{"id":13033,"date":"2025-10-06T10:38:29","date_gmt":"2025-10-06T10:38:29","guid":{"rendered":"https:\/\/icv-controlling.com\/?post_type=blog&p=13033"},"modified":"2025-10-26T11:57:10","modified_gmt":"2025-10-26T11:57:10","slug":"from-trees-to-trends-ai-supported-market-analyses-in-the-timber-sector","status":"publish","type":"blog","link":"https:\/\/icv-controlling.com\/en\/blog\/from-trees-to-trends-ai-supported-market-analyses-in-the-timber-sector\/","title":{"rendered":"From Trees to Trends: AI-supported market analyses in the timber sector"},"content":{"rendered":"
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From Trees to Trends: AI-supported market analyses in the timber sector<\/h2>
More transparency and planning reliability with machine learning<\/div> <\/div>\n <\/div>\n
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The timber market is under pressure. Climate change, bark beetle infestation, wars and other crises are shaping the development of many commodity markets and making reliable estimates increasingly difficult.<\/p>\n

This is exactly where the presentation “From Trees to Trends: AI-supported market analyses in the timber sector” by Sandra Heckmann (Consultant – Data Scientist, Syncwork AG Berlin) and Julia Kemmerer (Sales Management Specialist, Bayerische Staatsforsten A\u00d6R Regensburg) at the Controlling Competence Spaces at Metaverse comes in.<\/p>\n

“For our customer, Bayerische Staatsforsten A\u00f6R (BaySF), it is of economic interest to understand these fluctuations on the timber market. In addition to the maintenance and management of the Bavarian state forest, BaySF’s tasks also include the sale of the harvested timber. The price for this timber is the focus of our work. The aim is to predict three months in advance whether the price will have a rising or falling trend,” explains Sandra Heckmann. With this knowledge, BaySF gains a strategic advantage in order to achieve an adequate price.<\/p>\n

“We don’t have a crystal ball with which we can look into the future. But we can create an approximation of such a predictive “sphere”. We use a proven ML algorithm (machine learning) that uses data from the past to form rules for the predictions. This works well for data that is subject to minor fluctuations, such as periodic processes,” adds Heckmann.<\/p>\n

On 12 November at icvIspaces, the two experts will show how a machine learning algorithm is used to systematically analyze the timber market. The algorithm takes into account external influencing factors such as geopolitical events, logistical bottlenecks or seasonal fluctuations and can therefore recognize patterns that are difficult for humans to understand on their own. The aim is to predict price developments at an early stage and enable well-founded decisions in procurement and controlling.<\/p>\n

In the lecture, participants will learn:<\/p>\n