“…The choice of harvesting technology is typically a strategic planning issue [33,37], and in this study the gradual shift towards new technology was handled on an annual (tactical) basis during procurement procedures. In this study, contractors generally had a four to six year length in their machinery investment cycles which is similar to earlier studies [38][39][40].…”
Section: Procurement Of Harvesting Servicesmentioning
Increasing demands to harvesting production and quality require improved management practices. This study's purpose was to analyze the impact of industrial context on procurement, management, and development of harvesting services. Using interviews, functions were modeled at two forest owners associations (FOAs) with outsourced harvesting services. One FOA had its own sawmills, requiring frequent harvesting production adjustments to meet varying volume demand in the short-term. The long-term uncertainty was however low because of good visibility of future demand (>6 months). The other FOA did not own mills and produced wood according to fixed six-month delivery contracts. This meant few short-term production adjustments, but long-term uncertainty due to low visibility of future demand. Demand uncertainty resulted in corresponding needs for harvesting capacity flexibility. This could have been met by a corresponding proportion of short-term contracts for capacity. In this study, however, a large proportion (>90%) of long-term contracts was found, motivated by a perceived contractor shortage. It was also noted that although contractor investment cycles (4-6 years) matched the FOAs' strategic horizons (3-5 years), contractors' investment plans were not considered in the FOAs' strategic planning. The study concludes with a characterization of different FOA contexts and their corresponding needs for capacity flexibility.
“…The choice of harvesting technology is typically a strategic planning issue [33,37], and in this study the gradual shift towards new technology was handled on an annual (tactical) basis during procurement procedures. In this study, contractors generally had a four to six year length in their machinery investment cycles which is similar to earlier studies [38][39][40].…”
Section: Procurement Of Harvesting Servicesmentioning
Increasing demands to harvesting production and quality require improved management practices. This study's purpose was to analyze the impact of industrial context on procurement, management, and development of harvesting services. Using interviews, functions were modeled at two forest owners associations (FOAs) with outsourced harvesting services. One FOA had its own sawmills, requiring frequent harvesting production adjustments to meet varying volume demand in the short-term. The long-term uncertainty was however low because of good visibility of future demand (>6 months). The other FOA did not own mills and produced wood according to fixed six-month delivery contracts. This meant few short-term production adjustments, but long-term uncertainty due to low visibility of future demand. Demand uncertainty resulted in corresponding needs for harvesting capacity flexibility. This could have been met by a corresponding proportion of short-term contracts for capacity. In this study, however, a large proportion (>90%) of long-term contracts was found, motivated by a perceived contractor shortage. It was also noted that although contractor investment cycles (4-6 years) matched the FOAs' strategic horizons (3-5 years), contractors' investment plans were not considered in the FOAs' strategic planning. The study concludes with a characterization of different FOA contexts and their corresponding needs for capacity flexibility.
“…Supply chain planning in the forest product sector encompasses a wide range of decisions, from strategic to operational [11], and decision support tools can help with the quite often complex planning of wood supply. Optimisation and simulations models can be used to gain insight into the logistics of biomass supply chains [12].…”
Highlights A linear programming model that optimises wood biomass supply in Ireland. It uses MC to determine harvesting, chipping, storage and transportation costs. It analyses two supply chain scenarios and two truck configurations. Low wood MC increases supply cost due to longer transport distances. Optimal truck loads can be achieved by controlling wood MC.
AbstractIn the coming years, Ireland will continue to face an increasing demand for wood biomass as a renewable source of energy. This will result in strained supply/demand scenarios, which will call for new planning and logistics systems capable of optimizing the efficient use of the biomass resources.In this study, a linear programming tool was developed which includes moisture content (MC) as a driving factor for the cost optimisation of two supply chains that use short wood and whole trees from thinnings as material feedstock. The tool was designed and implemented to analyse the impact of moisture content and truck configurations (5-axle and 6-axle trucks) on supply chain costs and spatial distribution of the supply materials. The results indicate that the inclusion of wood chips from whole trees reduces the costs of wood energy supply in comparison with only producing wood chips from short wood to satisfy the demand, with 9.8% and 10.2% cost reduction when transported with 5-axle and 6-axle trucks, respectively. Constraining the MC of the wood chips delivered to the power plant increases both transport and overall supply chain costs, due, firstly to an increase in the haulage distance and secondly, to the number of counties providing the biomass material. In terms of truck configuration, the use of 6-axle trucks resulted in a 14.8% reduction in the number of truckloads and a 12.3% reduction in haulage costs in comparison to the use of 5-axle trucks across the MC scenarios analysed.
“…Operational planning of production is determined by single production and operation time framework at daily or hourly basis, which relates to different plants or plant units, with regard to the quantity of data the enterprise disposes with at operational level (D' Amours, Rönnqvist, & Weintraub, 2008;Stevenson, 2009). Most frequently, time framework referred to in operational production planning, is a month.…”
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