Future need of forest biomass supply

chains at the regional level of

South Savo in Finland

Karttunen, K., Aalto, M., Föhr, J. & Ranta, T. Lappeenranta University of Technology (LUT), School of Energy Systems, Laboratory of Bioenergy, Mikkeli, Finland FORMEC 2016 – From Theory to Practice: Challenges for Forest Engineering

September 4 – 7, 2016, Warsaw, Poland

Content 1. Background

- Aim of the study - Supply and demand of wood in Finland and South-Savo

2. Material and Methods - Material: Forest resource data & studies - Statistics - Productivity analyses - Simulation methods

3. Results

- Needed supply chains at the reginal level of South Savo

4. Conclusion

1. Background Aim of the study

− Aim of the study was to measure the future need of alternative forest

biomass supply chains at the regional level

− Aim of the project is to develop rural economic structure by highlighting

the importance of forest sector significance for the whole regional

economy of South Savo. The project will produce decision making information for the small entrepreneurs and forest owners at the South Savo province by using the forest management simulation and regional

economy modelling (CGE, Computable General Equilibrium). − The project schedule is 01.09.2015 - 30.08.2017. − The project is co-operated by Lappeenranta University of Technology (LUT),

and Natural Resource Institute Finland (LUKE) and University of Helsinki, Ruralia Institute.

Province of South-Savo in eastern Finland

South-Savo province

Other provinces

Supply and demand of wood in Finland and South-Savo

- Strong boom in the use of forest biomass both for industrial and energy purposes

- Strong difference between the regional supply and demand

Finland supply South Savo supply

South Savo demand

2. Material and Methods Material: Forest resource data

South Savo regional program´s supply aim by 2020: - 1 mil.m3 more round wood: From average (2010-2014) 6 mil.m3 round wood supply to 7 mil.m3 - 0.5 mil.m3 more energy wood: From average (2010-2014) 0.5 mil.m3 energy wood demand to 1 mil.m3

Aim line Trendline

− Share of terminal chipping system has been increasing in Finland

− Statistics of chipping systems in Finland (2015) (Metsäteho, Strandström 2016)

Stationary chipping Terminal chipping Roadside chipping

Trendline

Material: Studies

Material: Studies − There are studies of trucks used for industrial round wood (Venäläinen &

Poikela 2016) and energy biomass transportation (Föhr et al. 2016, Karttunen et al. 2012)

2015 2020 2025 2030

>70-76 tn 18 % 20 % 30 % 40 %

65-69 tn 55 % 60 % 60 % 60 %

< 65 tn 27 % 20 % 10 % 0 %

Total 100 % 100 % 100 % 100 %

2015 2020 2025 2030

Chips 1.Small diameter trees 53 % 50 % 40 % 30 %

2.Logging residues 77 % 70 % 60 % 50 %

3.Stumps 24 % 25 % 25 % 25 %

Loose residues 1.Small diameter trees 46 % 50 % 60 % 70 %

2.Logging residues 23 % 30 % 40 % 50 %

3.Stumps 75 % 75 % 75 % 75 %

Total (Chips + Loose residues) 1. / 2. / 3. 100 % 100 % 100 % 100 %

”HCT” High Capacity Trucks (figure: 94 tn) is expected to be increased in the future both for industrial wood and delimbed energy wood transportation with special permission (more in Formec: Korpinen et al. 2016)

Figu

re: O

lli-J

uss

i Ko

rpin

en

Material: Studies − Developing of truck systems (peat and

energy wood) have been studied before and after truck dimension

change in 2013, when the allowed capacity were increased from 60 tn to 76 tn (Föhr et al. 2016, Karttunen et al. 2012)

-> Average volume of chip/peat trucks has also increased (Volume > 140 m3)

Figu

re: J

arn

o F

öh

r

Chip truck

A. Statistics

- Three methods were compared in the study:

Statistics, productivity and simulation

- Number of harvesters, forwarders and timber lorries are statistically available in Finland (1982-2013) (Luke statistics), but those are only national level

- Entrepreneur and company information is statistically available also at regional level, but not include machine information itself (for example: 186 units of harvesting companies at the region of South Savo in 2015)

- National supply volumes were divided by national statistics (number of machines) and multiplied with regional supply volumes to estimate regional number of needed machines and vehicles (aim and trendline)

Trendline

B. Productivity analyses

Productivity, m3/h (E15) Annual volume (m3/a)

Round wood Cutting First thinning 8.2 21320

Other thinnings 16.7 43461

Final cutting 26.4 68764

Forwarding Thinnings 9.8 25384

Final cutting 20.5 53328

Transportation Timber wood 76 tn 11.5 39231

68 tn 9.6 32636

60-64 tn 7.8 26663

Pulp wood 76 tn 10.5 35799

68 tn 8.8 29907

60-64 tn 7.2 24387

Energy wood Cutting Delimbed stemwood 7.5 19411

Whole tree 8.5 22208

Logging residues

Stump lifting 6.8 17680

Forwarding Delimbed stemwood 12.5 32468

Whole tree 9.0 23341

Logging residues 11.5 29900

Stump lifting 7.5 19500

Transportation Roadside chipping, chips Small-diameter trees 6.9 23489

Logging residues 6.4 21716

Stumps 6.4 21716

Terminal chipping, loose Stemwood 9.9 33761

Whole tree 5.3 17910

Logging residues 5.3 17910

Stumps 4.6 15737

Terminal chipping, chips 6.9 22799

Chipping Roadside chipping Small-diameter trees 30 60000

Logging residues 26 52000

Stumps 26 52000

Terminal chipping Small-diameter trees 46 92300

Logging residues 40 80800

Stumps 40 80800

Stationary chipping Small-diameter trees 77 199300

Logging residues 67 174400

Stumps 67 174400

- All machines, vehicles and systems were separated between alternative round wood and energy wood supply chains

- Figures were based on previous studies and estimates to define average productivity, m3/h (E15)

- Annual working time was kept fixed -> Annual volume (m3/a)

- This method gives an estimate of each needed supply chains at national or regional area

C. Simulation methods Agent based simulation (supply chains) − Statistics and productivity analyses (A, B) provided

data also for simulation method (AnyLogic software) − Many fixed things were included from productivity

analyses (for example; 2 shifts working times, no seasonal differences)

− Public model available:

Input (user):

- Do nothing -> the current 2015 statistics - Put: Wood supply data past, current or future

- Put: Share of cutting styles (thinnings/clear cuts) - Put: Share of energy wood chipping systems in future

- Put: Machine and vehicle productivities (m3/h, E15) - Agent: min/mean/max size that one machine can only

work one agent at time Output:

- Amount of needed machines, vehicles and chipping systems (min, max and mean)

- forest machinery (harvesters, forwarders) - Transportation (trucks, chip trucks) - Chippers (stationary, terminal, roadside)

- Total time machines are working (h) - Wood processed in simulation round and % of the total

volume given in the beginning of simulation

http://www.runthemodel.com/models/3017/

C. Simulation methods Forest management simulation (supply in future)

Esri, HERE, DeLorme, MapmyIndia, © OpenStreetMap contributors,and the GIS user community

− Forest stand simulator MOTTI will be used in this project based on alternative forest management scenarios − National Forest Inventory provided data at regional level − All trees were separately simulated at each plot − Profitability (NPV); Timber revenues minus forest management cost included and

discounted to the present value − Scenarios: 1. Business as usual based on forest management recommendations 2. Industrial timber and pulp wood highlighted forest management 3. Industrial timber and energy wood highlighted forest management

3. Results Needed supply chains at the reginal level of South Savo − Results are presented between A. statistics, B. productivity and C. simulation

analyses

0

100

200

300

400

500

600

700

800

1980 1990 2000 2010 2020 2030

Num

ber o

f mac

hine

s an

d ve

hicl

es

Year

Harvesters

Forwarders

Timber lorries

Total

A. Statistics: Number of machines (from 1993-2003 to estimated future by 2030)

Need of harvesters will increase, forwarders may remain the same and timber lorries will decrease! ~Total 100 units more by 2020!

Aim 2020 and Trendline (2025-2030) with earlier (2000-2015) forest resource development!

Needed supply chains at the reginal level of South Savo

0

100

200

300

400

500

600

700

800

900

2000 2005 2010 2015 2020a 2025 2030

Num

ber o

f mac

hine

s an

d ve

hicl

es

Year

Energy wood,Energy wood,Energy wood,Round wood,Round wood,Total

B. Productivity: Number of machines based on productivity estimates

Results separated for round wood and energy wood supply chains showed the high growth potential for energy wood (78%, by 2020) compared to round wood (7%)! What happened in 15 years should be happened in 5 years!!! ~Total 100 units more by 2020!

Aim 2020 and Trendline, if the same machine increase continues!

Needed supply chains at the reginal level of South Savo C. Simulation: Number of machines based on simulation modeling (min, max and mean annual need).

The results showed large variation of needed machines. This was because total supply volume was divided into days and further into forest agent volumes (180-250 m3/machine). One machine can only work one agent at time. Someday a lot machines may be needed but in real life work could be done also next days but on the other hand…

”In real life variance is also large because of seasonal forest biomass procurement”

Aim 2020 and Trendline (2025-2030) with earlier (2000-2015) forest resource development!

Needed supply chains at the reginal level of South Savo C. Simulation: Average number of needed additional machines based on simulation modeling between 2015 and 2020!

Variance based on simulation drives (5 drives/year) of mean figures ~Total 120 (variance 76-210) units more by 2020!

Needed supply chains at the reginal level of South Savo There was anyway just a little difference between the methods: A. Statistics, B. Productivity, C. Simulation ~100 more, from 600 to 700 by 2020

4. Conclusion − Need of harvesting machines, transportation vehicles and chipping systems will be increasing at regional level

of South Savo in Finland!

− ~100 units more by 2020 aim is regional estimate! − The biggest increase (78%) is expected for energy wood supply chains − There was just a little difference between the study methods

− Simulation method gives more dynamic ways in future analyses! − Simulation model could be further develop to provide site-dependent and dynamic information for

individual company´s investment needs!

− Need of supply chains is however dependent on three things:

1. Supply and demand of forest biomass in future − Regional supply aims at increasing 1 mil.m3 more round wood and 0.5 mil.m3 more energy wood by

2020 -> 0.5 mil.m3 more round wood use (new pine saw mill), 0.5 mil.m3 more energy wood use (new biorefinery) in South Savo is aimed! -> Project: What kind of impact those investments may have on regional economy of South Savo? 2. Productivity development of supply chains

− If machine productivity is getting better or machine hours can be increased, we don´t need that many machines!

3. Special features in supply chains at regional level

− How the supply chains will be developing in the future? …chipping systems, transportation types, investments…

Thank you all!

Kalle Karttunen kalle.karttunen@lut.fi Lappeenranta University of Technology (LUT), School of Energy Systems, Laboratory of Bioenergy, Lönnrotinkatu 7 50100 Mikkeli, Finland http://www.lut.fi/web/en/lut-savo/laboratory-of-bioenergy