Search results

To improve supply chain performance, companies are now exploring new pathways including industry-wide data sharing initiatives along complex supply chains. The purpose of this paper is to stimulate research in this field by describing the benefits, obstacles and the governance required for supply chain data sharing initiatives.

Based on publicly available information complemented by interviews with practitioners, the authors describe how companies are establishing ambitious data sharing infrastructure and initiatives along their supply chains.

The authors describe how data sharing along supply chains is becoming increasingly important for many companies and how the automotive sector is working towards establishing a digital infrastructure for data sharing that could support a wide range of use cases. The article emphasises the importance of studying the governance of data ecosystems using new theoretical approaches. Finally, the authors suggest three areas for future research on data ecosystems, including their governance, the learning dynamics that will drive their adoption and their relationship with broader system-level changes.

This paper is the first, to the authors’ knowledge, that depicts how industry-wide data-sharing initiatives are expected to have an impact on supply chain performance. The authors highlight factors that affect the development and implementation of these initiatives along supply chains.

The internet of things (IoT) is one of the pillars of Industry 4.0. Prior OM research has conceptualized IoT, and analyzed potential applications and risks and challenges associated with its adoption. However, little empirical evidence exists on the main types of IoT projects undertaken by organizations and on their impacts. The purpose of this paper is to close this gap by searching for a taxonomy of IoT projects that may be associated to different IoT readiness levels. The dynamic capability lens is used as the theoretical background for the analysis.

A database of secondary IoT case studies is used to identify an IoT project taxonomy through two-step cluster analysis. The taxonomy obtained allows classifying projects into homogenous groups by technological novelty, IoT capabilities and functional areas of application. ANOVA is then used to test for the association between cluster membership and alternative operational impacts. Finally, the analysis of selected case studies from the database allows throwing light on the nature of the projects typical of each cluster.

Five clusters of projects have been identified and positioned along varying degrees of capabilities, novelty and scope. The taxonomy is consistent with a three layer IoT technological readiness model. In turn, the three IoT readiness levels correspond to three managerial capabilities: monitoring, control and optimization. Combining cluster results with detailed case analysis suggests that IoT technological readiness can be interpreted as a dynamic capability enabling knowledge creation that can support competitive advantage.

This is a first attempt to describe projects firms undertake when adopting IoT. Building on cluster analysis, the study suggests that different IoT readiness levels are needed to reach different impacts.

The purpose of this paper is to introduce and define the concept of purchasing ambidexterity in terms of two dimensions: balance dimension and combined dimension. The study proceeds to empirically examine the multiple performance effects generated for the buying firm and its key suppliers.

Ambidexterity theory informs the authors’ conceptual model. To test the hypotheses, the authors collected survey data from 95 purchasing functions of medium and large European firms and applied various estimation techniques.

This research indicates that ambidexterity substantially varies across purchasing functions. Further, it discovers that a purchasing function’s ability to advance the combined magnitude of exploratory and exploitative activities represents an essential determinant of supplier efficiency, supplier product innovation, and buyer financial performance. Notably, this research also discovers that balancing the magnitudes of exploratory and exploitative activities on a relative basis produces negative effects on the innovativeness of the supply network.

Although ambidexterity theory has been applied to supply chain management, limited attention has been dedicated to purchasing ambidexterity. This gap led us to study how purchasing impacts the competitiveness of the buying firm and of its supply network by balancing and combining exploratory and exploitative activities. This research is the first to advance the notion of purchasing ambidexterity, unpack its underlying dimensions, and examine its multiple performance implications. Such a conceptual and empirical development presents new perspectives on how purchasing can help the buying firm and its supply network to strengthen their competitiveness.

Smart industry initiatives focus on intelligent and interconnected cyber-physical systems. These initiatives develop complex technical architectures that integrate heterogenous technologies, causing significant organizational complexity. Tapping into the digital capabilities of distant partners while capturing profit from such innovation is demanding. Furthermore, firms often need to establish and orchestrate inter-organizational collaborations without prior relations or established trust. As a result, smart industry initiatives bring together disparate organizational forms and institutional environments, distinctive knowledge bases, and geographically dispersed organizations. We conceptualize this organizational capability as ‘distant capabilities integration’. This research explores the governance mechanisms that support such integration and their relation to value capture. We analyse 11 IoT case studies organized in three categories (process, product and technologies) of smart industry initiatives. Building on existing literature, we consider different ways to describe distance, including knowledge heterogeneity and organizational, geographical, institutional, cultural and cognitive distance. Finally, we describe the governance mode appropriate for upstream (developing foundational technologies) and downstream (leveraging existing distant technologies) smart industry initiatives.