At a glance

  • The next wave of industrial digitalization means moving from fragmented, single-use digital solutions to addressing value pools at scale as ecosystems.
  • These ecosystems build joint digital backbones on top of which partners and contributors innovate to address use cases with ever better data and learnings across the whole value chain.
  • In the driving seat are industrial sector companies realizing that unlocking value at scale requires technology, know-how and data beyond the capabilities of individual companies, and that strong winners-take-all dynamics are at play.
  • To win, companies need to identify and protect attractive control points in the value chain to shape a trusted ecosystem with business incentives for all partners, while capturing a considerable but fair share of the value created.
  • Ultimately it comes down to technology. The winning card is the ability to translate core competence, domain know-how and proprietary data into unique contributions to the ecosystem technology stack, controlled by intellectual property.

This article has two parts. First, it explores the driving forces behind and the characteristics of the next wave of industrial digital ecosystems. Second, it outlines a point of view on how to plan an industrial digital play to win. 

PART 1: The next wave of industrial digital ecosystems

Part 1 explores the driving forces behind and the characteristics of the next wave of industrial digital ecosystems.

Ecosystems to scale the benefits of digital

Although the buzz around Industry 4.0, Smart factories and the Industrial Internet of Things has been growing steadily for some 15 years, the benefits have been slower than expected and smaller than promised*. Issues have included a lack of digital capabilities, lack of trust and industry protectionism as industrials fear cannibalization on their core business as well as  disruption from software companies and BigTech entering the value chain. This has created a fragmented environment with countless digital solutions that aim to predict, optimize, automate, virtualize and augment industrial processes and products – but seldom reaching scale.

Exhibit 1: From fragmented digital solutions to ecosystem plays

The next wave of industrial digitalization is characterized by players collaborating to make ecosystem plays to address customer value at scale. This means moving away from having many single-use digital solutions, each one producing some customer value but not having a transformative impact. Single-use solutions may be connected but are not built to cooperate and amplify each other to create critical mass and thereby scaling value.

Instead, industrials are partnering in ecosystems to build joint digital backbones, i.e. connecting whole value chains so that data and know-how can be shared and accessed at the right places. On top of this digital backbone, ecosystem partners and contributors can develop apps and solutions to address a steadily growing number of use cases, leveraging data transparency and ever better learnings from across the value chain. This creates a value pool greater than the sum of its parts for the ecosystem to share.

Drivers for industrial digitalization ecosystem plays

A combination of five driving forces is powering the creation of industrial digital ecosystems. Three drivers are specific to player categories (industrial manufacturers, industrial machinery providers and platform capability providers), whereas two drivers go cross player types.

Exhibit 2: Driving forces for next wave of industrial digital ecosystems

Driver 1 – Downstream pull by industrial manufacturers: Industrial manufacturers have been under pressure for many years to deliver on the benefits promised by digital. The Covid-19 pandemic has escalated these expectations, with digital technology now considered the key toolbox for leading the way out of the recession. The market and shareholders are expecting industrial manufacturers to leverage their system-level know-how and customer insights to deliver both new digital value propositions and cost reductions at scale. This creates a strong downstream pull to scale digital efforts and results.

Driver 2 – Upstream push by industrial machinery providers: Industrial machinery providers fear being commoditized as “connected thing creators”. They are looking to reinvent themselves to instead claim its share of the new value. Most are looking to gradually transition from hardware-based sales to selling functionality and Performance-as-a-Service based on their specialized domain know-how and data. But many have also concluded that they are unlikely to create an attractive enough business case through independent stand-alone solutions.

Driver 3 – Capability push by platform providers: Platform providers of e.g. cloud, connectivity and analytics capabilities are looking to grow their business across multiple industry verticals. Platform providers scale their business by providing industry agnostic solutions. They bring knowledge and learnings across industries while making digital capabilities accessible and speeding up implementation. This allows industrials to overcome digital technology and skill gaps, with the trade-off of opening up the value chain to new types of players.

Driver 4 – Stack interdependency: The three player-category drivers motivate making scaled digital moves. But the tipping point for ecosystem plays is the need for technology, know-how and data beyond the capabilities of individual companies. The next wave of industrial digitalization requires large-scale collaboration based on shared data and codified domain knowledge from across the value chain. No single industrial company can build and sustain a complete stack on its own, as it ranges from multiple assets and things, through edge and connectivity layers, having the right backend, data and enablement platform layers, to apps and solutions addressing multiple use cases. Instead, the required technology stack is characterized by strong co-dependence and blurred boundaries between involved players’ contributions.

Driver 5 – Winners-take-all dynamics: Due to a combination of direct and indirect network effects, accentuated by big data and machine learning, digital ecosystem plays typically display strong winners-take-all dynamics. Next wave industrial digital ecosystems are set up to address a steady stream of new use cases, creating continuous learning loops and multi-layered innovation opportunities across the value chain. If successful, the few winning ecosystems can create insurmountable leads, allowing its orchestrators to set the rules for any new partners, or simply shut the door for outsiders. This creates a strong incentive to be part of a winning ecosystem play.

Competition between and within digital ecosystems

Next wave digital ecosystems create an intricate balance between collaboration and competition. The ecosystems are multi-layered both on relational levels and technology levels. They consist of multiple players from each player category. With customers, partners and competitors having to collaborate to win together against other ecosystems, while simultaneously competing in terms of how the winnings are distributed amongst themselves. Any industrial making an ecosystem play needs to manage both levels of competition, i.e. both contribute to the attractiveness and competitiveness of the ecosystem while preferably capturing a considerable but fair share of the value created.

Exhibit 3: Two levels of competition; between and within ecosystems

Control is key to success. To build and sustain a winning ecosystem, orchestrators must have the power to set rules, create business incentives and ensure robustness and trust in the ecosystem. They must be able to jointly protect the competitive edge versus other ecosystems, regulate the degree of openness and access in different layers, and steer how revenues and costs are distributed. Intellectual property is a critical tool to gain and exercise control. The main source of that intellectual property is the ability to translate core competence, domain know-how and proprietary data into unique contributions to the ecosystem technology stack. This is the winning card.


Condition-based and predictive maintenance for rail industrials

Global annual rail maintenance cost [BEUR] *


Improving efficiency and reducing total cost of ownership is key for the rail industry.

Condition-based and predictive maintenance solutions using big data and advanced analytics represent a great opportunity.

McKinsey & Co estimates efficiency gains of 10-15%, meaning EUR 7,5 billion savings per year for the global rail maintenance market.*

The market is expecting realization of these cost reductions, creating a pull for Rail Operators, Rail OEMs and Suppliers to act.

Competition between digital ecosystems


Rail industrials have realized that Performance- and Uptime-as-a-Service models is the way to address life cycle costs at scale.

For these to be effective, ecosystem plays are needed to utilize data and technology from players across the value chain.

The rail maintenance market is increasingly characterized by competition between ecosystems having different constellations, eg:

  • Rail OEM and Rail Supplier ecosystems like Siemens-led Railigent
  • Purely Rail OEM-led plays such as Alstom’s HealthHub
  • Rail Operator and Platform Provider partnerships such as the Trenitalia / SAP ecosystem play based on SAP Hana platform
  • Purely Rail Operator-led such as the Deutsche Bahn IoT Cloud

Collaboration and competition within ecosystems


In these Rail Life Cycle ecosystems partners and contributors collaborate to take as large share as possible of the total industry value pool.

They also compete for how the value shall be distributed, creating co-opetition relationships.

The Railigent ecosystem constellation as example:

  • Siemens Mobility's Railigent Application Suite is an open ecosystem that builds on Siemen’s MindShpere platform, with data applications running on Amazon Web Services (AWS).
  • The ecosystem currently has 14 members and includes companies across the value chain including partners like Voith, Strukton, SKF and Knorr-Bremse.

PART 2: How to make a winning digital ecosystem play

Part 2 outlines a point of view on how to plan an industrial digital play to win. 

Digital play strategy process

Any industrial aiming to make a play to scale customer value and digital revenue should start with some version of a digital play strategy – no matter if the aim is to take an ecosystem orchestrator role or to be a contributor in the winning ecosystem. Such a strategy process typically consists of four types of analyses; value pool analysis (what use cases create most value for customers and scale with a shared digital backbone), asset / capability analysis (what do we bring to the winning ecosystem that will also allow us to capture a disproportionate but fair share of the value pool), value chain / player analysis (what does the value chain look like through a digital lens and what competitive moves will players make), and technology stack analysis (what software, hardware and data is required to address the targeted value pool as an ecosystem).

These analyses are iteratively run in at least two loops. In the first loop, the aim is to target and position for value. The result is a control point map showing the attractive positions in the digital value chain that are also possible to claim and control. The second loop aims to determine how to capture value. The outcome is a competitive vector map that creates a red thread from the business objectives through the technology stack, outlining how to build the ecosystem and how to control the value distribution.

In the following chapters we will provide a step-by-step outline for how a digital play strategy process can be run, illustrating the analysis types and the resulting strategy maps.

Exhibit 4: Digital play strategy process

Target, position and capture value

Value pool analysis

To architect an ecosystem that out-competes other ecosystems, you need to be distinct about what value to target. Value pool analysis is very much business casing to understand where your profits will come from as well as what business incentives are needed for the right players to join the ecosystem as partners or contributors.

Value comes from helping customers remove pain points. You need to analyze the value chain to identify the use cases that create largest value impact for customers in terms of e.g. productivity gains, cost savings and new or enhanced revenue streams. The use case assessment should consider two perspectives. First, which use cases that scale most in an ecosystem play compared to isolated single-use digital solutions. I.e. the use cases benefitting the most from having a digital thread throughout the value chain, producing ever better data and learnings. Secondly, which use cases are in line with your business strategic priorities and build on your core competences. I.e. use cases where you have assets of importance to the ecosystem and which allow you to capture a considerable but fair share of the value pool.

The needs to understand the value chain, your assets and capabilities and the required technology stack in order to define the value pool, highlights that the four analysis types cannot be done in sequence but need to be iterative and reinforce each other.

Exhibit 5: Defining the value pool

Assets and capabilities analysis

For most industrials, digital ecosystem plays means reshaping the core competence to fit in the digital business setting. Assets and capability analysis is about understanding what you bring to the table in building and sustaining an ecosystem that outperforms other ecosystems, and what will allow you to capture a considerable share of the value pool. It’s also about how to be an attractive partner to the players you want to involve in the ecosystem and your ability to affect the rules in the ecosystem. Industrials typically perceive digital ecosystem plays as outside the comfort zone, especially in terms of openness. Defining what core assets to build on, develop and control is therefore key.

The asset and capability analysis is also very much about the people and cultural side of the digital play. It means finding the right models for merging domain know-how and digital talent in an effective way, overcoming the pitfalls that have made digital initiatives come up short in the past. The central part is the digital innovation organization; how to build winning teams, combine internal expertise with new recruits and amplify with external partners, as well as to find the right models for execution with speed. But the transformation cannot stop inside the innovation organization; it’s a cultural change that needs to permeate the whole company. For example, sales organizations need to shift mindset from selling products and services in traditional ways, to understand what it means e.g. to sell Performance-as-a-Service.

Exhibit 6: Understand the value chain and ecosystem conditions

Value chain and players analysis

In addition to the internal conditions, you also need to understand the competitive environment. Value chain and player analysis means creating a detailed and communicable map of the value chain and the involved players (typically industrial manufacturers, industrial machinery providers, platform capability providers, and end-customers).

This map is created by analyzing the value chain through a digital lens and assessing players as potential ecosystem partners/contributors and as threats (as individual companies or parts of competing ecosystems). The analysis typically involves parameters such as players’ current position and value propositions, expected competitive moves, distinguishing competitive factors and traits, technology stack coverage and compatibility with the ecosystem stack.

Technology stack analysis

The final piece of the analysis puzzle is to understand the required technology stack, i.e. the hardware, software and data needed by the ecosystem to address the value pool. Technology stack analysis means creating a detailed map (you may call it a breakdown, architecture or framework) of what the stack will look like and the characteristics of layers in the stack.

Industrial digital ecosystem stacks are typically built on similar base layers creating a robust and inclusive digital backbone, on top of which partners and contributors can build differentiating layers that create the competitive edge for the ecosystem as well as for the individual companies. Understanding the technology stack will help make it clear for partners how competing together creates a larger value pool, what their role in the ecosystem composition will be and what the business incentives are.

Exhibit 7: Designing the ecosystem stack

Control points and competitive vectors

Loop 1 outcome: Control points

The first loop of the digital play strategy aims to target and position for value. The result is a control point map of attractive positions in the digital value chain. I.e. positions where you can claim a considerable share of the value pool and that are possible to control. In this sense,  control means the ability to defend your targeted position, to claim your cut of the profits and give you a place at the table to affect the ecosystem rules. Control can be achieved using e.g. intellectual property rights, data access, contractual and secrecy-based control mechanisms.

Ecosystem orchestrators also need to map the control dynamics of the whole ecosystem. You need to ensure business incentives for partners and contributors and build robustness and trust in the ecosystem. Different player categories will have different fears and trust issues. Industrial machine providers will fear the risk of becoming commoditized and losing their edge from their unique domain knowledge. Industrial manufacturers will not want to become locked in to single suppliers, nor open up their system-level know-how to be common knowledge. Especially not in the hands of platform providers working in and cross verticals. For ecosystem orchestrators, it is key to set and communicate the rules of different ecosystem layers. E.g. what layers will be open (and controlled to remain open), where different players will make money, where competition between ecosystem players will take place, and how that internal competition will stimulate the continuous development of the joint effort towards a winners-take-all position.

Exhibit 8: Control point analysis to select right ecosystem positions

Throughout the first analysis loop, your business strategic priorities must guide the decisions you make. Business strategic priorities means clearly defined statements that together define the direction of the business and create a common understanding for how the company shall create its future competitiveness (customer value and internal performance). If your business strategic priorities cannot provide guidance to a digital play strategy, the process may also be a good opportunity to update them.

Loop 2 outcome: Competitive vectors

The second loop aims to determine how to capture value by creating a red thread – a competitive vector – from business objectives through the technology stack. Competitive vectors translate the strategic direction into actionable plans based on understanding which domains (parts and layers) in the technology stack contribute the most to business objectives and your selected control points. These plans include where to innovate to close gaps between current and wanted positions, and if gaps should be closed using your own resources, by others in the ecosystem or bought in (i.e. build/buy/partner decisions).

The final piece of competitive vectors is the control strategy, i.e. how to ensure power to direct and influence the course of events and behaviors in and around the ecosystem. The control strategy defines how to use different control mechanisms to manage the layers of the stack, both to set ecosystem rules and build trust, and to manage different interfaces in the external environment.

The control strategy should for example define how to ensure exclusivity or openness in different  layers and how to steer revenue and cost. This is both to ensure business incentives for ecosystem partners and contributors as well as to claim your share of the value pool. The control strategy should also define how to ensure differentiation and freedom to act for the ecosystem as a whole and for yourself, both in relation to individual competitors and to  competing ecosystems. It can also prepare for how to handle business situations such as key negotiations. E.g. having the right bargaining chips for negotiations with technology suppliers, collaboration partners and M&A targets.

Exhibit 9: Competitive vectors translating strategic direction to the technology

Looking ahead

In coming years we will see digital ecosystem plays at scale in many industry verticals including transportation, automotive, consumer durables, machinery and equipment, medtech, healthcare, energy, chemicals, metals and mining, and pulp and paper. Industrials will move from stand-alone connected products to platform plays that integrate technology, know-how and data across the value chain.

The success of the resulting ecosystems will depend on how collaboration and competition is managed. Most industrials will need to change their mindset in terms of controlling openness and sources of competitiveness. With winners-take-all dynamics at play, claiming stakes in the winning ecosystem with speed is key. Industrials that can translate core competence, domain know-how and proprietary data into unique contributions to the ecosystem technology stack – controlled by intellectual property – will have the winning card.