Point of View | Navigating structural uncertainty: Operational Efficiency and Financial Discipline in Wild-Capture Commodity Industries
By Martín Riquelme and Vicente Besa
In industries that rely on wild-capture products, the variability in resource availability is not a risk to be mitigated from time to time—it is the permanent nature of the business. The companies that thrive are not those with the most assets, but those that have built organizations designed for uncertainty: lean, with variable costs, disciplined in their use of capital, and capable of capturing value in upcycles without jeopardizing their survival in bad ones.
The Challenge of Operating on the Frontier of Uncertainty
In the vast universe of natural resource-based industries, managing volatility is a core competency. However, not all forms of volatility are the same. While most extractive or agricultural sectors deal with price cycles and risks that can be modeled, there is a unique category of industries where variability is not an accident but the very nature of the business: the wild-capture commodity industries.
Industrial fishing, krill harvesting, squid fishing, and wild seaweed harvesting operate under structural uncertainty. In these sectors, the availability of the raw material or resource—the supply—is dictated by oceanographic, biological, and regulatory factors beyond the company’s control. Added to this is a critical characteristic: once harvested, the resource is highly perishable and cannot be stored as a raw material in its original state.
This Point of View analyzes the structural differences between wild-capture industries and other natural resource sectors, explores the nature of this variability, and proposes a three-pillar framework—based on best practices in operational efficiency and financial discipline—to help companies in the sector transform uncertainty into a lasting competitive advantage.
I. The Spectrum of Predictability in Natural Resource Industries
To understand the unique challenges of wild-capture industries, compare them with other primary industries. This classification rests on two fundamental axes: the predictability of resource availability and the ability to inventory the resource before processing.
The Taxonomy of Primary Industries:
The following comparative table illustrates the spectrum of predictability that distinguishes the various natural resource industries. Each industry’s position on this spectrum largely determines the financial and operational management tools that are relevant to it:
Industry | Predictability of Resource Availability | Storage Capacity | Main Risk |
Mining | Very high — proven and probable (P&P) reserves are quantifiable | High — mineral can be stored | Price cycles, capital costs |
Forestry | High — long-term rotation cycles are known | High — standing timber is quantifiable | Extreme weather events, pests |
Agriculture | Medium-high — seasonal cycles can be modeled | High — post-harvest storage | Weather, price volatility |
Aquaculture | High — production cycles are controlled | High — live biomass in cages | Disease, feed costs |
Wild capture | Low-medium — variable biomass + regulation (TAC) | None — highly perishable | Biomass availability, ENSO, regulation |
The Unique Nature of Wild-Capture
As the table above illustrates, mining operates on quantifiable geological reserves. The business model is based on the projected life of the mine (Life of Mine, LOM), and if the price of copper or lithium falls, the company can reduce extraction; the ore will still be there. Forestry and agriculture face climate risks, but their cycles are predictable and allow for seasonal planning. Even aquaculture—often grouped with fishing—operates under a model much closer to manufacturing, where biomass is controlled in cages, and its growth is predictable.
In contrast, industrial fishing occupies the end of the spectrum with the highest uncertainty. It is the only primary industry where two extreme conditions converge simultaneously: availability is unpredictable, and the ability to store the resource is zero. The catch volume depends on natural biomass—which is affected by oceanographic phenomena such as the El Niño-Southern Oscillation (ENSO)—and on regulatory quotas—the Total Allowable Catch (TAC)—that authorities set, often in the middle of the season. Furthermore, fresh fish has a shelf life of only a few hours. Failing to catch it today means losing it, and catching it requires immediate processing.
“Industrial fishing is the only commodity industry where supply is both unpredictable and has zero inventory. This dual uniqueness sets it apart radically from all other natural resource industries and requires its own management tools, as well as additional spending discipline,” explains Martín Riquelme, vice president at SummaPartners.
Other Examples of Wild-Capture Industries
Industrial fishing is not the only example of this paradigm. The harvesting of Antarctic krill—carried out by companies such as Aker BioMarine (Norway)—is subject to quotas set by the CCAMLR (Convention for the Conservation of Antarctic Marine Living Resources) and to the natural variability of krill swarms. The harvest of the highly migratory giant squid (Dosidicus gigas) operates without fixed quotas in several countries. The collapse of the snow crab stock in the Bering Sea in 2022—which resulted in the total closure of the fishery—is a stark reminder that even seemingly stable industries can face major disruptions in supply. Wild seaweed harvesting and wild truffle harvesting share the same logic: uncontrollable supply variability and the perishability of the resource.
II. The Anatomy of Variability in Wild-Capture Industries
Variability in wild-capture industries is not a “black swan” event; it is the normal state of operation. Understanding the sources and impact of this variability is the first step toward managing it rigorously.
Sources of Structural Variability
Biological and oceanographic uncertainty:
Populations of pelagic species—such as the Peruvian and Chilean anchovy, which form the basis of the global fishmeal and fish oil industry—are extremely sensitive to changes in sea temperature. The 1997–1998 El Niño event caused a 47% drop in Peruvian fishmeal production in 1998 compared to the previous year. More recently, the 2023 Coastal El Niño caused estimated losses of more than USD 1.4 billion for the Peruvian fishing industry, impacting the national GDP by 0.5%. Peru, which supplies approximately 30% of the fish meal sold worldwide, clearly illustrates how biological variability has major macroeconomic consequences.
Regulatory Uncertainty:
Governments manage fishery resources through quota systems. However, these quotas are based on scientific biomass assessments that contain significant margins of error. It is common for fishing seasons to be shortened, suspended, or for TACs to be abruptly reduced to protect the resource. Companies must not only plan for an uncertain volume, but that volume may also change during the season itself. This makes planning for operations, logistics, and sales an ongoing exercise in adaptation, rather than the execution of a fixed plan.
The problem of endemic idle capacity:
To take advantage of periods of high abundance—when biomass is high and quotas are generous—companies tend to overinvest in their fleets and processing plants. The FAO (Food and Agriculture Organization) has estimated that global fishing capacity would need to be reduced by between 25% and 53% for revenues to efficiently cover operating costs. When catch availability declines, this overcapacity becomes a major financial burden: fixed assets that incur depreciation, debt, and maintenance costs without generating revenue.
The Impact: Operating Leverage and Value Destruction
The main enemy of profitability in wild-capture industries is operating leverage. Fishing fleets and capital-intensive processing plants represent very high fixed costs. When availability decreases due to closed seasons or weather events, these costs remain fixed.
Unlike in mining, where a price drop can be partially offset by halting production at the least profitable mines—leaving the ore in the ground—in industrial fishing, not fishing means the quota is lost (often reallocated or forfeited), and processing plants—which must be ready to operate 24/7 during the short season—consume cash without generating revenue. The result is accelerated destruction of EBITDA in years of low catch.
The seafood value chain adds additional pressure. Studies conducted by the European Parliament indicate that, on average, only 10% of the chain’s final value reaches the primary producer. With structurally tight margins and an inherently variable supply, survival depends on relentless efficiency at every link in the chain.
Impact comparison: wild-capture vs. predictable industries:
III. The Three Pillars of Financial Discipline
Faced with this landscape, leading companies do not attempt to predict the unpredictable: they build organizations designed to absorb shocks. Drawing on frameworks from McKinsey, BCG, and Bain applied to cyclical sectors and natural resource industries, we propose three pillars for management in wild capture industries:
i. First Pillar: Financial Planning Under Structural Uncertainty
The traditional linear budgeting approach—a single base-case scenario with marginal variations—is ineffective in wild-capture industries. Companies must plan using different scenarios and probabilities, explicitly acknowledging the distribution of possible outcomes.
Probabilistic scenarios instead of a single budget:
Planning should be based on multiple scenarios—for example, a strong El Niño year, a neutral year, and a La Niña year—assigning probabilities to each based on available oceanographic models. The operating budget should not be a fixed number, but rather a set of predefined responses for each scenario, which are activated based on biomass indicators and quota announcements. Each scenario should have a predefined operational and financial action plan associated with it, thereby avoiding decision paralysis when availability deviates from the base plan.
Through-cycle management and mid-cycle pricing:
McKinsey, in its analysis of mining investments, documents that leading companies evaluate their performance over the entire cycle, not year by year. This involves adopting a mid-cycle pricing and mid-cycle volume approach for long-term strategic planning, avoiding the extrapolation of record-breaking years of abundance into the future. In practice, this means that investment and expansion decisions must be evaluated using mid-cycle volumes and prices, not cyclical peaks that distort the perception of profitability.
Decoupling incentives from exogenous factors:
KPIs and executive compensation plans must be adjusted for exogenous factors. Penalizing operational teams for a drop in EBITDA caused by an El Niño event is just as counterproductive as rewarding them excessively for a year of record revenue driven by natural conditions. Incentives should measure relative efficiency—how much value was extracted given the available resources—not the absolute result, which is partly beyond the team’s control.
Actionable recommendations
Implement quarterly rolling forecasts based on probabilistic biomass models and oceanographic signals (ENSO, sea surface temperature).
Establish a “Supply Resilience” committee that brings together operations, finance, and oceanography to adjust the business plan in real time in response to changes in the TAC.
Redesign executive compensation plans to measure efficiency relative to availability, decoupling incentives from absolute results.
ii. Second pillar: A “breathing” cost structure
If supply is variable, the cost structure must be elastic. Rigidity is the greatest risk in wild capture. BCG emphasizes that in sectors with high raw material volatility, winning companies maintain flexible cost structures that allow them to lower their break-even point without sacrificing their ability to respond when supply recovers.
Making Direct Costs Variable:
The central objective is to reduce the percentage of fixed costs relative to the total cost structure. This is achieved by outsourcing non-core processes, using tolling agreements to absorb volume spikes without building new in-house capacity, and structuring flexible labor agreements for fishing seasons. For the fleet, this can mean a combination of company-owned vessels—sized for the base-case scenario—and chartering agreements to cover years with high quotas. At the plant, it involves designing modular processes that can be scaled up or down depending on the volume of available raw materials.
Lean organizations and lean teams:
Bain & Company reports that less complex companies grow 30% to 50% faster than their peers. In industrial fishing, this means consolidating operations, standardizing fleets to reduce maintenance costs and spare parts inventory, and maintaining a lean corporate structure that does not stifle operations during years of low catch. Organizational complexity is a fixed cost in disguise: oversized support functions, unnecessary hierarchies, and redundant processes that consume cash regardless of whether the vessel is fishing or not.
The concept of a “lean team” is not synonymous with a small team or indiscriminate cutbacks. It means that every position has a clear justification in terms of value generated, that critical capabilities are in-house, and that support capabilities are made as variable as possible. In years of high catch, the lean team expands with external resources; in years of low catch, it contracts without destroying the core of knowledge and operational capacity.
Dynamic Management of Installed Capacity:
Instead of owning 100% of the capacity needed to process 100% of the quota in a record year, efficient companies size their fixed assets for an average or below-average year and cover peaks through strategic alliances, vessel charters, or cross-processing agreements with competitors. This strategy reduces the cost of idle capacity in down years, which is the primary source of value destruction in the industry.
Actionable Recommendations
Conduct a detailed annual analysis to identify fixed costs that can be converted into variable costs, focusing on procurement (potential savings of 8–12% according to Bain), production, and overhead.
Establish pre-negotiated tolling agreements with third parties to manage biomass peaks without incurring additional Capex.
Define the “right size” for the organization based on the average-cycle scenario, not the record year, and build a flexible scaling model for years with high volume.
iii. Third pillar: Capital discipline and a conservative balance sheet
Capital management is where decades are won or lost in cyclical industries.
McKinsey reports that the correlation between high prices or volumes and investment peaks (Capex) is historically high in extractive sectors—up to 73% in mining—which leads to the classic trap of buying high and expanding just before the cycle turns downward.
Conservative Capex in Upcycles:
The extraordinary cash flow generated in years of high biomass should not be automatically allocated to expanding capacity. It should be used primarily to reduce debt, invest in efficiency technologies—such as precision fishing technologies to optimize fuel use and maximize yield per metric ton caught—and build liquidity reserves. McKinsey notes that leading companies in cyclical sectors “save during upcycles and invest during downcycles,” taking advantage of cheap assets and lower competition for capital that characterize downturns.
Through-cycle financial health metrics:
Wild-capture companies must operate with structurally more conservative debt ratios than manufacturing or agricultural companies. The balance sheet must be robust enough to withstand two consecutive years of fishing bans or minimum quotas without requiring forced restructurings or asset sales under unfavorable conditions. This implies defining explicit, cycle-adjusted net debt-to-EBITDA targets and maintaining them as hard limits in corporate financial policy, not merely as aspiration.
The Value of Optionality:
Capital allocation should prioritize investments that offer strategic flexibility. Multipurpose vessels that can switch fisheries based on biomass availability, modular plants capable of processing different species, and technologies that reduce the variable cost per metric ton processed are examples of investments that generate optionality. In contrast, highly specific investments—for example, a plant designed exclusively for a single species in a specific geographic location—concentrate risk and reduce the ability to adapt to disruptions in availability.
“In the wild-capture sector, having an efficient cost and expense structure isn’t a sign of a lack of ambition—it’s the key to surviving the bad years and capitalizing on the opportunities that upcycles bring. Liquidity is the most underrated competitive advantage in this industry,” adds Vicente Besa, a senior associate at SummaPartners.
Actionable Recommendations
Define an explicit dividend and cash retention policy linked to the company’s position in the biomass cycle and the level of leverage on the balance sheet.
Apply discount rates (WACC) adjusted for the risk associated with resource variability when evaluating capacity expansion projects, incorporating low-availability scenarios into the sensitivity analysis.
Prioritize investments in operational efficiency technology (precision fishing, route optimization, bycatch reduction) over gross capacity expansion.
IV. Efficiency as a Lasting Competitive Advantage
In the wild-capture commodities industries, supply variability isn't a risk you can mitigate from time to time; it is the playing field. Trying to run a fishing, krill, or wild-harvesting company using the financial tools of a manufacturing or agricultural company is a recipe for destroying value.
The companies that thrive in this environment are not necessarily those with the most vessels or the largest processing plants. They are the ones that have internalized the stochastic nature of their business and have built organizations that “breathe” in sync with the ocean: lean in their cost structure, disciplined in capital allocation, and capable of capturing maximum value in years of abundance without compromising their survival in years of scarcity.
The three pillars described in this document—probabilistic planning, an
elastic cost structure, and through-cycle capital discipline—are not independent initiatives. They are dimensions of a single management system that reinforce one another. A company that plans well under uncertainty but has rigid fixed costs will remain vulnerable.
A company with variable costs but no capital discipline during upcycles will accumulate debt that will suffocate it in the next downcycle. Only the combination of these three pillars generates the resilience needed to compete effectively in this environment.
Ultimately, in the wild-capture business, operational efficiency and financial discipline are not just levers for improving margins; they are the fundamental competitive advantage that separates enduring leaders from players who are at the mercy of the tide.
References
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NOAA Fisheries (2022), Alaska Snow Crab Fishery Status
Garcia, S. M., & Newton, C. (1997). Current situation, trends and prospects in world capture fisheries, FAO
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