In the project Digital Stambanan, we are developing demonstrators of this kind to test functionality and share knowledge and experiences from the project with other companies and organizations.

In work package 2 (WP2), we develop a demonstrator for a quality traceability demonstrator focused on improving the Root Cause Analysis (RCA) process from a supply chain level. Our demonstrator consists of a data lake with a dashboard to facilitate data transference and storage from the shop floor to quality teams. The technical solution integrates an IoT platform with a cloud computing platform to make data collection and storage more efficient.

Root Cause Analysis in the manufacturing industry

Root Cause Analysis (RCA) is a tool for identifying and addressing the causes of system or product quality disturbances, supporting the industry in coming across the journey to achieve resilient and sustainable systems [1]. Within the Digitala Stambanan Project [2], we assessed the main characteristics of the RCA process in a supply chain composed of three companies. The RCA is a valuable tool; however, it is a costly process that requires a lot of resources and time. It is usually said that it is hard to get data in place to perform the analysis, which is why the RCA process is constrained.

In other words, scaling up RCA practices remains challenging due to the complexity of manufacturing ecosystems and the need for more efficient knowledge transfer. Inside Work Package 2, we addressed this issue by mapping the pipeline process to track information from one company to another, as shown in Figure 1. Considering this pipeline and the maturity level of our supply chain, a transition to a platform approach could help the quality teams access data more efficiently.

Figure 1 The process for quality information between companies in a value chain.

Collaborative Platforms to support RCA

The adoption of platform thinking within this context promises a paradigm shift, facilitating rapid data exchange among diverse users and increasing the digitalization level of supply chains [3]. This approach significantly reduces the need for extensive interactions, enhancing the efficiency of RCA practices through data sharing and interoperability [4]. We propose that integrating the IoT and Cloud-computing platforms to achieve a unified Data Lake of traceable information can reduce resource allocation for data retrieval and enhance overall process efficiency.

Figure 2 Platforms as support for RCA in a value chain.

Implementation Roadmap and proof of concept

As the design of new business models starts with point solutions [5], a strategic roadmap is essential for successfully implementing platform solutions within the supply chain. This involves understanding partner dynamics and IT infrastructure and defining knowledge requirements for enabling desired interactions. Here, we follow a roadmap consisting of a digital maturity assessment, stakeholder analysis, definition of performance metrics, implementation, and empirical validation. The first results of this approach are meant to help with data visualization and dashboard to generate insights for quality teams performing RCA at a supply chain level. However, as further implications, this strategy enables ML/AI algorithms to be trained and big data analytics.

Figure 3 Roadmap for the demonstrator.

Observed benefits of the solution

The platform-based approach, mainly through IoT and cloud-based platforms, can improve the RCA process by facilitating data exchange and reducing the number of steps to perform tasks. This approach enhances the collection and integration of data across the supply chain, facilitating rapid RCA of disturbances and quality issues. Leveraging IoT enables real-time data acquisition and connectivity among devices, while cloud platforms offer a scalable environment to host a traceability data lake, ensuring efficient data management and accessibility. Together, they enable advanced analytics and AI-driven insights for proactive quality control and live monitoring, significantly improving operational efficiency and traceability.

Figure 4 Comparison of number of interactions between ICT systems based on silos vs. platforms.

With the transition to a platform strategy, we are addressing the traditional barriers that limit efficient data sharing and analysis within the industry. We aim to democratize access to valuable information, creating more resilient and self-improving production systems. The demonstrator we are developing within Digitala Stambanan is just the beginning of this journey.

Paulo Victor Lopes – WP-leader, Chalmers University.

Sofia Encinales – Volvo Group.

Read more about WP2 here.

Digitala Stambanan strengthens the Swedish industry through digitalization of value chains. The project is a collaborative project financed by Vinnova and participating companies. The work is now underway in two tracks through the strategic innovation programs PiiA (Processindustriell IT & Automation), which drives the Digitala Stambanan IndTech project, and Produktion2030, which runs the Digitala Stambanan Produktion project.


[1] Ito, Adriana. (2023). Root cause analysis for resilient production systems. ISSN 0346-718X. New serial no: 5287. Doctoral thesis at Chalmers University of Technology.

[2] Chalmers University of Technology – Division of Production Systems. (2021). Digitala Stambanan Project.

The assessment has already been applied in over 70 industries […].

[3] Schuh, G., Anderl, R., Dumitrescu, R., Krüger, A., & ten Hompel, M. (2020). Using the Industry 4.0 maturity index in the industry. Current Challenges, Case Studies, and Trends. Acatech COOPERATION.

[4] Parker, G. G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform revolution: How networked markets are transforming the economy and how to make them work for you. WW Norton & Company.

[5] Srai, J. S., Parker, G., Bärring, M.,… & Schönfuß, B. (2022). Unlocking Business Model Innovation through Advanced Manufacturing. In World Economic Forum, White Paper.