Researchers present a model for the equipment replacement decision using equipment maintenance schedules.
In forest engineering, the estimation of maintenance and repair costs has been considered the most unpredictable cost element, because operating conditions, operator skills, repair and maintenance strategies, and machine qualities influence this cost. Combined with the high price of new machines, the timing of investing in new machines requires careful planning. The investment risk exists because it is uncertain that the cost of the investment will be recovered, and a profit gained.
The approaches to the investment decision are based on the economic life (EL) method for three scenarios. In the first scenario, they considered buying a new machine during the first period. For the second scenario, they considered keeping the current machine for 24 months, and then buying a new one while the third scenario, they considered selling the current machine, leasing a new one for 24 months and then buying a new machine. The results draw from five logging machines that cut, delimb, and processes the tree stem into logs in forest plantations of a large integrated Brazilian forestry company.
The study used data from five Ponsse Ergo 8W (20.5 t, 210 kW) harvesters equipped with Ponsse H7 heads (1.15 t, 750 mm cutting capacity) with different ages over a period of three years. These machines began operating in 2015 and belong to the forestry company Klabin SA, located in Parana State, Brazil. The stands were planted with hybrid clones E. grandis and E. urophylla and are usually harvested between five and seven years depending on the productivity of the forest. At the time of harvest, the tree size ranged most commonly from 0.26 to 0.66 m3. All the study plantations were predominantly established with ground slope gradient below 30%. Areas with steeper terrain are harvested by other types of machines.
For the new machine, equipment replacement policy is given at 52 months, or around 19,000 hours of use, considering the company’s schedule, where the machines work an average of 4,500 hours per year. One important point is that, in Brazil, most forestry companies use forest harvesting machines in up to three shifts in a single day.
The best solution for all harvesters studied was that managers should opt for the sale of the equipment, and the lease of new machines for the period of 24 months for the subsequent purchase of new machines. The results show the new machine discounted cost (CNM) and rebuild machine discounted cost (CRM) scenarios presented higher values than the leased machine discounted cost (CRE) scenario. The economic life policy proved optimum with 52 months for a new machine, while the best decision to make is, sell the current machine, lease a new one for 24 months and then buy a new one. The solution and the algorithms used are described in the work.
In conclusion, this paper presented an approach to help in the decisions to either keep or release equipment based on the EL method of forestry equipment. When applied to a forest harvesting machine example, in southern Brazil, the economic life policy proved optimum with 52 months for a new machine, while the best decision to make is, sell the current machine, lease a new one for 24 months and then buy a new one. They showed the average production and cost per unit for the planning horizon and illustrated that new machines described in this study can produce more with less cost.
The research was published in the International Journal of Forest Engineering, 2019. The researchers were C Diniz, J Sessions, R Junior and R Robert. Source