Machine learning is still perceived as an innovative approach in business. The technological progress and the use of Big Data in business make ML-based solutions increasingly important. As Forbes magazine indicates, 76% of enterprises today prioritize artificial intelligence and machine learning over other IT initiatives .
The concept of machine learning is derived from advanced data analysis of pattern and dependency recognition. ML assumes that when models analyze new data, they can adapt independently and use this new knowledge to develop by learning from previous experiences. Machine learning models can enhance nearly every aspect of a business, from marketing and sales to maintenance.
Machine learning enables predictive monitoring, with algorithms anticipating equipment breakdowns before they occur and scheduling timely maintenance. With the work it did on predictive maintenance in medical devices, deepsense.ai reduced one client’s downtime by 15%.
But it isn’t just in straightforward failure prediction where machine learning supports maintenance. In another recent application, our team delivered a system that automates industrial documentation digitization, effectively reducing workflow time by up to 90%. We developed a model that recognizes and adds descriptions for all symbols used in the installation documentation. The schematics, including the technical descriptions of all components, are fully digitalized. The model reduces the work to a 30-minute review by a specialist. It also handles the most tedious tasks, thus reducing the effort required of human specialists and the number of errors they make in performing them.
An international manufacturer of medical devices was looking for a solution that would reduce device downtime. Our experts built a predictive maintenance model that pores over historical data, searching for anomalies and signs of a breakdown before one occurs. The model reduced breakdown-related downtime by more than 15%. Such a solution can be applied in machine-reliant industries, where breakdowns bring operations to a halt and hamper overall company performance.
Machine learning is also being adopted for product inspection and quality control. ML-based computer vision algorithms can learn from a set of samples to distinguish the “good” from the flawed. In particular, semi-supervised anomaly detection algorithms require only “good” samples in their training set, making a library of possible defects unnecessary. Alternatively, a solution can be developed that compares samples to typical cases of defects.
One of our clients asked us to tackle two of their visualization problems on a food production line – to detect sauce smears on the product’s inner packaging, and to identify correct positioning of the product’s topping. The system deepsense.ai delivered was able to identify 99% of faulty products with topping defects and while raising the alarm with a 99% accuracy rate for sauce smears. The model significantly reduced the need for manual quality control, hence lowering costs.
ML-based computer vision solutions can also be an essential component in the monitoring of hazardous areas in factories, tracking whether every worker is following safety requirements (including wearing helmets, glasses, vests, earmuffs etc.) and, if not, sending an instant alert to the supervisor with a detailed description of the event that has occurred.
In the field of predictive analytics demand forecasting can be used to predict consumer demand. Such forecasting is done by analyzing statistical data and looking for patterns and correlations. With machine learning taking the practice to a higher level, modern demand forecasting techniques go far beyond simple historical data analysis.
More recent techniques combine intuition with historical data. Modern merchants can dig into their data in a search for trends and patterns. At the pinnacle of these techniques sit demand forecasting machine learning models, including gradient boosting and neural networks, which are currently the most popular types and outperform classic statistics-based methods. Historical data from transactions form the basis of more recent demand forecasting techniques. These are data that sellers collect and store for fiscal and legal reasons. Because they are also searchable, these data are the easiest to use.
This modern approach is extremely effective. One of our clients from the retail industry was losing millions of euros a year due to out-of-stocks. There was a daily cap on how many new items its warehouse could receive. Our team built a demand forecasting model for products that were new to market. It enables the company to use the cap more efficiently by ordering more hot products and fewer of those that are less in demand. We used Gradient Boosting, Random Forest and Neural Networks to build the model, and the trifecta reduced out-of-stocks by 30%.
Companies can maximize ROI on their marketing activities by implementing machine learning into their customer analysis. Sophisticated data analysis helps identify customers with the highest ROI on ads to make the most of marketing campaigns. It also optimizes channel mix with advanced attribution models.
deepsense.ai designed a model for a leading mobile advertising platform that predicts the click-through rate of internet advertisements. The model analyzes historical data on site user behavior to spot patterns and uncover anomalies. It enabled clients to identify an abnormal pattern among users, which turned out to be bots engaging in fraudulent clicking. The solution effectively identified internet bots that click ads, significantly boosting CTR predictions – up to 90% of bots were spotted and CTR predictions were improved by up to 35% over existing heuristics.
Analyzing large amounts of data has become a crucial part of the retail and manufacturing business landscape. It has traditionally been done by experts, based on know-how honed through experience. With the power of machine learning, however, it is now possible to combine the astonishing scale of big data with the precision and intelligence of a machine-learning model. While the business community must remain aware of the multiple pitfalls it will face when employing machine learning, that it endows business processes with awesome power and flexibility is now beyond question.