Luke identifies surprising ways to enhance timber logistics using predictive simulation

The challenge

Finding a way to reduce operating costs while promoting more sustainable timber logistics

The operating environment for transporting timber by truck is hugely complex, especially in Finland. The high number of timber assortments and end-use facilities, high fluctuations in road traffic, weather conditions inhibiting road-bearing capacity, gravel roads and timber demand all cause logistics challenges.

Finland has a national strategy to enhance wood purchasing efficiency and reduce associated costs by 30% by 2025. Improved timber transport efficiency plays a key role in realising this, so the government changed the law to allow larger trucks to transport timber. Following that change, forestry company Metsäkolmio Oy commissioned Luke to research ways of reducing costs and promoting sustainability in its timber logistics. 

‘The project’s main objectives were to establish a new logistics model for moving timber via road networks, with a view to reducing costs while improving capacity, quality and standards,’ explained Kari Väätäinen, Research Scientist at Luke. 

The solution

Using Twinn Witness predictive simulation software to model the timber logistics supply chain 

Luke had previously used Twinn Witness predictive simulation software (formerly under the Lanner brand) on other projects involving complex assets and processes. Therefore, it was the ideal choice for creating an accurate and dynamic view of the timber supply chain, which would inform recommendations on performance improvements and cost reductions.

‘Timber logistics is hugely complex and involves many dynamic variables such as road conditions, truck size, wood assortment and variability in the operating environment,’ said Kari. ‘We needed a simulation-led approach to build out a realistic picture of the interdependent relationships between these complexities, showing a baseline case together with a number of scenarios that outlined potential performance improvements.’

Trucks were a crucial lever in achieving these improvements. In light of the new law, the Witness model analysed the cost and operational potential of using larger sizes.
The model looked at two load methods: single assortment and multi-assortment. It factored in the use of 4 timber truck sizes (64, 68 and 76 tonnes) to supply 25 timber assortments to 12 end-use facilities. It also accounted for the limitation that only 8 of the 12 facilities could accept multi-assortment deliveries. 

Importantly, the model incorporated key variables including:

• Roadside storage, such as types, wood assortments, volumes and distances to delivery facilities and parks
• Customer areas, storage sizes and assortment specifications 
• Operating environment characteristics such as shift patterns, wood reception times, routing rules and time-element functions
• Cost factors from Finnish Transport and Logistics (SKAL) and truck dealers

For each scenario, the team ran multiple simulations projecting across a year of operations, with each showing the likely timber volumes transported and operating hours per truck.

The impact

Debunked hypothesis – with substantial savings identified

Contrary to the team’s initial hypothesis, using larger trucks didn’t lead to major savings. 68- and 76-tonne trucks only reduced transport costs by 1.5-2.5% in single-assortment scenarios and by 0.4-0.8% in multi-assortment ones. This negligible cost reduction was largely because the full 76-tonne capacity wasn’t fully utilised, and the larger trucks required higher capex and opex.

However, the Witness model did highlight potential for substantial cost savings by using the multi-assortment load method:

• The multi-assortment load method was 3.3% cheaper than the single-assortment load one, on average
• Driving performance – presented as solid-m³ of timber per 100 kilometres – was 4% higher with the multi-assortment load method

Another conclusion was that small roadside assortment piles reduced transport efficiency because many small piles needed to be loaded to fill the space. The multi-assortment load method drastically reduced the number of rides between piles, thereby improving fleet performance. 

‘We were extremely pleased with the project’s success. We learned many new things and realised there’s a great deal of potential to improve timber logistics using the insights and recommendations from our predictive simulation model,’ said Kari. ‘Looking ahead, we would like to extend our research to evaluate the impact of different operating environments against each truck size, as well as looking in more detail at roadside storage and wood assortment pile sizes to reduce inventory and increase availability.’

Kari concluded: ‘Witness predictive simulation is hugely valuable in allowing us to effectively see into the future. It highlighted opportunities for improved performance and provided clarity and certainty in an unpredictable environment.’