Delay Mitigation in the Forestry Industry

Supply chain disruptions are currently impacting many industries across the globe. These delays can cause major problems to organizations, such as late deliveries and supply chain gridlock. It is also important to consider that early deliveries can cause various problems, as well. Both late and early deliveries generate significant waste in the form of additional costs in the supply chain. Late deliveries can cause costly production stoppages, lost sales and a loss of goodwill, while early deliveries can generate excess inventory holding costs.

A study conducted by researchers from Texas A&M University and the University of Wisconsin (Gong et al., 2012) examined the cost of delays to both shippers and those receiving shipments. They reported that the average cost of delay to shippers’ operations was $56 per hour. Another study conducted by Niemi et al. (2020) found that estimated lost sales due to late deliveries of about five days was 5.1% of the delivery value. Longer delays of at least 45 days caused almost 40% of the estimated lost sales. These studies show the value of delay prediction and mitigation and the importance of having the right tools and strategies. In this blog, we will look at delays in the forestry industry and how to mitigate them.

Common Causes of Delay

Forestry relies on moving goods from one place to another, so supply chain disruptions can mean serious losses for the business. Let’s look at one specific example. In timber harvesting, trees are felled, pulled out of the harvesting block, and delivered to a mill or wood yard. Delays in any of these steps are common and can cause supply chain issues. Since timber harvesting occurs exclusively outdoors and usually in extreme terrain, one of the most common causes of delay is weather conditions. Delays can also be caused by a suspension of the harvesting activity due to protected wildlife, and lower production rates that can happen for a variety of reasons.

Accurately Assess Weather Impacts

Both excessive rainfall and extremely dry conditions can heavily influence harvest and delivery operations, so understanding the exact weather conditions that affect the forest operation is crucial. One key question is, “What is the exact rainfall amount that begins to negatively impact my operation?” This valuable question can be answered with the support of Artificial Intelligence algorithms. Knowing the exact weather conditions that affect harvesting and delivery operations and combining this information with a 14-day weather forecast for each harvesting block can give forest planners a great perspective on how things will play out in the coming days. This allows organizations to be prepared ahead of time. Delay prediction is critical to minimize late deliveries and the additional costs that come with it. It will also result in more accurate schedules that can benefit other areas of the business.

AI Models Enhance Delay Prediction

Remsoft Artificial Intelligence algorithms can help you predict delays by providing you with data about the exact weather condition that will affect your timber harvesting operation, as well as predictions of production rate reductions or even a potential operation shutdown. These AI models can also provide insights and predictions on wildlife occurrences, such as the dates when certain species are seen more frequently, which increases the chances of a harvesting suspension. Lastly, they can indicate drops in the production rate caused by the proximity to holidays or other major events. These models look for a correlation between these events and dips in production rate, and the next time these dates are on the horizon, the model will provide the best estimation based on historical data. This information helps organizations have more accurate schedules and be prepared before delays occur.

Decision Support Powered by AI

Remsoft Operations is an integrated platform that provides an end-to-end solution to manage the complexity of the forestry supply chain. Our solution offers decision support capabilities powered by Artificial Intelligence. Earlier this year, we released the Harvest and Delivery Delay AI component, which is embedded into the application. It indicates assignments that may be late or early due to weather conditions, production rate variance, field estimate variance, and major events. This information is provided directly on the Crew Schedule page (figure 1). Users can get more details that show the reason for the delay, so they can adjust the schedule and mitigate the issue (figure 2).