Source-linked AI summary
Co-evolution of platform architecture, platform services, and platform governance: Expanding the platform value of industrial digital platforms
Marin Jovanovic, David Sjodin, Vinit Parida
TL;DR
Industrial B2B platform research lacks a process-based account of how architecture, services, and governance co-evolve to expand platform value. Using four manufacturer cases, the study identifies three mirrored platform archetypes and their associated innovation mechanisms.
Problem
Research lacks a holistic process perspective on the co-evolution of platform architecture, platform services, and platform governance in industrial B2B platforms.
Method
The study uses an inductive qualitative multiple-case design based on four global equipment manufacturers and rich field data.
Results
The study identifies product platform, supply chain platform, and platform ecosystem archetypes, each linking gradual architecture, governance, services, and innovation mechanisms.
Takeaways & Limitations
The framework extends platform ecosystem and digital servitization literature by showing how coordinated architecture and governance evolution expands platform value.
Takeaways & Limitations
The study’s findings have limited generalizability and transferability because industrial digital platform research remains nascent and B2C platform assumptions do not transfer directly to B2B.
Abstract
from arXiv · showhide
Industrial manufacturers increasingly develop digital platforms in the business-to-business (B2B) context. This emergent form of digital platforms requires a profound yet little understood holistic perspective that encompasses the co-evolution of platform architecture, platform services, and platform governance. To address this research gap, our study examines multiple platform sponsors from an industrial manufacturing context. The study demarcates three platform archetypes: product platform, supply chain platform, and platform ecosystem. We argue that each platform archetype involves a gradual development of platform architecture, platform services, and platform governance, which mirror each other. We also find that each platform archetype is characterized by a specific innovation mechanism that contributes to the platform service discovery and expands the platform value. Our study extends the co-evolution perspective of platform ecosystem literature and digital servitization literature.
1. Introduction
Industrial B2B digital platforms are linked to digital servitization, but their evolution and platform service discovery remain insufficiently understood. This study examines how platform architecture, services, and governance co-evolve to expand platform value.
- Digital servitization connects industrial assets to platforms that aggregate data and analytics for value creation and capture.
- Advanced industrial platforms remain nascent because sponsors must manage architecture, external modules, and governance while opening to complementors.
- The literature lacks a process perspective on how platform architecture, services, and governance co-evolve in B2B settings.
- The study investigates how industrial manufacturers can expand platform value through the evolution of industrial digital platforms.
- Four manufacturers reveal three archetypes—product platform, supply chain platform, and platform ecosystem—whose architecture, governance, services, and innovation mechanisms develop in parallel.
2. Theoretical background
The theoretical background presents industrial digital platforms as evolving technological and organizational systems whose architecture, governance, and services jointly shape value creation. It emphasizes gradual opening from proprietary platforms toward broader ecosystems and identifies ambiguity around service discovery.
- Industrial digitalization pushes manufacturers from firm-based product platforms toward platform ecosystems while creating governance tensions.
- A platform ecosystem combines a shared technological core with governance mechanisms that coordinate diverse members and support value creation.
- Higher-order optimization and autonomous services require external modules such as sensors, analytics, applications, and cloud storage.
- B2B platforms typically begin as proprietary systems with exclusive complementors, intermediaries, and customers before gradually opening.
- Platform services connect architecture and ecosystem members through joint exploitation, but their antecedents and discovery process remain ambiguous.
- Digital platforms support deeper search within existing knowledge structures and can identify previously undetectable patterns in data.
3. Methodology
The study uses an inductive, qualitative multiple-case design to examine how manufacturers develop industrial digital platforms. Data from four equipment manufacturers combines interviews, archival materials, thematic analysis, and cross-case comparison.
- The cases were selected to provide field-based insight into a complex and understudied process of industrial digital platform evolution.
- The research analyzes four global construction-equipment manufacturers through an inductive multiple-case study design.
- Data collection combined cross-functional interviews with archival materials gathered between May 2018 and January 2020.
- The authors conducted 48 interviews with informants involved in different phases of platform development.
- Thematic analysis coded interview data into empirical themes, conceptual categories, and aggregate dimensions before cross-case analysis.
- The analysis identified three platform archetypes and their service-discovery antecedents, then validated initial findings with 10 key informants.
4. The evolution of industrial digital platforms
Industrial digital platforms evolved through progressively richer architecture, services, and openness. Across three phases, manufacturers moved from product data collection and monitoring toward analytics, optimization, AI-enabled autonomy, and ecosystem-based value creation.
- Platform architecture: Platform architecture progressed from product data collection to analytics utilization and finally artificial intelligence enablement.These phases increased the platform’s capacity to gather installed-base data, analyze sensor streams, and support autonomous machine action.
- Platform architecture: 400 sensors per machine enabled geo-location, load, hydraulic, and other data to support proactive anomaly detection for safety and efficiency.Advanced sensors funneled data to the platform, allowing potential problems to be identified before corrective action became necessary.
- Platform value expansion: AI-enabled ecosystems expanded platform value through recombination, using micro-services to configure novel modular solutions.This ecosystem stage combined AI-driven analysis, open interfaces, and diverse partners to support autonomous services.
- Platform governance: Open APIs and authorized interfaces enabled collaboration with external partners, extending platform value beyond manufacturers’ core competencies.Examples included partnerships involving drone surveying and 3D topology technologies, although safety and legal requirements constrained complete openness.
- Platform services: Platform services mirrored architecture development through monitoring, optimization, and autonomous service development.Monitoring began with machine-level reports and early warnings, while optimization extended to fleets and entire customer sites.
- Platform services: Fleet and site-management services integrated data across equipment to improve productivity, reduce costs, and optimize traffic, production, and machine use.Site services could include production monitoring, route planning, and simulations, including equipment owned by partners or competitors.
5. Discussion
The study presents industrial platform evolution as a co-evolution of architecture, services, and governance across three platform archetypes. It also identifies innovation mechanisms and managerial requirements for expanding platform value, while noting important limits to generalization.
- The framework distinguishes product platforms, supply chain platforms, and platform ecosystems as stages of industrial B2B platform evolution.
- Architecture maturity is associated with a shift from simple monitoring services toward more advanced autonomous services.Platform sponsors need gradual architectural investments to expand platform functionality and support higher-order services.
- Platform architecture, platform services, and platform governance co-evolve as industrial platforms become more open and inclusive.The evolution typically moves from an internal focus toward engagement with supply-chain partners, customers, and other complementors.
- Digitally enabled search depth, search breadth, and recombination provide innovation mechanisms for platform service discovery and platform-value expansion.
- The study’s generalizability is constrained because industrial digital platforms remain nascent and B2C winner-takes-all assumptions do not transfer straightforwardly to B2B settings.
- Platform sponsors must simultaneously manage architecture, services, and governance while securing quality data and viable business cases for sponsors and customers.The paper identifies free telematics systems as one way sponsors may generate quality data for service development.