AI for Forestry Cost Optimization

Impact

• Fair assessment of logging truck drivers and coaching and training decisions based on real-world, accurate data
• Forecasted $200-700 thousand in annual savings through fuel optimization
• Sharp reductions in annual GHG emissions, which currently stand at roughly 8,000 tonnes of C02 a year
• Potential applications for AI in other areas of the business including Mill Operations and Sales & Logistics

Partner

Alberta Pacific Forest (Al-Pac) is one of Alberta’s largest forestry companies and produces pulp and wood products sold around the world. Their operation is supported by over 400 team members and has 3 main business areas:

1. Woodlands - areas of deciduous and coniferous growth are identified for harvest and brought to the Mill;
2. Mill Operations - raw wood is processed and turned into hardwood or pulp products; and
3. Sales and Logistics - to get finished products to global markets.

Al-Pac is a natural adopter of innovative technologies and approached AltaML in 2019 to help understand how an Applied AI Strategy can help maintain their competitive edge.
Working together, we started by identifying data strengths and challenges as well as team capabilities at Al-Pac. After an executive ML 101 and ideation session, the Woodlands operations were selected as their complexity and high costs would likely give the best early results.

Business Problem

Fuel for logging trucks is a significant expense in forestry operations with long distances traveled from remote logging areas to the mill for processing. Optimizing fuel consumption stood out as an ideal first use case.

Although truck onboard computers provide raw data about each truck’s driving patterns, the many variables affecting fuel usage (e.g. speed, distances, weather conditions, grade, load) make it difficult to assess driver behavior and its impact on fuel usage. The raw data generated by the onboard computer is simply too much for a human to consider, but this complexity is ideal for machine learning.

AltaML Solution

AltaML started by building and training an ML model using historical trip data found from the truck’s onboard computers. The first task was to work with the logistics managers to identify where data may be present to tell us more about good and bad driver trip behaviour. Then, we applied unsupervised learning techniques to the dataset to uncover signals that implied trucks were being driven less efficiently than necessary. Unsupervised learning is used when we don’t have human-labelled data to train the model on, making it the right technique for this job.

The model worked to uncover anomalies in the driving data and identify the root cause of excessive fuel consumption and is actively being deployed at Al-Pac.

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