Exploring the interplay between Smart Manufacturing and KIBS firms in configuring product-service innovation performance
Introduction
Constant changes in the competitive environment due to the increased complexity of offerings (Brax and Visintin, 2017; Visnjic et al., 2018) and constant updating of the available technologies (Onufrey and Bergek, 2020) are subjecting manufacturing firms to unprecedented pressures. Within this highly variable context, technology—in its different forms—has become a crucial catalyst in crafting business success by helping organizations to understand customer needs and optimize their operations along the value chain. These changes have spurred manufacturers to adapt their offerings by producing integrated bundles composed of products, services, and knowledge (Vandermerwe and Rada, 1988). By reforming and upgrading the traditional manufacturing paradigm, technology is transforming industry structure (Porter and Heppelmann, 2014).
Based on these changes and on the recognition that technology is disrupting competitive market forces at an increasingly rapid pace, manufacturers have developed new business models by embracing servitization-based innovation (Baines et al., 2017; Jovanovic et al., 2021), also referred to as product-service innovation (PSI) (Bustinza et al., 2018; Rabetino et al., 2018). PSI involves the various technology-enabled systems that can achieve competitive advantage by providing customer knowledge-based services throughout the life-cycle of manufacturing products. Successful development of PSI systems depends heavily, however, on selecting the right set of manufacturing technologies. The range of technologies required to configure an integrated Smart Manufacturing framework (Tao et al., 2018) is fast shifting from merely a resource base to a knowledge base. The technologies range from traditional supportive manufacturing modules—e.g., Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Manufacturing Enterprise Systems (MES), and Product Lifecycle Management (PLM)—to Smart Manufacturing technologies—e.g., Data-Driver (DD), Real-Time Monitoring (RTM), and Problem-Processing (PP) modules.
Previous studies of the decision to develop PSI (Bigdeli et al., 2018; Horváth and Rabetino, 2019; Jovanovic et al., 2019; Kamp and Alcalde, 2014; Opresnik and Taisch, 2015; Vaillant et al., 2019) show that manufacturers develop service business models conditioned by the business ecosystem in which they operate. Ecosystems condition the processes of both inter- and intra-firm change and decisions whether or not to adopt PSI (Jovanovic et al., 2021; Sklyar et al., 2019; Vendrell-Herrero et al., 2017). While knowledge-intensive business service (KIBS) firms have been shown to play an essential role in the development of specific types of services (Bustinza et al., 2019a) and in servitization in regional contexts (Lafuente et al., 2017, 2019), little is understood about their role supporting manufacturers who embark on servitization in the Smart Manufacturing context (Kiel et al., 2017). This role is of fundamental importance as Smart Manufacturing incorporates the digital technologies that enable the transformation of processes, offerings, and capabilities needed to create, deliver, and ultimately capture the value generated from PSI (Sjödin et al., 2020). Moreover, manufacturers can create new ecosystems through the interaction of smart products combined with Smart Manufacturing capabilities (Jovanovic et al., 2021), potentially generating new revenue streams.
An emerging line of research on the role of KIBS firms in PSI has attracted the interest of academics and practitioners (Lafuente et al., 2017; Vendrell-Herrero and Wilson, 2017). One reason for this interest is that manufacturers that develop PSI must undertake both product and service innovation, increasing the complexity of the firm's operations (Rabetino et al., 2017; Smith et al., 2014). KIBS firms may play a critical role in managing the ambidexterity required for PSI implementation (Bustinza et al., 2020). Implementing the new Smart Manufacturing technologies is also inherently complex (Bigdeli et al., 2018). Based on data analysis, continuous monitoring, and problem processing (Porter and Heppelman, 2014), these technologies can be managed either in-house or externally in collaboration with KIBS firms. This study suggests that aligning the development of PSI and the decision whether to manage Smart Manufacturing technologies in-house or with KIBS firms could determine the manufacturing firm's performance. The study therefore seeks to understand the critical managerial challenges that manufacturers face—a significant gap in the literature—by resolving two key uncertainties: What technologies could better support servitized and non-servitized firms? And what make-or-buy decisions should firms take to manage Smart Manufacturing technologies effectively?
Based on the foregoing, this study posits that both the influence of Smart Manufacturing technologies and the role of KIBS firms in managing these technologies can explain superior performance in servitized manufacturers. To test this hypothesis, a fuzzy-set QCA (fsQCA) was applied to assess the effect that using technological modules had on a set of performance measures. This methodology suits our context because it enables us to analyse which causal conditions were related to each specific outcome (Schneider et al., 2010). The results help to determine which modules pure manufacturers should use and which modules servitized manufacturers should use to achieve superior performance. We also analysed the role of KIBS firms by examining the performance outcomes of firms that manage Smart Manufacturing technologies internally vs. externally through KIBS firms. Overall, this study contributes to understanding the connection between internal and external technological management and manufacturers’ innovation trajectories, an issue that recent calls for further analysis have identified as critically important (Kohtamäki et al., 2019; Rabetino et al., 2018).
The paper is organized as follows. The following section presents literature reviews of the research on innovation management in a servitizing manufacturer setting and the role of KIBS firms in servitization processes. An empirical section then identifies the configurational approaches that lead to superior performance for pure and servitized manufacturers, respectively. Finally, the article concludes with discussion and conclusion sections that analyse the implications of the study and identify interesting avenues of future research.
Section snippets
Infusion of industrial services in manufacturing settings
Manufacturing firms are gradually shifting their focus from tangible resources and transactional agreements to intangible resources and the provision of relational customer services (Vargo and Lusch, 2004). The infusion of industrial services in manufacturing has been conceptualized as a multi-stage transformation in which each stage is influenced by a variety of internal and external contextual factors (Dmitrijeva et al., 2019). In an attempt to construct an overarching model of the
Data collection
This study addresses the development of PSI in Spanish manufacturing firms. Manufacturers are constantly attempting to meet their customers' needs better by innovating in products and/or services. The rise of data analysis as a source of value (Faroukhi et al., 2020) has led manufacturers to use various Smart Manufacturing modules to develop better innovative offerings and remain competitive. In this context, we follow Forza's (2002) guidelines and define the unit of analysis (manufacturing
Necessary or almost-always-necessary conditions
We used fsQCA 3.0 software to analyse whether a causal condition was necessary by adopting the consistency score (Ragin, 2014). Values of 1 identify a combination of causal conditions as representing a condition that is strictly necessary for the outcome. Because it is unusual for data to meet this strict rule, the consistency score measures the extent of rule compliance, with values higher than 0.9 indicating conditions that are necessary or almost always necessary. Table 2 displays the
Discussion
The results for the full study sample show that two modules are necessary or almost necessary to maximize performance: the MN module for OP and the DD module for FP. OP is a non-financial indicator, meaning that it complements traditional financial measures. The MN module thus helps to achieve superior performance from a business performance perspective as a necessary causal condition. Since the MN module incorporates CRM, MES, ERP, and PLM functions, this finding aligns with the operations
Conclusions, implications, and suggestions for future research
While extant research studies have adopted empirical and case-based methodological approaches to analyse the role that a few digital technologies (mostly the Internet of Things) play in developing PSI (Paschou et al., 2020), this study pioneers in determining the role of the full set of technologies in the Smart Manufacturing framework (Tao et al., 2018). To illuminate the various PSI development methods that manufacturers can pursue to achieve superior performance, this study identified the
Acknowledgement
This research was funded by FEDER/Ministerio de Ciencia, Innovación y Universidades– Agencia Estatal de Investigación, Spain. grant number PGC2018-101022-A-100.
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