Articles containing the keyword 'procurement'

Many kinds of planning systems have been labelled decision support systems (DSS), but few meet the most important features of real DSSs in planning and control of wood procurement. It has been concluded that many reasons exist to develop DSSs for wood procurement. The purchasing of timber seems to be one of the most promising areas for DSS, because there is no formal structure for these operations and decisions deal with human behaviour. Relations between DSSs and different features of the new approaches in wood procurement are also discussed, and hypotheses for future studies suggested.

• Harstela, E-mail: ph@mm.unknown

Linear programming (LP) is an important method for allocation of wood inventory stock. It is, for instance, used alone in tactical planning systems, which currently are in wide use at the higher hierarchical level in the functionally decentralized planning of the Finnish forest industry. Unfortunately, LP as a solution method has not been capable of handling spatial data that seem to characterize planning systems in geographical decentralization. In the present study, GIS was used to assimilate data from different wood procurement functions, to calculate transportation distances and cost figures, and to write the data in ASCII files, which were then used as input for the LP model. Using the experiments and methods of GIS on a planning system developed according to participatory planning, the results of this study suggest that the participatory method was faster than the conventional LP method, when solved using actual data. The participatory method was also capable of providing the same global optimum for a wood allocation problem. The implications of these results for improving operational and tactical planning of wood procurement in Finland are discussed.

• Palander, E-mail: tp@mm.unknown

The first aim of this study was to develop a simulation model describing the flow of different timber qualities to different firms. The second aim was to study preliminary the factors which affect timber distributions. In addition, we tested the hypothesis that in a small sawmill firm the traditional way of organizing timber procurement does not direct effectively good quality logs to the special production. The game theoretic approaching and the principles of Monte-Carlo simulation were applied in development of the simulation model. The most important factors of the model were tried to find for further studies with sensitive analysis. Empirical validation brought forth promising results in the area of one municipality. The buyer’s awareness of a marked stand, the seller’s willingness to sell a marked stand, the buyer’s ability to pay for wood and the proportion of first quality pine logs in a marked stand affected the distribution of pine logs. The results also supported the hypothesis that the traditional system, in which sawmills or their own forest departments procure themselves all timber needed, is not the most effective way to direct enough good quality timber to the special production.

• Tolvanen-Sikanen, E-mail: tt@mm.unknown
• Harstela, E-mail: ph@mm.unknown
• Sikanen, E-mail: ls@mm.unknown

Seasonal variation in the sawmill industry of Finland was studied in an investigation based on questionnaires answered by a random sample of sawmills concerning the time period of 1958-1960. The method is described in detail in a separate article in Acta Forestalia Fennica issue 75 no. 1.

The seasonal variations in purchase of roundwood was largest in big sawmills, which purchase the main part of the timber as standing sales and buy most of the wood from the State Forest auctions at the end of September. Also, they can afford to reserve their material earlier than the smaller companies. The saw logs are mainly felled in the winter in Finland because the climatic conditions and availability of labour are best at that time. Small sawmills begin fellings a little earlier than the larger ones.

In long-transport of timber the proportion of floating decreased from 47% in 1958 to 38% in 1960. At the same time, proportion of truck transport increased from 48% to 55%. Small sawmills use almost exclusively land transport. They received almost three-fourths of their logs between January and May, because the sawing is concentrated in the first half of the year. Therefore, floating does not suit for their transport method. The larger the sawmill, the later is the seasonal peak of log deliveries. The output of the big sawmills is distributed more evenly thoughout the year. The smaller the sawmill, the quicker is the turnover of raw material and the smaller the sawlog inventories.

The seasonal variation in output is sharper at small sawmills where sawing is concentrated in the first half of the year. The seasonal peak of the early spring is due to the aim at getting the sawn wood to dry early enough for shipments in the summer. Air drying takes an average of 4 ½ months. Kiln drying is more common at the larger sawmills, and gives them more flexibility. Due to the large seasonal variation in operation, the capacity of the small mills is poorly utilized. Domestic sales of sawn wood levels up the seasonality of the deliveries. Export sales are concentrated at the end and turn of the year. Also, the seasonal peak of expenditure occurs in the winter, but that of income in the summer.

The PDF includes a summary in English.

• Ervasti, E-mail: se@mm.unknown

Linear optimization model was used to calculate seven wood procurement scenarios for years 1990, 2000 and 2010. Productivity and cost functions for seven cutting, five terrain transport, three long distance transport and various work supervision and scaling methods were calculated from available work study reports. All methods are based on Nordic cut to length conditions. Finland was divided in three parts for description on harvesting conditions. Twenty imaginary wood processing points and their wood procurement areas were created for these areas. The procurement systems, which consists of the harvesting conditions and work productivity function, were described as a simulation model. In the LP-model the wood procurement system has to fulfil the volume and wood assortment requirements of processing points by minimizing the procurement cost. The model consists of 862 variables and 560 restrictions.

Results show that it is economical to increase the mechanical work in harvesting. Cost increment alternatives effect only little on profitability of manual work. The areas of later thinnings and seed tree- and shelterwood cuttings increase on cost of first thinnings. In mechanized work one method, 10-tonne one grip harvester and forwarder, is gaining advantage among other methods. Working hours of forwarder are decreasing opposite to the harverster. There is only little need to increase the number of harvesters and trucks or their drivers from today’s level. Quite large fluctuations in level of procurement and cost can be handled by constant number of machines, by alternating the number of season workers and by driving machines in two shifts. It is possible, if some environmental problems of large-scale summer time harvesting can be solved.

• Rummukainen, E-mail: ar@mm.unknown
• Alanne, E-mail: ha@mm.unknown
• Mikkonen, E-mail: em@mm.unknown

The applicability of five mathematical programming methods, namely standard linear programming, parametric programming, goal programming, mixed integer programming and integer programming is discussed as a planning tool for the choice of wood procurement method.

Theoretically, the goal programming approach seems to be the best routine for mathematical handling of problems related to wood procurement. The parametric approach must include enough large post-optimality analysis routine. If the effect of the variables expressed with different measures is to be studied, interpretation of the economic information given by the approach becomes a problem. The other drawback is that the approach does not allow determination of the hierarchy of the goals objectively as they depend on the subjective preferences of the decision maker.

From the practical point of view, standard linear programming is the best method if the objective function can be formulated in economic terms, for instance. If there are several goals to be attained or satisfied the best method is goal programming.

According to the sub-studies, every method under consideration can be used as a solution routine for the minimization of wood procurement costs. In cost minimization the best methods are goal programming and standard linear programming. The best method for harvesting system evaluation purposes is parametric because it allows varied cost calculations within a certain cost range. The best method for harvesting equipment investment planning is mixed integer programming with binary decision variables.

The more complicated and restricted the problem environment is, the better the mathematical programming approach will be, also in harvesting related problems.

The PDF includes a summary in English.

• Mikkonen, E-mail: em@mm.unknown

This study was aimed at determining the maximum cost level of artificial drying required for cost-efficient operation. This was done using a system analysis approach, in which the harvesting potential and procurement cost of alternative fuel chip production systems were compared at the stand and regional level. The accumulation and procurement cost of chipped delimbed stems from young forests were estimated within a 100 km transport distance from a hypothetical end use facility located in northern Finland. Logging and transportation costs, stumpage prices, tied up capital, dry matter losses and moisture content of harvested timber were considered in the study. Moisture content of artificially dried fuel chips made of fresh timber (55%) was set to 20%, 30% and 40% in the comparisons. Moisture content of fuel chips based on natural drying during storing was 40%. Transporting costs were calculated according to new higher permissible dimensions and weight limits for truck-trailers. The procurement cost calculations indicated that with artificial drying and by avoiding dry material losses of timber, it could be possible to reduce current costs of the prevailing procurement system based on natural drying of timber at roadside landings. The maximum cost level of artificial drying ranged between 1.2–3.2 € MWh^–1 depending on the supply chain, moisture content and procurement volume of fuel chips. This cost margin corresponds to, e.g., organization, forwarding and transportation costs or stumpage price of delimbed stems.

• Laitila, Natural Resources Institute Finland (Luke), Bio-based business and industry, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: juha.laitila@luke.fi
• Ahtikoski, Natural Resources Institute Finland (Luke), Management and Production of Renewable Resources, Paavo Havaksen tie 3, FI-90570 Oulu, Finland E-mail: anssi.ahtikoski@luke.fi
• Repola, Natural Resources Institute Finland (Luke), Management and Production of Renewable Resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi
• Routa, Natural Resources Institute Finland (Luke), Bio-based business and industry, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: johanna.routa@luke.fi

• Laitila, Natural Resources Institute Finland (Luke), Bio-based Business and Industry, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: juha.laitila@metla.fi
• Ranta, Lappeenranta University of Technology, Sammonkatu 12, FI-50130 Mikkeli, Finland E-mail: tapio.ranta@lut.fi
• Asikainen, Natural Resources Institute Finland (Luke), Bio-based Business and Industry, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: antti.asikainen@metla.fi
• Jäppinen, Lappeenranta University of Technology, Sammonkatu 12, FI-50130 Mikkeli, Finland E-mail: eero.jappinen@lut.fi
• Korpinen, Lappeenranta University of Technology, Sammonkatu 12, FI-50130 Mikkeli, Finland E-mail: olli-jussi.korpinen@lut.fi

• Hautamäki, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: sh@nn.fi
• Mutanen, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: antti.mutanen@metla.fi
• Viitanen, Finnish Forest Research Institute, Joensuu Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: jv@nn.fi

• Malinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: jukka.malinen@joensuu.fi

• Kärhä, Faculty of Forestry, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: karha@forest.joensuu.fi
• Oinas, Faculty of Forestry, University of Joensuu, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: so@nn.fi