The Seneka Viability Framework (SVF)

1. Summary

The SVF is a prediction tool* that evaluates a company's structural health by observing the relationship between how it allocates resources internally and how essential it is in the external market.

Unlike traditional financial analyses, such as P&L or balance sheets, which show what happened in the past, the SVF detects structural errors before they impact the income statement.

SVF is a highly accurate assessment tool for internal use by a company (which has all the exact data) and also provides valuable information even when using public data, inferred data, or approximations.

2. Objectives

SVF contextualizes current results over time. The main objective, and what makes it unique, is to create a matrix with moving, rather than static, elements. Its objectives are:

• Diagnosis: With a predictive component for detecting declines in core stability before they lead to lower sales or profits (identification of “collapsed castles”).
• Capital optimization: Guide investment (CapEx) toward areas that strengthen the company's role in its network or improve its adaptability. In other words, make better decisions to direct investment toward innovation or consolidation, depending on the needs of each case.
• Minimizing ballast: Recognizing the burden of legacy systems and methods to encourage the decision to sell or technologically upgrade them.
• Strategic alignment: Establishing an useful framework understandable for CEOs, CFOs (who manages WACC and CapEx), and CTOs/COOs (who manage technical debt, flexibility, and key positions).

3. Dimensions

4. Mathematical approach

The aim is for good performance in one specific area to overshadow weaknesses in others. The SVF uses a geometric mean for this purpose. If there is a significant problem in Speed or Power, the viability score decreases significantly.

The Displacement Vector is the key metric for forecasting the future viability of each company. Later on, we consider the implications of using the displacement vector as a predictive element. It measures how dynamic the company is in the face of risks.

V⃗ > 0 Structural resilience: The company is creating competitive advantages faster than market disruptions or capital costs can erode them.

V⃗ < 0 Structural atrophy: The company's position is weakening, pointing to upcoming declines in performance. Even in financially strong positions (EBITDA).

5. Strategic positioning matrix

6. Weaknesses of the SVF

The principle of reflexivity (feedback loop)

This is one of the weaknesses identified in using SVF as a predictive tool. Although the purpose is to anticipate the direction of a company regardless of its current position and size, the reality is that it also functions as a self-influencing mechanism. The results obtained from applying the framework can alter the approaches of the companies evaluated.

From another perspective, this principle of reflexivity exchanges the predictive capacity of the model for its guiding capacity:

• Corrective measures: Identifying the positioning of the “market leader” in advance allows companies to get rid of obsolete resources before accumulated technological problems become a drag that drags everything down.
• Risk mitigation: It uncovers structural problems that do not appear in fiscal reports but are already anticipating those to come.
• Investment direction: The displacement vector provides a forward-looking signal for the distribution of financing and for building stakeholder confidence.

Data acquisition levels

This is a relative limitation. To calculate the synergy factor and displacement vector with maximum accuracy, internal data would be required, which makes a fully accurate external analysis difficult. However, the use of external data does not invalidate the results due to the weighting of multiple parameters.

Sector bias (cross-sector benchmarking)

The velocity (IAV) of a software company is not comparable to that of a heavy manufacturing company. The framework requires clearly established industry standards to avoid mislabeling inherently gradual sectors as “zombies.” For more relevant results, it is not as important to match companies by size, but rather to classify them accurately within their industry.

For the same reason, it also yields better results when comparing companies with similar structures (e.g., holding company vs. holding company).

The Seneka Viability Matrix

To ensure that the mere calculation of DWC and IAV becomes a visual and illustrative element of both a particular company and a specific industry or sector (defined by the positions of a given set of players), the SVF is represented graphically by the SVM (Seneka Viability Matrix).

The matrix divides the space into four perfectly delimited quadrants, without attempting to simplify the reality of each company and define it categorically. However, for practical purposes and as an analysis tool, it strengthens the hard data.

The matrix is represented as follows:

With the possibility of “labeling” it metaphorically. This makes it easier to explore, intuitive to use as a tool for strategic ideas, and easy to comment on. For this, we establish the following equivalencies: