Executive Summary

Author: Brunno Guarniery Dos Santos — Advisor: Arthur Damasceno Vicente

^{Summary prepared by the ResumeAI tool, an artificial intelligence solution developed by the Pecege Institute focused on synthesis and writing.}

This study analyzed how budgetary planning and control acted as support tools for managerial decision-making and budgetary adherence in a mining company in the Carajás integration region, Pará. The investigation sought to understand the correlation between the main performance indicators of the mine operation and financial performance, especially in a context of negative budgetary variations of 98% in 2023 and 69% in 2024. Specific objectives included verifying the use of the budget as a management tool, evaluating the type of budget applied, and identifying elaboration techniques to enhance budgetary adherence, aiming to increase assertiveness in decisions.

The relevance of the research is situated in the scenario of fierce competitiveness that drives organizations to adopt continuous improvement practices to enhance their management (Wanzutt, 2009). In this environment, investment in new approaches that provide competitive advantages is essential (Silva et al., 2005). Competitiveness, according to Padoveze and Taranto (2009), is achieved through the strengthening of strategic planning and business control, instruments that support decision-making and are essential for the sustainability of companies. Business budgeting, in this context, transcends cost control, positioning itself as a pillar of tactical and operational planning.

The business budget should be executed along with the planning of expenses, investments, and revenue projections to forecast cash flows and assist in strategic decisions. The clear definition of the budget’s purpose is a crucial step and must be aligned with the guidelines of senior management (Padoveze, 2012). The success of the budgeting process is amplified when adequate planning is carried out and disseminated across all hierarchical levels. Bornia and Lunkes (2007) emphasize that a well-structured budget promotes greater alignment between strategic indicators and operational goals, contributing to the improvement of integrated management.

The budget’s function of economically quantifying operations is linked to the premises of strategic planning (Soares; Neves Júnior, 2004). Its use is widespread among Brazilian companies, demonstrating itself to be an indispensable management tool, also applicable to the mining industry. According to the Brazilian Mining Institute (IBRAM, 2023), mineral activity is strategic for Brazil’s development, with projected investments of US$ 50 billion between 2023 and 2027. Zaparolli (2024) adds that approximately 28% of the resources for the expansion of mineral activity in the country are concentrated in Pará, where investments of US$ 15.71 billion are foreseen between 2024 and 2028.

Despite the significant values, budgetary planning and control in mining often do not receive due attention, which can result in financial and operational problems. The selection of key performance indicators (KPIs) emerges as an effective tool for cost control and performance evaluation. Nader, Tomi, and Passos (2012) discuss the relationship between performance indicators and the benefits of integrating production processes in investment decision-making. By measuring the impact of operations with robust indicators, advantages are gained from the integration of the mineral value chain, a complex system where interdependent activities mutually influence each other (SACH, 2009). The justification for this research lies in the complexity of the topic and the scarcity of publications on the practical implementation of corporate budgeting in the mining sector in Brazil. Leite et al. (2008) highlight that research on corporate budgeting is generic, reinforcing the need to deepen the understanding of sector-specific processes.

The methodology was mixed, combining qualitative and quantitative approaches. The qualitative research, based on descriptive data, sought to interpret phenomena from the perspective of organizational practices for an in-depth understanding of the context (Neves, 1996). The quantitative approach objectively tested theories, examining statistical relationships between measurable variables to provide greater rigor to the findings (Creswell, 2010). The combination of methods allowed for the description of budgetary management practices and the quantification of cause-and-effect relationships between performance indicators and financial results. Data collection was carried out through documentary analysis and direct observation. Documentary analysis (Vergara, 1998) involved the examination of monthly cost reports, executive summaries, financial indicator reports, and the mining company’s budget models. Direct observation (Michel, 2005) allowed for an understanding of management processes and routines in the natural environment. For quantitative analysis, MS Excel tools were used for data consolidation and Minitab software for correlation and regression analyses, aiming to prove hypotheses about performance indicators and their adherence to the budget.

The object of study is a large-scale mining company in the Carajás region, Pará. The focus was on the mine operation sector, subdivided into mining, development, and infrastructure. Mining, a mineral extraction technique, is crucial, as the entire mining project is structured according to the chosen exploration method. Infrastructure and development activities are interdependent with the mining method (Macedo et al., 2001). This area is classified as operational and represents one of the company’s main cost centers, being a focal point for budgetary control. In observance of ethical precepts, data confidentiality was ensured with a Secrecy and Confidentiality Agreement, in accordance with Resolution nº 510/2016.

The essence of budgeting is to coordinate managers’ efforts to achieve institutional objectives. Welsch (2013) argues that administration is a continuous process of decision-making to reach goals. Budgeting emerges as an effective methodology for operationalizing management, especially in planning and control, allowing for the anticipation of actions and comparison of planned versus actual results to promote adjustments. Lunkes (2008) synthesizes the budgetary process into four phases: conception, control, comparison, and correction. Additionally, Lunkes (2003) classifies budgets into types such as continuous, zero-based, flexible, and activity-based, each with specific characteristics. The choice of the appropriate model is a strategic decision that impacts the effectiveness of managerial control.

The analyzed organization adopts a hybrid approach, combining two budgeting models. Firstly, it uses the flexible budgeting model to estimate annual costs, a process called “budget”. Secondly, on a quarterly basis, it resorts to the continuous budgeting model to update cost projections, providing more dynamic information, a process called “forecast”. This dual approach allows for maintaining a long-term strategic vision (budget) while adapting to short-term market fluctuations (forecast).

The flexible budget, according to Horngren, Foster, and Datar (2000), is a set of projections adjustable to different levels of operational activity, encompassing a range of volume variation. Its preparation is based on the segregation of costs into fixed and variable. In turn, the continuous budget is a relevant methodology in volatile scenarios that demand constant replanning. According to Welsch (2013), this method consists of a semiannual or annual results plan that is revised and redesigned monthly or quarterly, excluding the most recent period and including a new future period, maintaining a twelve-month planning horizon.

The data analysis showed that the AISC (All-In Sustaining Cost) indicator is mostly composed of the direct net cost (C1). In the twelve-month cumulative period ending in April 2025, C1 accounted for 88% of AISC, followed by 9% of sustaining capital and 3% of royalties. This highlights the predominance of direct operational costs, making C1 the main focus for optimization. The stratification of controllable C1 costs identified that 53% are related to mining (extraction, development, and infrastructure), 20% to logistics, 16% to administration, and 11% to mineral processing, reinforcing the need for in-depth analysis of mine management expenses.

The analysis of EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) identified that the main factor of negative impact was operating profit, which was below the annual target. The result was influenced by revenue and cost of goods sold (COGS) indicators, which were 9% and 4% below targets, respectively. These indicators also negatively influenced free cash flow, which fell short of the target. The cost per ton handled showed a negative delta of 29% compared to the target, a significant deviation impacted by operational efficiency, fleet utilization, and transportation productivity indicators. Similarly, mine development indicators showed performance below expectations, both in meters executed and specific cost, with the drilling rate being the main negative factor.

Given the relevance of the net direct cash cost (C1), which according to Brook Hunt (2013) encompasses direct mining, processing, administrative expenses, freight, and sales costs, and its representativeness of 88% of AISC, an in-depth statistical analysis was performed. Using Minitab software, we sought to verify the correlation between the C1 cost and the operational indicators: total ore production, adherence to the mining plan, cost per ton moved, and cost per meter developed. The sample universe considered data from January 2024 to May 2025. The scatter plot analysis revealed a more pronounced positive correlation between the C1 cost and the indicators “cost per ton moved” and “cost per meter developed”.

When analyzing predictive capacity, the “cost per ton moved” showed the highest correlation values with cost C1, indicating it as one of the best individual predictors. To deepen understanding, a multiple linear regression analysis was performed. The results indicated a statistically significant relationship between indicator C1 and the set of variables, with a p-value of 0.009, validating the model.

The regression analysis, however, revealed that, among the independent variables, only the “cost per meter developed” and the “cost per ton moved” presented statistically significant coefficients, adequately fitting the predictive model of C1. Total ore production and adherence to the mining plan did not demonstrate statistically relevant influence in the period. The analysis of incremental effects showed that both significant variables contribute relevant information to explain the variability of C1. The resulting regression model explained 65.10% of the variation in cost C1 (R² = 65.10%), evidencing its robustness in operational cost management.

This statistical finding has direct practical implications, demonstrating that the control of cost C1 is linked to the management of the efficiency of material handling and mine development operations. Managerial efforts to optimize these two processes, such as improving the productivity of transport equipment and increasing the drilling rate, can positively impact the cost structure. The quantification of this relationship allows management to make more informed decisions, prioritizing investments and continuous improvement projects in areas of greatest financial impact and aligning operational goals with strategic objectives.

The study found that the company systematically uses variance analyses and performance reports, prepared monthly by the controlling department, which support strategic decisions. The results show that budgeting, especially in the continuous modality (forecast), is an essential tool for efficient management. However, a challenge lies in the need to deepen the distinction between fixed and variable costs, given the complexity of the mining cost structure. An improvement in this segregation is fundamental to increasing the accuracy of flexible budgeting. The research limitations include the sample nature and the absence of a longer data history for specific indicators, which made it impossible to delve deeper into certain analyses.

It is concluded that the objective was achieved: it was demonstrated that budgetary planning and control, through the combination of flexible and continuous models and the analysis of performance indicators, act as crucial tools to support managerial decision-making and the pursuit of budgetary adherence in the studied mining company. Statistical analysis confirmed the strong correlation between direct net cost (C1) and the operational indicators “cost per ton moved” and “cost per meter developed”, explaining 65.10% of the variation in C1 and providing clear direction for continuous improvement and cost reduction actions. It is recommended that the company deepen the analysis of these indicators to identify optimization opportunities and replicate the methodology in other departments, incorporating the identified correlations in the preparation of future budgets to promote greater strategic alignment and financial assertiveness.

References:
^{Bornia, A. C.; Lunkes, R. J. 2007. A Contribution to the Improvement of the Budgetary Process. Contabilidade Vista & Revista 18(4): 37-59.
Creswell, J. W. 2010. Research Design: Qualitative, Quantitative, and Mixed Methods. 3rd ed. Porto Alegre, RS, Brazil.
Hunt, B. 2013. Brook Hunt cash costs – A Wood Mackenzie Co. M&M H. Colorado, USA.
Brazilian Institute of Mining [IBRAM]. 2023. Mining performance drops in 2022, but the sector creates more jobs and will increase investments to US$ 50 billion by 2027. Available at: <https://ibram. org. br/noticia/desempenho-da-mineracao-tem-queda-em-2022-mas-setor-cria-mais-empregos-e-aumentara-investimentos-para-us-50-bi-ate-2027/>. Accessed on: Mar. 22, 2025.
Leite, R. M.; Cherobim, A. P. M. S.; Silva, H. F. N.; Bufrem, L. S. 2008. Business Budgeting: Survey of Scientific Production from 1995 to 2006. Revista Contabilidade e Finanças, 19(47): 56 -72.
Lunkes, R. J. 2003. Contribution to the improvement of the business budgetary process. Doctoral Thesis in Production Engineering. Federal University of Santa Catarina, Florianópolis, SC, Brazil.
Lunkes, R. J. 2008. Budgeting Manual. 2nd ed. São Paulo, SP, Brazil.
Macedo, A. J. B.; Bazante, A. J.; Bonates, E. J. L. 2001. Selection of the mining method: Art and Science. Revista Escola de Minas, 54(3), 221-225.
Michel, M. H. 2005. Methodology and scientific research in social sciences. São Paulo, SP, Brazil.
Padoveze, C. L. 2012. Business Budgeting. Pearson Education do Brasil, São Paulo, SP, Brazil.
Padoveze, C. L.; Taranto, F. C. 2009. Business Budgeting: New concepts and techniques. Pearson Education do Brasil, São Paulo, SP, Brazil.
Silva, J. A. B. et al. 2005. Performance measures as a management tool. In: National Congress of Mechanical Engineering Students, São Paulo, SP, Brazil. 2005. Proceedings… pp. 1-12.
Soares, O. J.; Neves Júnior, I. J. 2004. Beyond Budgeting: How to optimize the management process without budgets. In: USP Congress, São Paulo, SP, Brazil. 2004. Proceedings… pp. 1-15.
Vergara, S. C. 1998. Research Projects and Reports in Administration. 2nd ed. São Paulo, SP, Brazil.
Wanzutt, D. R. D. 2009. Proposal for a systematic support for the implementation of the matrix budget – The case of a food industry. 2009, 88 f. Master’s Thesis in Production Engineering, School of Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
Welsch, G. A. 2013. Business Budgeting. 4th ed. São Paulo, SP, Brazil.
Zaparolli, D. 2024. Companies plan investments of US$ 15.7 billion. 2024. Available at: https://valor.globo.com/publicacoes/especiais/para/noticia/2024/02/29/companhias-planejam-investimento-de-us-157-bi. ghtml. Accessed Mar. 22, 2025.}

---

Executive Summary from the Final Coursework of the Specialization in Finance and Controllership of the MBA USP/Esalq

_{Learn more about the course; click here:}