kim J, Song S, Kim K. Discovering Sustainable Business Partnerships through a Deep Learning Approach to Maximize Potential Value. Sustainability. 2023; 15(22):15885. https://doi.org/10.3390/su152215885
Chicago/Turabian StyleLee, Donghun, Jongeun Kim, Seokwoo Song, and Kwanho Kim. 2023. "Discovering Sustainable Business Partnerships through a Deep Learning Approach to Maximize Potential Value" Sustainability 15, no. 22: 15885. https://doi.org/10.3390/su152215885