Optimizing Operations: An Economic Necessity

This article is produced in partnership with Lichens Innovation

In a tense economic context, operational efficiency has become a key factor in ensuring the sustainability of organizations. The recent 25% increase in tariffs imposed by the United States and the climate of uncertainty emphasize this necessity, forcing companies to rethink their processes. Artificial intelligence (AI) emerges as a pragmatic, accessible, and transformative lever, far from being an expensive technological fad for organizations. 

During the Baseline X Groupe MISA event, which our partner Lichens Innovation also participated in, eight companies submitted various innovative projects aimed at leveraging AI technology. This allowed these organizations to strengthen and anchor their business vision by developing high-value technological initiatives. In the form of roundtable workshops with experts, each table aimed to progressively explore an AI innovation hypothesis to meet the needs of a target market. These initiatives clearly demonstrate the potential of AI to optimize operations across various industrial sectors, without requiring overly lengthy or costly approaches. Below, we present five concrete examples inspired by this event.

With the significant growth of connected devices in the manufacturing and mining sectors, many industries depend on connectivity to collect data and make business decisions. A network interruption can lead to significant financial losses and disrupt operations. Implementing advanced system anomaly monitoring and diagnostic tools would make it possible to identify problems more quickly, better interpret information, and limit downtime.

Artificial intelligence detects abnormal trends in data, such as network interruptions, by identifying signals that deviate from usual patterns. It provides operators with a clear interpretation of the situation and an action plan to resolve problems. This approach reduces dependence on specialized telecom expertise and improves network activity continuity.

At forestry sites, timber loading trucks must transport cut trees to nearby sawmills. Before loading, the volume, species, and diameter of cut trees are estimated to meet the specific production requirements of each sawmill. However, these estimation methods are manual and imprecise, causing costly production interruptions. Having specific data on wood characteristics before sending them to sawmills would allow for better resource and production planning.

Using various data sources (e.g., location data, production data, images), AI can aggregate information to effectively respond to logistical flow needs. This approach reduces material and financial losses while promoting optimized planning and more sustainable and efficient resource use.

In the mining field, geological core analysis includes several tasks such as 1) identifying depth markers and 2) collecting various core information from sensors.

Depth marker identification represents an essential step in geological analysis. These markers are small blocks of materials inserted between cores that serve to precisely determine the sampling depth. However, manual entry of information related to these markers increases the risk of human error and causes significant delays during data validation.

Furthermore, the integration of various sensors, such as Lidar and image sensors, allows for collecting complementary data essential for geological analysis. However, in practice, the synchronization of these sensors often poses problems. For example, a slight divergence in orientation between the Lidar and the camera can alter data fusion. This causes complex and expensive manual adjustments.

Computer vision, an AI technique, is notably used to identify features in images (e.g., lines, contours). It then becomes possible to automate the identification of objects (e.g., depth markers) and apply alignment corrections to save time and resources. This costly manual process thus transitions to a reliable and efficient operation.

In all industrial sectors, a large amount of critical information is generated in the form of unstructured data (e.g., PDF, JPG). For example, technical reports and laboratory results may contain essential information such as names, dates, locations, quantities. The lack of predefined structure in these documents complicates the extraction of relevant information by traditional computer systems. Moreover, each organization, even each individual, adopts their own presentation practices, making document search and information analysis particularly complex. This increases the risk of overlooking key elements for decision-making and makes companies heavily dependent on document authors for interpretation.

Artificial intelligence, through advanced language processing techniques, makes it possible to exploit data sources that were previously difficult to use (e.g., text). It quickly identifies relevant documents and sections and structures them in a coherent and uniform manner, regardless of their original format. This approach considerably reduces the time spent on manual research, while facilitating and accelerating interpretation and decision-making. 

In a constantly evolving market, static pricing limits companies' ability to adapt to fluctuations in demand and economic context. Dynamic pricing, adjusted based on factors such as demand or time of year, can therefore prove more advantageous. However, manual rate revision extends analysis time, increases the risk of errors, and can lead to suboptimal decisions (e.g., setting a price below its potential value). It might be tempting to believe that predicting prices based solely on historical data is sufficient. However, the goal is not to reproduce past strategies, but to identify the most effective and novel pricing approaches, adapted to each specific situation.

In a changing economic world, AI enables learning dynamic pricing strategies that adjust in real-time by integrating various factors (e.g., stock availability, customer behavior). By continuously testing different strategies and adjusting its decisions based on the results obtained, it gradually identifies the most profitable approaches. This adaptability allows for optimizing prices in the face of market developments, while taking into account business objectives. It thus speeds up quote generation, strengthens company responsiveness, and allows for better margin control.

AI: An Accessible and Essential Tool

These projects, explored and deepened during the Baseline X Groupe MISA event, demonstrate that AI is no longer reserved only for large companies. SMEs can benefit from it too! In an economic environment where cost control and agility are essential, adopting AI becomes an unavoidable and accessible strategy for transforming industrial and organizational processes. 

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