The value of Big Data in servitization

By David Opresnik, Marco Taisch. 2015

490 citations recorded by the source corpus.

Servitization has become a pervasive business strategy among manufacturers, enabling them to undergird their competitive advantage. However, it has at least one weakness. While it is used worldwide also in economies with lower production costs, services in manufacturing are slowly becoming commoditized and will become a necessary, though not sufficient, condition for reaching an above average competitive advantage. Consequently, in this article we propose a new basis for competitive advantage for manufacturing enterprises called a Big Data Strategy in servitization. We scrutinize how manufacturers can exploit the opportunity arising from combined Big Data and servitization. Therefore, the concept of a Big Data Strategy framework in servitization is proposed. The findings are benchmarked against established frameworks in the Big Data and servitization literature. Its impact on competitive advantage is assessed through three theoretical perspectives that increase the validity of the results. The main finding is that, through the proposed strategy, new revenue streams can be created, while opening the possibility to decrease prices for product–services. Through the proposed strategy manufacturers can differentiate themselves from the ones that are already servitizing. This article introduces the possibility of influencing the most important of the five “Vs” in Big Data–Value, in addition to the other four “Vs”—Volume, Variety, Velocity and Verification. As in regards to servitization, the article adds a third layer of added value— “information”, beside the two existing ones: product and service. The results have strategic implications for managers.

Subfields: Management Information Systems, Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/235af1d1e142cfbecbd0c64ef8126c97c3cd921f

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Citations in the corpus

Territorial servitization: Exploring the virtuous circle connecting knowledge-intensive services and new manufacturing businesses (2017, background): “Opresnik & Taisch (2015) show that this service adds significant value to manufacturers’ offering especially in B2B relationships by providing customers with tools that can be used to enhance cost saving policies and develop more informed strategic decision-making processes.”
Big data analytics in supply chain management: A state-of-the-art literature review (2017, background): “On the other hand, it expands revenue streams from existing products to servitization, and creates new revenue streams from entirely new (data) products (Opresnik and Taisch, 2015).”
Transforming provider-customer relationships in digital servitization: A relational view on digitalization (2020, background): “Typically, product providers adopt a digital servitization strategy to differentiate themselves from competitors (Opresnik & Taisch, 2015) and to create new revenue streams by establishing closer collaboration with their customers (Scherer, Kloeckner, Ribeiro, Pezzotta, & Pirola, 2016).”
Internet of Things and Big Data as enablers for business digitalization strategies (2020, background): “…reliability and, thus, quality (Gandomi and Haider, 2015); value, which specifies the availability of hidden insights deriving from data analysis (Opresnik and Taisch, 2015; Fosso Wamba et al., 2015); and variability, which considers the context in which data are generated (Ebner et al., 2014).”
The impact of big data on world-class sustainable manufacturing (2015, background): “, [58]) have investigated howmanufacturers could harness the benefits of BDA for servitization, suggesting that BD is vital to this process.”
Big data analytics capabilities: a systematic literature review and research agenda (2017, background): “…tools to effectively capture, store, manage, analyze, and exploit them Big data analytics capabilities: a systematic literature … machines or sensors, social media, cell phones or other networked devices, news and network content, as well as GPS signals (Chen et al. 2016; Opresnik and Taisch 2015).”
Product-service innovation and performance: the role of collaborative partnerships and R&D intensity (2019, unknown): “Providing optimisation services through software and big data technologies, for example, gives product firms valuable feedback about product use and customer needs (Opresnik and Taisch, 2015).”
A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions (2019, methodology): “With the objective to explore the capacity of big data in product service, a framework of big data strategy in servitization for manufacturing enterprises was proposed (Opresnik and Taisch, 2015).”