650 Management und unterstützende Tätigkeiten
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The growing availability of customer and market data, together with advances in data analytics and artificial intelligence (AI), is transforming how manufacturing firms organize marketing. Drawing on a management innovation perspective, this study develops a process theory explaining how manufacturing firms progressively become data-centric through the continuous innovation of marketing practices. We employ an embedded longitudinal case study of a European manufacturing SME, Industrial Corp., and examine 16 data-centric marketing practices as embedded units of analysis. Our findings show that data-centric marketing evolves through three cumulative phases: data-enabled marketing practices, analytics-driven marketing practices, and integrated customer intelligence practices. A distinct innovation process characterizes each phase: adopt–implement–refine, explore–experiment–scale, and reframe–integrate–institutionalize—and by evolving configurations of internal and external change agents. As complementary bundles of marketing practices accumulate and become institutionalized, they progressively strengthen operational, dynamic, and transformational marketing capabilities, enabling increasingly sophisticated operational, strategic, and enterprise-wide marketing decisions. By integrating the management innovation, marketing practice, and marketing capability literatures, this study explains how manufacturing firms transform customer and market data into sustained marketing capabilities through the continuous innovation of marketing practices. The findings also provide managers with a process model for systematically developing data-centric marketing practices and capabilities.
From Scenario Farming to Learning: A Modular Low-Code Framework for Decision Support in Scheduling
(2025)
In a Letter to the Editor, Hans-Gert Gräbe and Ralf Laue commented on the paper “The Five Diamond Method for Explorative Business Process Management” by Grisold et al. (2022) which has been published in the BISE Issue 2/2022. Gräbe and Laue raise concerns of how Grisold et al. introduce the TRIZ method and relate it to their explorative BPM approach. In their response, Grisold et al. address the methodological differences between TRIZ and explorative BPM.
Additive Manufacturing (AM), also known as rapid prototyping or 3D printing, is widely used across various industries, including medical products and automotive spare parts. The COVID-19 pandemic has further accelerated its adoption to address supply chain disruptions caused by shortages in production resources and logistics constraints. However, as AM integrates into supply chains, structural changes in nodes and data flows create new challenges in information sharing and data standardization. Ontologies have proven effective in enhancing data interoperability and improving information quality through semantic modeling. Despite this, a comprehensive approach that combines AM and logistics ontologies to address cross-domain challenges remains underexplored. This study develops an ontology-based supply chain model for AM by integrating existing AM and logistics ontologies. Using the Design Science Research Methodology (DSRM), the proposed ontology is constructed and instantiated with a sample dataset for validation. The results provide a foundational framework for improving data management and coordination in AM supply chains.
Additive manufacturing (AM) revolutionises traditional manufacturing by enabling localised, on-demand production, reducing waste, and enhancing design flexibility. The adoption of the AM method also transforms supply chains (SCs) in several perspectives due to, removing and adding some nodes and arcs. While this transformation offers numerous benefits, it also presents significant challenges in configuring an optimal network for AM SCs, especially when a decentralization network is preferable. In this regard, this study investigates using the network optimisation modelling (NOM) method to optimise decentralised AM SCs. Utilising AnyLogistix software, the study models an AM SC to determine the optimal network configuration that minimises costs while ensuring timely deliveries. It explores the advantages of decentralised production, such as reduced lead times and costs. This study contributes to the growing body of literature by addressing gaps related to NOM in AM contexts, providing valuable insights for practical applications in SC management.
