Abstract
Organizations constantly seek ways to improve their business processes. This often involves using digital technologies to enable process improvements. However, simply substituting existing technology with newer technology has limited value as compared to using the capabilities of digital technologies to introduce changes to business processes. Therefore, process analysts need to understand how the capabilities of digital technologies can be used to redesign business processes. In this paper, we conducted a systematic literature review and examined 40 case studies where digital technologies were used to redesign business processes. We identified that, within the context of business process improvement, capabilities of digitalization, communication, analytics, digital representation, and connectivity can enable business process redesign. Furthermore, we note that these capabilities enable applying nine redesign heuristics. Based on our review, we map how each capability can facilitate the implementation of specific redesign heuristics to improve a business process. Thus, our mapping can aid analysts in identifying candidate redesigns that capitalize on the capabilities of digital technologies.
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1 Introduction
Organizations constantly seek to improve their business processes to optimize the efficiency and quality of their products and services [14]. Business processes are commonly improved by enacting changes, i.e., redesigning the business processes [32]. Technology has always played an important role in how business processes are redesigned [9]. For instance, client-server architecture enabled the re-engineering of business processes [17]. Likewise, the internet made it possible to automate certain business processes by enabling online self-service [35].
In the past decades, digital technologies have ushered in new opportunities for business process improvements. This has caused a surge in initiatives to digitally transform organizations through redesigning existing business processes [48]. However, merely substituting existing technology with a newer one has limited value [9]. Rather, value is gained when capabilities of digital technologies are used to innovate and redesign business processes [8]. Therefore, analysts working with improving business processes benefit from understanding how capabilities of digital technologies can enable the redesign of business processes [36].
Several studies analyzed digital technologies through the lens of business process management (BPM). For instance, Imgrund et al. [19] explain how to approach digitalization with business process management (BPM), Van Looy [50] explores synergies between BPM and digital innovation, and Löffler et al. [30] examine how to communicate business process change in the context of digital transformation. These works, however, consider digital transformation from an overall BPM perspective. They do not consider business process redesign, which is one of the main steps of BPM [14]. Furthermore, authors, such as [3, 18, 40, 46] propose frameworks for digital technologies. These studies discuss the impact of specific digital technologies on business processes but not how to use the capabilities of digital technologies for process redesign. Thus, to the best of our knowledge, there is a lack of research on how to connect the capabilities of digital technologies with business process redesign.
We address this gap by eliciting a classification framework for digital technology-driven redesign of business processes. More specifically, we explore what capabilities of digital technologies have been used to redesign business processes and how such capabilities have been used to redesign business processes. Therefore, first, we consider the capabilities of digital technologies that can be used to redesign business processes. With this RQ, we seek to understand the distinctive capabilities of digital technologies in light of business processes. However, digital technologies can deliver value if the business process is redesigned to exploit these capabilities [9]. Therefore, we also consider how business processes can be redesigned through the capabilities of digital technologies.
Thus, the contribution of this paper is a classification framework for how capabilities of digital technologies can enable the redesign of business processes. To address the RQs and develop the framework, we conducted a systematic literature review (SLR) [22] to identify case studies where digital technologies are used to redesign business processes. Then, we identified the capabilities of digital technologies and mapped them against business process redesign heuristics proposed by Mansar & Reijers [32] and synthesized the results. The classification framework can aid analysts working with incorporating digital technologies in new or existing business processes. More specifically, it can help them consider redesigns that capitalize on the capabilities of employed digital technologies.
The rest of this paper is structured as follows. Background is outlined in Sect. 2, and related work in Sect. 3. In Sect. 4, we describe the systematic literature review protocol and present the results Sect. 5. In Sect. 6, we present and discuss the classification framework. Finally, Sect. 7 concludes the paper.
2 Background
As defined by Dumas et al. [14], “business process management (BPM) is a body of principles, methods, and tools to discover, analyze, redesign, implement and monitor business processes.” Analysis provides input necessary for redesign. Analysts can use data-driven tools [25] to e.g., analyze batch behavior in a process [27], process performance [38], or other types of process analysis [37].
In the redesign phase, process redesigns are proposed based on the analysis conducted in the previous steps. There are different approaches to business process redesign. However, but the most widely adopted catalog of best practices in process redesigns are proposed in Mansar & Reijers [32] as business process redesign heuristics. There are 29 redesign heuristics that encompass different perspectives of a business process, such as customer, product, organizational, informational, and technological. Some of the heuristics can only be applied if a technology is used. For instance, task automation uses technology to automate repetitive manual tasks [32]. Other heuristics, such as control addition, can be applied manually. However, digital technologies enable applying such heuristics more efficiently. For instance, manual control addition can be replaced with a digital technology that enables data-driven and automated controls [11].
Currently, there is no consensus on the definition of digital technologies. Some researchers argue that “digital technology” are hype words that build on concepts that have been around for a long time [48] or that it is complex as it can be viewed from different perspectives (organizational, social, etc.) [20]. Nevertheless, Lipsmeier et al. [3] describe digital technologies as “something that is made up of knowledge, skills and know-how for the creation, processing, transmission and use of digital data as well systems and procedures for practical implementation.” In this paper, we adopt this definition.
Similarly, different definitions have been proposed for a digital capability [18]. However, Korhonen et al. [24] define digital capability as the “capacity” of an enterprise “to integrate and utilize digital data and information technologies in its products, services, business processes, and organizational systems and practices to create added value to its constituents and beneficiaries.” Here, we consider digital capabilities within the specific context of business process redesign. As such, we adopt this definition but define it as the capacity of a digital technology to enable the redesign of a business process to create added value.
Several attempts have been made to categorize digital technologies and their capabilities. Thus, Pousttchi et al. [40] categorize digital technologies into three categories: communication and other enabling technologies, those combining hardware and software in intelligent systems, and data technologies. Lipsmeier et al. [3], though, propose a more refined classification of digital technologies. They divide them into analytics, connectivity, fabrication, human-to-machine interface, interactivity, sensoring, storage, and visualization. Each category shares the commonality of having the same or similar digital capabilities. Henriette et al. [18] identified six capabilities of digital technologies: dematerialization, internet technologies, analytics, mobility, social network, knowledge, and skills.
3 Related Work
Studies that classify digital technologies according to different criteria are related to our work. For instance, Pousttchi et al. [40] provide a generic model to map the relationship between the use of digital technologies and their impact on companies. As a result, they propose a model that categorizes digital technologies. In Henriette et al. [18], the authors explore the nature of digital transformation. They report what digital capabilities are impacted by digital transformation and how digitalization transforms business models and user experience. Lipsmeier et al. [3], though, identify eight technology classes that are based on digital data operations. In da Silva et al. [46], the authors present 24 key factors that organizations should consider when implementing digital factoring in the context of Industry 4.0. These studies provide valuable insight into how digital technologies and capabilities can be classified. Therefore, they are complementary to our work. However, while these studies classify digital technologies, they do not connect them to BPM or provide insights into how processes can be redesigned.
A few studies have explored the intersection between digitalization and business processes. For instance, in Imgrund et al. [19], the authors propose a framework that can serve as the starting point for organizations seeking to embark on a digital transformation. The framework presents a set of organizational capabilities required for managing digital transformation and answers questions related to how BPM can be used to increase the feasibility of digital transformation.
Ahmad [1] addresses the question of how organizations can adopt technologies best fitted for specific business processes given their context. Mendling et al. [34] analyze previous works on BPM and digital innovation and conclude that they should be researched together rather than serve as complementary fields. Furthermore, Van Looy [50] identifies obstacles that companies face when using BPM for digital innovation. The study combines critical success factors and strategic dimensions of BPM with digital innovation and argues for combining them as they depend on the organization’s context. These studies consider capabilities relevant at the organizational level to initiate digital transformation or incorporate digital technologies. Our contribution is complementary as we focus on the capabilities of digital technologies applicable to business process redesign.
4 Systematic Literature Review
In this paper, we aim to elicit a classification framework for categorizing how capabilities of digital technologies can be employed to redesign business processes. This objective can be achieved by addressing two research questions. The first aims at examining which capabilities of digital technologies have been used to redesign business processes. Then, we seek to understand how such capabilities have been used to redesign business processes. Thus, our research questions are as follows.
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RQ\(_1\). What capabilities of digital technologies have been used to redesign business processes?
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RQ\(_2\). How have capabilities of digital technologies been used to redesign business processes?
To address the research questions, we employ a systematic literature review method as it allows for summarizing existing relevant research [22] and, based on the review of existing research, developing a classification framework. In conducting the SLR, we followed the guidelines proposed by Kitchenham [22]. Therefore, we defined the search strings, selected electronic databases, defined exclusion and inclusion criteria, defined data extraction strategy, applied the search strings to identify a list of potentially relevant papers, examined the papers using exclusion and inclusion criteria to filter out irrelevant publications, and extracted data from the final list of relevant papers [22].
To identify studies that, in some form, used digital technologies for process redesign, we derived search string from the paper’s objective and research questions. First, we applied a search string that contained terms such as digital, process, improvement, redesign, change, optimization, and case study. However, the derived search strings were too broad and resulted in highly irrelevant studies. Therefore, we narrowed the search string to include the key terms “business process” and “digital”. We also added “case study” to capture studies that have implemented and, in some form, validated their findings. Thus, we applied the following search string: “business process” AND “digital” AND “case study” on Scopus and Web of Science. We selected these electronic databases as they index most publications within the domain of BPM.
The next step, defining selection criteria, serves to identify relevant studies that can provide sufficient information for addressing the research questions. The initial list of papers was filtered following exclusion and inclusion criteria. The first exclusion criterion served to clean the data for entries that were not papers (EC1). The second was to remove duplicates (EC2). The third exclusion criterion served to ensure that the study can be accessed and understood (EC3). Papers with open access, accessible via the University or in other ways accessible on internet, were considered accessible. As such, papers behind paywalls were considered inaccessible. Papers in any other language than English were also removed. The first inclusion criterion ensured that the papers explicitly discuss a particular digital technology in the context of business process redesign (IC1). Thus, papers that use digital technologies for other purposes than redesigning business processes, e.g., to enable a new business strategy or to increase employee satisfaction, were removed. The second inclusion criteria filtered out studies that do not explicitly discuss case studies and, therefore, have not been tested in practice (IC2). For instance, in [36], the authors examine blockchain technology for redesigning the timber-to-charcoal process and include a theoretical case study. However, the case study is not implemented in real-life and, therefore, is not included in our review. Likewise, we excluded papers that did not provide sufficient information to address the research questions. Thus, papers stating that business value was achieved through to digital transformation but did not discuss the implementation were discarded. Finally, as the technology landscape is shifting fast, we excluded papers dating before 2017.
Our primary search resulted in 260 papers (181 from Scopus and 79 from Web of Science). We used the search filter functions to remove studies published before 2017. Thus, the 260 were all published from 2017 and on-wards. We applied the exclusion criteria and, therefore, discarded 23 non-papers (e.g., proceedings volumes) (EC1), removed 40 duplicate studies (EC2), and 6 non-English papers (EC3). For inclusion criteria, we filtered out 108 papers that were clearly out of the scope of our study (IC1) and additional 70 papers that did not provide sufficient information or had not conducted a case study (IC2). We also conducted backward referencing, which resulted in adding 27 papers. In the end, the final list of papers consisted of 40 studies (Table 1). The final list of papers can be accessed at: https://doi.org/10.6084/m9.figshare.21947564.v2
We then proceeded with data extraction. In addition to extracting metadata of the paper (authors, title, etc.), we extracted data on the main technology used, the business process before and after the change, what changes were implemented, how the digital technology enabled the process change, the capability of the digital technology, and what redesign heuristics were applied. It should be noted that some publications did not explicitly state which redesign heuristic was used. In such cases, we examined the case and assigned the redesign heuristics they applied based on heuristics descriptions in [32].
5 Results
In this section, we present the results of our review. We begin with results regarding capabilities of digital technologies that enable business process redesign (RQ\(_1\)) and continue with results on how such capabilities have been used to redesign business processes (RQ\(_2\)).
5.1 Capabilities of Digital Technologies for Process Redesign
Our review showed that digital technologies are used for digitalization of paper-based processes. For instance, in Di Vaio and Varriale [13], a case is presented where paper-based documentation and tasks are digitalized in a supply chain process. In another example, an insurance company moved from paper to digital and then used Robotic Process Automation (RPA) technology to enable digital information processing [33]. Likewise, in Sobczak and Ziora [47], RPA was implemented in an electricity billing management process. Another way technology was used for digitalization was to introduce new digital devices in a gas turbine maintenance process [5]. In this case, they modeled and analyzed their processes. Then, having identified issues due to the limitations of paper-based processing, they introduced digital pens that enabled digital information processing. Similarly, in other cases, digital tools have been implemented. For instance, to introduce digital information processing, a library used RFID technology to redesign its processes to enable self-loan [20]. Similar solutions where capabilities of digital technologies are used for redesigning processes by introducing digital information processes are found in healthcare [16], fashion [29], and automotive industry [43]. Therefore, the first identified capability is digitalization (Table 2).
Another capability we noted is communication. Some technologies have the capability to enable effective communication and flow of information between employees and stakeholders. For instance, a Swiss insurance company implemented a workflow management system that enabled managing insurance-related documents across departments [21]. Likewise, a railway company introduced a process-based management system that enabled them to centralize a highly scattered accounting process for efficiency gains [31]. Similar solutions have also been applied in healthcare [39] and to enhance global communication between subsidiaries at an international production company [10]. Thus, we define the second capability as communication.
We also noted that digital technologies enable analytics in business processes. Analytics include collecting and analyzing data for the purpose of supporting control, transparency, and decision-making. The capability of technologies to automatically collect and analyze data has been used to enable automated inventory control [11], enable tractability in production processes [44], and for data-driven analysis of business processes [6, 7, 21, 23, 33, 45]. In addition, this capability has been used to enable automated data collection and analysis for supporting decisions in manufacturing industries [5, 15]. We refer to the third identified capability as analytics.
Our review also revealed that some technologies enable the capability to digitally represent a physical reality. For instance, digitally replicating business processes for supply chain management [45] and insurance products [33] has enabled efficiency gains. Likewise, the process for managing incidents has been digitally replicated to provide transparency [5]. Finally, in a paper production process, sensor-enabled devices were introduced to fully move from centralized information and equipment control to flexible, decentralized production monitoring [44]. Thus, digital technologies are used to digitally represent business processes, i.e., enable the capability of digital twin.
The last category of capability we identified is connectivity, i.e., enabling stakeholders or customers to gain access to systems. Digital technologies enable connectivity by providing possibilities for standardization and data management, such as sharing documents and enabling quick access to information via a web application [23, 26, 28, 42]. For instance, in Becker et al. [6], a web-based modeling system was introduced to enable knowledge sharing in a manufacturing process. In Pufahl et al. [41], a blockchain-based digital platform was introduced to facilitate transparency for financial transactions in an agricultural supply chain. Thus, the fifth identified capability is connectivity.
5.2 Business Process Redesign
The capabilities of digital technologies can enable redesigning of business processes. We use business process redesigns heuristics [32] to structure the presentation of the results. We noted that nine of the listed 29 heuristics were applied in the reviewed papers. The applied heuristics are not exclusive as processes can be redesigned using different heuristics. Therefore, one capability can enable several redesign heuristics.
Digitalization. The capability of digital technologies to record, store, and process digital information enables redesign heuristics of integral technology, task elimination, and task automation. Integral technology redesign is when an information system is introduced that removes physical constraints in a business process [32]. To this end, RFID [20, 29], digital platforms [13], and workflow systems [33, 42] have been implemented to enable digitally managing a business process. Such technologies have enabled elimination of manual tasks, such as manual invoice processing [47] and manual confirmations [43]. Finally, digitalization of processes enables task automation, such as using RPA to automate invoice processing [47], automated scanning [29], and automated data entry [43].
Communication. The capability of communication enables redesigning business processes by applying the heuristics of integration, task elimination, centralization, and specialist-generalist. Implementing a new quality management system enabled applying integration as a heuristic, i.e., integrating a business process with that of a customer or supplier, to improve transparency [10]. Another company improved their logistics process by enabling resources to remove batch processing and adopt order-based workflow [39]. Likewise, an insurance company implemented an Adaptive Case Management solution enabling them to eliminate certain tasks and apply the specialist-generalist heuristic, i.e., making resources either more specialized or generalized for task execution [21] (Table 3).
Analytics. Analytics technologies can enable redesigning business processes by applying integral technology, task automation, task elimination, and control addition heuristics. A prerequisite for employing the analytic capability is to have the data in digital format. Therefore, other process redesigns heuristics are often enabled when the integral technology heuristic has been implemented. For instance, with integral technology heuristic in place it will be possible to apply task automation to, for instance, automate data collection [15] and eliminate certain tasks [5]. In addition, analytic capability enables control addition, i.e., checking completeness and correctness of inputs and outputs [32]. In the case of an inventory process, this redesign enabled increased control and traceability of an inventory process and, in addition, ensured better compliance [23].
Digital Twin. Technologies that can produce a digital representation of physical reality enable redesigning business processes by applying integral technology and re-sequencing heuristics. Integral technology is required to digitally represent physical reality. Similar to analytics, it is a prerequisite. By digitally duplicating the process with integrated sensor-enabled devices, the production process was made more efficient by enabling workers to apply re-sequencing, i.e., move the execution of an activity to a more appropriate place [44].
Connectivity. The capability of technologies that enable connectivity enables applying integral technology, integration, task elimination, centralization, interfacing, and re-sequencing redesign heuristics to business processes. As with the previous capability, integral technology is essential for connectivity. As such, companies have used web portals [28], document management systems [23], digital collaboration tools [49], quality management systems [10], and BPM systems [33] to redesign business processes by applying the integral technology heuristic. In another case, blockchain technology was used to redesign a business process by applying the integration heuristic [41]. Connectivity has enabled the elimination of tasks by replacing manual health data management by providing customers the necessary tools to do part of the work [4]. Likewise, technologies enabling connectivity have been used to redesign business processes by applying centralization [12, 31], interfacing [42], and re-sequencing [4] heuristics.
6 Discussion
In this section, we discuss the RQs. Then, we contextualize the capabilities to business processes and, in particular, their redesign. Furthermore, we summarize the results as a classification framework that presents the redesign heuristics that each capability enables. Finally, we discuss the limitations of our study.
Our RQ\(_1\) concerns capabilities of digital technologies and, more specifically, capabilities used to improve business processes. In our review, we identified capabilities that enable (1) digitalization and digital information processing, (2) communication and flow of information, (3) analytics in business processes, (4) digital representation of physical reality, and (5) connectivity, i.e., enabling stakeholders or customers to gain access to systems. Our findings match those observed in earlier categorizations of capabilities [3, 18, 40]. The capabilities of “digitalization and digital information” and “communication and flow of information” correspond to data technologies and communication, respectively, in the framework of [40]. Likewise, “analytics in business processes” corresponds to analytics in [3] and [18]. Digital representation of a physical reality is also included in [40] and [18]. Finally, connectivity is found in both [3] and [18].
However, in previous studies [3, 18], the authors listed capabilities of mobility, human-to-machine interface, sensoring, storage, and visualization. We did not identify these as separate capabilities in the context of business processes. Rather, capabilities of sensoring are included in the digital representation of physical reality. Sensors, such as IoT, provide the needed information to digitally represent a physical flow. Storage, in the same way, is a prerequisite for connectivity that enables stakeholders and customers to access data. Finally, human-to-machine interface and visualization are means that facilitate communication. These are, in the context of business processes, not separate capabilities but part of solutions to facilitate their usage.
Our RQ\(_2\) relates to how capabilities of digital technologies have been used to redesign business processes. The capabilities of digital technologies can enable nine redesign heuristics (see Fig. 1), the most common being integral technology, task elimination, and task automation. Mansar & Reijers [32] introduce a categorization for business process redesign. In this categorization, technology encompasses integral technology and task automation. However, in our study, we identify technology being used to enable eight additional redesign heuristics. At the same time, some redesign heuristics are not enabled by digital technologies. For instance, we did not find any case where parallelism heuristics, i.e., executing activities in parallel, was applied. However, it is reasonable to argue that analytics could indicate higher efficiency if certain cases are routed to specialists. One reason for not finding examples can be lack of academic research and case studies that enables technology enabling application of the parallelism heuristic. If this holds true, an implication for research is the need to explore more cases of technology-based redesign of business processes.
Furthermore, ML and AI based solutions have been proposed to optimize business processes [37]. In this regard, it might be valuable to study if optimizations solutions deliver higher value if they also consider dynamic process adaptations and changes based on time-tested redesign heuristics.
Classification framework for digital technology-driven business process redesign (papers that have a number “F#” are not referenced in the paper but included in supplementary material (sect. 4))
Based on the results of our review, we provide a classification framework for how capabilities of digital technologies can enable the redesign of business processes (see Fig. 1). The framework is read from left to right and begins with the capabilities of digital technologies in the context of business process redesign. Then, for each capability, we list which redesign heuristics they enable. As more than one capability can facilitate the same redesign heuristic, some heuristics occur under several capabilities. For instance, both analytics and digitalization enable task automation. Next, we present an example, and, finally, we provide additional references where the same capability has been used to enable the specific redesign heuristic in question (papers that have a number “F#” are not referenced in the paper but included in the final list of the publications).
Analysts can use the framework in their redesign projects in two ways. Given a digital technology, analysts can identify what capabilities the particular digital technology has, and, thereby, identify which candidate redesign heuristics to apply to the business process. Thus, analysts might find additional improvement opportunities in their process analysis. In addition, analysts can also first consider redesigns to apply and, then, identify which technology needs to be introduced to the process to enable such a change. Analysts can also consider how existing digital technologies used in the business process can be used to facilitate the implementation of redesign heuristics. In this way, analysts can leverage existing technology for process redesign.
6.1 Limitations
There are limitations inherent to SLR studies. One is excluding relevant studies or not including relevant studies [2]. We mitigated these limitations by following the guidelines when constructing search strings [22] and applying them to the electronic databases that include scientific publications in BPM. Relevant studies could have been excluded due to being not in English or inaccessible. We searched for inaccessible papers using the internet and, when possible, requested full-length text from the authors. We addressed the limitation of not including relevant papers by applying backward referencing to include relevant studies not identified with the search string. As such, we did not eliminate these limitations but reduced threats to validity.
7 Conclusion
In this paper, we aimed to explore how the capabilities of digital technologies can be applied to business process redesign. To achieve this objective, we conducted a systematic literature review of case studies where specific digital technologies are used to change a business process. From these case studies, we identified five main capabilities of digital technologies, namely, analytics, communication, connectivity, digitalization, and digital twin. Having identified these capabilities, we mapped them against business process redesign heuristics and identified nine specific redesign heuristics that can be enabled by the capabilities of digital technologies. Then, we elicited a classification framework that can be used by analysts to identify ways by which digital technologies can be incorporated in new or existing business processes. Specifically, analysts benefit from the classification framework by considering examples of redesigns of business processes enabled by digital technologies capabilities. Although our classification can aid process analysts in their redesign efforts, it is not detailed enough to provide specific guidance for less experienced analysts. As such, we identify two possible directions for future work. The first is to provide more detailed examples and conceptualization of how each capability can enable redesign. The second is to identify, given a capability, redesign opportunities from an event log.
References
Ahmad, T.: The impact of new IT adoption on business process management. In: 13th International Conference on Research Challenges in Information Science (RCIS), pp. 1–5. IEEE (2019)
Ampatzoglou, A., Bibi, S., Avgeriou, P., Verbeek, M., Chatzigeorgiou, A.: Identifying, categorizing and mitigating threats to validity in software engineering secondary studies. Inf. Softw. Technol. 106, 201–230 (2019)
Andre, L., Michael, B., Daniel, R., Christian, K.: Framework for the identification and demand-orientated classification of digital technologies. In: 2018 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), pp. 31–36. IEEE (2018)
Bandara, W., Syed, R., Ranathunga, B., Sampath Kulathilaka, K.B.: People-centric, ICT-enabled process innovations via community, public and private sector partnership, and e-leadership: the case of the Dompe eHospital in Sri Lanka. In: vom Brocke, J., Mendling, J. (eds.) Business Process Management Cases. MP, pp. 125–148. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58307-5_8
Barz, M., Poller, P., Schneider, M., Zillner, S., Sonntag, D.: Human-in-the-loop control processes in gas turbine maintenance. In: Mařík, V., Wahlster, W., Strasser, T., Kadera, P. (eds.) HoloMAS 2017. LNCS (LNAI), vol. 10444, pp. 255–268. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64635-0_19
Becker, J., Clever, N., Holler, J., Neumann, M.: Business process management in the manufacturing industry: ERP replacement and ISO 9001 recertification supported by the icebricks method. In: vom Brocke, J., Mendling, J. (eds.) Business Process Management Cases. MP, pp. 413–429. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58307-5_22
Blasini, J., Leist, S., Merkl, W.: Developing and implementing a process-performance management system: experiences from S-Y systems technologies Europe GmbH—A global automotive supplier. In: vom Brocke, J., Mendling, J. (eds.) Business Process Management Cases. MP, pp. 37–55. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58307-5_3
Brynjolfsson, E., Hitt, L.M.: Beyond computation: information technology, organizational transformation and business performance. J. Econ. Perspect. 14(4), 23–48 (2000)
Brynjolfsson, E., McAfee, A.: The Second Machine Age: Work, Progress, and Prosperity in A Time of Brilliant Technologies. WW Norton, New York (2014)
Cee, K., Bruns, I., Schachermeier, A., Kaiser, L.F.: Adoption of globally unified process standards: the case of the production company Marabu. In: vom Brocke, J., Mendling, J., Rosemann, M. (eds.) Business Process Management Cases Vol. 2, pp. 249–259. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-63047-1_19
Dantas, R., Barbalho, S.C.M.: The effect of islands of improvement on the maturity models for industry 4.0: the implementation of an inventory management system in a beverage factory. Braz. J. Oper. Prod. Manage. 18(3), 1–17 (2021)
Di Giuda, G.M., Marcandalli, G., Sanvito, L., Schievano, M., Paleari, F.: A workflow for building site digitalization. In: 11th International Structural Engineering and Construction Conference, ISEC-11 2021, pp. 1–6. ISEC Press (2021)
Di Vaio, A., Varriale, L.: Digitalization in the sea-land supply chain: experiences from Italy in rethinking the port operations within inter-organizational relationships. Prod. Plann. Control 31(2–3), 220–232 (2020)
Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-56509-4
Fernandes, J., Reis, J., Melão, N., Teixeira, L., Amorim, M.: The role of industry 4.0 and BPMN in the arise of condition-based and predictive maintenance: a case study in the automotive industry. Appl. Sci. 11(8), 3438 (2021)
Gavrilov, G., Simov, O., Trajkovik, V.: Analysis of digitalization in healthcare: case study. In: Dimitrova, V., Dimitrovski, I. (eds.) ICT Innovations 2020. CCIS, vol. 1316, pp. 202–216. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62098-1_17
Hammer, M., Champy, J.: Reengineering the Corporation: Manifesto for Business Revolution, a. Zondervan (2009)
Henriette, E., Feki, M., Boughzala, I.: The shape of digital transformation: a systematic literature review. In: MCIS 2015 proceedings, vol. 10, pp. 431–443 (2015)
Imgrund, F., Fischer, M., Janiesch, C., Winkelmann, A.: Approaching digitalization with business process management. In: Proceedings of MKWI, pp. 1725–1736 (2018)
Karna, N., Pratama, D., Ramzani, M.: Self service system for library automation: case study at Telkom university open library. In: 2019 International Conference on Information and Communications Technology (ICOIACT), pp. 689–693. IEEE (2019)
Kim, T.T.T., Weiss, E., Ruhsam, C., Czepa, C., Tran, H., Zdun, U.: Enabling flexibility of business processes using compliance rules: the case of mobiliar. In: vom Brocke, J., Mendling, J. (eds.) Business Process Management Cases. MP, pp. 91–109. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58307-5_6
Kitchenham, B.A., Charters, S.: Guidelines for performing systematic literature reviews in software engineering. Technical report EBSE 2007–001 (2007)
Kloppenburg, M., Kettenbohrer, J., Beimborn, D., Bögle, M.: Leading 20,000+ employees with a process-oriented management system: insights into process management at Lufthansa Technik group. In: vom Brocke, J., Mendling, J. (eds.) Business Process Management Cases. MP, pp. 505–520. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58307-5_27
Korhonen, J.J., Gill, A.Q.: Digital capability dissected. In: ACIS 2018–29th Australasian Conference on Information Systems (2018)
Kubrak, K., Milani, F., Nolte, A.: Process mining for process improvement - an evaluation of analysis practices. In: Guizzardi, R., Ralyte, J., Franch, X. (eds.) RCIS. Lecture Notes in Business Information Processing, vol. 446, pp. 214–230. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05760-1_13
Lafquih, H., Elhaq, S.L., Krimi, I., Berquedich, M.: Modeling and analysis of a quality traceability framework for phosphate extraction process: evidence from morocco. Int. J. Qual. Reliab. Manage. 39, 1412–1428 (2021)
Lashkevich, K., Milani, F., Chapela-Campa, D., Dumas, M.: Data-driven analysis of batch processing inefficiencies in business processes. In: Guizzardi, R., Ralyte, J., Franch, X. (eds.) RCIS. Lecture Notes in Business Information Processing, vol. 446, pp. 231–247. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05760-1_14
Laurenza, E., Quintano, M., Schiavone, F., Vrontis, D.: The effect of digital technologies adoption in healthcare industry: a case based analysis. Bus. Process Manage. J. 24(5), 1124–1144 (2018)
Leitz, R., Solti, A., Weinhard, A., Mendling, J.: Adoption of RFID technology: the case of Adler—A European fashion retail company. In: vom Brocke, J., Mendling, J. (eds.) Business Process Management Cases. MP, pp. 449–461. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58307-5_24
Löffler, A., Prifti, L., Knigge, M., Kienegger, H., Krcmar, H.: Teaching business process change in the context of the digital transformation: a review on requirements for a simulation game. In: Proceedings of MKWI, pp. 759–770 (2018)
Ludacka, F., Duell, J., Waibel, P.: Digital transformation of global accounting at deutsche Bahn group: the case of the TIM BPM suite. In: vom Brocke, J., Mendling, J., Rosemann, M. (eds.) Business Process Management Cases Vol. 2, pp. 57–68. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-63047-1_5
Mansar, S.L., Reijers, H.A.: Best practices in business process redesign: use and impact. Bus. Process Manage. J. (2007)
Marek, J., Blümlein, K., Wehking, C.: Process automation at Generali CEE holding: a journey to digitalization. In: vom Brocke, J., Mendling, J., Rosemann, M. (eds.) Business Process Management Cases Vol. 2, pp. 19–28. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-63047-1_2
Mendling, J., Pentland, B.T., Recker, J.: Building a complementary agenda for business process management and digital innovation. Eur. J. Inf. Syst. 29(3), 208–219 (2020)
Milani, F., García-Bañuelos, L., Dumas, M.: Blockchain and business process improvement. BPTrends newsletter (2016) (2016)
Milani, F., García-Bañuelos, L., Reijers, H.A., Stepanyan, L.: Business process redesign heuristics for blockchain solutions. In: EDOC, pp. 209–216 (2020)
Milani, F., Lashkevich, K., Maggi, F.M., Francescomarino, C.D.: Process mining: A guide for practitioners. In: Guizzardi, R., Ralyte, J., Franch, X. (eds.) RCIS. Lecture Notes in Business Information Processing, vol. 446, pp. 265–282. Springer, Cham (2022)
Milani, F., Maggi, F.M.: A comparative evaluation of log-based process performance analysis techniques. In: Abramowicz, W., Paschke, A. (eds.) BIS 2018. LNBIP, vol. 320, pp. 371–383. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93931-5_27
Øvrelid, E., Halvorsen, M.: Supporting process innovation with lightweight IT at an emergency unit. J. Integr. Des. Process. Sci. 22(2), 27–44 (2018)
Pousttchi, K., Gleiss, A., Buzzi, B., Kohlhagen, M.: Technology impact types for digital transformation. In: 2019 IEEE 21st Conference on Business Informatics (CBI), vol. 1, pp. 487–494. IEEE (2019)
Pufahl, L., Ohlsson, B., Weber, I., Harper, G., Weston, E.: Enabling financing in agricultural supply chains through blockchain. In: vom Brocke, J., Mendling, J., Rosemann, M. (eds.) Business Process Management Cases Vol. 2, pp. 41–56. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-63047-1_4
Rau, I., Rabener, I., Neumann, J., Bloching, S.: Managing environmental protection processes via BPM at Deutsche Bahn. In: vom Brocke, J., Mendling, J. (eds.) Business Process Management Cases. MP, pp. 381–396. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58307-5_20
Schindlbeck, B., Kleinschmidt, P.: Integrate your partners into your business processes using interactive forms: the case of automotive industry company HEYCO. In: vom Brocke, J., Mendling, J. (eds.) Business Process Management Cases. MP, pp. 485–501. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58307-5_26
Schönig, S., Ermer, A., Jablonski, S.: Sensor-enabled wearable process support in the production industry. In: vom Brocke, J., Mendling, J., Rosemann, M. (eds.) Business Process Management Cases Vol. 2, pp. 29–40. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-63047-1_3
Shevtshenko, E., Mahmood, K., Karaulova, T., Raji, I.O.: Multitier digital twin approach for agile supply chain management. In: ASME International Mechanical Engineering Congress and Exposition, vol. 84492, p. V02BT02A012. American Society of Mechanical Engineers (2020)
da Silva, E.H.D.R., Angelis, J., de Lima, E.P.: In pursuit of digital manufacturing. Procedia Manuf. 28, 63–69 (2019)
Sobczak, A., Ziora, L.: The use of robotic process automation (RPA) as an element of smart city implementation: a case study of electricity billing document management at Bydgoszcz city hall. Energies 14(16), 5191 (2021)
Tomat, L., Trkman, P.: Digital transformation-the hype and conceptual changes. Econ. Bus. Rev. 21(3), 351–370 (2019)
Vaia, G., DeLone, W., Arkhipova, D., Moretti, A.: Achieving trust, relational governance and innovation in information technology outsourcing through digital collaboration. In: Agrifoglio, R., Lamboglia, R., Mancini, D., Ricciardi, F. (eds.) Digital Business Transformation. LNISO, vol. 38, pp. 285–300. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47355-6_19
Van Looy, A.: On the synergies between business process management and digital innovation. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 359–375. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_21
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This research is supported by the Estonian Research Council (PRG1226) and the European Research Council (PIX Project).
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Kubrak, K., Milani, F., Nava, J. (2023). Digital Technology-Driven Business Process Redesign: A Classification Framework. In: Nurcan, S., Opdahl, A.L., Mouratidis, H., Tsohou, A. (eds) Research Challenges in Information Science: Information Science and the Connected World. RCIS 2023. Lecture Notes in Business Information Processing, vol 476. Springer, Cham. https://doi.org/10.1007/978-3-031-33080-3_13
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