Abstract Business modeling and strategic planning are crucial for navigating competitive market environments. This study explores how Villa Exclusivo B.V, a pre-assembled and custom-made kitchen factory located in Curaçao (Netherlands Antilles), upgraded its business strategy by applying Kolyada’s business modeling and strategic planning theory and methodology, referred to as, “Kolyada’s methodology.” Specifically, Kolyada’s methodology enables firm leaders to evaluate target cluster selection and market segmentation, and perform business model optimization based on projected industry trends. By combining these tools, this study illustrates the methodology’s potential to effectively assess a competitive environment, create a market model, and forecast its volume, as well as evaluate and forecast the performance of companies operating within a market niche. Additionally, it provides scenario-based approaches to evaluate potential business outcomes under different economic conditions. While applications of Kolyada’s methodology were previously limited to Russian-language scholarship (Russia and CIS), this study’s findings provide practical insights into strategic decision-making for English-speaking professionals, demonstrating how structured business modeling can improve a company’s market positioning. Keywords: Business modeling, market forecasting, strategic management, strategic planning, competitive positioning, target market segmentation, clustering, clusterization. An analysis of kitchen manufacturers in the Curaçao Market by applying Kolyada’s Business Modeling and Strategic Planning Methodology 1 General Manager of Villa Exclusivo B.V - Curaçao (Netherlands Antilles). Eurasian Management and Administration School (EMAS Business School). Email: anthonyjhosset@gmail.com. ORCID: https://orcid.org/0009-0001- 8745-4827 2 Eurasian Management and Administration School (EMAS Business School). Email: rector@emasglobe.com. ORCID: https://orcid.org/0009-0005-9725- 9370 * Corresponding author. Declaration of competing interest: No conflicts declared. BioProducts Business 10(3), 2025, pp. 29–50. ISSN 2378-1394 https://doi.org/10.22382/bpb-2025-003 Anthony Jhosset Gonzalez Rodriguez1, Andrey A. Kolyada2 * 1. Introduction In today’s dynamic and uncertain business world, business models must evolve and innovate, leverag- ing technological advancements to capitalize on new opportunities (Morris et al. 2005; Nielsen and Aagaard 2021). Forecasting tools are crucial for this process because they enable organizations to simulate sce- narios, evaluate potential outcomes, and align their strategies with future goals. Ultimately, forecasting enables firms to adapt their business models and create customer value in an increasingly globalized market (Baden-Fuller and Haefliger 2013). Although Magretta (2002) defined business mod- els fairly simply as stories that show how organizations operate, the management literature has struggled to reach a consensus on their operational defini- tion. For example, in a review of 103 publications on business models, Zott et al. (2010) found that 37% did not provide a definition of the concept, 19% referenced definitions from other authors, and 44% proposed distinct business models based on one or more specific elements. Following Gambardella and McGahan (2010), this study defines a business model as a company’s method to produce revenue at a logical cost that integrates assumptions regarding how to generate and obtain value. More concretely, a business model also demonstrates how an orga- nization plans to surpass competitors and succeed mailto:anthonyjhosset@gmail.com https://orcid.org/0009-0001-8745-4827 https://orcid.org/0009-0001-8745-4827 mailto:rector@emasglobe.com https://orcid.org/0009-0005-9725-9370 https://orcid.org/0009-0005-9725-9370 30 BioProducts Business 10(3) 2025 in saturated “red ocean” markets through strategic planning and management. Business models often operate in collaboration with business strategy, to the extent that Massa et al. (2017) viewed a business model as a continuation of the firm’s strategy, rather than a new element. Along similar lines, Slávik and Zagoršek (2016) highlighted that a business model outlines how a firm operates and generates revenue, whereas a business strategy defines and explicates in what way, where, and for what reason a business model is applied. George and Bock (2011) argued, however, that business models can be non-reflexive, stationary, and centered on opportunities, whereas strategy is dynamic, re- flexive, and focused on competitors/environments. According to Teece (2018), a business model must be effective if it is aligned with a strategy. Overall, while strategy plays a significant role in combination with business model, the business model itself can predetermine strategic choices (Kolyada 2023). Despite the expanding recognition of business models as strategic tools, a gap remains in empirical research on how structured methodologies can be applied in niche manufacturing industries. Existing frameworks, such as the Business Model Canvas (Osterwalder and Pigneur 2010) and the Balanced Scorecard (Kaplan and Norton 1996), offer strategic planning insights but remain primarily descriptive and do not incorporate structured market forecast- ing or competitive positioning approaches tailored to smaller, industry-specific businesses. By compari- son, “Kolyada’s methodology”—a business model- ing and strategic planning theory and methodology developed by Andrey Kolyada (Kolyada 2014, 2023, 2025)—enables firm leaders to evaluate target clus- ter selection and market segmentation and perform business model optimization based on projected industry trends. However, empirical applications of Kolyada’s methodology have appeared almost exclusively in Russian-language outlets (Russia and CIS), limiting both its visibility and validation in the international English-speaking literature. This study addresses these gaps by applying Kolyada’s methodology, which emphasizes market forecasting as a means to anticipate future market trends more precisely, within the context of a kitchen manufacturing firm in Curaçao. Accordingly, the crucial research question is: How can Kolyada’s meth- odology be applied to forecast market trends and optimize business strategies in kitchen manufactur- ing firms? Villa Exclusivo B.V served as an illustrative case for this analysis. Situating the case study within the broader field of strategic management highlights a key principle: forecasting is not only a methodological tool but also a critical enabler of organizational performance. Effective business performance relies heavily on the ability to forecast with precision, use data to establish goals, allocate resources, and minimize potential risks (Chambers et al. 1971; Theodorou et al. 2025). These forecasts can be effectively employed in manage- rial decision-making by creating knowledge, leading policy decisions, and tracking system performance (Lindberg and Zackrisson 1991). Maximizing the return on invested resources is another vital advantage of integrating business modeling and market forecasting. By incorporat- ing projections of market fluctuations and com- petitors’ strategic actions into business modeling, rather than merely reacting to market shifts and competitors’ achievements, organizations can take proactive steps to stay ahead of their competitors in the market. Companies gain a competitive edge by identifying high-return opportunities in environments with limited resources. These projections inform key decisions, help align a company’s goals better, and bridge the gap between forecasting and effective planning. In particular, specialized manufacturing firms must strategically choose the appropriate niche. Without that clarity, a business risks spreading too thin across many opportunities, wasting valuable time and resources. Dalgic and Leeuw (1994) noted that, as competition intensifies, only the most adaptable businesses tend to survive. Adapting within the right niche is equally crucial; resilient companies stand out in their ability and capacity to adapt in response to the ever-evolving marketplace. Mintzberg (1994) emphasizes that, at times, strategies for decision- making should remain a broad and flexible vision rather than being precisely defined, allowing for adaptation to a changing environment. Strategic management encompasses the methods and tools necessary to execute these steps effectively, including the selection of an effective business model for a company’s market niche (Casadesus-Masanell and Ricart 2010). Many existing studies on this pro- Gonzalez and Kolyada — An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s methodology 31 cess focus on large firms, thereby creating a gap in the literature on small- to medium-sized niche manufac- turers. Accordingly, this study applies the algorithm present in Kolyada’s methodology to present a case study of business model development for a small and medium-sized enterprise (SME) in the custom kitchen industry. It investigates whether Kolyada’s methodology can bridge the gap between models and strategy by offering a structured method for busi- ness modeling, strategic planning, and forecasting in a niche manufacturing environment. According to Kolyada (2014, 2023), a strategic algorithm involves a continuous cycle of three key steps: (1) establish the target market and find a market niche, (2) design and implement a business model and strategy to achieve the desired position (share) in the market, and, (3) change strategic goals from growth to reten- tion, eliminating growth-oriented costs to maximize profit in the target market. Villa Exclusivo B.V, a kitchen manufacturing company operating within a small niche market in Curaçao (Netherlands Antilles), is the setting for the case study. In 2023, this company had a market share of 32.38% in monetary terms, employed 150 people, and had over 17 years of experience in the industry. By focusing on a niche manufacturing company, this study offers insights into the applicability of struc- tured business modeling in dynamic competitive environments. 2. Literature review 2.1 Business models and forecasting In recent decades, business models have become a critical element of strategic management. For exam- ple, Van de Ven et al. (2023) pointed to the Business Model Canvas (BMC) as a broadly used framework for representing business models in research and practice, and for evaluating their performance. However, many studies have criticized the concept for its ambiguity and lack of clarity, given the absence of consensus regarding its definition and components (Fielt 2013). Several studies have attempted to resolve this issue. Notably, Shafer et al. (2005) categorized business model components into four domains: strategic selection, value network, value generation, and value retention. They emphasized the need for clarity in distinguishing business models from strategy and proposed a synthesized definition that bridges theoretical and practical perspectives. This structured approach clarifies how business models operate and how they can be effectively utilized in corporate management. Building on this work, Samavi et al. (2009) estab- lished a strategic modeling framework designed to clarify and analyze the goals, intentions, roles, and underlying rationale of strategic actions within a business context. Casadesus-Masanell and Ricart (2010) introduced a framework that facilitates the integration of strategies, business models, and tac- tics. In their formulation, a business model is directly derived from a business strategy; however, the two are distinct concepts and should not be equated. This recognition also enabled their framework to differentiate between tactics and strategies. Wirtz et al. (2010) emphasized that developing and adapting a business model is crucial for a firm’s executives to respond effectively to technological advance- ments, competitive shifts, and regulatory changes. To tackle this managerial challenge, they proposed a comprehensive framework of Web 2.0 features and success elements, enabling managers to identify crucial tendencies relevant to their Internet business models. They asserted that a firm’s environment fun- damentally influences the business models capable of creating value within a specific market context. Teece (2010) contended that a business model refers to the way a company produces, delivers, and retains value. He argued that a well-designed business model consolidates the assumptions of management regarding customer needs, and it is the most effective way to provide value while generating profits. Furthermore, he examined the connection between business models, strategy, and sustainable competitive advantage, emphasizing that while a business model is more general than a business strategy, integrating the two is essential to protect the competitive advantage created through the design of new business models. Braun et al. (2019) reinforced this idea, stress- ing that achieving a delicate equilibrium between strategy and business model is crucial for securing an organization’s sustained competitive advantage and rentability, while McGrath (2010) noted that business models provide strategists with a novel approach to navigating unpredictable and rapidly 32 BioProducts Business 10(3) 2025 changing environments. Finally, Zott and Amit (2013, 2015) extended this theoretical foundation by defin- ing business models as systems of interdependent activities. Their work provides a robust conceptual base for understanding the interconnection between actors and resources in delivering value, making it highly relevant to contemporary research in strategic design. Forecasting plays a pivotal role in enhancing the agility of business models, especially in dynamic mar- ket environments. Predictions made using modeling tools are crucial for companies to adapt flexibly to changing conditions. Sakas et al. (2014) pioneered the use of dynamic simulation models (DSMs), which are indispensable tools for IT companies to simulate operational scenarios before committing actual re- sources. These models help to establish strategies, ultimately leading to precise decision-making pro- cesses. The primary concepts underlying DSMs can be universally applied, enhancing strategy practices across various sectors and navigating rapid techno- logical progress. Groesser and Jovy (2015) applied this insight by advocating a systems-based simulation methodol- ogy that allows strategic experimentation under volatile conditions. They emphasized the importance of examining and understanding the structure of a business model, as well as the changes driven by stra- tegic initiatives. Specifically, they argued that existing qualitative models lack robust guidelines, particularly in uncertain and volatile situations characterized by rapid technological change and agile competition. They recommended adopting a quantitative ap- proach using a systemic simulation methodology, allowing decision-makers to gain valuable insights through experimental designs within a company’s business model. Pröllochs and Feuerriegel (2020) integrated fore- casting with business analytics and topic modeling to effectively assess corporate scenarios. Their au- tomated text-mining tool employs techniques such as SWOT analysis, examining company profiles to identify potential risks and opportunities, and posi- tioning forecasting as a critical component of strategic modeling. This enables businesses to predict market fluctuations, safeguard themselves against risks, and assess their competitive positioning in relation to their competitors. Globocnik et al. (2020) further explored the correlation between strategic planning and business model management. They introduced a structured management framework that aligns activities across various levels of an organization, including strategy, business models, and tactics, to streamline operations as a unified and effective unit. This systematic approach guarantees coherence and enables quick adaptation, allowing companies to tailor their models to market changes. 2.2 Kolyada’s methodology This section summarizes Kolyada’s business model- ing and strategic planning theory and methodology, which emphasizes the multi-level nature of busi- ness models and strategic planning, was initially developed in Russia at the Eurasian Management and Administration School (EMAS Business School). Several earlier publications detail its key provisions, particularly the idea that business models and strat- egy operate on at least three levels: product-market, corporate, and holding (Kolyada 2014, 2023). Business modeling and strategic planning are implemented at each of these levels. The business model is con- sidered the foundation of strategy. The economic efficiency of the chosen business model compared to competitors’ business models should determine the limits (maximum values) of the company’s strategic goals. A company cannot achieve market results ex- ceeding those predetermined by the effectiveness of its business model. Strategy represents the answer to the question “How” to implement the chosen busi- ness model and achieve strategic goals based on it. The same business model and the set of strategic goals based on it can have different strategies for their achievement (Kolyada 2014, 2023, 2024, 2025). Kolyada’s methodology, particularly its predic- tive economic-mathematical model component, comprises a set of interconnected tools and has undergone continuous validation since its creation. Following initial tests from 2010 to 2014, this se- quence of business modeling and strategic planning was intensively tested between 2021 and 2024, when graduate students and faculty at EMAS implemented it in nearly 300 projects for Russian, CIS, and global firms (Kolyada, 2025). These applications confirmed the forecasting accuracy and reliability of the meth- odology, as the models were tested against real- world results and adjusted where necessary. Several Gonzalez and Kolyada — An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s methodology 33 publications in Russian scientific journals demon- strate the reliability of the methodology (Abramchuk, Lyashenko, and Kolyada 2024a, 2024b; Abramchuk and Kolyada 2024). The methodology ensured the initial validation and continuous fine-tuning of the predictive model in two stages: (1) initial tuning by reconciliation of reverse forecasts performed by the model with the actual results/data in historically completed periods, and (2) continuous comparison of the forecasts for the years in the planning horizon with the actual results/data obtained in these years. After the period of systematic validation and iterative refinement, the methodology provides that the forecasts aligned closely with actual outcomes. Each analysis is to be implemented within a plan- ning horizon that is divided into a set of correspond- ing years, and relies on a market scenario-based approach and “As-Is” and “To-Be” formats. The sce- nario approach distinguishes between three pos- sible futures: (1) a main scenario representing the most probable trajectory of market and competitive changes, (2) an optimistic scenario assuming favor- able external factors such as rapid growth or reduced risks, and (3) a pessimistic scenario incorporating adverse conditions such as decreasing demand or unfavorable external factors or heavy risks. These scenarios differ not only in their assumptions but also in their strategic implications. The main scenario pro- vides a baseline for planning, the optimistic scenario highlights extra opportunities that could accelerate growth, and the pessimistic scenario identifies extra negative factors that may threaten performance. The idea behind Kolyada’s methodology of testing business models under these alternative assump- tions is not only to enhance forecasting accuracy but also to help organizations evaluate the robustness of their business models across different possible scenarios while anticipating volatile markets. This aligns with the scenario-planning tradition estab- lished in strategic management (notably Schoemaker 1995; Ringland 2006; Van der Heijden 2005), which emphasizes the disciplined anticipation of contrast- ing futures to reduce cognitive biases, strengthen decision-making, and better manage uncertainty. Kolyada’s contribution, as discussed above, extends this tradition by embedding quantitative, competitor- focused forecasting into scenario analysis, providing a structured mechanism for evaluating business model resilience in turbulent markets. Kolyada’s book on the methodology (2023) was recognized as the best business book by a Russian author in 2024 (TEDO 2024), highlighting its growing impact in the field. Currently, active scientific research con- tinues in Russia to further develop the theory and methodology. Kolyada’s methodology has undergone extensive application in Russia and the CIS, including valida- tion across hundreds of business projects and the publication of award-winning work (TEDO 2024). However, any emerging framework carries strengths and boundaries of applicability. Therefore, a balanced appraisal requires acknowledging its potential limita- tions alongside its contributions. First, the methodology’s adoption has thus far been geographically concentrated, with most valida- tions occurring in Russia and the CIS. Despite multiple testing outside Russia and CIS, its transferability to other institutional and cultural contexts remains underexplored, raising questions regarding its gen- eralizability. Second, implementing this methodology is resource-intensive, requiring access to reliable da- tasets and advanced analytical expertise, conditions that many small and medium-sized enterprises may struggle to meet. As a final point, while its structured, quantitative nature is a strength, Kolyada’s methodol- ogy is not a one-size-fits-all solution; its effectiveness depends on an organization’s readiness to deal with complex analytical tasks. At the same time, situating Kolyada’s methodology within the broader international literature highlights its relevance beyond Russian and CIS contexts. In addition to its alignment with scenario planning ap- proaches (Schoemaker 1995; Ringland 2006; Van der Heijden 2005), parallels can be drawn with scenario planning approaches developed by Schoemaker (1995). It emphasizes that strategic scenarios are relevant in any context in which decision-makers need to anticipate possible future developments under conditions of uncertainty. Kolyada’s methodology extends this observation by adding a quantitative, competitor-focused dimen- sion to the scenario-based comparative forecasting present in his methodology (Kolyada 2014, 2023). This move also aligns with the dynamic capabilities framework by Teece et al. (1997) and Teece (2007), 34 BioProducts Business 10(3) 2025 which highlights a firm’s capability to incorporate, develop, and reconfigure internal and external com- petencies in response to environmental changes. In the context of the “Concept of Dynamic Capabilities,” according to Kolyada’s theory, business modeling is one of a company’s high-level routines. This routine ensures the firm’s dynamic capability to create innovative and cost-effective business models (Kolyada and Plekhova, 2025). Overall, by quanti- fying market niches and forecasting competitive moves, Kolyada’s methodology offers a structured mechanism for operationalizing dynamic capabilities, particularly in turbulent markets. The framework connects to the growing literature on predictive analytics in strategic management (Cote 2021; Adwani 2025), where predictive analytics are applied to anticipate market trends and fore- cast scenarios that help guide strategic decisions. Rooted in economic and mathematical modeling, yet forward-looking in its data-driven strategy design, Kolyada’s approach can thus be positioned within the broader international movement toward antici- patory, analytics-based management. For a detailed description of the step-by-step procedures, “As-Is/To-Be” formats, base year defini- tions, and validation processes underlying Kolyada’s methodology, see Appendix A. 2.3 Comparative analysis of strategic and business modeling frameworks To evaluate Kolyada’s methodology in context, comparing it with other commonly used business modeling frameworks is helpful. Although Kolyada’s methodology, like other frameworks, naturally has many limitations, its distinguishing features lie in scenario-based quantitative forecasting of the effec- tiveness of different business models and strategy variants for a company, compared to those of its competitors. This enables the company to evalu- ate the economic efficiency of potential business models and strategy options, and select the optimal solution based on the forecasted outcomes. A com- parative analysis is provided in Table 1 to enhance understanding. 3. Methodology The following section describes the application of a specific part of Kolyada’s methodology to Villa Exclusivo B.V, a kitchen manufacturer in Curaçao (Netherlands Antilles). The case study seeks to dem- onstrate the predictive and strategic utility of this methodology. However, the study only applies a re- duced version of the market volume and complexity analysis (MVC-1) tool to market modeling (forecasting volume and market structure) and clustering. The approach targets cluster segmentation and develops positioning within the target cluster and segment. The full version of MVC-1, including the mathematical algorithm for forecasting market and market cluster dynamics, remained outside the scope of this study. Accordingly, the study does not consider the method’s broader set of forecasting tools, such the business modeling economic-mathematical predic- tive mechanism that ensures the selection of the most economically efficient business model for the company’s operations in the chosen market niche, in particular Decision-Making Matrix Analysis (DMM), Z-equation, Market Risk Analysis (MARIS), Customer Satisfaction Index (CSI) calculations, and other pre- dictive tools. Nevertheless, these tools were utilized in the project to obtain the results described in this article. Strategic goal setting and target-based man- agement, brand management, corporate culture management, budgeting, and other parts of the methodology also remained outside of the scope of the article. The research employs a single-case study de- sign (Yin, 2003), which is appropriate for addressing “how” questions and is also particularly valuable in research areas where existing theory is insufficient or underdeveloped (Eisenhardt 1989). Villa Exclusivo B.V was used as an illustrative case study to assess the applicability of Kolyada’s methodology. As Yin (2003) and Baxter and Jack (2008) emphasize, case study research is especially suitable for investigating complex phenomena within real-world contexts. In this study, the context is a niche manufacturing firm in Curaçao (Netherlands Antilles), making the design particularly appropriate for addressing the central research question. The case study was also selected to address a potential weakness in Kolyada’s methodology: though it has been extensively validated in Russia and CIS, its applicability to international contexts remains under- explored. Villa Exclusivo B.V represents a theoretically Gonzalez and Kolyada — An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s methodology 35 significant example: a niche manufacturer operating in a dynamic, highly competitive local market, facing several key challenges that the methodology aims to address, namely establishing a market position and predicting future market trends. Likewise, the context of the kitchen manufacturing sector in Curaçao allows for a detailed exploration of how Kolyada’s meth- odology can guide strategic forecasting and market positioning under environmental uncertainty within a specific industrial domain, utilizing a scenario-based approach. This empirical study draws on a combination of primary and secondary data sources. As Saunders et al. (2009) emphasize, most studies rely on such data to adequately answer their research questions and ensure reliability through triangulation (Yin 2003; Creswell and Miller 2000). Primary data were gathered to complement and validate Villa Exclusivo B.V’s internal company records for 2023, thereby enhancing reliability through data triangulation (Creswell and Miller 2000). These re- cords provided detailed quantitative information on revenue by product category, number of clients per cluster, sales volumes, pricing structures, and direct costs. In addition, semi-structured interviews were conducted with Villa Exclusivo B.V’s General Manager and Sales Director in 2022 and 2023, focusing on segments based on psychographic motives as well as operational capacities and constraints. Secondary data sources were also collected from diverse sources, which were examined, summarized, and categorized to construct a robust picture of the kitchen manufacturing sector in Curaçao and Villa Exclusivo B.V’s competitive positioning. These in- cluded: (1) industry reports from the Central Bureau of Statistics of Curaçao (Central Bureau of Statistics of Curaçao 2023,2024) (e.g., sector KPIs, market size es- timates), (2) historical benchmarking of sales, market shares, and business positioning, and (3) competitor pricing and cost data covering 2023–2024. Secondary sources provide essential insights into the industry’s structure and external dynamics that cannot be captured through primary data alone. As in other case study research (e.g., Saunders et al. 2009), reliance on secondary data has both advantages and disadvantages. On the one hand, it may have fewer resource requirements, be less obtrusive, and offer better data performance. On the other hand, limitations include the possibility that it was collected for a purpose that does not align with Table 1. Kolyada’s theory and methodology vs. other common strategies and business modeling frameworks. Framework (author, year) Emphasis and advantages Limitations Balanced Scorecard (Robert Kaplan and David Norton, 1996) • Emphasis: Strategic goal setting and planning. • Advantage: Clear logic of goal setting. Theoretical ambiguity in distinguishing between business model and strategy concepts; lack of in-depth analysis of current business model and strategy effectiveness; absence of thorough market modeling, analysis, and forecasting; no tools for forecasting quantitative effectiveness of developed business model elements and strategic initiatives; no comparative forecasting with competitors. Business Model Canvas (Alexander Osterwalder and Yves Pigneur, 2010) • Emphasis: Business model design with a focus on graphical representation. • Advantage: Visual clarity for the business modeling process. Similar to the Balanced Scorecard limitations, plus insufficient attention to strategic goal-setting, target-based management, and strategic planning frameworks. SWOT Analysis and associated strategy development methods (Albert Humphrey, 1960s/2005; popularized in the 1980s) • Emphasis: Planning. • Advantages: User-friendly and simple-to-implement system. Similar to the Business Model Canvas limitations. Takes a reactive approach to strategizing, focusing on responding to emerging opportunities and threats. Kolyada’s theory and methodology (2014, 2023, 2025) • Emphasis: Economic-mathematical modeling with scenario-based forecasting. • Advantage: Enables quantitative competitor- focused forecasting and integrates business modeling, strategic goal-setting, target-based management, and strategic planning. Validation outside Russia and CIS remains limited. The methodology requires initial predictive model tuning using reliable historical data, as well as ongoing multi-year calibration. It is labor-intensive and requires professional skills for effective implementation. 36 BioProducts Business 10(3) 2025 the study’s needs, access may be difficult or costly, and there may be limited control over data quality. Ambrosini et al. (2010) argued that data reliabil- ity increases when researchers draw on replicable teaching sources and integrate them with additional evidence to achieve triangulation. To address these issues and safeguard the quality of the secondary data used, we triangulated secondary sources with internal company information and managerial inter- views, thereby enhancing validity and reducing bias. Clustering was performed to identify the natural structure of the market, using three characteristics: price range, product type, and purchasing method. The rationale for selecting these variables, and their significance for competition in Curaçao’s kitchen market, is elaborated in Section 4 (Case Study). A similar approach is described in the case of applying the Theory and Methodology to the furniture market in Russia (Kolyada 2023). To perform scenario-based forecasting, an ap- proach similar to that of Schoemaker and Van der Heijden (1992) was employed, utilizing scenarios as tools for enhancing decision-making under conditions of uncertainty. For this study, we developed three scenarios: a main scenario, an optimistic scenario, and a pessimistic scenario. This choice reflects the standard practice in strategic planning: working with a limited number of scenarios that capture plausible developments of key risks (Fotr et al. 2014) These scenarios were constructed through a combination of expert interviews (to identify potential shocks, such as competitor entry), economic projections (to establish baseline assumptions for inflation and pricing), and historical trend analysis (to capture demand patterns and market behavior). The key variables considered across the scenarios included customer demand volume, average purchase per customer, operating costs, shifts in customer preferences, and the entry or exit of competitors. The main scenario represents the most prob- able trajectory of market and competitive changes, assuming stable economic conditions, modest de- mand growth, and continued competition without significant disruptions. The optimistic scenario as- sumes favorable external factors such as acceler- ated demand growth and reduced risks, resulting in higher customer volumes and stronger profitability. Conversely, the pessimistic scenario anticipates ad- verse conditions, including demand contraction, rising operating costs, and intensified competition, with customers delaying or reducing their purchases. Together, these scenarios provide a structured range of possible futures rather than a single deterministic forecast. Significantly, these scenarios also shape strategic choices regarding cluster selection and positioning. Under an optimistic scenario, management could pursue more aggressive expansion into the high- price custom-made segment, leveraging increased demand and margins. Under the pessimistic sce- nario, however, the emphasis would likely shift to the medium-price segment, focusing on operational efficiency and resilience against cost pressures. The main scenario supports a balanced choice; sustaining presence in the core niche while preparing contingent actions should shift conditions. Thus, scenario-based forecasting provides a structured basis for evaluating which target cluster is most viable under different competitive and economic assumptions. This ensures that scenario-based forecasting not only provides alternative futures but also directly informs strate- gic cluster selection, which is central to evaluating the applicability of Kolyada’s methodology in niche manufacturing. While this article presents results based on the main scenario, the whole project incorporated all three to stress-test strategic options against vary- ing market conditions. The forecasting component of Kolyada’s methodology is grounded in a predic- tive economic-mathematical model that applies a sensitivity analysis and scenario simulation. Inputs include base-year data on sales, prices, direct costs, and customer purchases by cluster, combined with projections of future demand, competitor behavior, and external risks. This combined dataset enhanced the credibility and reliability of the case study, providing a robust foundation for applying Kolyada’s methodology with a one-year planning horizon. The timeframe was selected based on Curaçao’s dynamic market con- ditions, availability of validated input data, and the need to produce actionable insights within a limited timeframe. Before applying the model prospectively, initial tuning and validation were conducted by forecasting Villa Exclusivo B.V’s 2024 sales volume and those of Gonzalez and Kolyada — An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s methodology 37 its competitors using 2023 data as the base year. The projections were then compared to the actual outcomes, confirming the reliability of the model and highlighting the need for ongoing recalibration to maintain accuracy in longer planning horizons. In practice, the methodology is effective in short- term forecasting but achieves higher accuracy when recalibrated continuously over multiple years as new market and competitor data become available. This helps ensure that forecasts remain both credible and adaptable, supporting the study’s aim of evalu- ating its applicability to strategic planning in niche manufacturing (see Appendix B for technical details of the MVC-1 tool). To clarify the methodological framework, Table 2 summarizes the tools applied at each step of the study, their respective purposes, data sources, type of analysis, and associated notes or limitations. 4. Case study: Villa Exclusivo B.V The following subsections illustrate how Kolyada’s methodology was applied to Villa Exclusivo B.V, a niche-oriented kitchen manufacturer in Curaçao. Each stage of the methodology is described in terms of its analytical purpose, data inputs employed, and contribution to answering the central research question. This research was conducted as part of the on- going development of the scientific school in busi- ness modeling, strategic planning, and strategic management at the Eurasian Management and Administration School (EMAS Business School). 4.1 Market modeling and clustering, scenario- based forecasting (MVC-1 tool) The first stage of the methodology involved market modeling and clustering using a simplified version of the MVC-1 tool. Clustering was conducted using three criteria: price range, product type, and purchasing method. These characteristics capture the primary drivers of competitive dynamics in Curaçao’s kitchen market. Price was categorized into low ($1,000–3,000), medium ($3,000.01–5,000), and high ($5,000.01– 25,000) ranges.1 These ranges were selected to re- flect the purchasing power and market stratification typical of Curaçao’s population, as also observed in the company’s 2023 transactions. The low-price 1 Note: all financial figures are presented in USD. Table 2. Methodological framework applied in the study. Step/tool from Kolyada’s methodology Purpose/function Data sources Notes/limitations Market modeling and clustering, scenario- based forecasting (MVC-1 tool) • To model the market and identify relevant clusters based on product type, pricing, and purchasing method. • To assess volumes, profitability, and gross profit in individual clusters and the market as a whole in the base year. • To perform scenario forecasting of the dynamics of volumes, sales profitability, and gross profit in individual clusters and in the market as a whole over the planning horizon (Main, Optimistic, Pessimistic scenarios). • To choose target cluster(s) for the company. Villa Exclusivo BV sales, pricing, and client data. CBS Curaçao reports industry data. Expert interviews, historical trends, macroeconomic projections, and 2023–2024 operational data. Quantitative analysis and forecasting. The economic-mathematical forecasting model has undergone only two stages of validation: reconciliation of reverse forecasts with the actual results of the previous period (2022/2023) and reconciliation of the forecast for 2024 with the actual data obtained this year. According to the methodology, further validation is required to improve the accuracy of forecasts in future periods. Short-term reliability is high. Cluster segmentation • To divide the target cluster into segments based on consumer motives. • To choose target segment(s) for the company. Interviews with Villa Exclusivo BV management. Observed purchasing behavior. Qualitative analysis. Not statistically tested, relies on expert judgment, and offers strategic insight for positioning. Positioning development • To develop the company’s positioning in the target cluster and segment. • To align the company’s value proposition and unique selling proposition with the dominant consumer motive and positioning in the target cluster and segment. Derived from previous segmentation and internal strategy discussions. Strategic and qualitative analysis. Relies on accurate segmentation. Implementation depends on internal alignment and the capability of resources. 38 BioProducts Business 10(3) 2025 range corresponds to mass-market affordability, the medium range captures middle-income households seeking both value and quality, and the high-price range reflects the premium custom-made market. Product type distinguishes between pre-assem- bled and custom-made products, reflecting differ- ences in lead times, customization depth, and gross margin. This criterion is critical for Villa Exclusivo B.V, since the company’s core competence lies in cus- tom design, which demands greater craftsmanship but also offers higher margins. In contrast, the pre- assembled part represents standardized products that compete primarily on cost efficiency, presenting different strategic risks and opportunities. Finally, purchasing methods were divided into direct sales and tender-based transactions, a distinc- tion that separates retail/household demand from institutional and project-based procurement. This factor is particularly important in Curaçao’s market, where B2B and B2C dynamics coexist and most B2B transactions are conducted through tenders. Tenders, such as contracts for hotels, government buildings, or large housing projects, can significantly influence demand volumes and revenue stability. By contrast, direct sales rely on household-level deci- sions and are more sensitive to shifts in consumer sentiment. These categories were chosen because they represent the most salient dimensions of com- petition in Curaçao’s kitchen manufacturing market, thereby providing decision-useful forecasts by cluster that inform positioning and resource allocation. Typically, the methodology requires considering B2C and B2B as separate markets, i.e., developing a distinct MVC-1 for each of them. However, in this particular case, an exception was made to combine both markets within a single MVC-1, i.e., treating them as different parts (clusters) of one market. The reason for this decision is the very small size of these markets in Curaçao, which makes it impracti- cal to increase labor and financial costs for separate modeling and forecasting of B2C and B2B markets in this territory. Applying the MVC-1 tool, twelve (12) possible clus- ters emerged from these combinations, of which six were viable (see Appendix C) and six were excluded as artificial because they did not exist in the actual market. In line with Schiffman and Wisenblit’s (2019) criteria that effective segments must be identifiable, profitable, accessible, and reachable, only these six clusters qualified as realistic strategic options. The viable clusters displayed distinct sales volumes, cus- tomer bases, profitability levels, and growth potential. The analysis established the market structure for 2023 (the base year) and projected cluster dynam- ics for 2024. This study focused on identifying the key eco- nomic parameters of the clusters in the base year, such as volume in physical and monetary terms, sales profitability, gross profit, and customer numbers. It also conducted scenario forecasting for changes in these parameters in 2024 and identified the most profitable clusters, taking into account competitive density and resource availability for Villa Exclusivo B.V. This facilitates the analysis and projection of the target market and cluster data, cluster characteristics, pricing structures, and client bases. This stage not only revealed the structure of the market but also established an economic baseline for scenario-based forecasting, thereby linking market clustering and the resulting segmentation to the study’s broader aim of testing how Kolyada’s methodology can guide strategic positioning under uncertainty. 4.2 Selection of target cluster After market modeling and clustering, the second stage of Kolyada’s methodology involves the selec- tion of a target cluster. A target cluster is defined as the cluster offering the most advantageous bal- ance of profitability, competitive density, and growth potential. It must also remain attainable, given the firm’s intellectual, financial, and technical resources. In this case, Villa Exclusivo B.V’s assessment focused on identifying the cluster that combined high pro- jected margin income with manageable competition intensity and reasonable growth opportunities. This logic reflects Kotler and Keller’s (2016) view that target market selection must consider both external at- tractiveness and internal fit with company resources. This stage is critical for addressing the research question, as it demonstrates how scenario-based forecasting and comparative analysis within Kolyada’s methodology inform practical decisions regarding where to compete. For organizations such as Villa Exclusivo B.V, this guidance is particularly important in dynamic markets, where selecting the wrong clus- ter could result in wasted resources and strategic mis- Gonzalez and Kolyada — An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s methodology 39 alignment. Following the identification of the target cluster, the analysis proceeded with psychographic segmentation of its clients to refine the company’s strategic focus. 4.3 Segmentation of the target cluster The third stage of applying Kolyada’s methodology is the segmentation of the selected target cluster to refine Villa Exclusivo B.V’s strategic focus. While segmentation may include socio-demographic char- acteristics (for B2C), physical, operational, situational, and other factors (for B2B), and psychographic char- acteristics (for both B2C and B2B), Kolyada (2023) argues that the first two criteria are the most com- mon, but also the least effective way to segment a cluster. Instead, psychographic analysis, based on natural and consumer motives (what clients want) and psychological type (how clients decide), is used in this case study. This choice is supported by Wedel and Kamakura (2000), who emphasize that motives- and values- based segmentation provide deeper insight into consumer behavior than demographics, since cus- tomers with similar profiles may differ significantly in their purchase motivations (Kotler and Keller 2016; Wedel and Kamakura 2000). This distinction is particularly relevant in the Curaçao kitchen market, where customers with the same demographic profile may differ significantly in whether they value tangible incentives, such as extended warranties, or intangible ones, such as the prestige of owning a custom-designed kitchen (Kotler and Keller 2016). Therefore, segmenting based on motives provides a more accurate and action- able understanding of demand than demographic profiling alone. Practically, this approach allowed Villa Exclusivo B.V to evaluate how different subgroups within the chosen cluster would respond under alternative market scenarios. For example, in an optimistic sce- nario, segments driven by consumer motives such as prestige might expand, encouraging greater invest- ment in high-end custom kitchens. In the pessimistic scenario, the cost-saving motive dominates, where customers prioritize the opportunity to purchase a product at a lower price (while remaining within the price range that defines the cluster) or to obtain more for the same amount of money. The integration of motives with scenario-based forecasting also shaped the selection of the tar- get cluster: optimistic conditions made high-price custom-made clusters more attractive, whereas pes- simistic conditions favored the medium-price cluster. The main scenario reinforced Villa Exclusivo B.V’s cur- rent niche, while highlighting adjacent opportunities. By connecting segmentation to motives and scenarios, this stage demonstrates how Kolyada’s methodology translates forecasting into concrete strategic marketing and product design choices. This directly addresses the central research question by demonstrating how structured business modeling enables Villa Exclusivo B.V to select and adapt its niche effectively under conditions of environmental uncertainty. 4.4 Position development The fourth stage of Kolyada’s methodology involves translating segmentation insights into a clear market position. Positioning, as Kolyada (2023) emphasizes, goes beyond a simple slogan; it is a strategic asser- tion that this company or product is the best choice for customers in the target segment, based on their natural and dominant consumer motives (Kolyada 2023). Building on Schiffman and Wisenblit’s (2019) idea that positioning is fundamentally about shap- ing a distinct image and identity in the minds of consumers, and Ries and Trout’s (2001) insight that positioning is ultimately determined by perception rather than product features, the analysis focused on how Villa Exclusivo B.V could translate segmentation insights into market advantage Together, these perspectives underscore that effective positioning requires alignment with the target segment’s intrinsic motivations while also differentiating the company from its competitors. For Villa Exclusivo B.V, effective positioning requires aligning its value proposition with the dominant consumer motive identified in the target segment. By tailoring its message to emphasize both functional benefits (e.g., quality and durability) and symbolic value (e.g., prestige from custom design), the firm reinforced its credibility as the best-equipped provider to meet client needs and aspirations. This stage addresses the research question be- cause it demonstrates how Kolyada’s methodology moves from forecasting and segmentation to an 40 BioProducts Business 10(3) 2025 actionable strategy. Positioning based on psycho- graphic insights ensures that Villa Exclusivo B.V not only selects the right niche, but also creates and com- municates its competitive advantage in a way that resonates with customer motives, thereby translating strategic forecasting into tangible market leadership. 5. Results This section presents the results derived from the ap- plication of Kolyada’s methodology to Villa Exclusivo B.V. The findings reflect the application of the main scenario defined during the forecasting phase, fo- cusing on the high-end custom-made kitchen mar- ket in Curaçao. Each phase, market modeling and clustering, target cluster selection, segmentation, and positioning, generated quantitative outputs as well as strategic insights that directly informed Villa Exclusivo B.V’s decision-making. By illustrating how forecasts inform strategic choices, the results also demonstrate the value of the methodology as a tool for guiding business modeling and strategies under uncertainty. 5.1 Market modeling and clustering Clustering using the MVC-1 tool identified six viable clusters in Curaçao’s kitchen market, defined by the price range, product type, and purchasing method. It illustrates data from Villa Exclusivo B.V and two competitors (as well as from a “minor player” group of smaller companies) operating within the selected product category of built-in kitchens. These clusters revealed the natural structure of competition, includ- ing price level, production type, sales channel, cluster volume, and projected growth. They highlighted the cluster with the highest concentration of profitability and growth potential. Table 3 presents the distribu- tion of clusters based on the sales volume, customer base, and profitability. It also includes both the base year (2023) and the forecast year (2024) data. The analysis indicates that the high-price, custom- made clusters represented a smaller share of the market, but generated high profitability. In contrast, the medium-price clusters were larger, but character- ized by stronger competitive pressure. The low-price clusters were less attractive, with narrower margins and greater sensitivity to economic fluctuations. This stage provides a baseline for subsequent forecasting. It clarifies how Villa Exclusivo B.V can evaluate opportunities across clusters, shows how the methodology organizes competitive landscapes into decision-useful clusters for strategic forecasting, and translates raw data into actionable strategic insights, rather than static market categories. 5.2 Selection of target cluster Applying Kolyada’s methodology, the next step is to identify the target cluster that offers the best balance of profitability, competition intensity, and resource feasibility. Villa Exclusivo B.V identified Clusters 3, 4, and 5 as strategic targets. Although Clusters 3 and 4 demonstrated an acceptable profitability, the company already held a significant market share of both 52.6% and 66.7%, respectively. Therefore, the company chose to continue developing these markets incrementally, while prioritizing entry to Cluster 5, where it had no presence. Cluster 5, distinguished by high prices, custom- made products, and direct purchases, emerges as a particularly attractive opportunity. It combines strong profit potential (expected profit of $101,789 and projected profitability of 31.60%), forecasted market growth (from $300,000 to $322,088), and low competition density, with only one major competitor. These conditions signaled a strategic opening in a profitable niche where Villa Exclusivo B.V’s capabilities could provide immediate differentiation. Table 4 sum- marizes the comparative indicators across clusters. The results indicate that Cluster 5 was the most attractive for Villa Exclusivo B.V. It combines strong gross margins with manageable competition and high entry barriers, protecting against rapid imita- tion. Whereas the medium-price clusters showed a greater volume, they also presented fiercer compe- tition and lower profitability. This choice was also supported by organizational fit: Villa Exclusivo B.V possessed the technical capabilities, infrastructure, and customization expertise required to compete effectively in this cluster. Thus, the selection of this target cluster demon- strates how Kolyada’s methodology operationalizes comparative forecasting into a rational and evidence- based choice of strategic niche, directly addressing the central research question. 5.3 Segmentation of the target cluster Once the target cluster was identified, segmentation analysis was performed to refine Villa Exclusivo B.V’s understanding of the customer groups. Psychographic Gonzalez and Kolyada — An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s methodology 41 Table 3. MVC-1: Market model, clusters, and their characteristics - Main scenario. Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Company name Total sales Low price, pre- assembled, direct purchase Low price, pre- assembled, tenders Medium price, custom- made, direct purchase Medium price, custom- made, tenders High price, custom- made, direct purchase High price, custom- made, tenders Company’s market share, % Competitor 1 $525,000 $250,000 $225,000 $50,000 20.00% Competitor 2 $325,000 $100,000 $175,000 $50,000 12.38% Group: Minor players $925,000 $200,000 $200,000 $300,000 $225,000 35.24% Villa Exclusivo BV $850,000 $75,000 $75,000 $250,000 $350,000 $100,000 32.38% Cluster volume/market volume, $ $275,000 $625,000 $475,000 $525,000 $300,000 $425,000 $2,625,000 Sales profitability (rate of return), % 17.09% 14.58% 21.53% 22.14% 32.45% 31.72% 22.43% Total profit volume, $ $47,007 $91,116 $102,284 $116,238 $97,345 $134,795 $588,785 Cluster share, % 10.5% 23.8% 18.1% 20.0% 11.4% 16.2% Change in cluster volume/market volume (in money), next year, % 109.87% 111.13% 160.27% 109.83% 107.36% 58.26% Expected cluster volume/market volume, next year, $ $302,148 $694,588 $761,276 $576,601 $322,088 $247,609 $2,904,310 Expected sales profitability (rate of return), % 16.48% 13.92% 20.51% 21.18% 31.60% 30.88% 20.76% Expected total profit volume in the cluster/market next year, $ $49,797 $96,683 $156,137 $122,143 $101,789 $76,465 $603,014 Expected cluster share in the market volume, % 10.4% 23.9% 26.2% 19.9% 11.1% 8.5% 100.0% Colors - the company’s share in the cluster 0-5% 5%-10% 10%-15% 15%-20% 20%-25% 25%-40% >40% Table 4. Selected target cluster. Cluster 5 Company name Total sales High prices, custom-made, direct purchase Company’s market share, % Competitor 1 $500,000 18.87% Competitor 2 $325,000 12.26% Group: Minor players $1025,000 $300,000 38.68% Villa Exclusivo BV $800,000 30.19% Cluster volume/market volume, $ $300,000 $2,650,000 Sales profitability (rate of return), % 32.45% 22.84% Total profit volume, $ $97,345 $605,228 Cluster’s share, % 11.4% 100.0% Dynamics of the cluster volume/market volume (in money), next year, % 107.36% Expected cluster volume/market volume, next year, $ $322,088 $2,902,936 Expected sales profitability (rate of return), % 31.60% 21.18% Expected total profit volume in the cluster/market next year, $ $101,789 $614,898 Expected cluster share in the market volume, % 11.1% 100.0% Target market: Product group: Built-in kitchens Geographical region: Curaçao 42 BioProducts Business 10(3) 2025 motives, natural motives (functional, value-for-money concerns), and consumer motives (shortest delivery time, prestige, and personalization preferences) were used to distinguish segments. Table 5 presents the segmentation of Cluster 5 under the main scenario. The cluster was divided into three segments based on dominant customer mo- tives, as previously explained. All segments comprise small, medium, and large-sized enterprises, reflecting differing strategic needs and profit potential. Segment 1. Dominant consumer motive — “Shortest delivery time (maximum speed)”: Clients in this segment desire to receive the prod- uct as quickly as possible and value efficiency and prompt service. Segment 2. Dominant consumer motive — “Prestigious status”: Clients in this segment are driven by the aspiration to own products that signify their association with an exclusive or high-status group. They value items that fulfill practical needs while simultaneously acting as symbols of belonging to a prestigious community. Segment 3. Dominant consumer motive — “Maximum customer focus (personalization)”: Clients in this segment value personalization and attentiveness, expecting suppliers to thoroughly understand and prioritize their unique needs and preferences. To demonstrate how the methodology accounts for competitive dynamics, an expert-based estimate assessed the degree of customer association with competitor brands in each segment. As shown in Table 5, Segments 2 and 3 exhibited low competitor brand association levels of 15% and 10%, respectively. This indicates that these segments remain largely unoccupied by strong competitor perceptions, of- fering Villa Exclusivo B.V a strategic advantage by establishing a stable brand association as consumers’ preferred choice. Based on these findings, Villa Exclusivo B.V se- lected segments 2 and 3 within cluster 5, as they align most closely with its operational capabilities. The company’s value proposition was modified in accordance with the dominant consumer motives of the selected segments. These results suggest that integrating psychographic segmentation with scenario forecasting provides a stronger foundation for strategic decision-making than demographic seg- mentation alone. They also illustrate how Kolyada’s methodology translates customer motives into ac- tionable strategies, ensuring that cluster and segment choices are anchored in both forecasting accuracy and consumer psychology. 5.4 Development of positioning The final stage translated the segmentation insights into a clear market position for Villa Exclusivo B.V. The positioning strategy was designed to align with the dominant consumer motives in segments 2 and 3, prestige and personalized services, ensuring that the company’s value proposition addressed the psycho- logical and functional expectations of high-end clients seeking exclusivity, quality, and attentive interaction. The following position statement guided this strat- egy: “Villa Exclusivo B.V delivers high-priced custom- Table 5. Segmentation of the target cluster (main scenario) and selection of the target segment. Segmenting Cluster 5 (high prices, custom-made, direct purchase). Segment # Dominant consumer motive Customer profile Expected profit volume $/next year Company that formed a strong association with its brand in the base year, % of respondents in the segment 1 Shortest delivery time (maximum speed) Small, medium, and large-sized enterprises $ 47,332 Competitor 1+2: 73% 2 Prestigious status Small, medium, and large-sized enterprises $ 18,831 Competitor 1+2: 15% 3 Maximum customer focus (personalization) Small, medium, and large-sized enterprises $ 35,626 Competitor 1+2: 10% Cluster total $ 101,789 Target segments Gonzalez and Kolyada — An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s methodology 43 made kitchens, combining the highest quality and prestige with exceptional personalized customer service.” The results show that aligning positioning with dominant consumer motives strengthens Villa Exclusivo B.V’s credibility as the leading provider of high-end custom-made kitchens in Curaçao. This stage demonstrates how the methodology ensures that strategic positioning is systematic, grounded in forecasting, segmentation, and validation, rather than arbitrary. 5.5 Validation of forecast accuracy using 2024 data The reliability of the model was validated by compar- ing its 2024 forecasts with actual performance data for Cluster 5. As shown in Table 6, the deviation be- tween the projected and real figures is minimal; for instance, the sales volume deviates by only +0.65%. These results confirmed that the model was tuned correctly during the initial calibration phase and was capable of producing accurate short-term forecasts. This reinforces its value in strategic planning within dynamic market environments such as the kitchen manufacturing sector in Curaçao. Additionally, sen- sitivity analysis was embedded into the scenario modeling process, enabling the model to assess how changes in variables, such as customer demand, costs, and competitor behavior, impact key outputs. This enhances the robustness and adaptability of the model in forecasting future conditions. The results of this validation confirm the effec- tiveness of the forecasting model in identifying vi- able strategic opportunities. Cluster 5, characterized by high pricing, custom-made solutions, and direct purchasing, proved to be a key opportunity for Villa Exclusivo B.V. These forecasts guided the selection of target segments and the development of a po- sitioning strategy aligned with customer motives. Kolyada’s methodology provided a structured and reliable framework for market modeling, clustering, segmentation, and positioning. Ultimately, it supports the development of a business model that is both strategically aligned and market-responsive. 6. Strategic discussion This study applied a key component of Kolyada’s methodology to identify an optimal market niche for Villa Exclusivo B.V. The findings suggest that the company is well-positioned to capture two high-value segments in Cluster 5: customers who prefer high prices, custom-made products, and direct purchases, by aligning its capabilities with consumer motives such as prestigious status and maximum customer focus (personalization). The projected growth in this cluster indicates a rising demand for bespoke, high- end products and requires that Villa Exclusivo B.V be well-positioned to meet this demand through its commitment to quality craftsmanship and personal- ized offerings. Beyond this case, however, Kolyada’s methodology offers unique advantages for strategic decision-mak- ing. Unlike better-known tools such as the Business Model Canvas (Osterwalder and Pigneur 2010), SWOT analysis, and Balanced Scorecard (Kaplan and Norton 1996), which primarily provide qualitative assess- ments, Kolyada’s methodology enables quantitative forecasting of core business metrics such as revenue, market share, cluster share, sales profitability, and gross profit. This data-driven approach supports more precise and risk-informed business modeling and strategic planning. A key differentiator of the methodology is its built-in sensitivity analysis, which allows decision-makers to simulate and assess alter- native business models and strategy options under different market scenarios. Additionally, the model can simulate competitor behavior and quantify the effects of strategic rivalry, an uncommon but es- Table 6. Comparison between base year (2023), forecast year (2024), and actual year (2024) for Cluster 5. Metric Base (2023) Forecast (2024) Actual (2024) Deviation (%) Sales volume (Cluster 5, ANG) $300,000 $322,088 $320,000 +0.65% Sales profitability (rate of return), % 32.45% 31.60% 30.00% +1.6% Total profit volume, $ $97,345 $101,789 $103,250 -1.42% Cluster’s share, % 11.4% 11.1% 11.6% -0.5% 44 BioProducts Business 10(3) 2025 sential feature for companies operating in dynamic, segmented markets. From a cost-benefit perspective, while the methodology requires moderate invest- ment in data collection and initial model calibration, the ability to make analytically supported strategic choices offers a high return, especially for companies seeking to optimize positioning and profitability in volatile sectors. It is essential to note that this study was based solely on a part of Kolyada’s methodology, which focused on determining the market position as one element of the organization’s business model. Other parts of the methodology are likewise helpful but lie outside the scope of this study. These include digital business modeling, quantitative forecasting of productivity, assessing the economic efficiency of a business model, strategic goal setting, target- based management, brand management, corporate culture management, budgeting, and other related tools and methods. 7. Conclusion This study introduces Kolyada’s business modeling and strategic planning theory and methodology and demonstrates how it can be utilized to forecast mar- ket trends and support business modeling and stra- tegic planning in a niche manufacturing context. By applying a selected part of the methodology focused on identifying and validating a target market niche, the case study of Villa Exclusivo B.V demonstrates the methodology’s predictive potency and strate- gic usefulness. The analysis successfully identified and validated a profitable, low-competition niche (Cluster 5) for the firm in Curaçao’s kitchen manu- facturing industry. By combining scenario-based forecasting with consumer-motive segmentation, this study provided actionable guidance for positioning and strategic focus. The findings indicate that even partial application of Kolyada’s methodology yields quantifiable forecasts of revenue, profitability, and market share, while supporting managers in aligning operations with consumer expectations in high-value segments. Projection validations through historical tuning confirmed the predictive strength and practi- cal relevance of the methodology. The results of this case study highlight that Kolyada’s methodology brings distinctive value by enabling quantitative forecasting of a potential busi- ness model (e.g., revenue, market share, and share in the target cluster). This differentiates it from qualita- tive business modeling methods that rely on relative assessments. Moreover, by integrating sensitivity analysis, the methodology creates a digital business model (predictive economic-mathematical model) for the firm, allowing managers to test multiple strategic alternatives under different scenarios. This structured experimentation helps to identify the most effec- tive options based on expended resources and the economic results obtained. This is very important for practical management, as company managers prefer to make decisions based on analytically sound models and tests performed with their help, rather than based on abstract and relative assessments. Additionally, the methodology evaluates the eco- nomic efficiency of both the organization’s business model and those of its competitors. It further illus- trates the economic consequences of competitive rivalry arising from different business model variants. This supports more informed management deci- sions in business modeling and strategic planning by considering not only incurred costs and outcomes but also the dynamics of competitive rivalry. This is important, as most modern markets are character- ized by a very high level of competition. From a policy perspective, the methodology can be applied by governments, development agencies, or industries, enabling them to guide the strategies of small, me- dium, and large businesses and identify economically viable clusters and niches for sustainable growth and innovation. 7.1 Theoretical implications The main contribution of this research lies in expand- ing the concept of business modeling and strate- gic planning. It demonstrates how new theory and economic-mathematical methodology of business modeling and strategic planning (Kolyada 2014, 2023, 2025) can be applied in the context of niche manu- facturing. The study contributes to the literature on business modeling and strategy development by demonstrating how quantitative scenario model- ing and forecasting can be integrated with psycho- graphic segmentation to identify market niches and develop effective company positioning—an aspect often overlooked in existing methodologies such Gonzalez and Kolyada — An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s methodology 45 as the Business Model Canvas (Osterwalder and Pigneur 2010). Specifically, three theoretical insights have emerged. First, by applying Kolyada’s methodology outside the Russian and CIS markets, this study pro- vides evidence of its transferability. It discusses how forecasting-based business modeling and strategic planning tools can be used in other markets. Second, it addresses a longstanding concern in the forecast- ing literature. As Makridakis et al. (2009) emphasize, precise forecasting in economics and business is rarely achievable because most activities are exposed to unpredictable events that generate significant uncertainty. Thus, scenario-based approaches are particularly valuable. Kolyada’s methodology comple- ments traditional forecasting by simulating multiple plausible outcomes rather than relying on a single- point estimate. Third, the findings illustrate how the theory and methodology support the Concept of Dynamic Capabilities (Teece et al. 1997; Teece 2007) by con- sidering business modeling as a high-level routine and a structured mechanism for tracking changes, leveraging opportunities, and making strategic deci- sions regarding company development in the market under unstable conditions. 7.2 Managerial implications This study has several interesting implications for business practitioners. First, forecasting-based busi- ness modeling and strategic planning tools require time, calibration, and historical data to generate value. However, firms that adopt them early are better positioned to adapt to disruptive change. Second, market niche search should be approached as a two-step process: first, model and forecast the market to select a target cluster, and then segment that cluster to identify the most viable target segment. Third, developing the company’s positioning, value proposition, and unique selling proposition (USP) in accordance with the natural and consumer motives of the target segment ensures optimal alignment. This alignment connects the company, its product, and its service with the expectations of target customers. This ensures that firms choose the most economically advantageous market position, one that is aligned with available resources and responsive to client motives, thereby winning in the competitive struggle. Ultimately, forecasting models provide not only quantitative predictions but also a disciplined framework for strategic dialogue, reducing reliance on intuition and improving decision quality under uncertainty. 7.3 Limitations and further research directions As noted throughout this paper, this study has sev- eral limitations. First, the results described in the article are based on a single case study in Curaçao. Second, due to the limited scope of the article, only a reduced version of the MVC-1 tool and the approach to segmentation based on consumer motives were applied. These represent just a portion of Kolyada’s entire set of tools and approaches—specifically, the toolkit dedicated to determining the market position within a company’s business model. The full version of MVC-1, including the math- ematical algorithm for forecasting market and market cluster dynamics, as well as other business modeling and strategic planning tools that are integral parts of the methodology, remained outside the scope of this article. Also, the economic-mathematical predic- tive model that ensures the selection of the most economically efficient business model variant for the company’s operations in the chosen market niche. However, these tools were utilized in the project to obtain the results described in this article. Third, the economic-mathematical forecasting model underwent only two stages of validation: re- verse forecasting for 2022/2023 and forward fore- casting for 2024. Although these exercises confirmed short-term reliability, further longitudinal testing is necessary to strengthen predictive accuracy across longer horizons and different environments. The methodology requires initial model tuning using reliable historical data and ongoing multi-year cali- bration, which may limit the immediate applicability for small firms that lack robust datasets or analytical resources. Therefore, future research should explore sim- plified or hybrid approaches that adapt Kolyada’s methodology for resource-constrained SMEs. Fourth, Kolyada’s methodology is labor-intensive and re- quires professional skills for its effective implemen- tation. Fifth, empirical validation outside Russia and CIS remains limited. A broader application in an international context would help establish the gen- 46 BioProducts Business 10(3) 2025 eralizability of Kolyada’s methodology and reveal potential cultural or institutional constraints. By acknowledging these limitations, this study adheres to best practices in case study research, where transparency about scope and constraints is crucial for credibility (Ross and Zaidi 2019). 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IESE Research Papers D/862, IESE Business School. https://www.iese.edu/media/research/pdfs/ DI-0862-E.pdf 7. Strategy development (strategic actions to build a target business model and achieve strategic goals based on it). 8. Budgeting. Economic-mathematical predictive model in the methodology The set of projects included an economic-mathe- matical predictive model of the company’s business model and strategic plan, with an emphasis on the following: • MVC-1 tool: Development of a market model, including clustering of the target market, determination of key economic characteristics of clusters and the market as a whole (structure, volumes, sales profitability, and other indicators) in the base year, and forecasting the dynamics of these indicators within the planning horizon. • MVC-1 and MARIS tools: Forecasting external factors (macro and micro-environments) and risks (internal and external sources) within the planning horizon. • Business Process Matrices: Forecasting of internal business process efficiency. • DMM tool and Z-equation: Creation of a digital business model of the organization and projection of key economic indicators: sales volume in physical terms, revenue, market shares, target cluster share, sales profitability, etc. As-Is and To-Be Formats • “As-Is”: The “As-Is” format assumes that during the forecast period, the business model and strategic Appendix A: Procedural Details of Kolyada’s Methodology To preserve analytical clarity in the main text, a brief description of the procedural elements of Kolyada’s methodology is presented here. Step-by-Step Sequence in the methodology 1. Scenario-based market modeling and forecasting, including market cluster (niche) selection. 2. Scenario-based comparative quantitative fore- casting of the proposed business model/strat- egy’s economic efficiency, as well as competitors’ business models and strategies. This step uses the “As-Is” forecasting format to track projected sales volume in physical terms, revenue, profit, profitability, market shares, and other economic indicators. 3. Scenario-based comparative quantitative forecast- ing of a firm’s potential business model/strategy variants’ economic efficiency (and competitors’ efficiency). This step uses the “To-Be” forecasting format to select the optimal business model and strategy, based on the projected indicators and required resources. 4. Strategic goal-setting and target-based manage- ment are both derived from the selected business model. 5. Brand management. 6. Planning and management of corporate culture in the context of the selected business model and strategic goals. file:///F:\__CASCADIA\______BIOProductsBIZ_VICKI_ERIC\__Volume%2010\10_3%20Gonzales\%2043(2–3 https://doi.org/10.1016/j.lrp.2010.01.005 https://doi.org/10.1177/1476127013510466 https://doi.org/10.1177/1476127013510466 https://doi.org/10.1016/B978-0-08-097086-8.73040-6 https://doi.org/10.1016/B978-0-08-097086-8.73040-6 https://ideas.repec.org/p/ebg/iesewp/d-0862.html https://ideas.repec.org/p/ebg/iesewp/d-0862.html https://ideas.repec.org/p/ebg/iesewp/d-0862.html https://ideas.repec.org/s/ebg/iesewp.html https://www.iese.edu/media/research/pdfs/DI-0862-E.pdf https://www.iese.edu/media/research/pdfs/DI-0862-E.pdf Gonzalez and Kolyada — An analysis of kitchen manufacturers in the Curaçao market by applying Kolyada’s methodology 49 decisions (characteristics) of the organization and competitors would be identical to those in the base year. • “To-Be”: Assumes that during the forecast period, the organization selects the optimal business model and strategic decisions to outperform its competitors, taking into account their anticipated actions (selection of business model and strategy). Base Year and Planning Horizon • The base year is defined as the last completed calendar year when the project is initiated. • The planning horizon is the current unfinished year and the following three years or more. Predictive models are developed annually, incorporating a scenario-based analysis. The “As-Is” and “To- Be” formats consider the main, optimistic, and pessimistic scenarios. Follow-Up Projects and Validation Follow-up projects are implemented annually, on average, starting from one year after the original projects. Each follow-up project extends a project in an earlier period, conducted for the same organiza- tion and competitors, within the same market and target cluster. They focus on validating the forecasts made for the first year in the planning (forecasting) horizon based on the model and fine-tuning the model by comparing the projected indicators with the actual results obtained that year. Alongside the primary tasks of the initial projects, these projects also include necessary corrections in the strategic goal-setting, brand management, defining a target corporate culture profile, developing strategic initia- tives, and establishing budgeting processes. This approach ensures both the initial validation and continuous fine-tuning of the economic and mathematical predictive model. Developers either confirm the alignment of forecasts for the previous year of the planning horizon with the actual results (under the realized market scenario) or make adjust- ments if the forecast did not materialize. Adjustments include: • Changing input factor values when actual values differed from projections. • Revising cause-and-effect relationships between inputs and forecasts. • Implementing a combination of both methods. Appendix B. Technical Details of Predictive Economic-Mathematical Model for the Curaçao Kitchen Manufacturing Market The forecasting component of Kolyada’s methodol- ogy applies sensitivity analysis and scenario simula- tion through an economic-mathematical predictive model that links input variables to projected business outcomes. In the Villa Exclusivo B.V’s project, the base year was taken as the last completed calendar year at the time the project was carried out. In contrast, the planning horizon was the current unfinished calen- dar year in which the project was being executed. Initially, the predictive model was configured through “forecasting” past periods for which the developers had the necessary data. Main Inputs and Initial calculations (Base Year, 2023): • Market modeling and clustering characteristics. • Monetary sales volume for Villa Exclusivo B.V and competitors. • Pricing and direct costs (Villa Exclusivo B.V and competitors). • Average customer purchase size by cluster. • Unit sales volume for Villa Exclusivo B.V and competitors (calculated). • Customer numbers by cluster and companies in cluster (calculated). • Sales profitability by cluster (calculated); • Companies market and cluster shares (calculated); • Other market and cluster characteristics (calculated). Projected Inputs, scenario mode (2024 Horizon, Main, Optimistic, and Pessimistic scenarios): • Pricing and direct costs. • Average customer purchase size by cluster. • Customer numbers by cluster. • Other market and cluster characteristics. • External market factors and risks. • Comparative efficiency in business processes. 50 BioProducts Business 10(3) 2025 Appendix Table 1. Clustering results for the Curaçao kitchen manufacturing market Cluster Price range (USD) Product type Purchasing method Market characteristics Strategic relevance 1 1,000–3,000 Pre-assembled Direct sales Mass-market, cost-driven, sensitive to consumer sentiment Low margins, high volume 2 1,000–3,000 Pre-assembled Tender-based Bulk contracts for low-cost units (e.g., social housing) Stable demand, price competition 3 3,000.01–5,000 Pre-assembled Direct sales Middle-income households seeking quality & value Balanced growth potential 4 3,000.01–5,000 Custom-made Direct sales Middle/upper-middle customers wanting affordable customization Attractive margins, moderate demand 5 5,000.01–25,000 Custom-made Direct sales High-income clients valuing exclusivity & craftsmanship Premium niche, high margins 6 5,000.01–25,000 Custom-made Tender-based Luxury projects (hotels, offices, upscale housing) High-value, B2B-driven demand Outputs, scenario mode (2024 Horizon, Main, Optimistic, and Pessimistic scenarios): • Forecasted sales volumes (monetary and physical terms) for Villa Exclusivo B.V and competitors. • Expected customer numbers for Villa Exclusivo B.V and competitors. • Companies market and cluster shares for Villa Exclusivo B.V and competitors. • Sales profitability by cluster for Villa Exclusivo B.V and competitors. • Gross Profit for Villa Exclusivo B.V and competitors. Validation Process: The predictive model was first tuned using 2023 data points as the base year. Forecasts were generated for 2024 and compared with the actual outcomes. Discrepancies prompted adjustments to input val- ues and, where necessary, revisions to the causal relationships within the model. This iterative process of validation and recalibra- tion is integral to maintaining forecasting accuracy. Each cycle of back-testing not only strengthens pre- dictive reliability but also improves managerial deci- sion-making by refining assumptions about demand, competitor behavior, and risk exposure. Appendix C. Clustering Results for the Curaçao Kitchen Manufacturing Market Twelve clusters were initially modeled by combining three price ranges (low, medium, and high), two prod- uct types (pre-assembled and custom-made kitchen furniture), and two purchasing methods (direct sales and tender-based). Of these, six clusters were con- sidered viable (Appendix Table 1). The remaining six represented fictitious combinations that were not observed in the Curaçao market and were excluded.