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.
Know why you do work, how you do work, what you get out it, and you are ready to digitally engineer your office. DE can pay dividends, if it is embraced instead of complied with.
References
Department of Defense. (2018c). Digital Engineering Strategy. https://ac.cto.mil/digital_engineering
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