Over the last two years, industrial companies have faced unexpected global crises whilst simultaneously they are going through a transformation process with the aim of increasing renewable energy usage. To maintain competitiveness in a changing business environment, dynamic capabilities are needed in supply chain resilience (SCRes) management to proactively identify threats, respond agilely and implement change. This study aimed to identify what kinds of dynamic capabilities and microfoundations are needed in SCRes management in the energy industry. The research was conducted at Finnish energy industry companies. Research data from 19 semi-structured interviews were analysed according to the analysis framework of sensing, learning, integrating and coordinating. Eight microfoundations in the energy industry were identified. From those network collaboration, open communication and knowledge sharing, systematic supplier relationship management (SRM) and leadership were identified as key microfoundations for dynamic capabilities that affect sensing, learning, integrating and coordinating.

The accuracy of the measurement system is vital for reliable process monitoring using statistical process control charts. The applied chart’s effectiveness depends on the measurement system's performance. Measurement uncertainty can lead to incorrect decisions like unnecessary stops or failure to intervene. In this paper, we investigated the effect of measurement errors on the performance of four well-established combined charts for monitoring the mean of normally distributed processes: Shewhart-CUSUM, Shewhart-Crosier’s CUSUM, Shewhart-EWMA and Shewhart-GWMA charts. To deal with measurement errors we considered the additive measurement error model. Detailed run length profiles of these charts are studied in terms of average run length (ARL), extra quadratic loss, relative ARL, and performance comparison index through Monte Carlo simulations under different sizes of measurement errors. It was found that measurement errors significantly reduce the power of the combined charts. Thus, multiple measurements scheme is incorporated as a remedy to this effect. The Shewhart-Crosier’s CUSUM performed best of four charts, while the Shewhart-EWMA chart did worst. To demonstrate the effect of measurement uncertainty and highlight implications further, a simulated dataset with a shift in the process mean is considered.

Industry 4.0—also known as the modern industrial revolution—nurtures close collaboration between various organizations so that they can come together for innovation. While aiming for digital transformation through such innovation, these organizations form certain interdependencies due to the pool of resources and tasks they agree to share to reach both common and independent goals. To understand those interdependencies, we studied a national innovation project in Finland called “Reboot IoT Factory,” which leveraged several resources, processes, and practices to successfully combine modern technologies in manufacturing in a competitive and sustainable way. The participants included in the project were factories, research organizations, and small and medium enterprises (SMEs). An actor dependency model was used to analyze the observed interdependencies through survey and interview data. The results showed strong goal, task, and resource dependencies between the participants. A conventional understanding of advantages and opportunities, such as increased experience sharing and possible long-term synergies, is elaborated; moreover, an analysis of the disadvantages and risks caused by interdependencies, such as delays in tasks and possible inefficiency through unnecessary complexity, is also conducted.

Digitalisation and Industry 4.0 promote a fundamental technological disruption that requires industry, research and government institutions to revisit their roles within the innovation ecosystems. Actors in this environment need to understand value co-creation during interaction and collaboration. The purpose of this study is to investigate the triple helix collaborative capabilities in an Industry 4.0 ecosystem context. The case under study is a Finnish national publicly funded research project involving five global manufacturers, three research institutions, and several small-and medium-size enterprises (SMEs). The results demonstrate that practices related to adaptivity, experience sharing, SME co-innovation and scale up can enable the ecosystem to be managed in a dynamic way. Yet, this type of operation requires the adoption of the ecosystem approach with mutual trust, intensive collaboration and the identification of common aims among the project participants. The presented co-innovation model can be used to design innovation ecosystem projects in the future.

Railway track maintenance and renewal are vital for railway safety, train punctuality, and travel comfort. Therefore, having cost-effective maintenance is critical in managing railway infrastructure assets. There has been a considerable amount of research performed on mathematical and decision support models for improving the application of railway track maintenance planning and scheduling. This article reviews the literature in decision support models for railway track maintenance planning and scheduling and transforms the results into a problem taxonomy. Furthermore, the article discusses current approaches in optimising maintenance planning and scheduling, research trends, and possible gaps in the related decision-making models.

Being multinational and complex, global supply chains may be extremely vulnerable to sudden crises, such as the recent covid-19 pandemic. In addition to issues related to the health and lives of people, the pandemic has also had negative impacts on supply chains, and many companies dependent on them have been struggling. The ability to recover from disruptions is called resilience. In this study, the resilience of supply chains is evaluated by studying three globally operating Finnish b2b manufacturing companies during the covid-19 pandemic. The empirical research was conducted by interviewing the companies’ supply chain and purchasing operations representatives, analysing the results and comparing the result sto the literature. As a result, essential methods to survive during crises and increase the resilience of supply chains were discovered. The results can be utilised in preparing for future crises.

Process industries focus on their core processes whilst certain activities are bought as outsourced services from a variety of suppliers. Corporate social responsibility highlights the need to manage and develop suppliers’ HSEQ capabilities. This study focuses on large Finnish industrial companies’ HSEQ cluster, and its’ actions for supplier development. This study includes interviews for large Finnish industrial companies and a database analysis on a sample of supplier audit reports. A macroergonomics analysis is conducted to show how the observations made in the audits, i.e. identified development topics or deviations, are of internal origin and mainly identify issues related to suppliers’ management processes and health and safety performance. In addition, suppliers’ own stakeholder management practices and processes were questioned in some cases. This study shows that this current HSEQ assessment practice by the cluster somewhat strongly identifies issues related to CSR. Yet, some development topics and future research aspects are identified that could help in tying the assessment procedure better with CSR. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

In business-to-business sales, instinct-based human interaction is often the norm, while the potential of process-based and data-driven approaches has not been thoroughly studied, especially in combination with artificial intelligence. This study examines this potential through an analysis of dualistic factors related to implementation of data-driven sales at B2B companies of all sizes. Based on empirical observations from 162 companies, four dualities are identified: waste/opportunities, customer satisfaction/buying behaviour, large/small sample sizes and manual data input/closed-loop learning.  It is suggested that a balanced focus on these dualities can enhance customer value creation and act as a platform for growth. We also present “IoC growth ratio” to demonstrate the universal link between compound annual growth rate (CAGR) and Win Rate (WR).