Enterprise strategies stratification based on the fuzzy matrix approach

This article is a summary of the scientific work published by Valeriy Balan 

The original source can be accessed via DOI: 10.33111/nfmte.2022.124

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Introduction

Global shocks—from the prolonged pandemic to the war in Ukraine—are transforming how businesses operate. Today’s environment is defined by uncertainty, rapid change, and hard-to-predict trends. This forces companies to rethink not only daily operations but also long-term strategic priorities.

A key challenge is choosing the right strategy amid ambiguous data and shifting conditions. Traditional tools often fall short. That’s why forward-looking firms are adopting advanced, research-backed methods to better analyze options and respond swiftly to external pressures.

In an era of volatility, the ability to navigate uncertainty with smarter strategic analysis is more than a competitive edge—it’s a necessity.

Emerging Trends in Strategic Planning Research

Over the years, numerous scholars—from Ansoff and Mintzberg to Kotler and Porter—have laid the groundwork for modern strategic planning. Their frameworks continue to shape how companies navigate competitive landscapes.

In the past decade, however, a powerful new direction has emerged: applying fuzzy set theory to strategic management. Unlike traditional models, fuzzy methods excel at handling vague, uncertain data—making them ideal for today’s unpredictable environments.

Recent studies showcase a wide range of innovative applications:

• Using fuzzy-enhanced SWOT, QSPM, and ANP models to prioritize strategies in manufacturing, education, and even marketing.
• Leveraging hybrid approaches like Fuzzy AHP and VIKOR to better rank strategic options.
• Adopting fuzzy inference systems for sharper decision-making in areas from financial stability to bankruptcy diagnostics.
  
  

Despite this progress, there’s still significant room to refine these methodologies—particularly in improving how businesses evaluate and select strategies under uncertainty. This opens the door for forward-looking companies to gain a competitive edge by adopting more advanced, adaptable planning tools.

Research Objective

This study aims to bridge gaps in how companies evaluate and select strategies by:

• Reviewing the latest research on strategic decision-making under uncertainty.
• Developing a new approach that combines the Fuzzy Extension of the Simplified Best-Worst Method (F-SBWM), layered fuzzy evaluation matrices, and tailored decision rules.

The goal? To help businesses more effectively classify and prioritize strategic alternatives, ensuring smarter choices in complex, rapidly changing markets.

Results

Evaluating strategies is a critical part of enterprise strategic planning, as mistakes at this stage can be extremely costly—misallocating resources, wasting time, and even pushing a company toward failure, as noted by experts like G. Day and W. Glueck. Today’s turbulent business environment—with rising uncertainty, shrinking planning horizons, faster product cycles, and the need for complex scenario forecasting—demands a shift from traditional evaluation methods to more advanced tools. This is compounded by intense global competition, evolving business rules, and the explosion of data-driven decision support systems. Strategic evaluation now happens both when selecting which strategies to implement and later during execution to adjust for shifting internal or external factors. As shown in Fig. 1, four main approaches are used: goal-centred (to gauge alignment with strategic objectives), comparative (benchmarking against peers), improvement-focused (tracking strategic progress), and normative (checking for traits typical of successful strategies, per R. Rumelt). From these, the goal-centred and normative approaches are especially valuable for assessing the long-term merits of strategic alternatives and guiding smart choices under uncertainty.

The normative approach stands out for its focus on key factors that shape future business scenarios, applying a disciplined, logic-based assessment, while the goal-centred, comparative, and improvement approaches are more operational, emphasizing direct measurements of performance and efficiency. This study concentrates on evaluating strategic alternatives to identify the best options for implementation. To handle uncertainty and expert judgment rigorously, it employs principles of fuzzy set theory. Specifically, it uses a triangular fuzzy number model (illustrated in Fig. 2) along with its membership function, which provides a practical way to quantify ambiguous data and incorporate it into strategic decision-making.

This approach uses triangular fuzzy numbers and operations—such as addition, subtraction, multiplication, and defuzzification via the Center of Area method—to quantify expert judgments and handle uncertainty in strategy evaluation (illustrated by the membership function and algebraic rules). As shown in Fig. 3, the process involves a structured sequence: diagnosing the company and environment, forming expert groups, identifying strategic options and key factors, then evaluating their importance with the F-SBWM method. This is followed by consistency checks using the Fuzzy Delphi technique, aggregating weights, building fuzzy matrices for opportunities-threats and strengths-weaknesses, and finally stratifying strategic alternatives. This systematic methodology ensures that strategic decisions are grounded in rigorous, expert-informed, and uncertainty-aware analysis—reducing risks and improving the quality of strategic choices.

Stage 1‍

At this stage, enterprise and strategy analysts conduct a thorough diagnosis of the company and its environment using tools such as EFE and IFE matrices, the ETOM framework, PEST and SWOT analyses, and various competitive assessment methods.

Stage 2‍

At this stage, a working group of experts with deep knowledge, experience, and authority is formed, ideally also including external specialists with relevant expertise.

Stage 3

This stage is critical, as it involves developing a diverse list of strategic options using traditional planning tools like correlation SWOT, Ansoff, IEM, BCG, GE-McKinsey, and SPACE matrices, enhanced by fuzzy methodologies. As G. Day highlights, the best strategic decisions emerge when leaders explore multiple alternatives simultaneously—encouraging comparison and fostering creative combinations. The resulting set of strategic alternatives, denoted here for evaluation, forms the basis for selecting the optimal path forward.

Stage 4‍

Now selecting the right criteria to evaluate strategic alternatives is one of the most challenging tasks, heavily influenced by industry dynamics, competitive pressures, company size, and position. Experts like G. Day, D. Hussey, Ph. Kotler, R. Rumelt, and S. Abraham emphasize multiple dimensions: alignment with environmental and organizational realities, creation of a sustainable competitive advantage, financial efficiency, feasibility, and acceptance by stakeholders. These include tests of market attractiveness, risk levels, strategic consistency, adaptability, and managerial fit. As shown in Tables 1 and 2, critical internal factors (strengths and weaknesses) and external factors (opportunities and threats) are identified, shaping the company’s strategic landscape. To rigorously prioritize alternatives, this study uses the QSPM method, which quantifies how well each strategy leverages opportunities and strengths while mitigating threats and weaknesses, ensuring decisions are data-driven and robust under complex, uncertain conditions.

Stage 5‍

At this stage, the importance of factors grouped as O (opportunities), T (threats), S (strengths), and W (weaknesses) is assessed using the Fuzzy SBWM method, which blends “best” and “worst” pairwise comparisons under fuzzy logic with triangular fuzzy numbers to derive balanced priority weights as the average of these two approaches (see Fig. 4).

Stage 6‍

Here the group consistency of experts’ assessments is verified by calculating concordance coefficients for each area of analysis. If significant discrepancies are found, the fuzzy Delphi method [7] is applied to achieve consensus.

Stage 7

If the experts’ assessments are found to be consistent, the aggregation of factor weights is performed using averaging formulas for each analysis area (O, T, S, W). The aggregated fuzzy weights are then ready for further use, typically after defuzzification by formula (9) to obtain crisp priority values.

Stage 8‍

The strategic alternatives identified earlier are evaluated against SWOT criteria:

• how well they exploit opportunities,
• counter threats,
• strengthen existing strengths,
• and reduce weaknesses.

This is done using fuzzy linguistic terms (EL, VL, L, M, H, VH, EH), mapped to triangular fuzzy numbers (see Fig. 5). Expert evaluations are converted into fuzzy triangular forms for analysis.

Stage 9

‍This stage checks the group consistency of expert evaluations of strategic alternatives by calculating concordance coefficients for each SWOT analysis line (O, T, S, W). If the consistency is insufficient, similar to Stage 6, the Fuzzy Delphi method is applied to refine expert judgments and achieve acceptable agreement.

Stage 10‍

At this stage, the fuzzy assessments provided by experts for each strategic alternative are combined into overall group assessments.

For each area — opportunities, threats, strengths, and weaknesses — the fuzzy estimates given by all experts are averaged. This results in a consolidated fuzzy evaluation for each factor under each strategic alternative.

After that, using the Fuzzy SAW method, these consolidated evaluations are combined with the previously determined importance weights for each factor. This gives integral values that reflect how each strategic alternative performs with respect to opportunities, threats, strengths, and weaknesses.

This completes the process of summarizing expert opinions into comprehensive fuzzy evaluations for each strategic alternative, covering all SWOT directions.

Stage 11

At this stage, all strategic alternatives are plotted on evaluation matrices that compare two sets of criteria: opportunities versus threats, and strengths versus weaknesses. This helps visually position each strategy according to how well it takes advantage of opportunities, reduces threats, builds on strengths, and addresses weaknesses.

Additionally, to account for different levels of uncertainty in expert evaluations, a special adjustment method is used (called an “alpha cut”), which refines the fuzzy values and allows analysis under various assumptions about the degree of uncertainty.

Stage 12‍

At this stage, the fuzzy matrices for “Opportunities-Threats” and “Strengths-Weaknesses” are combined by overlaying them on top of each other. This helps to analyze different combinations of how strategic alternatives are positioned in these matrices. Based on this, experts develop production rules that specify which group (or stratum) each strategic alternative should belong to.

For example, experts might set rules like:

• If a strategic alternative is in the top right of the Opportunities-Threats matrix and also in a certain area of the Strengths-Weaknesses matrix, then it belongs to the first group.
• If it is in another combination of positions, it belongs to the second group, and so on.
  
  

This allows for sorting or classifying all strategic alternatives into several levels (strata), depending on how attractive or important they are.

When doing this, it is important to note:

1. The significance of each factor can be taken into account when forming the rules. This can be done using other fuzzy methods such as fuzzy SMART, fuzzy AHP, or fuzzy SBWM.
2. The rules can use the membership functions (the fuzzy evaluations) calculated earlier, making it possible to automate this stratification process.
3. The total number of groups (strata) is decided based on the specific goals of the analysis. For instance, it could be used to find the single best strategic alternative or to rank all of them by priority.

Stage 13‍

At this stage, the strategic alternatives are grouped into different levels (called strata) based on the production rules that were developed earlier. For example, in the case shown in Figure 8, applying these rules resulted in 8 groups. Each group contains certain strategic alternatives that were found to have similar positions in the fuzzy evaluation matrices.

Additionally, the fuzzy SAW method can be used here to rank the strategic alternatives. This method combines the overall evaluations of each strategic alternative — taking into account opportunities, threats, strengths, and weaknesses — along with their respective weights. As a result, it is possible to determine the priority or importance level of each alternative.

This approach allows either to double-check the groupings obtained through the production rules or to directly rank all strategic alternatives from most to least preferable.

Stage 14‍

At this stage, either a single strategy is chosen for implementation at the enterprise or a group of the most promising alternatives is selected for consideration by top management.

A few comments about why this proposed model is reliable:

1. The experts reach a consensus when identifying the most and least important factors for each analysis direction.
2. There is a check on the consistency of each expert’s individual opinions.
3. There is also a check on the overall consistency of the experts’ group assessments in both the “best” and “worst” evaluations.
4. The approach ensures coherence among experts when they evaluate strategic alternatives across all analysis directions.
   
   

To support this, the entire framework was implemented in Excel. It includes the main functional blocks shown in Figure 7, which make it possible to run simulation scenarios depending on the experts’ input assessments.

Conclusions and discussion.

The growing turbulence and uncertainty in business environments make it crucial to improve how companies choose their strategic directions. A wrong choice can be very costly or even lead to bankruptcy. This study tackles two main problems:

1. Building a system of criteria to evaluate strategic alternatives, accounting for fuzzy expert opinions. It uses SWOT factors with weights determined by the Fuzzy SBWM method, based on expert linguistic assessments transformed into fuzzy triangular numbers.
   
   
2. Selecting strategies under fuzziness, using the Fuzzy SAW method and fuzzy matrices (“Opportunities–Threats” and “Strengths–Weaknesses”). Strategies are positioned and stratified through matrix overlay and production rules from a Mamdani fuzzy inference system.
   

An Excel-based framework supports these calculations and can help create decision support tools for strategic management. Future work may refine criteria, combine fuzzy methods (like Fuzzy AHP and SMART), optimize the fuzzy inference system, and build complete decision support systems.

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