Strategic Situation Analysis with SWOT

While no organization or individual can predict the future, organizations that aren’t ready for the future will be disadvantaged. I’ve asked one of my long-time associates to be a guest blogger on a topic that plays well to be prepared for the future. Frank Kowalkowski, the President of Knowledge Consultants, Inc. (see bio below), gives us a quick overview, delivering an excellent approach to being ready for the future by leveraging intelligent SWOTs. 

Summary – Enabling Situation Analysis/SWOT with Analytics

Today’s external environment is a considerable challenge in developing strategic foresight. How can we anticipate the volatile state of the landscape and separate out the stable part? Where do our opportunities lie for successful continuity, not just survival. What short-term and long-term issues lurk that may prevent achieving organizational goals? What should you act on, and what should you start watching? So many questions going forward and so little insight. Situation Analysis and SWT were designed to assist in assessing this condition. However, it has been the victim of aging usefulness.

Let us review for a moment. The goal of a strategic management effort is to develop a viable strategic foresight perspective for the organization. Situation analysis drives that foresight. Strategic change may include direction that ranges from simple changes to radical disruptive changes, depending on the stage of the organization's performance and the interests of management and related parties. The degree of change impacts the scope of the strategy effort, especially the effort for situation analysis.

Situation analysis with SWOT is used on a macro-strategic (enterprise-wide) basis or on a micro-scale applied to the tactical or operational part of the organization. SWOT analysis can also be applied to the study of competitors.

Why make changes to how Situation Analysis is done?

Major business modeling experts such as Michael Porter, Henry Mintzberg, and others have identified several reasons for concern and the need to upgrade to Situation Analysis with SWOT (SA/SWOT). Here is a summary of the issues for improving SA/SWOT value and quality:

1. The lack of rigor such as ‘forward looking’ analytics and lack of extended analytics to business models has left the result incomplete.
2. There is a lack of well-defined steps in applying SA to the strategy process. The approach varies with whoever is doing the analysis. There are as many variations as there are consulting firms.
3. The current SA/SWOT method is labor-intensive, human-intensive research taking up at least 50% of the effort.
4. The analytics that exist are historical in nature, and many predictive analytics are difficult to use. Few tools for strategic and tactical business analysis exist. Those have limited and complex analytic algorithms that discourage rigor in analysis.

Resolving each of these points

1. Forward-looking analytics

The history-based approach works but has limitations. History is extended into the future through various estimating techniques such as trumpet diagrams, linear trend analysis, and so on.

What is needed is the simplification of forward-looking decision algorithms that relate to capturing expected subjective conjectures and preferences through criteria evaluation. Recent advances in analytics using newer algorithms, Gen AI, and neural net AI techniques have made the SA/SWOT analysis steps more productive and of better quality.

On the left, you have a landscape category, in this case, Key Economic Factors. This is linked to Technology Trends, which in turn is linked to Social Trends, and last is the target, Business Strategies. Of course, the strategies you start with are the ones you have today, but you will also do this for future strategic foresight when you finally have that. The result is an assessment of a gap analysis for benefit and value determination if desired.

The first time you do this, it is usually a two-cycle effort: first, assess the impact on today’s strategies, which helps explain what is going on, and second, assess the impact on future direction. At the end of the day, you want to know the impact of the external landscape on the set of strategies for the next time. A path-to-point diagram such as the one below in Figure 1 helps expose the relationships needed to make it happen.

 

                                                                      Figure 1

This diagram can be extended beyond strategy to include capabilities (a tactical interest), Processes, Applications, and, eventually, Databases (an operational interest). At the end of the day, you want to know the impact of the external landscape on the set of strategies for the next time. A path-to-point diagram such as the one above in Figure 1 helps expose the relationships needed to make it happen. This type of analysis also provides the insight needed for strategic alignment with operations.

2. Using well-defined stages of SA/SWOT

Historically, each step of situation analysis and strategic planning has evolved into its own way of analysis with no underlying analytics framework. The linkage between steps is dependent on the human effort of intuitive alignment. Well-defined workflows using analytic agents that focus on analytic ensembles as agents are available today to make applying the analytics by managers and business strategic and tactical analysts simpler.

Situation insight makes visible the potential future direction the organization will take. It is part of the overall strategy process and critical to identifying the suite of strategies an organization should pursue. The net result of all this is to get a higher percentage of success in assessing direction. There are four stages of analysis to consider for SA/SWOT:

Figure 2 below shows the relationship of the four stages:

                                                                          Figure 2



Here is a brief comment on each stage:

Stage 1—External Environment—Landscape Analysis (e.g., categories like PESTLE, Industry Factors, World Economic Forum assessment, and so on as added categories) The output is a suite of externally ranked category elements of interest to the organization.

Stage 2 – Internal Environment – Strategic/Tactical/ Operational Macro views focusing on Existing Strategic categories (e.g., existing strategies, capabilities, initiatives, etc.) The output is ranked and related categories regarding the current strategic and tactical structure.

Stage 3 - SWOT Quadrant Mapping and Analysis, simple quadrant analysis, and External/Internal comparative integrated quadrant analysis. The output is a set of quadrant contents that combine the external and internal views.

Stage 4 – Strategy Formulation linkage, namely the Scenario and Forecast Strategy Development. The output from this stage is the set of strategies for the next period, along with scenarios and drivers that explain the strategy.

These four stages are typical situation analyses that lead to the rest of the strategic planning processes in many organizations.

 
3. Reducing the labor burden in SA/SWOT

Situation Analysis with SWOT is, by nature, a human endeavor supplemented by methods and support tools. The key to efficient and effective improvements in Situation Analysis is AI-enabled stages, especially the Landscape Assessment and SWOT parts.

The insight and analysis efficiency gained using automated analytics, such as Gen AI search tools, text generation for scenarios, and subjective Multi-Criteria Decision Making (MCDM) analytics, is significant. In Landscape and SWOT research, the gain is as much as a 75% improvement in time and cost.

 
4. Improving the analytics

Avoid ‘the devil is in the details’ efforts and focus on reducing complexity to focus on strategic and tactical issues. There are several key improvements in achieving the situation analysis goal through applying current advances in analytics.

The analytic-based method described here resolves many of the objections to the current approach. The theme here is ‘let technology do the legwork.’ Technology used here, especially AI-based technology, augments human insight for strategy development. This more rigorous approach resolves the concerns of experts in business modeling and strategy. The core idea is to have AI be the assistant to the manager/analyst doing the analysis.

Recent articles claim improvements of 25 to 35 % in MCDM analysis by using hybrid subject/objective analytics.

The list of suggested solutions below provides a starting point for analytics improvement.

Comments by Business Modeling Experts

Here are three of the several expert comments on issues with SWOT:

Michael Porter: Lack of analytical rigor. According to Porter, SWOT analysis does not account for the competitive forces in an industry.

Henry Mintzberg: SWOT analysis oversimplifies strategic planning by categorizing factors into strengths, weaknesses, opportunities, and threats. This leads to a narrow view of strategic issues and might result in missed opportunities or underestimated threats.

Kim Warren: SWOT analysis often lacks a clear link to organizational performance and decision-making. This leads to vague, and generic statements that do not drive specific actions or improvements.

Some Analytic Solutions that Address Weaknesses in SWOT analysis

Here are the five most significant considerations the updated SWOT approach has regarding analytics:

• Use Multi-Criteria Decision-Making Concepts. Applying MCDM analytic criteria to analyze the landscape categories and the 4 SWOT quadrants for element significance. This provides accounting for the influence of several preferences not just one or two plus it can uncover accelerators and barriers to success.
• Using multiple ranking approaches (composite ranking, correlation matrices, and Neural Nets) to confirm the validity of ranks and significance of relationships. This prevents domination by one analytic algorithm.
• Use context analysis and DNA algorithms to assess and uncover hidden or significant relationships that offer valuable strategic insight.
• Using labor savings to expand the perspectives of the landscape using added categories reflecting the current larger and industry-specific scope today of external impacts
• Provide scenario generation and strategic implications through AI tools that utilize the results of insights gained from SWOT quadrant analytics.

For further information contact:

For training, Consulting, and SWOT Demos, contact Frank Kowalkowski at kci_frank@knowledgebiz.com

For more information on the software used, contact www.WIZSM.io


BIO:

Frank Kowalkowski is President of Knowledge Consultants, Inc., a firm focusing on business performance, business analysis, data science, business intelligence, artificial intelligence, and statistical techniques across industries. More recently, Frank has been involved in conducting workshops, professional training sessions, and assessments of business structures and transformation, data science, analytics for process management efforts. He is the author of a 1996 book on Enterprise Analysis. His most recent publications are a featured chapter in the business book Digital Transformation with BPM. His chapter is titled “Improve, Automate, Digitize.” he also has a chapter in the business architecture book titled Business and Dynamic Change, and a chapter on semantic process analytics in the book Passports to Success in BPM, and most recently, a key chapter titled Intelligent Automation and Intelligent Analytics in the 2020 book Intelligent Automation.