Flunger & Company structures high-impact decisions in environments of uncertainty. For more than 20 years, we have combined proprietary frameworks and analytical instruments to transform business complexity into concrete decisions that generate robust results.
Our capabilities are organized in two complementary layers. The first brings together the frameworks and methodologies that structure the decision: how to define strategy, model markets and operations, and quantify risk. The second brings together the tools that give precision to this analysis: simulation, analytics, decision support, and visualization. Frameworks structure the question; instruments and tools transform that question into a tangible, quantitative, and actionable answer.
We treat strategy as a Wicked Problem: without a single formula, with multiple criteria and strong uncertainty. We integrate systemic vision, scenario exploration, and decision process modeling in three proprietary dimensions, in the sequence in which the decision actually unfolds.
A Wicked Problem is a complex problem that admits no single solution or closed formula. It combines multiple legitimate criteria, strong interdependence between parts, and persistent uncertainty. Each attempt at a solution transforms the problem itself, and the quality of the response is measured in degrees, from best to worst. Strategic decisions are of this type: they call for a method to structure the choice.
The concept comes from planning and design theory (Rittel and Webber, 1973).
Every strategy begins with mastery of the terrain. Scenario Planning maps the business environment and builds structurally distinct futures along the fundamental drivers of the sector. We identify the uncertainty drivers that determine success and test the robustness of decisions in each future. The result is a clear reading of where the company competes and which uncertainties actually matter.
With the terrain understood, the Strategic Puzzle gives shape to the strategy. It is our proprietary reference instrument: it articulates the four foundations that every strategy must make explicit — the Arena in which the company decides to compete, the Value Proposition that differentiates it, the Delivery Model that sustains it operationally, and the Goals that make it measurable. By closing these four pieces into a coherent whole, it converts strategic intention into a formalized strategy ready to implement.
With strategy defined, the Strategic Management Process keeps it alive. It organizes the continuous cycle of strategic management: vision alignment, organizational learning, market monitoring with direct and peripheral vision, and tracking of strategic actions to identify execution adjustments. Strategy thus operates as a permanent decision process, sensitive to market changes.
A strategy only creates value when it translates into management models that work in practice. In this layer we bring together the methods with which we design these models, adapted to each client's problem and anchored in results.
Customer Journey in the Free Energy Market: winning the consumer who now has a choice.
The opening of the free market transformed captive consumers into clients who choose their supplier. We map the journey of this consumer — from awareness to the migration decision — and design the approach by segment: which products and services to offer, through which channel and at what moment. The result is an acquisition and retention strategy oriented toward the segments with the highest migration potential.
Download: Customer Journey article
Management Model: the system that drives the organization toward strategy.
The Management Model goes beyond operations. It is the steering system that defines how the organization structures itself, decides, incentivizes, and develops people to realize the strategy. Our methodology structures four interrelated axes based on the required strategic capabilities: organizational design and governance, process maturity, incentive model, and human talent management. We evaluate each axis in detail so that structure, decision, and incentives all point in the direction of the strategy.
Value Sourcing: purchasing that creates value, beyond reducing cost.
Value Sourcing evolves the purchasing function from cost reduction to value creation. We prioritize purchasing categories by crossing savings potential with implementation complexity, and build strategies based on needs analysis and the supplier market. To cost reduction we add value engineering and advanced negotiation techniques, supported by a technology implementation platform. The result is fast and sustainable savings.
Sales & Operations (S&OP): the dialogue between sales and operations that unlocks revenue.
Sales and operations frequently decide in isolation, and the company loses revenue at the junctions. We structure this dialogue: we align forecasting, capacity, and priorities and distribute responsibilities in the coordination between selling and delivering. The result is more captured revenue and greater adherence between the commercial promise and the operational delivery.
Download: Sales & Operations article
The list above is illustrative, not exhaustive. Beyond consolidated frameworks, we develop models and tools tailored to each client's specific problem — from logistics network design to warehouse layout, among others. Much of our value lies in building the method that each problem demands.
The decisions that matter rarely have a single criterion or guaranteed outcome. This is our decision architecture: it structures choices marked by uncertainty, conflicting objectives, and Wicked Problems, integrating criteria, preferences, and trade-offs into a defensible decision. It orchestrates the instruments detailed in the next layer — from simulation to analytics — in service of the decision. Risk management is one of its components.
Multi-criteria decision: choosing with method when criteria compete.
At the center is multi-criteria decision support. We structure the problem explicitly: the objectives, criteria, preferences, and alternatives. From there, we support choosing, classifying, ordering, or describing options according to the nature of the decision. Decisions with many criteria and strong uncertainty follow an auditable logic.
Risk Management: a component of the decision, not a separate exercise.
Risk is one of the dimensions the decision must incorporate. We measure financial exposure quantitatively — potential loss and loss under extreme scenarios — with indicators such as VaR and CVaR. We translate these numbers into limits and triggers that enter directly into the decision architecture. In volatile markets, such as energy and agricultural commodities, this is a critical competency.
Download: Risk Management article
CDPs: concentrating the decision where it determines the outcome.
Not every decision weighs the same. Critical Decision Points (CDPs) identify, in a process, the moments at which a choice determines the outcome. By mapping them, we concentrate analysis, indicators, and governance where the decision can actually change the result. In a project for a premium coffee producer and exporter, we applied this approach to identify the process points that determined quality and production variability, structuring a new decision model supported by a dashboard.
Download: Critical Decision Points article
AgriDecision: the decision architecture applied to agribusiness.
AgriDecision is our proprietary tool that brings multi-criteria decision support to coffee and soybean markets. It brings together data, indicators, and scenarios in a panel that translates agribusiness complexity into more informed commercial and hedging decisions. It is the concrete and ongoing expression of our decision architecture.
Download: Coffee Producer Challenge 2026
Frameworks define the question; instruments produce the answer. This layer brings together the technical tools with which we quantify, test, and visualize the decisions structured in the first layer.
Simulation: test decisions before making them.
Simulation allows you to experiment with the future without risking the present. We model business behavior over time with systems dynamics — which reveals how the structure of a system generates its behavior — and with Monte Carlo simulation, which measures the effect of uncertainty on outcomes. We test scenarios, policies, and decisions and observe their consequences before committing resources.
Analytics: extract from data the signal that guides the decision.
Data analysis reveals patterns that intuition cannot reach. We apply techniques from established statistical methods — such as time series and analysis of variance — to choice modeling and Machine Learning, to estimate relationships, predict behaviors, and reveal preferences. An example is conjoint analysis (conjoint/DCE), which statistically decomposes preferences to show what actually drives client decisions. Our analytics serves strategic decision-making, not technique for its own sake.
Decision Support: the instrument that calculates the choice.
Where the decision architecture structures the problem, the multi-criteria instrument resolves it. It weights conflicting criteria, incorporates the decision-maker's preferences, and orders, classifies, or selects alternatives in a transparent and reproducible way. It is the mechanism that transforms a well-structured decision into a calculated recommendation.
Visualization: make visible what complexity hides.
A decision is only good if the decision-maker can see it. We translate models, data, and scenarios into panels and interfaces that show, directly, the decision variables, performance indicators, and consequences of each choice. Visualization closes the loop: it transforms analysis into something one decides upon and acts on.