Overcoming Scalability Barriers in Manufacturing Marketplaces: A Framework for Trust-Centric Platforms

Authors

  • Stephan Biller Edwardson School of Industrial Engineering, Purdue University, 315 N. Grant Street, West Lafayette, IN 47907, Mitch Daniels School of Business, Purdue University, 315 N. Grant Street, West Lafayette, IN 47907
  • Hazel He Edwardson School of Industrial Engineering, Purdue University, 315 N. Grant Street, West Lafayette, IN 47907
  • Shivani Suresh Kumar Edwardson School of Industrial Engineering, Purdue University, 315 N. Grant Street, West Lafayette, IN 47907
  • Franz Stoll Edwardson School of Industrial Engineering, Purdue University, 315 N. Grant Street, West Lafayette, IN 47907

DOI:

https://doi.org/10.47672/ajscm.2786

Keywords:

Manufacturing Platforms; Supply Chain Management; Digital Marketplace; Trust, Transaction Cost Theory

Abstract

Purpose: Despite heavy investment, digital manufacturing marketplaces have struggled to scale beyond niche adoption. This study examines the underlying causes and proposes a new framework for building a trust-centric, scalable platform. The framework identifies three core principles: (1) efficient matchmaking, (2) enduring trust, and (3) effortless collaboration, as levers for transforming fragmented manufacturing networks. It demonstrates how trust-enabled platforms can trigger network effects, lower external transaction costs, and reshape supply chain strategies.

Materials and Methods: An exploratory mixed-method approach was used, combining an extensive literature review of academic and industry sources with semi-structured interviews involving platform architects, manufacturing SMEs, and supply chain managers. Insights from these interviews, analyzed thematically, were synthesized with the literature to develop the proposed framework. 

Findings: The study finds that today’s manufacturing marketplaces lack scalability and broad adoption due to limited transparency in supplier capabilities, cost-centric matchmaking, and “black box” models that hinder communication and customization. To overcome these barriers, platforms must actively engineer trust by promoting performance-based visibility and enabling direct, accountable collaboration. A platform grounded in Efficient Matchmaking, Enduring Trust, and Effortless Collaboration can create self-reinforcing network effects and substantially reduce external transaction costs. As these costs decline, firms are increasingly incentivized to outsource manufacturing, potentially decoupling production from product development and allowing greater focus on innovation.

Unique Contribution to Theory, Practice, and Policy: The findings extend Transaction Cost Theory (TCT) by demonstrating that trust-enforced digital marketplaces can reduce firm boundaries. Theoretically, this suggests expanding TCT to account for platform-enabled trust mechanisms that lower coordination costs. In practice, firms are advised to reconsider make-or-buy decisions as outsourcing via high-trust platforms becomes safer and cheaper. This allows firms to externalize manufacturing and devote internal resources to innovation. Policymakers are urged to update trade and antitrust policies to accommodate hyper-scalable manufacturing service platforms that are redefining industrial structures.

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References

1 Bahga, A. and Madisetti, V. (2016). Blockchain platform for industrial internet of things. Journal of Software Engineering and Applications, 09(10), 533-546. https://doi.org/10.4236/jsea.2016.910036

2 Beck, B. B., Wuyts, S., & Jap, S. D. (2023). Guardians of trust: how review platforms can fight fakery and build consumer trust. Journal of Marketing Research, 61(4), 682-699. https://doi.org/10.1177/00222437231195576

3 Cennamo, C., & Santalo, J. (2013). Platform competition: Strategic trade‐offs in platform markets. Strategic management journal, 34(11), 1331-1350.

4 Chan, H., Yang, M. X., & Zeng, K. J. (2022). Bolstering ratings and reviews systems on multi-sided platforms: a co-creation perspective. Journal of Business Research, 139, 208217.

Chikhi, T., Santa-Eulália, L., Mosconi, E., RISSO, L., Filho, M., & Ganga, G. (2022). Going beyond blockchain adoption’s hype to improve supply chain sustainability: evidence from empirical and modelling studies. https://doi.org/10.24251/hicss.2022.735

5 Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386–405. https://doi.org/10.1111/j.1468-0335.1937.tb00002.x

6 Compagnucci, L., Spigarelli, F., Sernani, P., Frontoni, E., & Seri, P. (2025). A systematic literature review of business-to-business platforms for the digital transformation of the manufacturing industry: Taking stock and advancing through research. Technovation, 148, 103330. https://doi.org/10.1016/j.technovation.2025.103330

7 Dann, D., Peukert, C., Martin, C., Weinhardt, C., & Hawlitschek, F. (2020). Blockchain and trust in the platform economy: the case of peer-to-peer sharing. 1459-1473. https://doi.org/10.30844/wi_2020_n2-dann

8 Design2Market. (2023). 97% of patents never make money – here’s why and what you can do about it. https://www.design2market.co.uk/academy/97-of-patents-never-make-money/ 10. Doroudi, R., Sequeira, P., Marsella, S., Ergun, O., Azghandi, R., Kaeli, D., & Griffin, J. (2020). Effects of trust-based decision making in disrupted supply chains. PloS one, 15(2), e0224761.

9 Evans, D., & Timme, S. (2024). The Economic Impact of a Digital Manufacturing Partner for Production Workloads. Fictiv.

10 Fraile, F., Sanchís, R., Poler, R., & Ortíz, Á. (2019). Reference models for digital manufacturing platforms. Applied Sciences, 9(20), 4433. https://doi.org/10.3390/app9204433

11 Frandsen, T., & Lefebvre, V. M. (2022). Manufacturing-as-a-Service: A systematic literature review. Journal of Manufacturing Systems, 62, 738–751.

13 Frank, A. G., Mendes, G. H., Ayala, N. F., & Ghezzi, A. (2019). Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective. Technological Forecasting and Social Change, 141, 341–351. https://doi.org/10.1016/j.techfore.2019.01.014

14 Ganne, E. (2020). Can blockchain revolutionize international trade? World Trade Organization.

15 Gerrikagoitia, J., Unamuno, G., Urkia, E., & Serna, A. (2019). Digital manufacturing platforms in the industry 4.0 from private and public perspectives. Applied Sciences, 9(14), 2934. https://doi.org/10.3390/app9142934

16 Ghobakhloo, M., & Fathi, M. (2019). Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing. Journal of Manufacturing Technology Management, 31(1), 1–30. https://doi.org/10.1108/JMTM-11-2018-0417

17 Gupta, N., Gunawan, I., & Kamineni, R. (2024). Analysing resilience and leagility in postpandemic sustainable supply chain management: a systematic literature review. Built Environment Project and Asset Management, 14(3), 432-448. https://doi.org/10.1108/bepam-10-2022-0151

18 Hagiu, A., & Wright, J. (2015). Multi-sided platforms. International journal of industrial organization, 43, 162-174.

19 Hayman, B. and Dennehy, M. (2021). Developing-country vaccine manufacturers’ technical capabilities can make a difference in global immunization. Vaccine, 39(36), 5153- 5161. https://doi.org/10.1016/j.vaccine.2021.07.044

20 Hesse, M., Dann, D., Braesemann, F., & Teubner, T. (2020). Understanding the platform economy: signals, trust, and social interaction. Proceedings of the Annual Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2020.631

21 Horstmann, M., Ahlers, D., & Krause, D. (2023). Trust and security in industrial platforms: A quantitative study on the adoption of IoT platforms in manufacturing. Technovation, 119, 102548. https://doi.org/10.1016/j.technovation.2022.102548

22 Höse, K., Amaral, A., Götze, U., & Peças, P. (2023). Manufacturing flexibility through industry 4.0 technological concepts—impact and assessment. Global Journal of Flexible Systems Management, 24(2), 271-289.

23 Hossain, M. (2022). The Shenzhen ecosystem: What it means for the western world. Technology in Society, 68, 101919. https://doi.org/10.1016/j.techsoc.2022.101919.

24 Hu, K., Kong, L., & Jia, Z. (2024). Supplier selection criteria under heterogeneous sourcing needs: evidence from an online marketplace for selling production capacity.

25 Production and Operations Management, 34(2), 168-186. https://doi.org/10.1177/10591478241279384

26 Hu, Y., Jia, Q., Yao, Y., Lee, Y., Lee, M., Wang, C., & Yu, F. R. (2024). Industrial internet of things intelligence empowering smart manufacturing: A literature review. IEEE Internet of Things Journal, 11(11), 19143-19167.

27 Jacobides, M. G., Cennamo, C., & Gawer, A. (2018). Towards a theory of ecosystems. Strategic management journal, 39(8), 2255-2276.

28 Ji, F., He, Z., & Cheng, G. (2025). Manufacturing enterprise digital platform capabilities: structural dimensions and scale development. Technology Analysis & Strategic Management, 1–15. https://doi.org/10.1080/09537325.2025.2492228

29 Jiga. (2023, November 27). Xometry competitors: 14 alternatives to consider. https://jiga.io/articles/xometry-competitors/

30 Kanike, U. K. (2023). Factors disrupting supply chain management in manufacturing industries. Journal of Supply Chain Management Science, 4(1–2), 1–24. https://doi.org/10.18757/jscms.2023.6986

31 Kayhan, H. (2022). Ensuring trust in pharmaceutical supply chains by data protection by design approach to blockchains. Blockchain in Healthcare Today. https://doi.org/10.30953/bhty.v5.232

32 King, G., & Navarra, D. (2024). Transaction cost theory, organisational ambidexterity, and digital platforms: an interpretative case study exploring the implementation of a businessto-business logistics digital platform in a South African organisation. The Business & Management Review, 15(3), 36-44.

33 Lee, Z. W. Y., Chan, T. K. H., Balaji, M., & Chong, A. Y. (2018). Why people participate in the sharing economy: an empirical investigation of uber. Internet Research, 28(3), 829850. https://doi.org/10.1108/intr-01-2017-0037

34 Li, L. and Wang, W. (2020). The effects of online trust-building mechanisms on trust in the sharing economy: the perspective of providers. Sustainability, 12(5), 1717. https://doi.org/10.3390/su12051717

35 Liu, Y. and Tang, X. (2018). The effects of online trust-building mechanisms on trust and repurchase intentions. Information Technology and People, 31(3), 666-687. https://doi.org/10.1108/itp-10-2016-0242

36 Luca, M. (2017). Designing online marketplaces: Trust and reputation mechanisms. Innovation Policy and the Economy, 17(1), 77-93.

37 Luca, M., & Zervas, G. (2016). Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Science, 62(12), 3412–3427. https://doi.org/10.1287/mnsc.2015.2304

38 Ma, Z., Chen, X., Sun, T., Wang, X., Wu, Y., & Zhou, M. (2024). Blockchain-based zerotrust supply chain security integrated with deep reinforcement learning for inventory optimization. Future Internet, 16(5), 163. https://doi.org/10.3390/fi16050163

39 Markham, S. K. (2002). Moving technologies from lab to market. Research-technology management, 45(6), 31-42.

40 McIntyre, D. P., & Srinivasan, A. (2017). Networks, platforms, and strategy: Emerging views and next steps. Strategic management journal, 38(1), 141-160.

41 Menon, K., Kärkkäinen, H., & Wuest, T. (2017). Role of openness in industrial internet platform providers’ strategy. 92-105. https://doi.org/10.1007/978-3-319-72905-3_9

42 Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49, 194–214. https://doi.org/10.1016/j.jmsy.2018.10.005

43 Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., & Ueda, K. (2016). Cyber-physical systems in manufacturing. Cirp Annals, 65(2), 621-641.

44 Okano, M., Antunes, S., & Fernandes, M. (2021). Digital transformation in the manufacturing industry under the optics of digital platforms and ecosystems. Independent Journal of Management & Production, 12(4), 1139-1159. https://doi.org/10.14807/ijmp.v12i4.1375

45 Oks, S. J., Jalowski, M., Lechner, M., Mirschberger, S., Merklein, M., Vogel-Heuser, B., & Möslein, K. M. (2024). Cyber-physical systems in the context of industry 4.0: A review, categorization and outlook. Information Systems Frontiers, 26(5), 1731-1772.

46 Onyeme, C., & Liyanage, K. (2021). A Critical Review of Smart Manufacturing & Industry 4.0 Maturity Models: Applicability in the O&G Upstream Industry. Advances in Manufacturing Technology XXXIV, 347-354.

47 Oriekhoe, O. I., Oyeyemi, O. P., Bello, B. G., Omotoye, G. B., Daraojimba, A. I., & Adefemi, A. (2024). Blockchain in supply chain management: a review of efficiency, transparency, and innovation. International Journal of Science and Research Archive, 11(1), 173-181. https://doi.org/10.30574/ijsra.2024.11.1.0028

48 Park, J., Lee, M., & Kim, H. (2024). The neighborhood advantage: exploring the impact of negotiation costs on transaction satisfaction in local second-hand trading platforms. Fashion and Textiles, 11(1). https://doi.org/10.1186/s40691-024-00386-8

49 Paul, S. K., Chowdhury, P., Moktadir, M. A., & Lau, K. H. (2021). Supply chain recovery challenges in the wake of covid-19 pandemic. Journal of Business Research, 136, 316-

329. https://doi.org/10.1016/j.jbusres.2021.07.056

50 Procurement Tactics. (2023, May 3). 35 supply chain statistics in 2023: Facts, trends & insights. https://procurementtactics.com/supply-chain-statistics/

51 RAHMAN, M. S. A., Mohamad, E., & Rahman, A. A. A. (2020). Enhancement of overall equipment effectiveness (oee) data by using simulation as decision making tools for line balancing. Indonesian Journal of Electrical Engineering and Computer Science, 18(2), 1040. https://doi.org/10.11591/ijeecs.v18.i2.pp1040-1047

52 Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A. B. L., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 107546. https://doi.org/10.1016/j.ijpe.2019.107546

53 Rindfleisch, A. (2019). Transaction cost theory: past, present and future. AMS Review, 10(1-2), 85-97. https://doi.org/10.1007/s13162-019-00151-x

54 Saari, L. M., Kääriäinen, J., & Ylikerälä, M. (2024). Maturity Model for the Manufacturing Industry with Case Experiences. Intelligent and Sustainable Manufacturing, 1(2), 10010.

55 Schmitz, J. and Tang, Y. (2018). The genmark eplex ®: another weapon in the syndromic arsenal for infection diagnosis. Future Microbiology, 13(16), 1697-1708. https://doi.org/10.2217/fmb-2018-0258

56 Schöppenthau, F., Patzer, F., Schnebel, B., Watson, K., Baryschnikov, N., Obst, B., Chauhan, Y., Kaever, D., Usländer, T., & Kulkarni, P. (2023). Building a Digital Manufacturing as a Service Ecosystem for Catena-X. Sensors, 23, 7396. https://doi.org/10.3390/s23177396

57 Senna, P. P., Barros, A. C., Roca, J. B., & Azevedo, A. (2023). Development of a digital maturity model for Industry 4.0 based on the technology-organization-environment framework. Computers & Industrial Engineering, 185, 109645.

58 Singha, J., Grahamb, G., Lorentzc, H., Phillipsd, W., Kapletiad, D., & Hennelly, P. (2020). Distributed Manufacturing: A new form of localized production?. International Journal of Operations and Production Management.

59 Spieske, A., & Birkel, H. (2021). Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic. Computers & Industrial Engineering, 158, 107452.

60 Springer, V., Randhawa, K., Jovanović, M., Ritala, P., & Piller, F. T. (2025). Platform design and governance in industrial markets: Charting the meta-organizational logic. Research Policy, 54(6), 105236. https://doi.org/10.1016/j.respol.2025.105236

61 Springer, V., Randhawa, K., Jovanović, M., Ritala, P., & Piller, F. T. (2025). Platform design and governance in industrial markets: Charting the meta-organizational logic. Research Policy, 54(6), 105236. https://doi.org/10.1016/j.respol.2025.105236

62 Sudha, R., Bennulf, M., Zhang, X., Hammar, S., & Danielsson, F. (2022). Online path planning in a multi-agent-controlled manufacturing system. Lecture Notes in Mechanical

Engineering, 124-134. https://doi.org/10.1007/978-3-031-18326-3_13

63 Sudirman, I. D., Astuty, E., & Aryanto, R. (2025). Enhancing digital technology adoption in SMEs through sustainable resilience strategy: examining the role of entrepreneurial orientation and competencies. Journal of Small Business Strategy, 35(1), 97-114.

64 Sun, Y. and Qu, Q. (2025). Platform governance, institutional distance, and seller trust in cross-border e-commerce. Behavioral Sciences, 15(2), 183. https://doi.org/10.3390/bs15020183

65 Vance, D., Jin, M., Price, C., Nimbalkar, S. U., & Wenning, T. (2023). Smart manufacturing maturity models and their applicability: a review. Journal of Manufacturing Technology Management, 34(5), 735-770.

66 Wan, P., Huang, L., & Holtskog, H. (2020). Blockchain-enabled information sharing within a supply chain: a systematic literature review. Ieee Access, 8, 49645-49656. https://doi.org/10.1109/access.2020.2980142

67 Wang, J., Bao, Y., & Zhai, L. (2022). Tripartite evolutionary game analysis of trust relationship between enterprises in a cloud manufacturing environment: a service composition perspective. Discrete Dynamics in Nature and Society, 2022(1). https://doi.org/10.1155/2022/6922627

68 Williamson, O. E. (1975). Markets and hierarchies: Analysis and antitrust implications. New York: Free Press.

69 Williamson, O. E. (1985). The economic institutions of capitalism. New York: Free Press.

70 Williamson, O. E. (2010). Transaction cost economics: The natural progression. American Economic Review, 100(3), 673-690.

71 World Intellectual Property Organization. (2024). World intellectual property indicators 2024: Highlights. https://www.wipo.int/web-publications/world-intellectual-propertyindicators-2024-highlights/en/index.html

72 Xin, Y., Liu, D., & Zhou, X. (2022). Evolutionary analysis of cloud manufacturing platform service innovation based on a multiagent game perspective. Ieee Access, 10, 104543-104554. https://doi.org/10.1109/access.2022.3208915

73 Xue, J., Lu, S., Ben-shan, S., & Yang, X. (2017). Applying complexity theory to explain partner cooperation: the role of transaction cost‐related factors and elements of relational exchanges. Canadian Journal of Administrative Sciences / Revue Canadienne Des Sciences De L Administration, 35(3), 488-500. https://doi.org/10.1002/cjas.1458

74 Yang, S., Zhou, Q., Yang, L., Xue, Y., Xu, J., & Xue, C. (2015). Effect of thermal processing on astaxanthin and astaxanthin esters in pacific white shrimp. Journal of Oleo Science, 64(3), 243-253. https://doi.org/10.5650/jos.ess14219

75 Yarbrough, A. C., & Peters, C. (2023, December 12). Smart manufacturing adoption study 2023 (Technical Report No. 23-01). Interdisciplinary Center for Advanced Manufacturing Systems, Auburn University. https://eng.auburn.edu/icams/files/Smart-ManufacturingStudy.pdf.

76 Yenipazarli, A. (2020). The marketplace dilemma: selling to the marketplace vs. selling on the marketplace. Naval Research Logistics (NRL), 68(6), 761-778. https://doi.org/10.1002/nav.21964

77 Zhang, L. and Hou, K. (2023). An intelligent manufacturing models recommendation model based on knowledge graph and recommendation algorithms. Journal of Physics: Conference Series, 2665(1), 012009. https://doi.org/10.1088/1742-6596/2665/1/012009

78 Zhu, Z. (2024). The Application of Transaction Cost Theory in Supply Chain Management. Open Journal of Applied Sciences, 14(11), 3216-3225.

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Published

2025-10-21

How to Cite

Biller, S., He, H., Kumar, S. S., & Stoll, F. (2025). Overcoming Scalability Barriers in Manufacturing Marketplaces: A Framework for Trust-Centric Platforms. American Journal of Supply Chain Management, 10(1), 1–29. https://doi.org/10.47672/ajscm.2786

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