Comparative Analysis of Maintenance Models in Forest Machines

Diniz, Carlos Cézar Cavassin; Lopes, Eduardo da Silva; Koehler, Henrique Soares; Miranda, Gabriel Magalhães; Paccola, José

doi:10.1590/2179-8087.099417

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…In this study, this was observed at 2 years of harvester use. For this specific analysis, the methodology is inadequate because the replacement of a forestry machine typically occurs after a longer period of use, given current preventive and predictive maintenance routines (Diniz et al, 2020). This discrepancy is justified by the high acquisition and operational costs, and the method's failure to consider capital variation over time (interest rate) and the revenues or production of the harvester, which significantly impacts the analysis.…”

Section: Discussionmentioning

confidence: 99%

The optimal replacement time for Harvesters: An economic analysis

Rodrigues,

Silva,

Fernandes

et al. 2024

RARV

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This research sought to identify the ideal timeframe for replacing a Harvester, used in forest harvesting, employing a comparative evaluation of different economic methods. A John Deere 1270D harvester, equipped with a 270-model head was utilized in this study. The machine’s cost per hour worked was calculated by adding fixed costs with variable costs. The methods used to determine the machine’s optimal replacement time were: the Total Average Cost (TAC) and the Equivalent Annual Cost (EAC), which consider discrete functions, and the Terminal Cycle and Constant Chain Replacement, which consider continuous functions. The four methods differed in their indication of the optimal replacement time for the harvester. Although the TAC is a simple and well-known method, it did not yield an adequate result (2 years). This is because replacing a forest machine usually occurs over a longer period of use, with current preventive and predictive maintenance routines. This discrepancy arises from the high acquisition and operational costs, as well as the method’s failure to account for the variation of capital over time (interest rate) and the revenues or production of the harvester, which significantly affects the analysis. Utilizing the discount rate, the EAC proved to be more efficient than the TAC, yielding a result that is more aligned with the reality of the forest sector (6 years). Furthermore, it is the most widespread method in machine and equipment replacement studies due to its simplicity, ease of application, and efficiency. The Chain Replacement method provided an optimal replacement time between 7 and 8 years, also consistent with the reality of companies in the forest sector engaged in forest harvesting for an extended period. Therefore, this method can be considered in decision-making as it takes into account the cost and revenue variables of the machine. Determining the useful life of a machine used in forest harvesting from an economic perspective, or the period in which it performs its activities with the lowest operational cost, is directly related to its lowest production cost. In the present study, the most suitable methods to determine the optimal replacement time for the harvester were the EAC and the Chain Replacement method.

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Section: Discussionmentioning

confidence: 99%

The optimal replacement time for Harvesters: An economic analysis

Rodrigues,

Silva,

Fernandes

et al. 2024

RARV

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“…In the last two decades, with the accelerated growth of computer science and emerging IoT technologies, maintenance strategies have revolutionized from preventive maintenance to predictive maintenance (PdM) [58][59][60]. Recently, key industrial manufacturers have invested in PdM to maximize the availability of machine parts and equipment uptime and deploy maintenance more cost effectively [46].…”

Section: Predictive Maintenance (Pdm)mentioning

confidence: 99%

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Maktoubian

Taskhiri

Turner

2021

Forests

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The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

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“…This information can lead the decision maker to make better purchasing agreements, as well as to seek better maintenance practices and test new technologies (next section) to reduce production cost. Diniz et al (2020b) show in their work that better maintenance practices lead to increases in the forestry equipment utilization rate.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Espds

Diniz

Sessions

2021

Croat. j. for. eng. (Online)

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In this paper, we introduce a Microsoft Excel Workbook containing the software Equipment Selection Problem DS (ESPDS) that recognizes the special structure of the equipment selection problem. The ESPDS approach is based on the context of the Brazilian forestry sector using detailed equipment maintenance schedules. No special restrictions are needed on cost inputs over time or technologies. The output is an equipment schedule that can be used to project equipment investment needs, operational costs, and tree harvesting costs. ESPDS can be applied to support companies and contractors in order to choose the best option for their operations, as well as to achieve better equipment purchase agreements. We will show how ESPDS will also be useful in providing longer term estimates of production costs. The sensitivity analysis shows how different inputs and maintenance polices can affect the best alternative. A numerical example is included considering the entrance of a specific technology that increases the equipment productivity in order to examine whether it can change the solution. ESPDS is intuitive, flexible, and easy to calculate. Although designed for the forestry industry, the approach is readily transferable to other sectors. ESPDS may be found on the web at the following URL: <a target="_blank" rel="nofollow" href="https://www.researchgate.net/publication/350811380_ESPDS_workbook">https://www.researchgate.net/publication/350811380_ESPDS_workbook</a>.

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Comparative Analysis of Maintenance Models in Forest Machines

Cited by 5 publications

References 19 publications

The optimal replacement time for Harvesters: An economic analysis

The optimal replacement time for Harvesters: An economic analysis

Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

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