The SPEED Act (H.R. 3838) and the FORGED Act (S. 5618) are two legislative proposals aimed at reforming the Department of Defense (DoD) acquisition system to enhance efficiency, agility, and alignment with warfighter needs. Both bills address overlapping challenges in defense acquisition, such as bureaucratic inefficiencies, slow delivery of capabilities, and the need for greater integration of commercial solutions. However, they differ in scope, approach, and specific reforms. Below is a detailed analysis of how the bills are complementary and how they differ.
The SPEED Act and FORGED Act share a common goal of modernizing DoD acquisition processes to deliver capabilities faster, reduce costs, and strengthen the defense industrial base. Their complementary aspects include:
Streamlining Acquisition Processes
SPEED Act: Focuses on aligning acquisition with warfighter priorities, streamlining procurement thresholds (e.g., raising simplified acquisition thresholds), and reducing bureaucratic hurdles through targeted amendments (e.g., Sections 101, 303).
FORGED Act: Takes a broader approach by repealing over 250 outdated or redundant provisions (Section 101) and introducing automatic sunset clauses for reporting requirements (Section 103), aiming to eliminate unnecessary administrative burdens.
Complementarity: Both bills aim to reduce bureaucracy, but SPEED Act focuses on refining existing processes, while FORGED Act aggressively eliminates obsolete laws, together creating a leaner acquisition framework.
FORGED Act: Mandates treating certain products as commercial unless justified otherwise (Section 310), simplifies commercial contracting clauses (Section 313), and authorizes consumption-based solutions (Section 315).
Complementarity: Both bills prioritize commercial integration, with SPEED Act focusing on specific mechanisms (e.g., data-as-a-service) and FORGED Act establishing broader commercial preference policies, enhancing the DoD’s ability to leverage private-sector innovations.
Rapid Acquisition Pathways
SPEED Act: Enhances rapid acquisition through modular open system approaches (Section 4401) and emphasizes iterative development to address evolving threats.
FORGED Act: Formalizes rapid prototyping and fielding pathways (Section 317, Section 3602) and exempts certain programs from traditional requirements like the Joint Capabilities Integration and Development System (JCIDS) (Section 101).
Complementarity: SPEED Act’s modular systems and FORGED Act’s rapid pathways work together to accelerate capability delivery, with SPEED focusing on system design flexibility and FORGED on procedural exemptions.
Workforce Development
SPEED Act: Mandates a Comptroller General review of the Defense Acquisition University (DAU) and acquisition workforce training (Title V, Section 101) to address gaps in skills and retention.
FORGED Act: Establishes an engineering workforce development program (Section 401(h)) to train personnel in qualification, testing, and airworthiness for secondary sourcing.
Complementarity: Both bills strengthen the acquisition workforce, with SPEED Act focusing on broad training and retention and FORGED Act targeting specialized skills for supply chain management, collectively enhancing workforce capability.
Supply Chain and Industrial Base Resilience
SPEED Act: Includes provisions for advanced manufacturing (Section 405) and surge capacity (Section 406) to bolster the industrial base.
FORGED Act: Establishes a secondary sourcing program (Section 401) and an industrial expansion program (Section 402) to enhance supply chain flexibility and rapid qualification.
Complementarity: Both bills address supply chain vulnerabilities, with SPEED Act emphasizing manufacturing innovation and FORGED Act focusing on rapid sourcing and qualification, together improving industrial readiness.
Joint Requirements and Integration
SPEED Act: Creates the Requirements, Acquisition, and Programming Integration Directorate (RAPID) (Section 186) to coordinate joint requirements and acquisition strategies.
FORGED Act: Reforms the Joint Requirements Oversight Council (JROC) and establishes the Joint Requirements and Programming Board (Section 202, Section 186) to streamline requirements.
Complementarity: Both bills enhance coordination of joint requirements, with SPEED Act’s RAPID and FORGED Act’s Board providing overlapping mechanisms to align acquisition with operational needs.
How the Bills Are Different
While the bills share common goals, they differ in scope, approach, and specific focus areas:
Scope of Deregulation
SPEED Act: Takes a targeted approach, amending specific sections of Title 10 (e.g., Sections 139, 3103, 1737) to refine processes without broad repeals.
FORGED Act: Aggressively repeals over 250 provisions across Title 10 and multiple NDAAs (Section 101), targeting entire chapters (e.g., Chapters 205, 258, 327) to eliminate outdated requirements.
Impact: FORGED Act’s extensive deregulation is more sweeping, potentially creating a cleaner slate but risking gaps in oversight, while SPEED Act’s incremental changes are more conservative and focused.
Acquisition Role Definitions
SPEED Act: Enhances the Program Executive Officer (PEO) role (Section 102) with responsibilities for trade-offs, program termination, and innovation, maintaining the traditional PEO structure.
FORGED Act: Replaces PEOs with Portfolio Acquisition Executives (PAEs) (Section 201), granting broader authority over portfolios and direct control over requirements and programming.
Impact: FORGED Act’s PAE model is more transformative, shifting to portfolio-level management, while SPEED Act strengthens existing roles, reflecting different philosophies on organizational reform.
Approach to Commercial Solutions
SPEED Act: Focuses on specific commercial mechanisms, such as data-as-a-service (Section 4324) and payment clarifications (Section 305), with targeted threshold adjustments.
FORGED Act: Establishes a broader commercial preference policy (Section 314), mandates single-clause requirements for commercial contracts (Section 313), and introduces consumption-based solutions (Section 315).
Impact: FORGED Act’s commercial reforms are more systemic, aiming to standardize commercial practices, while SPEED Act’s are more specific, targeting niche areas like data access.
Supply Chain and Industrial Base Focus
SPEED Act: Emphasizes advanced manufacturing (Section 405) and surge capacity (Section 406), with a focus on technological innovation and industrial readiness.
FORGED Act: Prioritizes rapid secondary sourcing (Section 401) and industrial expansion (Section 402), with detailed processes for qualification and life-of-type buys.
Impact: FORGED Act provides more granular supply chain reforms, particularly for wartime scenarios, while SPEED Act focuses on broader manufacturing advancements.
Budgetary Reforms
SPEED Act: Does not explicitly address budgetary structure but implies cost savings through streamlined processes and commercial integration.
FORGED Act: Includes a comprehensive budget structure review (Section 501) and repeals specific budgetary requirements (Section 503) to consolidate program elements.
Impact: FORGED Act’s budgetary focus is more explicit, aiming to reduce fragmentation, while SPEED Act’s cost-efficiency measures are embedded in process reforms.
Reporting and Oversight
SPEED Act: Relies on existing oversight mechanisms, with a Comptroller General review (Section 101) to assess workforce programs.
FORGED Act: Introduces automatic sunset clauses for future reports (Section 103) and requires public contract award notices (Section 312) for transparency.
Impact: FORGED Act reduces long-term reporting burdens but increases transparency requirements, while SPEED Act maintains traditional oversight with targeted reviews.
Legislative Context and Timing
SPEED Act: Introduced in the 119th Congress, 1st Session (2025), reflecting a forward-looking approach aligned with Executive Order 14265 (April 15, 2025).
FORGED Act: Introduced in the 118th Congress, 2nd Session (December 19, 2024), indicating an earlier legislative push.
Impact: SPEED Act may build on FORGED Act’s reforms, potentially refining or expanding them in a later session, but their timing suggests different legislative priorities.
Potential Synergies and Conflicts
Synergies: The bills complement each other by addressing different facets of acquisition reform. SPEED Act’s focus on workforce training, modular systems, and data-as-a-service could enhance FORGED Act’s rapid acquisition pathways and commercial preferences. Together, they could create a comprehensive framework for agile, cost-effective acquisition with a robust industrial base.
Conflicts: The FORGED Act’s extensive repeals could create gaps that SPEED Act’s targeted amendments may not address, potentially leading to inconsistencies. The PAE model (FORGED) might conflict with SPEED Act’s PEO enhancements, requiring clarification on roles. Overlapping requirements (e.g., RAPID vs. Joint Requirements and Programming Board) could cause confusion if not harmonized.
Recommendations for Harmonization
To maximize their complementary potential and minimize conflicts:
Align Role Definitions: Clarify whether PAEs (FORGED) supersede or coexist with enhanced PEOs (SPEED) to avoid organizational overlap.
Coordinate Repeals and Amendments: Ensure FORGED Act’s repeals do not undermine SPEED Act’s amendments, particularly for overlapping Title 10 sections (e.g., 181, 186).
Integrate Supply Chain Efforts: Combine SPEED Act’s manufacturing focus with FORGED Act’s secondary sourcing to create a unified industrial base strategy.
Harmonize Commercial Policies: Align SPEED Act’s data-as-a-service and payment reforms with FORGED Act’s consumption-based solutions for consistent commercial integration.
Legislative Coordination: If both bills progress, Congress should reconcile them to avoid redundant or conflicting provisions, potentially merging key elements into a single reform package.
Conclusion
The SPEED Act and FORGED Act are highly complementary, both aiming to modernize DoD acquisition by reducing bureaucracy, leveraging commercial solutions, and enhancing supply chain resilience. SPEED Act focuses on refining existing processes, workforce development, and specific innovations like modular systems, while FORGED Act pursues broader deregulation, portfolio management, and rapid acquisition pathways. Their differences in scope and approach reflect distinct strategies—SPEED Act’s incrementalism versus FORGED Act’s transformative sweep—but together, they could create a robust framework for agile, efficient defense acquisition. Careful coordination will be needed to align their provisions and avoid conflicts, ensuring a cohesive reform effort.
This study is a systematic review to determine a conceptual framework for digital engineering, the objective being to select what and how to digitalize Department of Defense (DoD) acquisition processes, data, and decisions. The research question was, What are the best practices for Digitalization and Industry 4.0 to inform DoD acquisition programs? The study analyzed 20 peer-reviewed scholarly articles from the last five years,written by academics and practitioners from 19 countries, focused on Digitalization and Industry 4.0 methods and technologies. This study had five major findings: digitalization projects begin with strategic choices; digitalization is done within an ecosystem that constrains the technical options; digitalization requires a method of execution that assesses opportunity and limits risk; digitalization results in new processes using new data models that enable better decisions; feedback on that new business model will come internally from users and externally from customers.
Keywords: digital engineering, digitalization, Industry 4.0, framework, implementation, strategy
Determining a Digital Engineering Framework
DoD published its Digital Engineering (DE) Strategy in 2018. That was followed in 2020 by the Naval Digital Systems Engineering Transformation (DSET) Strategy. Both have the same five goals. The question has arisen of whether or not digital engineering (DE) is a new interdisciplinary branch of engineering, like systems engineering (SE) is a branch of industrial engineering. At this time, it has no distinct scientific principles applied to build particular things, no unique processes, methods or protocols; it is only a policy. However, the commercial world embraced Digitalization and Industry 4.0 out of necessity and has realized great opportunities that government can leverage.
Problem Statement
Executing acquisition plans in a predictable, fully resourced manner is challenging (Kraft, 2015). The National Defense Strategy states greater efficiency in procurement is a national priority (Department of Defense, 2018a). The National Defense Business Operations Plan declares that reforming the business processes is a key strategic goal (Department of Defense, 2018b). The resulting Digital Engineering Strategy admits that DoD lags industry on digital transformation solutions (Department of Defense, 2018c).
The DoD Digital Engineering Strategy has five Goals:
Formalize the development, integration and use of models to inform enterprise and program decision-making.
Provide enduring, authoritative source of truth.
Incorporate technological innovation to improve the engineering practice.
Establish a supporting infrastructure and environments to perform activities, collaborate, and communicate across stakeholders.
Transform the culture and workforce to adopt and support digital engineering across the lifecycle.
The DAU glossary defines digital engineering as “… an integrated digital approach that uses authoritative sources of systems’ data and models as a continuum across disciplines to support life cycle activities from concept through disposal (Defense Acquisition University, n.d.).” However, neither the goals nor the definition answers the critical questions of what or how to implement digitalization.
Rationale
Blackburn et al. (2018) was the report that formed the foundation of the DoD Digital Engineering Strategy, later restated and published in Bone et al. (2019). Neither articulated a conceptual framework for implementation. That is the rationale for this study.
Digital Engineering discussions often include unfamiliar and somewhat fluid terms. These may include Digital Thread (Kraft, 2020), Digital Twin (Madni et al., 2019), Digital Surrogate (Chakraborty et al., 2020), Electronic Prototype (Rieken et al., 2020), Authoritative Source of Truth (Kraft, 2019), Government Reference Architecture (Department of Defense, 2010), Open Architecture (Keller, 2021), and Agile Software (SAFe 5, n.d.). This study generally avoids them.
Objective
The objective of this study is to identify the current state of Digitalization practices and methods, and to identify a conceptual framework and notional integration of business processes to data products to structured decisions that would satisfy the goals of the DoD Digital Engineering Strategy. This study is a systematic review.
Potential Significance
Newly digitalized processes would be documented and constrained, with their triggers, inputs and outputs defined. Policy mandates imposed on a major defense acquisition program (MDAP) would be knowable and trackable over the life cycle of an acquisition program. Program decisions could be made with a common operating picture of the technical and managerial context around a given problem on a variety of levels, in a variety of functions, across the enterprise.
Theoretical Framework
General Systems Theory (von Bertalanffy, 1972) provides a framework that can bridge between systems engineering, business process management, and decision science. A biologist, Bertalanffy published his Theory of Organic Shape, “Gestalt”, in 1926. He published his view of organisms as physical systems in 1940, and ultimately the seminal General Systems Theory (von Bertalanffy, 1950). A modern conceptual framework adapted from Marcketti et al. (2009) is shown in Figure 1. General Systems Theory Conceptual Framework.
The International Council on Systems Engineering (INCOSE) states that Systems Engineering emerged concurrently with Bertalanffy, at Bell Telephone Labs (INCOSE, n.d.). Hall (1962) defined a methodology for systems engineering to formalize and teach the principles of it. Kossiakoff & Sweet. (2002) cite several approaches, including one adopted by the Defense Acquisition University for instruction, shown in Figure 2. Systems Engineering Process. It bears note this engineering process is defined by inputs, a multi-step process, outputs, and feedback loops like Systems Theory.
Note: Adapted from Defense Acquisition University (2001, p. 6)
Business Process Management
Dumas et al. (2013) state that Business Process Management (BPM) is how work should be performed in order to ensure consistent outputs and to take advantage of improvement opportunities. This includes a circular lifecycle of process identification, monitoring, modeling, analysis, and redesign. Business Process Model Notation (BPMN) is the industry standard and is defined by the Object Management Group (OMG), as they do for Systems Modeling Language (SysML). Figure 3. Business Process Model and Notation Example shows the Microsoft Visio default process modeled with inputs, outputs, and feedback loop, also like Systems Theory.
Note: Adapted from Deitrick & Wentz (2015, p.548).
Synthesis
General Systems Theory (von Bertalanffy, 1972) is the parent theoretical lens for this study. It describes open systems as organisms that have input, throughput, output, react to external forces and have a feedback loop. Systems engineering as described by Kossiakoff & Sweet (2002) also has inputs, a process, output, feedback loops, and external forces. Deitrick & Wentz (2015) described decision processes with similar components. Inputs, output, feedback loops, and external forces are the archetypal objects in a BPMN process. Therefore, this framework is a convenient means to bridge these disciplines.
Methodology
General
This study is a systematic review of scholarly journals to provide the evidence-based current state of Digitalization and Industry 4.0 practice & methods. Petticrew & Roberts (2006) describe one method of performing a systematic review. Barends et al. (2017) offers a more streamlined approach for narrower questions that require rapid evidence assessments. The generally accepted method for more exhaustive review is the PRISMA, recently updated by Page et al. (2020). The PRISMA Checklist identified 27 items for consideration of inclusion in a systematic review. The research approach for this study was adapted from that, and is shown below in Figure 5. Overview of Research Approach.
Figure 5
Overview of Research Approach
Note. Adapted from PRISMA Checklist (Page et al., 2020)
Research Question
The research question was developed using the PICOC (Population, Intervention, Comparison, Outcome, Context) framework (Barends et al., 2017). Initially, the population was to be the financial technology industry but scholarly research on that segment proved too narrow, so the population was broadened to general business. The intervention was Digitalization and Industry 4.0 practices & methods, as they are being applied by business operations. Comparison would be to the existing DoD practices. The outcome was improved business processes to create better data products to make better decisions. The context was DoD acquisition programs. Application of PICOC is summarized in Table 1 and led to development of the following research question, What are the best practices for Digitalization and Industry 4.0 to inform DoD acquisition programs?
Table 1
PICOC Framework
Population
Who
Commercial Industry
Intervention
What or How
Digitalization and Industry 4.0 practices & methods
Comparison
Basis
Existing DoD processes, data products, and decisions
Outcome
Goal
Better planned and dynamic decision making, with lean processes.
Context
Circumstance
DoD acquisition program
Note: Adapted from Barends et al. (2017)
Eligibility
The inclusion criteria was restricted to peer-reviewed, scholarly journal articles. Only full-text articles were sought. For a fast-moving field, only articles from the last five years were accepted. While natural language processing translations provide extraordinary access, English language publications offer less risk of miscommunication. Sources were restricted to journals titled “business” or “management” that had published more than three articles on topic within the last three years, demonstrating sustained interest by the publisher, reviewers, authors and readers.
Information Sources
Google Scholar and ResearchGate were used to conduct initial scoping studies and find preliminary evidence on ‘digital engineering’ that address the current state of digitalization best practices, frameworks, strategies, and implementations, for process, or data, or decisions. The final search of UMGC OneSearch for evidence was reported using the PRISMA flow diagram (Moher et al., 2009).
Search Strategy
A set of Boolean search terms was developed with the assistance of UMGC librarians. Table 2 explains how they were derived. The final search set was: business AND technology, digitization OR digitalization, “best practice” OR framework, strateg* OR implement*, process OR data OR decision.
Table 2
Search Terms & Strings
Concept
Search Term
Technology Industry
business AND technology
Digitalization and Industry 4.0
digitization OR digitalization
practices & methods
“best practice” OR framework AND strateg* OR implement*
Existing DoD processes, data products, and decisions
For the study to be of value the source articles must be of quality. Critically appraising data sources prevents information overload, ensures relevance and is a best practice for evidence-based management (Rousseau, 2006). Weight of Evidence (Gough, 2007) was used to assess the coherence, appropriateness and relevance of articles. TAPUPAS (Pawson et al., 2003) was used to evaluate the selected articles for transparency, rigor, ethics, and quality assessments for inclusion.
Data Collection
Article meta data extraction involved collecting information such as year of publication, research design, sample size, population (e.g., industry, type of employees), and type of study. Overall trustworthiness was judged. Core data extracted were the explicit findings, discussions, or conclusions of each article.
Synthesis
Collected data was recoded using the theoretical lens of Systems Theory, as relevant to the input, throughput, output, external force, or feedback of the open system. The categorized data was viewed for emergent themes. In the end, inputs clearly shaped strategic decisions, the throughput was the process of digitalization, external forces were part of the ecosystem or technical options, the output was a new business model, and feedback was provided by users and customers.
Findings
Input: Strategy Decisions
Blackburn et al. (2017) studied big data implications on research and development (R&D). They explored three important questions in the degree of change: how would big data refine, innovate, or transform R&D. Those mapped to impacts on strategy, people, technology and process.
Tortorella et al. (2021) explored the impact of Industry 4.0 on lean automation. They found process-oriented technologies had more impact on lean production than product and service technologies. This suggests a choice depending upon the desired target for impact.
Kristoffersen et al. (2020) proposed a Smart Circular Economy for manufacturing companies. This framework translates strategies into business analytics outcomes with digital technologies. It has three major dimensions that are relevant, each with degrees of implementation: Data Transformation, Resource Optimization, and Data Flow Process. This is shown in Figure 6.
Figure 6
Decision Tool Example
Note: Adapted from Kristoffersen et al. (p248, 2020)
Nosalska et al. (2019) found Industry 4.0 to be a multidimensional system with numerous terms, categories and variables across its dualistic nature of technical and business. They documented the most common Industry 4.0 design principles over several years of publication. The top recurrent principles were flexibility, real-time capability, decentralization, and modularity.
There were warnings as well. Donnelly (2019) cautioned to avoid over-digitization, while encouraging formal and informal knowledge exchange. This was a key strategic consideration given the tensions of digital transformation.
Throughput: Process
Almost uniformly, the focus was not only on the process of how to digitalize, but emphasizing that business process is the most important target of digitalization. Specifically, the value of digitalization is realized through the transformed underlying business processes (Antonucci et al., 2021). Further, lean production is most affected by process technology (Tortorella et al., 2021). Lastly, process is a critical component of Industry 4.0 implementation in supply chains (Ghadge et al., 2020).
Janiesch et al. (2019) used the 6-step design science research (DSR) process for the design of autonomous agents in the Internet of Things (IoT). They described the DSR steps as 1. Problem Identification, 2. Objectives of a Solution, 3. Design and Development, 4. Demonstration, 5. Evaluation, and 6. Conclusion. They applied this to a scenario of a cyber-physical system (CPS), a self-driving car.
Linde et al., (2021) evaluated opportunities for digital modeling, and identified traps to avoid. They found a structured approach for evaluating digital business models had three phases: assessing the opportunity, managing risks, and modeling the future. Concurrently, Linde et al., (2021) found several common traps that must be avoided. First, companies in a rush may not understand the customer value they are creating, and fail to satisfy customer needs. Second, not understanding the value delivery process and how the new digitalized process fits within the rest of the corporate context has risks. Last, companies may not understand the new profit formula and means of realizing revenue, simply trusting that digitalization will have made things better.
Output: New Business Model
One critical component of Industry 4.0 implementation is the new digital business model (Ghadge et al., 2020). Another recurring theme is that technical and business-related aspects are interlocked factors (Nosalska et al., 2019). While business model change is enabled by digitalization (Laïfi & Josserand, 2016), the new business model progresses with the business modeling process (Mattsson & Andersson, 2019).
Particularly important for a government procurement agency, Mattsson & Andersson (2019) determined that public-private interaction reveals tensions that drive BPM: structural, behavioral, and organizational. Mattsson & Andersson (2019) concluded a public actor in the complex public network is a much more complex implementation.
External Force: Ecosystem and Technical
There are many external forces to consider. Cong et al. (2021) identified partners as part of the IoT ecosystem. Correani et al. (2020) described a digital transformation ecosystem in which the data platform worked with customers and other players. Dethine et al. (2020) suggested that the ecosystem adapts over time, as did Ghadge et al. (2020). Garay-Rondero et al. (2020) stated the ecosystem is digital and physical. Gastaldi et al. (2018) considered the firm’s larger ecosystem important to a transformation. Linde et al. (2021) described the ecosystem in terms of relationships. Thus the ecosystem could be recoded as people, resources, organization and supply chain, and a digitalization project will have relationships with all of them.
Ivančić et al. (2019) identified seven main dimensions of digital transformation to include strategy, people, organization, customer, ecosystem, technology and innovation. In the framework shown below, Correani et al. (2020) included data sources, platform, and artificial intelligence (AI) as shown in Figure 7. Example Digital Implementation Framework. Ghadge et al. (2020) listed data sharing and management as critical.
Figure 7
Example Digital Implementation Framework
Note: Adapted from Correani et al. (p45, 2020).
Nosalska et al. (2019) listed many Industry 4.0 key technologies such as CPS, Big Data, IIOT, Cloud Computing/Cloud Manufacturing, Services/Product-as-a-Service/Internet of Services, and System/Architecture. Tortorella et al., (2021) determined that some Industry 4.0 technologies are positively correlated with Lean Production (LP) practices, but not all. The emerging technical factors appear to be the platform, technologies, and the data.
Feedback: Users & Customers
Correani et al. (2020) stated that customers could be given immediate feedback if part of the feedback loop. Donnelly (2019) noted opportunity to provide interpersonal feedback to clients and colleagues from digitalization. Garay-Rondero et al. (2020) found feedback to the value chain, and where physical processes affect computations and vice versa. Ghadge et al. (2020) found low customer service could be due to a backlog of feedback on demand. Mattsson & Andersson (2019) found small companies were quicker to adopt platform and content changes based on user feedback, and that customer feedback was important during development.
A Digital Community Infrastructure is digital sharing platforms to share designs and social networks and/or blogs to discuss ideas, questions and projects (Rieken et al., 2020). IoT technology and Artificial Intelligence of Things (AIoT) empowers the acceleration of digital transformation, and real-time collection of data from customers to monitor their conditions or assets to update risk. (Cong et al., 2021). Matzler et al. (2018) cautions that within the existing organization, implementation is highly unlikely to succeed, therefore organizational change is essential to success.
Certainty Assessment
Lewin et al. (2018) described a method of applying the Confidence in Evidence from Reviews of Qualitative research (CERQual) approach to identify the confidence in findings. CERQual is a framework to evaluate the methods, coherence, adequacy and relevance of the data used, effectively a self-report card that adds rigor and transparency.
Discussion
Developing the Conceptual Framework
The proposed framework consists of input, throughput, output, feedback and external forces. In this model, the inputs are the strategic choices to be made for implementation:
1. Degree of Change (Blackburn et al., 2017).
Refine, or Innovate, or Transform.
2. Target for Lean Impact (Tortorella et al., 2021).
Process, or Product & Service.
3. Degree of Circular Economy (Kristoffersen et al., 2020).
Data Transformation, Resource Optimization, Data Flow Process.
4. Primary Design Principles: (Nosalska et al., 2019).
These choices should be made with the intent of best achieving the DE Strategy goals of using models to inform decision making, creating an authoritative source of truth, technological innovation, supporting infrastructure and environments, and transforming the culture and workforce.
Throughput is the process of selecting processes, then digitalizing them. A best practice is to use 6-step design science research process (Janiesch et al., 2019). During execution, evaluate the opportunities and avoid the common traps (Linde et al., 2021). The method of digitally engineering is a process itself. That process is a mini-project plan for each business process under consideration of constraining the problem, setting goals, finding a solution, test, demonstration and deployment. Constraining the problem necessarily includes assessing the opportunity for process improvement, because some process improvements may not yield sufficient benefits to make the solutions cost effective or the margin for improvement may be too small. Lastly, as the process moves forward the team must continually assess risks. If the project understands the value the process creates (why we do it), the value delivery process (how we do it), and value realization (what we get out of it) those typical traps will be escaped.
The output is a new digitalized business model, where technical and business aspects are intertwined (Nosalska et al., 2019). The more the business model changes, the more the relationships with customers, the supply chain and internal users will change, and new opportunities will arise (Cong et al., 2021; Dethine et al., 2020; Garay-Rondero et al., 2020; Laïfi & Josserand, 2016). This the entire purpose of digitalization. While a DoD acquisition program does not realize revenue (they do not get ‘paid’ by the Pentagon for systems delivered), they certainly realize costs and deliver product. Having a well-documented business model, especially one that is digitally accessible will enable resource managers to see how their funds are being used, and will enable warfighters to see how their capabilities are being delivered. In addition, legislative authorizers and appropriators will be more easily persuaded to fund programs that are transparent to them.
Many external forces are at work, but they can be grouped into ecosystem constraints and technology opportunities. The ecosystem includes people, resources, organization and the supply chain, which the entity may or may not have control of (Cong et al., 2021; Correani et al., 2020; Dethine et al., 2020; Garay-Rondero et al., 2020; Gastaldi et al., 2018; Linde et al., 2021). Technical forces include the computing environment platforms, technologies, and data (Correani et al., 2020; Ghadge et al., 2020; Ivančić et al., 2019). Technologies do not equally benefit all desired outcomes (Tortorella et al., 2021), but several are key to Industry 4.0 application (Nosalska et al., 2019).
While the number of external forces at work could be infinite, the list must be constrained to provide meaningful decision points. The ecosystem forces were selected because their presence is necessary for success, even if they are constraints beyond the immediate control of the process owner. A process owner may not be able to change the people assigned, or may not have the authority to redirect resources, but both must be present in some limited quantity to succeed. A small operation may have complete control of its organization and culture, while many will be part of a larger organization with a set culture. Both can succeed, but the choices available are different. The digital supply chain for an office is crucial, and every office can identify who it depends on for data to execute owned processes, and what other offices consume data produced. Those players constitute the digital supply chain, and the participation of data suppliers and data consumers in digitally engineering a process is critical. The more they are integrated to the effort, the more opportunities may be exposed for further refinement, enhancing the recursive nature of digitalization.
Technical forces are more likely to be options than constraints. This is where people naturally gravitate to when considering digitalization. An office must consider its computing environment (platform), the technologies available (and affordable), and the data repositories it will require, create, and share. A small office may able to change its platforms, whereas a larger office inside a large organization may have no control, or limited choices within a menu. A major choice will be between on-premises (e.g. desktop) and off-premises (e.g. cloud) computing, and that choice could be driven by security considerations. Industry 4.0 technologies are centered on IoT, and there are many technologies associated with that. Application of technologies like AI, ML, NFC or Bluetooth may accelerate IoT deployment, or they may have limited impact efficacy; being judicious is important. The new business model will hinge on the new data model. Businesses can collect data they never use, or fail to relate or visualize the data they have in a usable manner. A vast repository of stove piped data serves nobody. Data that is interrelated cross-functionally is more likely to have meaning. Data should be collected, created and shared because it is required to execute a process or make a decision.
Feedback will come first from internal users, eventually from external customers, as well as the digital supply chain (Cong et al., 2021; Garay-Rondero et al., 2020; Rieken et al., 2020). Communication with them is essential to success and subsequent adjustments. Providing a means for users to provide faster feedback via a Digital Community Infrastructure will lead to changes in the organizational culture and increase likelihood of acceptance, as users feel they are an integral part of changing the way they do their work. If feedback from those users is not aggressively sought, there is a risk they will obstruct change or sabotage the project. Those users must include not only the performers of a given process, but the users of its results, the decision makers. The best process with poor visualizations may not improve outcomes.
Figure 8 illustrates the derived conceptual framework for digital engineering. DoD Goals feed project strategy decisions, the ecosystem constrains technology choices, process defines execution, a new business model delivers efficiencies, and feedback informs recursion.
Figure 8
Digital Engineering Conceptual Framework
The goal of digitalization is to arrive at a new set of processes that use a new set of data to achieve value. It is easy to see digitalization merely as a problem of new applications, or the introduction of Artificial Intelligence (AI) into processes, or new data models depending upon personal perspective or experience. However, none of those solutions alone will have sustained or meaningful impact. New models may be better, but may not result in better decisions if disconnected from a unified data model. A web services firm may be able to house petabytes of data for decades, but if it is not designed for people to use with their digital supply chain its customer value is limited. Using AI as support infrastructure to communicate with customers is common, but without integration with the business process it may not deliver value.
Entities have known they should digitalize, but did not know what or how to implement it. This framework provides a means to choose what projects to do and how to execute them in a balanced way.
Recommendations
Establish the implementation framework. Decide what external forces are strengths, weaknesses, opportunities or threats. To achieve DE goals, decide the strategy. Determine the desired degree of change, impact target, circular economy, design principles, and delimit the changeable processes. Model those processes as-is, to-be, and assess risk, as part of a disciplined project plan. Engineer a new data model on the proper platform with select technology, fed by new processes, and feeding others internally and externally. Communicate with the affected users, customers, and suppliers continuously, seeking failure early and rewarding good outcomes. Plan on necessary organizational changes. Monitor changes to the business model; prepare to adjust.
This systematic review was streamlined for rapid completion. While the search was conducted on UMGC library databases, a significant number of results were excluded based solely on the title or abstract, and may be subject to selection bias (Nunan et al., 2017). The search terms may be subject to selection bias. Article content may have been ignored or highlighted based on the authors experience, injecting confirmation bias (Spencer et al., 2018). Digitalization is a rapidly evolving practice with hotly competing providers who need a proprietary edge, which resists scholarly publication.
Engineering is commonly defined as the application of scientific principles to build things. The branches and sub-branches are differentiated by using particular scientific principles to build particular things: this is what differentiates mechanical engineering from software engineering. This paper associates the principles of systems engineering, business process management and decision science for the purposes of describing a DE framework.
The Accreditation Board for Engineering and Technology (ABET) certifies more than 3,000 programs at over 600 US institutions with 75 engineering programs, yet none are ‘digital engineering.’ Digital Engineering is not currently a defined branch of engineering; therefore, few journal articles reference it. DE might be a sub-branch sibling of Systems Engineering if a distinct DE process is proposed and accepted.
Entities have known they should digitalize, but did not know what or how to implement it. This framework provides a means to choose those projects and execute them in a balanced way. This framework is being deployed in a case study integrated product team (IPT) this summer.
According to Matzler et al. (2018) the biggest risk is the existing organization; therefore, companies need a new culture, with great incentives to innovate and small penalties for mistakes. Failing faster, cheaper, will lead to success in digital transformation.
The research question was: What are the best practices for Digitalization and Industry 4.0 to inform DoD acquisition programs? The study found broadly that an implementation framework is necessary to properly apply Industry 4.0 technology to the digitalization of business processes. In the case of the DoD, the proposed framework shows Digital Engineering Strategy goals guide implementation decisions, the ecosystem constrains technology choices, an executable process is defined, the resulting new business model delivers efficiencies, and feedback informs recursion.
A conceptual framework was proposed that integrates these elements, as an evidence-based recommendation. A DoD agency that applied this method would be a cutting edge digitally engineered entity, capable of continuous digital evolution.
References
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ACKNOWLEDGEMENTS
This paper was inspired by the practical challenges encountered with sponsored work, and would not have been possible without the involvement of several colleagues: Dr. Timothy Davis, Dr. Matthew Tillman, and Dr. Bryan Herdlick. All three gave unique insights and identified particular applications. This research was unfunded. There is no known conflict of interest. This paper was required as part of doctoral course work at University of Maryland, Global Campus, under the tutelage of Dr. Walter McCollum, to whom I am sincerely grateful.
DATA TABLES
Appendix A Acronyms
ABET
Accreditation Board for Engineering and Technology
AI
Artificial Intelligence
BPM
Business Process Management
BPMN
Business Process Management Notation
CPS
Cyber-Physical System
DAU
Defense Acquisition University
DE
Digital Engineering
DoD
Department of Defense
DSET
Digital Systems Engineering Transformation
DSR
Design Science Research
IIOT
Industrial Internet of Things
INCOSE
International Council on Systems Engineering
IPT
Integrated Product Team
LP
Lean Production
MDAP
Major Defense Acquisition Program
OASD/NII
Office of the Assistant Secretary of Defense, Networks and Information Integration
“best practice” OR framework AND strateg* OR implement*
Existing DoD processes, data products, and decisions
process OR data OR decision
Appendix C PRISMA Flow Chart
Note: Adapted from Moher et al.32
Appendix D Data Extraction
Author & Year
Population
Design
Main Findings
Effect
Limitations
Level of Trust
Antonucci et al. (2021)
165 BPM professionals, across 4 continents
Quantitative cross-sectional study (survey)
1. The value of digitalization is realized through the transformed underlying business processes. 2. Mangers can target specific BPM capabilities to achieve targeted benefits. 3. The digitalization initiative needs to be established, and then the BPM capability can use it to create additional benefits. 4. Higher BPM capabilities lead to increased value through digitalization
Cited: n/a Views: n/a Internal consistency is good (α 5 0.919) and communalities analysis indicates a shared variance. Furthermore, the removal of any item would not result in a lower Cronbach’s α. A factor analysis was found to account for a good amount of variance (KMO50.929) and to be suitable (p < 0.001).
data for the dependent and independent variables were collected through a cross-sectional survey
High
Blackburn et al. (2017)
hundreds of interviews
literature review, interviews, and case studies
Framework: Strategy, People, Technology, Process integration 1. Insights provided by big data can inform both the kinds of innovations an organization pursues and the process it uses to produce new products and services. 2. Big data can enable more efficient and effective innovation by increasing the ability of researchers to obtain needed information, allowing faster iteration on designs and supporting virtual design and experimentation. 3. Big data developments will impact the future of their organizations
Cited: 26 Views: 486
None self-identified. Interviews with 22 attendees at workshop, literature review of nine case studies
Medium
Cho & Lee (2016)
Self-driven algorithmic analysis
Quantitative framework analysis
knowledge-intensive collaboration is taking the place of more traditional cost-driven collaboration intellectual property (IP) profiles may be more indicative of capability than ‘past performance’ Invented Anywhere alliance (patent overlap, industry overlap, citation count gap small) highest potential for synergy
Cited: 4 Views: 205
None self-identified. Did not leverage other works, entirely novel. Limited validation tests.
Medium
Cong et al. (2021)
2 case studies
survey of industry applications, several case studies
the advance of IoT technology and Artificial Intelligence of Things (AIoT) empowers the retail, industrialization, and finance industries to accelerate the pace of digital transformation (1) improved customer experience and (2) optimized supply chain operations InsureTech IoTs enable real-time collection of data from customers to monitor their conditions or assets (health, home, car, etc.) in order to update risk. Artificial Intelligence of Things (AIoT) = machine learning capabilities to the connectivity, signaling, and data-exchange capabilities
Cited: n/a Views: n/a
Article does not examine all possibilities provided by the potential and large scale of IoT-based alternative data. Small sample size.
High
Correani et al. (2020)
3 companies
case studies
Framework: Scope, Data Sources, Data Platform People, Partners, AI Information and Knowledge Processes and Procedures Transformed Activities, Tasks, and Services, Customers
1. Digital facilitators impact the export, strategic and organizational management practices in a global way. 2. Operational and structuring practices are significantly less impacted. 3. e-business tools keep track of impacts and implications, and to develop the distinct capabilities needed to reach international markets.
Cited: 5 Views: 496
literature review, no original research
High
Donnelly (2019)
line and HR managers in S&ITS multinationals
388 completed surveys
1. it is important to encourage employees to divulge articulable tacit knowledge that may be valuable 2. avoid over-digitization and balance the formal and informal encouragement of knowledge exchange. 3. recognition and reward (R&R) played key roles in fostering knowledge sharing behaviors and the degree to which individuals engage in interpersonal exchanges and with KM initiatives
influenced by the participants’ work environment and relations, and the methods of data collection
High
Garay-Rondero et al. (2020)
267 articles from 1989 to 2019
systematic literature review, in-depth screening of the papers
1. Industry 4.0 is expected to enable factories to: organize and control themselves autonomously, in a decentralized fashion and in real time 2. main features of the digitalized supply chains (DSC) are: accelerated, adaptable, smart, real-time data gathering, transparent, globally connected, scalable and clustered, breakthrough, inventive and sustainable. 3. Digital supply chain model with 6 dimensions
Cited: 24 Views: [-unknon-]
the exclusion criteria that were used to perform the literature review, which eliminated papers not written in English and which may also have ignored research in other languages, as well as terms other than those defined in the search protocol
High
Gastaldi et al. (2018)
107 semi-structured interviews 14 Italian hospitals
multiple case studies with embedded analysis
input-process-output (IPO) framework P1. Hospitals can solve the innovation-refinement paradox through the implementation of different digital transformation programs. P2. The implementation of digital transformation programs entails a time variant approach, where different time frames allow for different digital solutions. P3. In case of asset digitization, the efficiency frontier can be pushed further out by undertaking different innovation-refinement (re)configurations. P4. Through different digital transformation programs, hospitals accomplish different innovation and efficiency outcomes.
Cited: 8 Views: 96
study is exploratory, generalizability is limited.
Medium
Ghadge et al. (2020)
Not specified; quantitative model development
literature review, systems dynamics model
Industry 4.0 is predicted to bring new challenges and opportunities for future supply chains. The study discussed several implementation challenges and proposed a framework for an effective adaption and transition of the Industry 4.0 concept into supply chains. Ten critical components of Industry 4.0 implementation in supply chains
Cited: 25 Views: 321
results of the simulation model are utilized to develop a conceptual model. study did not use empirical data to test the SD models only a few parameters of Industry 4.0 (RFID and cloud) were considered in the model development and evaluation
Medium
Ivančić et al. (2019)
3 case studies
case studies
important factors for successful digital transformation are the ability of an organization to change and operational excellence in the integration of external digital services with internal IT support The seven main dimensions of the digital transformation are strategy, people, organization, customer, ecosystem, technology, and innovation digital strategy does not coexist as a separate document; rather, it is a coherent part of other strategies and a way of doing business
Cited: 0 Views: 248
limited number of companies that originated from the same country
High
Janiesch et al. (2019)
unspecified
literature review problem-centered Design Science Research (DSR) approach as suggested by Peffers et al. (2007) aligned with the seven guidelines by Hevner et al. (2004).
12 requirements for the design of autonomous agents in the IoT. Design science research (DSR) process generally includes six steps or activities identification of the problem, defining the research problem and justifying the value of a solution; definition of objectives for a solution; design and development of artifacts (constructs, models, methods, etc.);demonstration by using the artifact to solve the problem; evaluation of the solution, comparing the objectives and the actual observed results from the use of the artifact; and communication
Cited: 3 Views: 683
Modeling language may lack precision for some Domains must conduct more use cases to validate the suitability of notation Must research whether our construct of an agent can be integrated with existing modeling languages
Medium
Kristoffersen et al. (2020)
32 included papers
theory- and practice-based review academic sources, practice case study examples and “grey literature”
Smart CE framework that supports translating the circular strategies central to the goals of manufacturing companies – operational maturity could be linked to both an increase in the level of resource productivity and unburdening of human decision makers. 1. data transformation levels (5): connected resources, data, information, knowledge, wisdom 2. resource optimization capabilities (5): descriptive, diagnostic, discovery, predictive, prescriptive 3. data flow processes (3): data collection, data integration, data analysis The smart CE matrix>> A> Restore, Reduce, and Avoid: prevent excessive resource use and improve the inherent efficiency and circularity potential B> Reinvent: fully decouple value creation from the consumption of finite resources.
Cited: 18 Views: 276
requires further empirical research on the Smart CE research stream theoretical development process, the proposed framework should be empirically validated
High
Laïfi & Josserand (2016)
single entity
longitudinal case study and an intensive case study
1. progressively convincing targeted audiences in specific contexts of the legitimacy of their innovative business model 2. business model change enabled by digitalization occurs at the micro level – local arrangements can allow for the emergence of new value distribution arrangements
Cited: 16 Views: 353
None self-identified. Single entity case study.
Medium
Linde et al. (2021)
14 leading industrial companies that operate in mining, manufacturing, transportation, construction, energy, forestry, telecommunications, and pulp and paper
case studies
3 digitalization traps: – Trap 1: Not Understanding Customer Value – Trap 2: Not Understanding the Value Delivery Process – Trap 3: Not Understanding the Profit Formula 3 phase framework to evaluate digital business model opportunities: – Phase A: assessing digital opportunity value, – Phase B: managing digital opportunity risks, – Phase C: modeling digital opportunity financials
Cited: 2 Views: 1,442
None self-identified.
High
Mattsson & Andersson (2019)
single entity
longitudinal case study
A business model progresses with the business modeling process: public actors concurrently change their service provisioning models. Public-private interaction reveals tensions that drive business modeling: structural, behavioral, organizational public actor in the complex public network > much more complex implementation
Cited: 6 Views: 134
Need a closer look on the public service provision modeling Framework does not examine interaction between public actors.
High
Matzler et al. (2018)
N/A
editorial
3 levels of digitalization: products & services, processes & decisions, business models >>Within the existing organization, implementation is highly unlikely to succeed
Cited: 25 Views: 3,129
opinion
Low
Nosalska et al. (2019)
675 papers
systematic literature review
“Industry 4.0 is a concept of organizational and technological changes along with value chains integration and new business models development that are driven by customer needs and mass customization requirements and enabled by innovative technologies, connectivity and IT integration.” Industry 4.0 Design Principles: Flexibility, Real-Time Capability, Decentralization, Modularity, etc. Industry 4.0 Key technologies: CPS, Big Data, IIOT, Cloud Computing/Cloud Manufacturing, Services/Product-as-a-Service/Internet of Services, System/Architecture >>technical and business-related aspects are intertwined factors
Cited: 17 Views: 166
Further analysis of interdependencies between the technical and business aspects of Industry 4.0 to better understand its influence on the modern manufacturing sector. Value changes concerning cooperation and communication between manufacturers and clients who expect personalized products; an investigation of possibilities of using data from intelligent products to improve businesses’ efficiency, and the changes in the role of marketing.
High
Rieken et al. (2020)
116 relevant articles (61 journal articles and 55 conference papers)
critical review of 116 scientific articles
“digital community infrastructure” = digital sharing platforms to share digital designs and social networks and/or blogs to discuss ideas, questions and projects with other makers Facilitation of the makerspace is an important aspect for successful operations.
Cited: 8 Views: 286
The framework on CMSs is based on a critical review of makerspace literature and not on empirical research data.
High
Tortorella et al. (2021)
147 manufacturers that are undergoing a LP implementation aided by novel information and communication technologies from I4.0.
cross sector survey
1. Industry 4.0 (I4.0) technologies are positively correlated with Lean Production (LP) practices, but not all. *Process-oriented I4.0 technologies present the most synergistic relationship with practices from nine LP operational constructs 2. Lean Automation (LA) framework can potentially overcome traditional barriers and challenges of a LP implementation. **Lean Automation (LA) refers to the incorporation of digital automation technologies into the operationalization of Lean Production (LP) practices.
Cited: 9 Views: 206
Technologies applied in this study may present a limitation, since we used the adoption level of only ten digital automation technologies as a proxy for I4.0 implementation.
High
Note. The data include evidence derived from qualitative and quantitative research studies
Appendix E Weight of Evidence
Study
Author & Year
Coherence
Appropriateness
Relevance
Average
1
Antonucci et al. (2021)
3
3
3
3
2
Blackburn et al. (2017)
3
3
1
2.33
3
Cho & Lee (2016)
3
3
1
2.33
4
Cong et al. (2021)
3
3
3
3
5
Correani et al. (2020)
3
3
3
3
6
Dethine et al. (2020)
3
3
3
3
7
Donnelly (2019)
3
3
3
3
8
Garay-Rondero et al. (2020)
3
3
3
3
9
Gastaldi et al. (2018)
3
3
3
3
10
Ghadge et al. (2020)
3
2
2
2.33
11
Ivančić et al. (2019)
3
3
3
3
12
Janiesch et al. (2019)
3
3
1
2.33
13
Kristoffersen et al. (2020)
3
3
3
3
14
Laïfi & Josserand (2016)
3
3
2
2.67
15
Linde et al. (2021)
3
3
3
3
16
Mattsson & Andersson (2019)
3
3
3
3
17
Matzler et al. (2018)
0
3
3
2.00
18
Nosalska et al. (2019)
3
3
3
3
19
Rieken et al. (2020)
3
3
3
3
20
Tortorella et al. (2021)
3
3
3
3
Note. No explicit evidence = 0, Author explains methodology briefly = 1. Author has written considerable complementary works = 2, Extensive justification and supporting body of knowledge = 3. WoE resulted in a strength rating of high (2.5 to 3.0), medium (1.5 to 2.5), or low (0 to 1.5) for each article. Adapted from Gough.34
Appendix F TAPUPAS
Author & Year
Transparency
Accuracy
Purposivity
Utility
Proprietary
Accessibility
Specificity
SUM
Antonucci et al. (2021)
3
3
3
1
3
3
3
19
Blackburn et al. (2017)
1
3
3
3
3
2
3
18
Cho & Lee (2016)
1
2
2
3
3
2
3
16
Cong et al. (2021)
3
3
3
3
3
3
3
21
Correani et al. (2020)
3
3
3
3
3
3
3
21
Dethine et al. (2020)
3
3
3
3
3
3
3
21
Donnelly (2019)
3
3
3
3
3
3
3
21
Garay-Rondero et al. (2020)
3
3
3
3
3
3
3
21
Gastaldi et al. (2018)
3
3
2
2
3
3
3
19
Ghadge et al. (2020)
2
2
2
3
3
3
3
18
Ivančić et al. (2019)
3
1
3
1
3
3
3
17
Janiesch et al. (2019)
1
2
3
2
3
3
3
17
Kristoffersen et al. (2020)
3
3
3
3
3
3
3
21
Laïfi & Josserand (2016)
3
3
2
1
3
3
3
18
Linde et al. (2021)
3
3
3
3
3
3
3
21
Mattsson & Andersson (2019)
3
3
3
3
3
3
3
21
Matzler et al. (2018)
3
3
3
1
3
3
3
19
Nosalska et al. (2019)
3
3
3
3
3
3
3
21
Rieken et al. (2020)
3
3
3
3
3
3
3
21
Tortorella et al. (2021)
3
3
3
3
3
3
3
21
Note: Scoring: 1 = some standards met, flaws detected but do not compromise the evidence, 2 = most standards met, no flaws, 3 = highest standards met. Adapted from Pawson et al.35
Appendix G CERQual
Summary of review finding
Studies contributing
Methodological limitations
Coherence
Adequacy
Relevance
CERQual assessment
Explanation
Input: Strategy Decisions 1. Degree of Change (Refine, or Innovate, or Transform). 2. Lean Impact (Process, or Product & Service). 3. Smart Circular Economy (Data Transformation, Resource Optimization, Data Flow Process, Reuse). 4. Industry 4.0 Design Principles: (Flexibility, Real-Time Capability, Decentralization, Modularity). 5. Avoid over-digitization
2, 7, 13, 18, 20
Very minor concerns. 2 was based exclusively on interviews of 22 attendees at a workshop, plus literature survey of nine case studies. 7 was analysis of interviews and 388 online surveys. 13 was a theory and practice review. Academic sources but also practice case study examples and “grey literature.” 18 was a literature review of 52 publications. 20 was a cross sectional survey of 147 manufacturers.
No concerns
Minor concerns on degree of change. None were directly related to the new DoD DE policy, so did not directly address those goals.
No concerns
High confidence
All studies were transparent and rigorous. Each was from a specific perspective, focused on a particular aspect of upfront choices to be made. However, each reinforced the need for exactly such decisions before launching a digitalization project.
Throughput: Process The value of digitalization is realized through the transformed underlying business processes. Lean production most affected by process technology Process is a critical component of Industry 4.0 implementation in supply chains. Use 6-step design science research process. Evaluate opportunities, and avoid traps.
1, 10, 12, 15, 20
1 was a quantitative cross-sectional survey of 165 BPM professionals, lacking randomness. 10 was a literature review leading to a model, then derived analysis. 12 was a comprehensive literature review. 15 was a set of case studies. 20 was a cross sectional survey of 147 manufacturers.
No concerns.
No concerns. Combination of data is robust from over 300 surveys, literature reviews and case studies.
Minor concerns. 12 was focused on specifying autonomy in the Internet of Things, which is not directly on topic, but the methods used were directly relevant (DSR approach).
High confidence
All studies were transparent and rigorous, and were seeking to establish evidence from raw data.
Output: New Business Model Technical and business-related aspects are intertwined factors Business model change is enabled by digitalization. A business model progresses with the business modeling process. Public-private interaction reveals tensions that drive BPM: structural, behavioral, organizational. Public actor in the complex public network is a much more complex implementation.
14, 16, 18
14 was a longitudinal case study. 16 was a longitudinal case study. 18 was a literature review of 52 publications.
No concerns. 18 was particularly disciplined.
Minor concern. Two studies were a single case study, however, both were longitudinal.
Minor concerns. 18 was focused on library technology and digital publishing business models. The relevant finding was the recursive nature of BPM modeling.
Medium confidence
The study’s findings externally validated each other, even on limited raw data.
External Force: Technologies Industry 4.0 key technologies: CPS, Big Data, IIOT, Cloud Computing / Cloud Manufacturing, Services / Product-as-a-Service / Internet of Services, System / Architecture. Some Industry 4.0 technologies are positively correlated with Lean Production practices, but not all.
18, 20
18 was a literature review of 52 publications. 20 was a cross sectional survey of 147 manufacturers.
No concerns. 18 was particularly disciplined.
Minor concerns. With only 2 studies directly contributing to findings, this was the least broadly supported.
No concerns
High confidence
This key finding was focused on only one of the external forces, and the evidence serves more as an exemplar than complete set.
Feedback: Users & Customers “Digital Community Infrastructure” = digital sharing platforms to share digital designs and social networks and/or blogs to discuss ideas, questions and projects. IoT technology and Artificial Intelligence of Things (AIoT) empowers the acceleration of digital transformation, and real-time collection of data from customers to monitor their conditions or assets to update risk. Within the existing organization, implementation is unlikely to succeed.
4, 17, 19
4 was two case studies. 17 was a magazine format research question. 19 was a critical review of 116 scientific articles.
No concerns
Minor concerns. 4 only performed two case studies. 17 was not presented as a data driven study, but appears to have been revised into a narrative format.
Minor concerns. 19 was focused on corporate makerspaces, however the findings are generalizable.
High confidence
19 was particularly robust, and the findings are fairly general.
Note: Adapted from Lewin et al..54 Study numbers listed in Appendix E
Everyone who tires of the latest trending buzzword salad might reflect before acting on the most recent ‘paradigm shifting transformational’ strategy from the Pentagon. However, if they have ever wondered why the advanced technology program office has more detailed insight on a pizza ordered online than a jet engine repair, they may withhold judgement (briefly). The commercial world has known for decades that the Internet changed the way business is done in very fundamental ways, and businesses must adapt or yield to more agile, efficient competition. Government, the Federal Government in particular and most especially the Department of Defense, lives in isolation of those competitive forces. But the Digital Engineering Strategy changed that.
Few people working in acquisition would disagree that programs are taking longer and costing more to deliver capabilities. The National Defense Strategy of 2018 put procurement efficiency as a national priority. The National Defense Business Plan subsequently targeted business processes a key strategic goal. The Digital Engineering Strategy then acknowledged that DoD lags industry in digital transformation. A problem has been publicly recognized, and several goals identified to address it. Unfortunately, that strategy did not specify who should fix it, and it is not apparent to the ground-level leaders if they are expected to act, or even if they can act. Leaders need a means to decide if they can, or should, and how.
Any government office at any level is responsible for a set of processes they own, and uses or participates in many other processes they do not. You can change the processes you own. Take three examples of who might consider applying digital engineering.
First, a major defense acquisition program office. As defined in the Defense Acquisition System (DoDI 5000.02), every program office owns its acquisition process. They own their acquisition strategy, their engineering plan, their test and evaluation plan, and many more. The program office is required to present and update about 50 different plans and assessments over the course of development. Each of these plans describes a process by which the program office will maintain a data repository of some form, for example, the systems engineering plan (SEP) controls the requirements management process. The SEP is presented or updated at every milestone decision. Because the office owns the process (engineering management) that controls the data (system specification) in order to get a decision (milestone), this is a candidate for digitalization.
Second, consider a functional organization, like the source selection office. In a matrix organization the functional offices own processes that create products others need. The source selection office owns the process for competitive acquisitions, a service used by program offices to compete and award small and large contracts. They are responsible for compliance with Navy, DoD and Federal regulations regarding such competitions. Such a process is a candidate for digitalization because they own the process (source selection), that controls data (government requests for proposal and industry responses), to make a decision (contract award).
Third, examine a tenant activity, like a test squadron. While the squadron might be a tenant activity that executes primarily using resources associated with program office projects, they own the process of writing and executing test plans. That process may be generally standardized, but each squadron has unique patterns of content and method driven (jet test plans may be different from helicopter test plans). Each squadron has decision makers who approve those plans. The squadron owns the process (test planning), the data (test plans), and the decision (approval). Such a process is a candidate for digitalization.
Every office has some staff, and some budget, but both are likely at a premium. Every office resides within a larger organization – even the Office of the Secretary of Defense has limits to its authority. Every office has a supply chain of providers, perhaps of products and services, or providers of data. If your office can identify the ecosystem it resides within (the people, resources, organization, and supply chain), you may be able to digitalize.
The technical options can be overwhelming. In today’s Internet world digital solutions can be sourced globally, which is both an opportunity and a threat. However, if an office can understand the platforms it has (or can procure) that will scope the computing environment (i.e. everyone works on laptop computers running Windows, plugged into the organizational network). Industry 4.0 technology options are remarkable, but the payoff is uneven so they should be considered for impact. Everything is not better with Bluetooth. Does your office maintain its data, or rely on disparate functional services to meld their data to answer questions? If you can change the platforms you own, the technology you use, or the data you maintain, those technical options are available for you to digitalize.
Bottom line: if you own a process and its data, can characterize your ecosystem and understand your technical options, you can choose a strategy for digital engineering.
References
Department of Defense. (2015). Operation of the Defense Acquisition System. https://www.dau.edu/tools/t/DoDI-5000-02
Department of Defense. (2018a). National Defense Strategy. https://www.defense.gov/Explore/Spotlight/National-Defense-Strategy
Department of Defense. (2018b). National Defense Business Operations Plan. https://cmo.defense.gov/Publications/NDBOP.aspx
Department of Defense. (2018c). Digital Engineering Strategy. https://ac.cto.mil/digital_engineering
Digital Engineering is a relatively new term and almost exclusively used in the Department of Defense, creating a great deal of confusion about what it means. Program managers and other leaders within defense acquisition recognize the existence of a new Digital Engineering Strategy (Department of Defense, 2018) from the Pentagon, and want to comply, but may not be certain what can or ought to be digitalized. It is easy to get caught in a trap of simply considering it model-based systems engineering (MBSE) with some enhancements.
Digital Engineering discussions often include unfamiliar and somewhat fluid terms. These may include Digital Thread (Kraft, 2020), Digital Twin (Madni et al., 2019), Digital Surrogate (Chakraborty et al., 2020), Electronic Prototype (Rieken et al., 2020), Authoritative Source of Truth (Kraft, 2019), Government Reference Architecture (Department of Defense, 2010), Open Architecture (Keller, 2021), and Agile Software (SAFe 5, n.d.).
INCOSE (2020) defines MBSE as “…the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases.” It is doing classic systems engineering using the traditional systems engineering methods (analyzing requirements, performing a functional analysis, design synthesis, with a verification feedback). But instead of using documents MBSE is done with models.
The DE Strategy focuses on decision making: using models and data that endures with the program over years, crosses functional areas, communicating across stakeholders, and performing activities. Whereas MBSE is a digital substitute for a list of engineering specifications, or a replacement for an engineering blueprint, digital engineering can include the digitalization of the logistics supply chain, math model of the cost estimate, Monte Carlo simulation of risk causal factors, test and evaluation discrepancy reporting, or the business process for managing training plans. Business process is the most important target of digitalization. Specifically, the value of digitalization is realized through the transformed underlying business processes (Antonucci et al., 2021). Further, lean production is most affected by process technology (Tortorella et al., 2021). Lastly, process is a critical component of Industry 4.0 implementation in supply chains (Ghadge et al., 2020). A typical program office may own more than 50 processes that create reports or plans which in turn impact their product. Any one of them is a candidate for digital engineering.
Three examples might be the Life-Cycle Sustainment Plan (LCSP), Systems Engineering Plan (SEP), and the Test and Evaluation Master Plan (TEMP). A typical program office may have an assistant program manager for each functional area whose job is to draft that plan, then execute the processes and use the resulting data to make decisions. That is a good test to determine if something is a candidate for digitalization: there is a functional manager, with a written plan that describes processes, a data repository and decisions to be made. But not everything is a good candidate, there are traps.
Linde et al., (2021) found several common traps that must be avoided. First, offices in a rush may not understand the customer value they are creating, and fail to satisfy customer needs. Second, not understanding the value delivery process and how the new digitalized process fits within the rest of the organizational context has risks. Last, offices may not understand the new operations model and means of realizing value, simply trusting that digitalization will have made things better. An office can understand the value the process creates (why we do it), the value delivery process (how we do it), and value realization (what we and they get out of it) and those typical traps will be escaped.
The commercial goal of digitalization is to arrive at a new set of processes that use a new set of data to achieve value. It is easy to see digitalization merely as a problem of new applications, or the introduction of Artificial Intelligence (AI) into processes, or new data models depending upon personal perspective or experience. However, none of those solutions alone will have sustained or meaningful impact. New models may be better, but may not result in better decisions if disconnected from a unified data model. An office may be able to house petabytes of data for decades, but if it is not designed for people to use with their digital supply chain its value is limited. Using AI as support infrastructure to communicate with customers is common, but without integration with the business process it may not deliver value.
The commercial world has paved a wide path of opportunity for digitalization. Program offices, matrix organizations and others within DoD need to leverage those lessons as they modernize their business operations as they modernize their weapon systems. Digital Engineering is a means to that end.
References
Antonucci, Y. L., Fortune, A., & Kirchmer, M. (2021). An examination of associations between business process management capabilities and the benefits of digitalization: all capabilities are not equal. Business Process Management Journal, 27(1), 124–144. https://doi-org.ezproxy.umgc.edu/10.1108/BPMJ-02-2020-0079
Department of Defense. (2018). Digital Engineering Strategy. https://ac.cto.mil/digital_engineering
Ghadge, A., Er Kara, M., Moradlou, H., & Goswami, M. (2020). The impact of Industry 4.0 implementation on supply chains. Journal of Manufacturing Technology Management, 31(4), 669–686. https://doi-org.ezproxy.umgc.edu/10.1108/JMTM-10-2019-0368
Keller, J. (2021). The latest trends in power electronics: It’s not just about stand-alone devices anymore, as power systems designers tackle power density, thermal management, open-architecture standards, systems integration, and obsolescence management. Military & Aerospace Electronics, 32(4), 23–29.
Kraft, E.M. (2015, January 5-9). HPCMP CREATE-AV and the Air Force Digital Thread. [Paper presentation]. AIAA SciTech Forum Kissimmee, FL.
Linde, L., Sjödin, D., Parida, V., & Gebauer, H. (2021). Evaluation of Digital Business Model Opportunities: A Framework for Avoiding Digitalization Traps. Research Technology Management, 64(1), 43–53. https://doi-org.ezproxy.umgc.edu/10.1080/08956308.2021.1842664
Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model-based systems engineering. Systems, 7(1), 7.
Rieken, F., Boehm, T., Heinzen, M., & Meboldt, M. (2020). Corporate makerspaces as innovation driver in companies: a literature review-based framework. Journal of Manufacturing Technology Management, 31(1), 91–123. https://doi-org.ezproxy.umgc.edu/10.1108/JMTM-03-2019-0098
Tortorella, G., Sawhney, R., Jurburg, D., de Paula, I. C., Tlapa, D., & Thurer, M. (2021). Towards the proposition of a Lean Automation framework: Integrating Industry 4.0 into Lean Production. Journal of Manufacturing Technology Management, 32(3), 593–620. https://doi-org.ezproxy.umgc.edu/10.1108/JMTM-01-2019-0032
You decided that your program must comply with the recent DoD Digital Engineering Strategy. Your team identified some candidate processes, data and decisions for digitalization. How do you get on with the project without competing and awarding a contract for service support? Naturally, your program office does not have JCIDS requirements to digitally engineer anything, or PPBS authority to expend if you did. You need a framework to understand your environment to make some strategic choices and execute.
The first step is to set some objectives and limits. Blackburn et al. (2017) studied big data implications on research and development (R&D), and three questions in particular: how would big data refine R&D, innovate R&D, or transform R&D. The answers to the three questions matrix to impacts on strategy, people, technology and process. You must choose the degree of change, from refinement to transformation.
Tortorella et al. (2021) focused on improving lean production by integrating Industry 4.0 lean automation. They discovered that technologies are not uniformly effective, that some have more impact on lean production than product and service technologies. When making choices of technologies to apply, you must consider the desired target for impact.
How important is recycling (a circular economy) to you? For manufacturing companies that may extend beyond just parts and materials to include data, for which Kristoffersen et al. (2020) proposed a Smart Circular Economy. You can translate your strategy into business analytics outcomes with digital technologies using their framework. That framework has three major dimensions that are relevant to any business with information technology: Data Transformation, Resource Optimization, and Data Flow Process. Consider each dimension and choose your desired degree of implementation.
Industry 4.0 is a multidimensional system and a dualistic nature of technical and business, with numerous terms, categories and variables (Nosalska et al., 2019). There are many design principles attributed to Industry 4.0 and the Internet of Things (IoT). They found the top recurrent principles were flexibility, real-time capability, decentralization, and modularity. You need to choose which design principles should be the design drivers of your digitalization project.
Do not try to boil the ocean or solve world hunger. Set realistic limits on how many processes you will consider for a given digitalization project. Over-digitization is a trap that Donnelly (2019) cautioned to avoid, while encouraging formal and informal knowledge exchange. This was a key strategic consideration given the heightened tensions of digital transformation between public and private organizations that must exchange data.
In order to meet DE Strategy goals, those are the important objectives to decide for each project:
the degree of change
the target for impact
the degree of transformation, optimization and flow
the design principles
the limit of digitalization
There are numerous models for project management, the flagship being the Project Management Institute (PMI) techniques. For the design of autonomous agents, Janiesch et al. (2019) used the 6-step design science research (DSR) process. They described the DSR steps as 1. Problem Identification, 2. Objectives of a Solution, 3. Design and Development, 4. Demonstration, 5. Evaluation, and 6. Conclusion. They applied this to a scenario of a cyber-physical system (CPS), a self-driving car and it will apply well in other scenarios. Digitalizing your business processes and data models is a project unto itself and should be treated as such, with a manager, a team, a plan and oversight.
There is no limit of business processes that could be modeled and redesigned, and every one you model you will redesign, and it will tempt you to do more. Linde et al., (2021) found a method to assess each opportunity, and identified common traps to avoid.
Their structured approach for assessing business process models has three phases: assessing the opportunity, modeling the future, and managing risks. Model your process as-is, redesign it to-be, then think about how that changes your business model, your internal work and external relationships. Do you have the resources to change? Will your people accept the new process? Will your organization allow it? Can your platform handle it?
Several common traps that must be avoided came out in the research of Linde et al., (2021). Know why you do work, how you do work, and what you (or your team, suppliers or customers) get out it. Companies don’t always know what value they’re creating for the customer, and if you rush a digitalization project you may fail to satisfy customer needs. Sometimes an entity doesn’t fully appreciate the context of each process within their organization, and how it delivers value to you. Remember that the whole point of digitalization is to change your business model, meaning the way you do your work and the way your realize profit/revenue/objectives/goals. Digitalization is no guarantee things will be better, but they will be different.
Donnelly, R. (2019). Aligning knowledge sharing interventions with the promotion of firm success: The need for SHRM to balance tensions and challenges. Journal of Business Research, 94, 344–352. https://doi-org.ezproxy.umgc.edu/10.1016/j.jbusres.2018.02.007
Janiesch, C., Fischer, M., Winkelmann, A., & Nentwich, V. (2019). Specifying autonomy in the Internet of Things: the autonomy model and notation. Information Systems & E-Business Management, 17(1), 159–194. https://doi-org.ezproxy.umgc.edu/10.1007/s10257-018-0379-x
Kristoffersen, E., Blomsma, F., Mikalef, P., & Li, J. (2020). The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies. Journal of Business Research, 120, 241–261. https://doi-org.ezproxy.umgc.edu/10.1016/j.jbusres.2020.07.044
Linde, L., Sjödin, D., Parida, V., & Gebauer, H. (2021). Evaluation of Digital Business Model Opportunities: A Framework for Avoiding Digitalization Traps. Research Technology Management, 64(1), 43–53. https://doi-org.ezproxy.umgc.edu/10.1080/08956308.2021.1842664
Nosalska, K., Piątek, Z.M., Mazurek, G. & Rządca, R. (2019). Industry 4.0: coherent definition framework with technological and organizational interdependencies. Journal of Manufacturing Technology Management, 31(5), 837–862. https://doi-org.ezproxy.umgc.edu/10.1108/JMTM-08-2018-0238
Tortorella, G., Sawhney, R., Jurburg, D., de Paula, I. C., Tlapa, D., & Thurer, M. (2021). Towards the proposition of a Lean Automation framework: Integrating Industry 4.0 into Lean Production. Journal of Manufacturing Technology Management, 32(3), 593–620. https://doi-org.ezproxy.umgc.edu/10.1108/JMTM-01-2019-0032
The proposed framework for executing a DE project consists of input, throughput, output, feedback and external forces. These five strategic choices must be made as input to implementation:
Degree of Change: Refine, or Innovate, or Transform (Blackburn et al., 2017).
Target for Lean Impact: Process, or Product & Service. (Tortorella et al., 2021).
Degree of Circular Economy: Data Transformation, Resource Optimization, Data Flow Process. (Kristoffersen et al., 2020).
The intent is to achieve the DE Strategy goals: using models to inform decision making, creating an authoritative source of truth, technological innovation, supporting infrastructure and environments, and transforming the culture and workforce.
Throughput is the process of selecting processes, then digitalizing them. One alternative is to use the 6-step design science research process (Janiesch et al., 2019), while another may be classic Project Management Institute project planning. While executing the project, continuously assess the opportunities as they emerge (Linde et al., 2021). Digital Engineering is a process itself. That process is a mini-project plan for each business process under consideration of constraining the problem, setting goals, finding a solution, test, demonstration and deployment. Constraining the problem necessarily includes assessing the opportunity for process improvement, because some process improvements may not yield sufficient benefits to make the solutions cost effective or the margin for improvement may be too small. Lastly, as the process moves forward the team must continually assess risks. If the project understands the value the process creates (why we do it), the value delivery process (how we do it), and value realization (what we get out of it) those typical traps will be escaped.
The end-state (output) is a new digitalized business model (process and database that enables better decisions), where technical and business aspects are intertwined (Nosalska et al., 2019). As the internal business model changes, relationships with internal users, external customers, and the supply chain will change, then new opportunities will arise (Cong et al., 2021; Dethine et al., 2020; Garay-Rondero et al., 2020; Laïfi & Josserand, 2016). This the entire purpose of digitalization. While a DoD acquisition program does not realize revenue (they do not get ‘paid’ by the Pentagon for systems delivered), they certainly realize costs and deliver product. Having a well-documented business model, especially one that is digitally accessible will enable resource managers to see how their funds are being used, and will enable warfighters to see how their capabilities are being delivered. In addition, legislative authorizers and appropriators will be more easily persuaded to fund programs that are transparent to them.
Many external forces are at work, either constraints or opportunities. People, resources, organization and the supply chain form an ecosystem, which you may or may not have control of (Cong et al., 2021; Correani et al., 2020; Dethine et al., 2020; Garay-Rondero et al., 2020; Gastaldi et al., 2018; Linde et al., 2021). The computing environment platforms, technologies, and data are technical options or forces (Correani et al., 2020; Ghadge et al., 2020; Ivančić et al., 2019). While some might say “Everything is better with Bluetooth,” a given technology will not equally benefit every business objective (Tortorella et al., 2021), several are key to Industry 4.0 (Nosalska et al., 2019).
While the number of external forces at work could be infinite, the list must be constrained to provide meaningful decision points. The ecosystem forces were selected because their presence is necessary for success, even if they are constraints beyond the immediate control of the process owner. A process owner may not be able to change the people assigned, or may not have the authority to redirect resources, but both must be present in some limited quantity to succeed. A small operation may have complete control of its organization and culture, while many will be part of a larger organization with a set culture. Both can succeed, but the choices available are different. The digital supply chain for an office is crucial, and every office can identify who it depends on for data to execute owned processes, and what other offices consume data produced. Those players constitute the digital supply chain, and the participation of data suppliers and data consumers in digitally engineering a process is critical. The more they are integrated to the effort, the more opportunities may be exposed for further refinement, enhancing the recursive nature of digitalization.
Technical forces are more likely to be options than constraints. This is where people naturally gravitate to when considering digitalization. An office must consider its computing environment (platform), the technologies available (and affordable), and the data repositories it will require, create, and share. A small office may able to change its platforms, whereas a larger office inside a large organization may have no control, or limited choices within a menu. A major choice will be between on-premises (e.g. desktop) and off-premises (e.g. cloud) computing, and that choice could be driven by security considerations. Industry 4.0 technologies are centered on IoT, and there are many technologies associated with that. Application of technologies like AI, ML, NFC or Bluetooth may accelerate IoT deployment, or they may have limited impact efficacy; being judicious is important. The new business model will hinge on the new data model. Businesses can collect data they never use, or fail to relate or visualize the data they have in a usable manner. A vast repository of stove piped data serves nobody. Data that is interrelated cross-functionally is more likely to have meaning. Data should be collected, created and shared because it is required to execute a process or make a decision.
Feedback will come first from internal users, eventually from external customers, as well as the digital supply chain (Cong et al., 2021; Garay-Rondero et al., 2020; Rieken et al., 2020). Communication with them is essential to success and subsequent adjustments. Providing a means for users to provide faster feedback via a Digital Community Infrastructure will lead to changes in the organizational culture and increase likelihood of acceptance, as users feel they are an integral part of changing the way they do their work. If feedback from those users is not aggressively sought, there is a risk they will obstruct change or sabotage the project. Those users must include not only the performers of a given process, but the users of its results, the decision makers. The best process with poor visualizations may not improve outcomes.
The figure above illustrates the derived conceptual framework for digital engineering. DoD Goals feed project strategy decisions, the ecosystem constrains technology choices, process defines execution, a new business model delivers efficiencies, and feedback informs recursion.
The goal of digitalization is to arrive at a new set of processes that use a new set of data to achieve value. It is easy to see digitalization merely as a problem of new applications, or the introduction of Artificial Intelligence (AI) into processes, or new data models depending upon personal perspective or experience. However, none of those solutions alone will have sustained or meaningful impact. New models may be better, but may not result in better decisions if disconnected from a unified data model. A web services firm may be able to house petabytes of data for decades, but if it is not designed for people to use with their digital supply chain its customer value is limited. Using AI as support infrastructure to communicate with customers is common, but without integration with the business process it may not deliver value.
Entities have known they should digitalize, but did not know what or how to implement it. This framework provides a means to choose what projects to do and how to execute them in a balanced way.
References
Blackburn, M., Alexander, J., Legan, J. D., & Klabjan, D. (2017). Big Data and the Future of R&D Management: The rise of big data and big data analytics will have significant implications for R&D and innovation management in the next decade. Research Technology Management, 60(5), 43–50. https://doi-org.ezproxy.umgc.edu/10.1080/08956308.2017.1348135
Cong, L.W., Li,B., & Zhang, Q.T. (2021). Internet of Things: Business Economics and Applications. Review of Business, 41(1), 15–29
Correani, A., DeMassis, A., Frattini, F., Petruzzelli, A. M., & Natalicchio, A. (2020). Implementing a digital strategy: Learning from the experience of three digital transformation projects. California Management Review, 62(4), 37-56. https://doi.org/10.1177/0008125620934864
Dethine, B., Enjolras, M., & Monticolo, D. (2020). Digitalization and SMEs’ Export Management: Impacts on Resources and Capabilities. Technology Innovation Management Review, 10(4), 18–34. https://doi-org.ezproxy.umgc.edu/10.22215/timreview/1344
Donnelly, R. (2019). Aligning knowledge sharing interventions with the promotion of firm success: The need for SHRM to balance tensions and challenges. Journal of Business Research, 94, 344–352. https://doi-org.ezproxy.umgc.edu/10.1016/j.jbusres.2018.02.007
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