Digital transformation in operations management: Fundamental change through agency reversal

Abstract

The emergence of digital technologies across all aspects of operations management (OM) has enabled shifts in decision making, shaping new operational dynamics and business opportunities. The associated scholarly discussions in information systems (IS) and OM span digital manufacturing (e.g., Roscoe et al., 2019), the digitalization of OM and supply chain management (e.g., Holmström et al., 2019), platform outcomes (e.g., Friesike et al., 2019), and economies of collaboration (e.g., Hedenstierna et al., 2019). For such changes to be successful, however, there is a need for organizations to go beyond the mere adoption of digital technologies. Instead, successful changes are transformational, delving into digital transformation (DT) endeavors (Vial, 2019), which in turn can enable operational improvements in organizational performance (Davies et al., 2017), lead to structural changes in operations processes, and may result in new business models being deployed.

Appropriately, DT endeavors are increasingly treated in both the IS and OM literature as an ongoing process rather than an isolated project with a clear start and finish (e.g., Struijk et al., 2022). Here, we adopt this line of reasoning and specifically treat DT endeavors as: “the use of digital technologies to evolve operational activities by creating new or transforming existing processes, cultures, and customer experiences to meet changing business and market requirements.” Such a perspective is somewhat distinct from widely adopted definitions of DT in IS and OM (e.g., Vial, 2019), as well as from the strict consideration of radical operational innovation (cf. Hammer, 2004). Specifically, our perspective is neither predicated on “disruption” per se, nor limited by such transformations being fundamentally strategic ones for the focal organization. In other words, DT endeavors can (i) extend into the creation of new organizational processes, (ii) transform existing processes either incrementally or more substantially, (iii) shift decision making with regard to those processes, (iv) enable the consideration of new business models, and (v) largely serve as a source of facilitation and synergy in existing ones. In this special issue, we characterize the specific role of DT in OM as follows: through DT endeavors, digital technologies have the potential to affect OM processes and decision-making with regard to finance, design, production, and the delivery of products, services, or combinations of them.

The broader OM literature has already set the stage for the consideration of new business models and innovation tournaments that have been extensively influenced by DT endeavors, such as platform services, omnichannel retail, supply chain information exchange, and Internet of Things (IoT)-enabled operations. This line of research can contribute to contemporary and ongoing discussions within the broader field (e.g., Holmström et al., 2019), including the opportunities for organizations to leverage presence in one market into other areas; the emergence of ecosystems that take into consideration all players in the value chain; the appeal of multi-sided platform business models that bring together disparate actors; the value of new data sources when serving new customers; and the importance of artificial intelligence (AI) in the form of advanced algorithmic solutions as a competitive advantage for organizations. Such scholarly discussions can further consider failures caused by the complexity and comprehensiveness of actions that organizations attempt to undertake during DT endeavors (Struijk et al., 2020, 2023).

Empirical research as well as theoretical insights into DT endeavors, therefore, can challenge our established understanding of OM theory and practice, and highlight the importance of organizational dynamics as intertwined with higher levels (Struijk et al., 2022). Our aim here, thus, is to provide an epistemic platform to advance our understanding of how DT endeavors, including the adoption of digital technologies, business model innovations, and innovations in collaboration mechanisms and methods of operations improvement, can affect various aspects of OM. In the discussion that follows, we delineate a review and conceptualization of DT in OM, taking stock of the topic within the field and exploring pathways for moving forward beyond the hype. In doing so, we draw attention to a change in the relationship between humans and technology, where the roles of an agent and a principal are being reversed for the first time in the evolution of the broader IS theory and practice. Specifically, we argue that the transformative nature of DT lies in an agency reversal in many organizational processes that are affected by it.

Technology evolution has been a central topic for the broader management literature, due to the transformative effect of technological change on organizations, individuals, and society at large (Grodal et al., 2023). Technology is inherent in OM theory and practice, and its role in the value-adding processes of organizations is crucial to the extent that early management theorists used the word “technology” in place of “process” when discussing what we now know as OM (Thompson, 2017). The evolution of OM, thus, has been tightly linked to the evolution of both physical technology as well as advanced IS, from the invention of the spinning jenny in the early 18th century to modern advanced algorithmic solutions. Our special issue focuses on the latter, within the context of DT and the broader consumerization of digital technologies (Gregory et al., 2018; Struijk et al., 2022). Although we use that term (DT) and argue that the contemporary forms of such technologies bear an exceptional potential for fundamental change, it is still useful to view contemporary technologies within the greater picture of the evolution of organizational IS. In doing so, we see three distinct phases in that evolution as shown in Table 1. This view departs from the idea that the contemporary digital technologies are merely linear extensions of technological evolution, in the sense that they deliver similar benefits as all of the previous technologies such as reducing the costs of data collection, storage, as well as processing, and enable faster and better decision making. Instead, we view the historical development in the role of digital technologies in OM as encompassing three major stages: stand-alone tools, integrated tools, and, contemporaneously, increasingly autonomous tools that have the potential to deliver an unprecedented change in the human-technology relationship, where DT in OM resides. We further discuss these three stages through an elaboration on the leading technologies of the time, providing a brief overview on how various digital technologies have contributed to OM practice.

From the 1970s, when IBM developed the COPICS software package for MRP, until the turn of the millennium, when vendors like Manugistics and i2 marketed advanced planning and scheduling (APS) systems for integrated supply chain optimization, the field of OM has experienced an explosion in the use of IS. In those early days, while MRP systems facilitated the day-to-day planning of manufacturing activities, CAD tools were developed to enable the design of complex components with an unprecedented level of precision. To close the loop, CIM systems emerged to facilitate the use and supervision of automated production tools resulting from the evolution of physical technologies. Although such IS combination provided support for the design, planning, and control loop of OM, each one was function specific. As additional IS got added into the picture, including sales support and procurement systems, the inherent standalone nature of such tools created interface maintenance challenges and quality problems due to redundant databases, incompatible protocols, and data formats. Such challenges, in turn, created the need for the first fundamental change in the role of IS, as depicted in Table 1. Instead of providing function-specific support, digital tools would have to provide comprehensive process-wide support. Additional benefits to such integration would ostensibly include reductions in data and software incompatibilities as well as redundancies (Jacobs & Weston, 2007).

The challenges in such organizational and technology silos were addressed by a new cohort of IS vendors. Aided by the emergence of the client–server information architecture in the 1990s, companies like SAP embraced the challenge of combining the features of the previously function-specific tools into a single, companywide software suite and database. The implementation of these ERP systems turned out to be fraught with challenges, resulting in many well-publicized failures (Davenport, 1998), yet through their inherent support for business-wide integration (Gattiker & Goodhue, 2005) and process standardization (Cotteleer & Bendoly, 2006), they ultimately proved their worth for many organizations (Tenhiälä & Helkiö, 2015). Nevertheless, it also became evident that a single ERP system was not the optimal solution for everyone, and organizations with lesser needs for integration and standardization could perform well with standalone tools (Tenhiälä et al., 2018). To serve the needs of those organizations, a supplemental group of vendors, including Appian and Pegasystems, emerged to resolve the interface and redundancy problems in organizational workflows with a new digital tool called an iBPM system. As a natural extension to the broadening scope of the support of digital tools from individual business functions to entire business processes, a variety of technologies also emerged to support processes that spanned organizational boundaries, including radio-frequency identification for interorganizational product tracking (Bendoly et al., 2007) and APS systems featuring interorganizational supply network planning capabilities (Stadtler, 2005).

By around 2015, the industry began to witness yet another critical development in the use of digital technologies. The decades-long trajectory in physical technologies that had led to ever-increasing industrial automation started to find ways to connect directly to digital technologies without a need for a human mediator. Equipped with sensors and algorithmic solutions, advanced robotics reached a new level of autonomy, leading to breakthroughs in a variety of operational settings from warehouse automation to robotic surgeries (Mukherjee & Sinha, 2020) and increasingly in the domain of knowledge-intensive professional services (Spring et al., 2022). Contemporary robotic solutions can relieve human operators from the physical burden of work or enable doing it beyond humanly achievable precision and consistency. In combination with AI, such solutions could assume an increasing proportion of the cognitive burden, as well. To resolve cognitive challenges, AI needs large datasets for training, which are increasingly drawn from constellations of sensors and communication tools known as IoT. While earlier sensor technologies enabled remote monitoring and predictive maintenance of industrial equipment (Persona et al., 2007) as well as real-time sharing of inventory data (Bendoly et al., 2007), current AI-enabled technologies are increasingly capable of proactively controlling and adjusting equipment to optimize maintenance and the timing and quantities of inventory replenishment. Advances in data analytics and in-memory computing (IMC) have critically improved the performance of these digital technologies, kicking off a trend where humans are no longer so much the users of the technology as they are its mere supervisors. In fact, even such a supervisory role could be already questioned, as recent research shows that human interventions and adjustments to the automated decisions of digital tools are more often counterproductive than they are beneficial (e.g., Caro & de Tejada Cuenca, 2023; Ibanez et al., 2018; Kesavan & Kushwaha, 2020). Although the evolution of IS in OM can be viewed through various lenses and perspectives (Grodal et al., 2023), here we emphasize the changing roles in the human-technology relationship (see Table 1) to better understand DT in OM as far more than a simple extrapolation of prior advancements.

Concurrent with the emergence of digital technologies, and the rise of DT in OM, has been the appearance of critical questions related to how decision making can be informed or automated, as well as to how the pervasive use of digital technologies and DT impacts individual responsibilities and shifts power among producers, and consumers. Critically, decision support is increasingly provided by both human-driven analysis of such data, and advanced algorithmic solutions. In the extreme, this can represent a significant role reversal in decision-making, positioning non-human actors as decision makers and directing operational moves carried out by humans (Mims, 2021; Schechner, 2017). To best leverage the potential of both actors in advanced decision-making, human-machine interaction needs to be carefully designed (Gante & Angelopoulos, 2022, 2023; Hoberg & Imdahl, 2022). The spectrum from human driven, technology supported to technology driven, human-supported dynamics—with various degrees of concentration along this spectrum (i.e., a distribution of use)—increasingly characterizes and distinguishes contemporary organizations. This applies to both the case of administrative processes as well as to processes such as order-picking in warehouses (e.g., Sun et al., 2022). Less clear are the costs and benefits of specific levels of agency reversal for organizations, for example, when technology usurps the traditional principal role held by humans, or the pressures that these place on the stewardship of the datasets needed to train algorithmic solutions (Angelopoulos et al., 2021).

The papers in this special issue exemplify the impact that DT is currently having in OM and the strategic considerations that are rapidly emerging. For instance, Stark et al. (2022) provide a rich discussion of how comprehensive digitalization can be leveraged to replace traditional procedural control. The authors highlight, through example, the potential shift in decision making and power attributable to DT in operational process settings, as well as related impacts across the supply chain. They argue that a key to comprehensive impact is a shift from what they refer to as “procedural syntax” to “object-interactive syntax.” In short, the claim is that the manner in which manufacturing activity is encoded for both managerial discussion purposes, as well as in many associated legacy IS, is essentially as a confederation of separate concepts. Overlap exists only to the degree that information can be transferred at a level of minimal sufficiency between adjacent functions. For example, only certain details of the design process are shared with manufacturing, sourcing, and delivery. Similarly, only minimally sufficient data flows from these functions back to design. This tactic, while meaningful in an era where data transmission and storage were highly limited, is nevertheless baked into many approaches to OM and even pervades modern approaches to technical integration, thus imposing constraints that do not actually represent the digital capabilities of contemporary organizations.

With the advent of digital counterparts, through the addition of part functionality in the design models used in digital equipment contexts, a more interactive syntax arises with pervasive touch points that permits far deeper scopes of automation—automation that is self-correcting in some instances and capable of making the kind of process changes only possible earlier through human diagnosis and intervention. In short, Stark et al. (2022) describe one critical aspect of the conditions under which shifts in digital technology from agent to principal roles become viable. However, the authors also discuss the potential for hybrid, simultaneous use of both forms of encoding, either as a transitional mechanism or as a strategic steady-state. That is, they recognize that a “big-bang” shift from fully procedural to fully object-interactive syntax is likely not a reasonable option for many existing organizations. Rather, they expect procedural and object-interactive syntax to co-exist, with DT likely serving in both agent and distinctly principal roles for the foreseeable future.

A related discussion of encoding-for-integration is demonstrated in Sampson and Pires dos Santos (2023). While the contextual focus of the authors is distinct from that of Stark et al. (2022) in that the concern is that of professional services rather than manufacturing, a very similar message emerges: there are virtues to increased process automation with regards to offloading menial work from employees, as well as reducing cost and increasing speed and consistency. Demonstrated through empirical field data and applied simulation methods, the authors suggest that achieving these gains depends on encoding and open communication. Delegation to automated agency, and in some respects de facto principal decision-making, are described as playing a key role in enabling hybrid DT solutions to these ends, with resulting shifts in control without loss of strategic advantage.

Kude et al. (2023) make an alternative argument, suggesting that greater designation of separability in work can be influential in driving digital outcomes. While integration and delineation may appear to be at odds with one another, they are all part of the same virtuous ideal: a comprehensive, systematic organization of data and workflows. In OM, we have for long appreciated that the extremes of specialization and generalization—depth and breadth—are neither points to which organizations should aspire, but that a healthy and appropriate mix of the two is needed. Consequently, we can also argue that modularization and integration both play critical roles. Indeed, it is hard to imagine one without the other, as “integration” implies deeper connection of elements that have functionality unto themselves but which in combination form a system, and “modularity” implies the ability to compartmentalize specific elements of a larger system in a fashion that permits various forms of reintegration. In the arguments posed by Kude et al. (2023), such an architectural modularity is core to both the success of digital innovation and the wellbeing of those who are tasked with it. Since the discussion of Kude et al. (2023) focuses on the development of software, one might also ask: Are modularity and integration not only characteristics of effective DT artifacts, but also core DT implementation processes? Further, what are the effects of other key architectural patterns, such as cyclicality (cf. Sosa et al., 2013)? If we are to take the insights from the papers in this special issue to heart, the answers would seem to be a resounding “yes” and “we need to find out!”

Lastly, and relating to our discussions of value that can be generated through well designed and integrated approaches to human-technology interaction, Brau et al. (2023) investigate the fusion of human judgment with algorithmic solutions in demand planning. They introduce the innovative approach of Human-Guided Learning. Their approach revolutionizes the training of algorithmic models by incorporating human judgment through an iterative, linear weighting process, resulting in significantly improved accuracy compared to the established integration methods. By highlighting the impact of integration techniques, the study establishes that the effectiveness of human judgment in demand planning hinges on the specific integration method employed, thereby paving the way for further exploration and research in this area. Research such as this should prove instrumental as firms rationalize shifting co-producer and agent/principal roles in a manner that capitalizes on strengths and avoids sidelining contextual intelligence.

As illustrated in the previous section, our special issue attempt to provide an epistemic platform for advancing our understanding of how DT endeavors, including the adoption of digital technologies, business model innovations, as well as innovations in collaboration mechanisms and methods of operations improvement, can affect various aspects of OM. In doing so, we emphasize the urgency of focusing on the implications of agency reversal in many organizational processes affected by the transformative nature of digital technologies. Specifically, we highlight a change in the relationship between humans and technology, where the roles of an agent and a principal are being reversed for the first time in the history of IS. After having delineated a review and conceptualization of DT in OM and taken stock of the topic within the broader field, here we explore pathways for moving forward beyond the hype. Given the growing importance of DT in OM, we see fruitful pathways for future research along the themes discussed in the previous sections that can incorporate conceptual, modeling, and empirical approaches.

Our special issue showcases how OM is being transformed by the implementation and adoption of novel digital technologies and how DT is increasingly becoming a key concept across the broader OM research and practice. The associated articles of the special issue address the importance of the topic, while in this guest editorial we bridge the IS and OM disciplines to further conceptualize the shifting role of agency in decision making due to the adoption of digital technologies. We do so both conceptually as well as, more specifically, with regard to advanced algorithmic solutions. An examination of the literature situated at the intersection of IS and OM suggests that this is only the beginning of what is likely to be an ongoing consideration of role shifts between human and technology agents and principals. Such an agency reversal brings forward novel issues for OM practice, new business models, and renovated supply chain architectures, as well as increased industry competition, and vital ethical considerations. To carefully study such a novel phenomenon, we need to approach it with new perspectives. The advances in algorithmic solutions have showcased that, when trained well, they can provide us with the right answers; going forward, it is more imperative than ever that we ask the right questions.