The goal of TimberTracks™ is to not only benchmark industry performance but to also be a management tool that help contractors improve their businesses. One component of improving a business is to understand what factors lead to greater efficiency and in turn sharing of best practices that lead to incremental improvements in the industry as a whole.
This article is the first in a series of articles where we start analyzing the data TimberTracks™ has collected to start identifying the factors that affect productivity. In this article we are going to take a macro approach to productivity in two interior phases – falling and processing.
TimberTracks™ has been collecting, validating, and auditing contractor productivity data provided to us. Productivity is analyzed by collecting volumes delivered and contractor hours by phase. We then divide the volume delivered by the contractor phase hours to arrive at productivity per phase. The contractor hours by phase excludes any travel time to or from the block and other non-productive time such as mechanical breakdowns, safety meetings, etc.
TimberTracks™ verifies the information provided by contractors and then conducts random audits of sample data to confirm the information provided is accurate. The audit samples for productivity would include verification of volumes paid by a customer to confirm actual volumes delivered and worker timesheets through to payroll to confirm workers are recording productive hours and the worker was actually paid for those hours. This verification and random audit process provides independent verification that information submitted to TimberTracks™ accurately reflects reality.
For the purposes of the analysis we conducted for this article, we selected all approved and audited data from the Central Interior region and sorted that data by phase and then by piece size. Within each piece size we averaged the productivity for that piece size and then applied polynomial regression to the data to establish a trendline across piece sizes.
The data for the analysis comprised almost 10 million cubic metres with piece sizes ranging from 0.12 to 0.98 cubic metres per tree.
Falling productivity is somewhat volatile with low productivity in small piece sizes, increasing to solid productivity in middle piece sizes before softening in larger piece sizes. Based on the data, the sweet spot in piece size could be considered between 0.30 cubic metres per tree to about 0.70 but the sweetest range would be about 0.37 to 0.60.
Using an estimate of an interior standard buncher hourly rate in today’s fuel prices, the all-found rate paid for falling shows extremes from $8.73 per cubic metre to $3.62 per cubic metre at the peak of productivity and falling back to $4.47 per cubic metre at the larger piece sizes. An average of $4.00 per cubic metre is evident across all piece sizes.
Processing productivity largely followed an expected curve of low productivity in the small piece sizes to high productivity in the larger piece sizes. The expectation of lower productivity in small piece sizes largely comes from a relatively fixed time frame of moving a single tree through the processor head does not really change from a small diameter tree versus a large diameter tree. Multi-stemming capability has existed for years for smaller piece size trees but there are still a number of limiting factors of moving whole trees through the processor head.The graph clearly shows the dramatic increase in productivity from 0.12 through to about 0.30 where it starts to slow down through to the upper end of the piece size range where it starts to almost level off. This productivity differential can have a massive impact on the rate paid for processing services.
Using an estimate of an interior processing hourly rate in today’s fuel prices, the all-found rate paid for processing shows extremes from $7.90 per cubic metre to $3.67 per cubic metre with an average of about $4.38 per cubic metre across all piece sizes.
The analysis conducted for this article is extremely high-level with no sensitivity to other factors that could affect productivity across the piece sizes. However, the data illustrates that both falling and processing productivity are significantly impacted by piece size. It demonstrates that successful contractors need to know what affects their productivity and the impact of the affect when managing their operations for efficiency.
Interested in the analytics TimberTracks™ provides or what us to consider another topic for analysis in an upcoming artcle? Send us an email at email@example.com or tweet us @TimberTracks.