Systems thinking for engineers: Four techniques for digital transformation

To succeed at digital transformation, you need a digital mindset. For engineers and manufacturers looking to get the most out of digital tools, systems thinking is an essential one.

Systems thinking is an analytical methodology that makes sense of digital transformation complexity by viewing the world as a hierarchy of systems and interconnections, rather than splitting complexity into multiple smaller parts. Thankfully, this mindset is easy to learn—and it could pay dividends for any digital transformation project.

What is systems thinking, and how can it help digital transformation?

Systems thinking is a holistic way for engineers to investigate digital transformation opportunities, problems and alternative solutions.

First, engineers use systems thinking techniques to analyze as many factors, variables and interactions as possible that could contribute to the problem.

Then, they continue to use the same techniques to identify potential improvements and solutions.

Finally, they evaluate the improvement alternatives to reach a recommendation for action. Systems thinking is more a mindset than a thoroughly prescribed practice or software.

An overview of systems thinking techniques

The most frequently employed techniques that apply the systems thinking methodology are as follows:

CLDs produce qualitative visualizations of mental models of business processes focused on highlighting causality and feedback loops. CLDs are often developed using a collaborative approach, because no one understands all aspects of the business process being analyzed. CLDs reveal process problems that are used to develop intervention strategies.

The iceberg metaphor reminds us that what we see initially is only the surface of the digital transformation problem. Engineers ask questions like these to explore what might be below the surface:

• Can we improve the problem definition?
• Is the problem statement derived from what we see in reality only a symptom of a more significant system performance problem below the surface?
• What are the laws, policies or information that affect the problem we’re trying to solve?
• What issues, impediments or concerns, such as poor application operation or missing digital data, might lead to what we see above the water?
• Are information technology performance issues or constraints, such as insufficient computing capacity or unreliable data, contributing to the problem?
• Are people performance issues, such as a lack of data management training or poorly written software, contributing to the problem?
• What is the risk that one or more of the proposed solutions will worsen the problem, cost too much to operate or be problematic to implement?

Always start with what you know or are highly confident is accurate. Apply the iceberg metaphor to ask lots of questions to reveal what might lurk below.

Process maps provide a pictorial representation of a sequence of digital transformation actions and responses. Process maps first confirm the current process diagrammatically and then identify bottlenecks, gaps or inefficient steps. Finally, a future-state process map illustrates the recommended process.

RACI stands for Responsible, Accountable, Consulted, Informed. Completing a RACI table with these four columns and the names of the involved individuals or groups as rows ensures that everyone with a role in the digital transformation problem or process has been recognized and consulted.

What are the benefits of systems thinking?

When engineers apply systems thinking to their digital transformation work, they reap the following benefits:

• Comprehensive and defensible recommendations to advance digital transformation.
• Reduced time, effort and money spent on advancing digital transformation.
• Active involvement of affected stakeholders, especially those who can slow digital transformation by not participating.
• Low risk of missing factors, such as vendor involvement or process understanding, that should be considered in the analysis of a digital transformation.
• Stakeholder buy-in to the digital transformation.
• Lower organization silos that are often a prerequisite to effective digital transformation.

When should you use systems thinking?

Systems thinking becomes more necessary and valuable as digital transformation increases in complexity, because it has to navigate the intricate relationships among technology, processes, people and the external environment. For example, engineers should employ systems thinking to attack business situations like the following:

• Globalization of manufacturing is making companies more interdependent and creating more supply chain complexity. The associated data exchanges are sufficiently complex that they become difficult to manage without digital transformation.
• Information technology continues to advance. In addition to on-premises applications, organizations are adding cloud-hosted, Software-as-a-Service (SaaS) and AI-based applications. These multiple hosts for applications increase the complexity of integrating data sources for digital transformation.
• Incompatible data structures and column values create extract, transfer and load (ETL) complexity when integrating data sources for digital transformation.
• Missing and inaccurate data lead to complex rules for choosing the best available data values when integrating data sources for digital transformation.
• Designing the digital transformation of complex business processes. Most organizations previously completed the digital transformation of simple business processes.
• Increasing societal expectations for the performance of companies, governments and individuals are adding to the legal and regulatory thicket. This trend creates reporting requirements that organizations can handle more efficiently through digital transformation.

What types of problems can be solved with systems thinking?

Systems thinking provides the most value when the digital transformation opportunity or problem is:

• Important – Solving the problem will be valuable to advancing the business plan. It’s not trivial or inconsequential.
• Chronic or persistent – It’s not a one-time or infrequent event.
• Familiar – The organization has experienced a long and likely costly history with the problem.
• Difficult to resolve – Previous attempts to solve the problem failed. The organization must learn from the failures, and new ideas are needed.
• Large – The solution will require collaboration and cross-functional cooperation.

How can engineers encourage best practices for systems thinking?

Most digital transformation problems that can be solved within one group or department have been solved previously.

However, the digital transformation opportunities that produce the most value require cross-departmental cooperation and data sharing. Implementing solutions to these opportunities requires multi-disciplinary collaboration and cross-departmental cooperation.

Engineers can create an environment that encourages experimentation and prototypes. A thorough analysis of the situation often does not reveal the optimum solution. Creating a culture that supports experimentation and accepts failures without assigning blame produces superior solutions.

Apply the iceberg framework. It suggests comprehensively describing the digital transformation problem from the angles of events, patterns and structure. When engineers facilitate the use of the iceberg framework, it:

• Arouses the group’s curiosity about the problem.
• Focuses on aspects of the problem that others seem to gloss over or ignore.
• Stimulates discussion about aspects of the problem that are not well understood.

What are the alternatives to systems thinking?

The commonly observed alternative to systems thinking is called event thinking. It’s a shallow, quick, tactical response to the situation and risks an inadequate solution.

Another alternative to systems thinking is traditional analysis, which studies systems by breaking them into discrete elements. This approach is limited in value because it focuses on individual systems but neglects interconnections.

Systems thinking invites engineers to look deeper at patterns of events and related data, structures and mental models we all use, often unconsciously.

How can you kill systems thinking?

Engineers or other team members can kill systems thinking and thereby leave the digital transformation problem unresolved by:

• Assigning blame for the problem.
• Criticizing the potential solution ideas developed by others.
• Aggressively promoting their solution as superior compared to what others are proposing.
• Sweeping the problem away by insisting the event that triggered the discussion is extremely rare and inconsequential.
• Failing to collaborate with stakeholders.

Systems thinking is a disciplined approach to examining digital transformation opportunities and problems more completely and accurately before acting. It encourages engineers to:

• Consider the bigger picture by looking well outside their current box.
• Ask better questions before jumping to conclusions.
• Explore ideas, alternatives and the experiences of others on the road to developing a superior solution.

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Yogi Schulz has over 40 years of Information Technology experience in various industries. He writes for IT World Canada and other trade publications. Yogi works extensively in the petroleum industry to select and implement financial, production revenue accounting, land & contracts, and geotechnical systems. He manages projects that arise from changes in business requirements, from the need to leverage technology opportunities and from mergers. His specialties include IT strategy, web strategy, and systems project management.