Wood logistics in Sweden Forest Road planning, development in transport and wood flow

Outline  Forest Road planning  Diesel consumption  One Pile More  CTI  FlowOpt  RuttOpt

Better planning?

Less storage?

VägRust  Aim –To develop a decision support system for identifying the most cost effective road investments  Collaboration project –Skogforsk –Linköping university –Stora Enso, Sveaskog, SCA, Holmenskog

VägRust  System for planning of road maintenance  GIS and optimization model  Road information, costs and cutting plan for 10 years  Which roads should be upgraded to secure delivery of raw material while minimizing costs?

Data  Planned cutting (stand information) –Geography –Volume per assortments  Road information –Accessibility –Link length  Mill demand –Divided in four periods which corresponds to the accessibility  Costs –Upgrading –Transportation

Present situation  Finishing part one –Prototype developed for ArcMap –Optimization model developed –Two case studies carried out  Important experiences for the future  Take off for part two –New application and furhter development of the optimization model

Case study II

 Road information from NVDB –Quality check together with road manager –Roads closed during spring thaw  Stand data from Holmen Skog –Cutting in two five year periods –Thinning and clearcutting –Totally 4853 stands –Yield calculation for volume per stand –Assortments timber (pine and spruce) pulp wood (conifer and birch) –Aproximately 2,5 million m3fub

Case study II  Mills and their demand –6 mills –Demand over one year divided into accessibility classes  If the thawing period is 6 weeks, the demand during that period is 6/52 of the year demand  Cost for upgrading –Standard cost for the whole area –Cost for upgrading from one class to another  Sensitivity analysis –Variation of thawing period

Case study II  Lack of volume in the starting point The task is to find the road investments which can free that volume to the lowest cost.

Case study II  Suggestions of upgrading (meters) 4 weeks6 weeks8 weeks B – A C – A Sum

Case study II

 Costs (SEK) 4 weeks6 weeks8 weeks B – A C – A Sum Alternative cost weeks6 weeks8 weeks Cost/m39,77,65,6 Marginal cost-3,60,1

Conclusion from case studies  The model give us reliable answers –The results give a hint of how costs for upgrading and stock keeping can be reduced compared with todays costs  Very time consuming analyses  Accurate road information is very important –Accessibility class on private roads –Shutting of public roads  More detailed upgrading costs  Sensitivity analyses very important  Include stand accessiblity in optimization model?

Further development  Connection to SNVDB –Route calculation  Alternative routes?  Connection to Heureka  New application –Not connected to ArcMap –Faster –Better visualisation –More detailed costs for upgrading (distance to gravel pit)  New optimization model –Include stand accessibility? –Include present value of the stands  Time schedule –Q4 2008

Diesel consumption

It is possible to decrease CO 2 and diesel with 40% to year 2020 Diesel consumption År l/m³s 5,4 3,7 2,5 1,7 0,8 2,1 90 ton Hybrid technique Future steps - New fuels DME within 10 years -More efficient engines -Hybrid technique -New machine systems -ECO driving -Larger machines -One more pile -Better logistics Larger loading capacity for forwarders Hydrostatic transmission Single grip harvester GVW of trucks increased from 51 ton to 60 ton between to % decrease

Diesel – heavy expenses  Today  In the forest –1,7 l/m³s –15-20% (of total cost)  On the road –2 l/m³s –40 % (of total cost)  New systems: 20-40% reduction  Hybrid technique: large potential  El-Forest = 3,14 l/h (7 l for a 14 tons forwarder) ”The beast” The harvarder El-Forest

One pile more 1.A dolly with a turn-table is attached to the roundwood haulage truck. 2.A link with another turn- table is attached to the dolly. 3.A semi-trailer is attached to the link. 4.The ”OPM-vehicle” is now 30 m long and has a gross weight of 90 ton. The axle load is only 8–9 ton, due to the weight is distributed to 11 axles

High tire pressure concentrates vehicle weight to a small contact surface area. Reducing tire pressure creates a longer footprint and distributes the weight over a larger area.

Forestry

Demonstrated Results High PressureLow Pressure

FlowOpt

Wood supply - FlowOpt  Allocation  Back haulage  Combination of truck and train  Timber exchange between companies  Planning over several time periods

Optimized wood supply with truck and train

SNVDB Database Excel Supply Demand Costs Constraints etc. Application Optimization Generate road network -Node snapping -Distance calculation -Maps -Editing -Generate files for optimization Reports

Distances – Route calculation  Critical for all types of transportation analyses  SNVDB (Skogens Nationella VägDataBas) –Copy of NVDB –Resistance

Results  Maps –Catchments areas –Truck and train transportation  Excel/Access reports –Costs and savings –Average transport distance –Volume transported (truck, train, back haulage etc.) –Transport work –Effects on the environment –Etc.

Timber exchange

Combination of truck and train Only trucksTruck and train

Wood flow maps

RuttOpt

When? Where? How? Where to?

Decision support system: RuttOpt GIS - program Database Road database NVDB Optimization application Presentation Supply Trucks Home bases Change of drivers Time windows Demand

Driving times – real and RuttOpt

Gantt schedule for a day

Case Holmen Skog  12 trucks logged by Holmen Skog during three days in June 2004  Case 1 –Schedule/routes for one day  Case 2 –Schedule/routes for three days

Results  Holmen Skog - annual cost for truck transports:100 MSEK

Knowledge of ”home area” Professional skill IT-support DSS Transport management

Logistic challenges  Lean  Agile  Green