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Optimized supply chain management

Our data scientists can apply an ARIMA model or a deep neural network to generate reliable demand predictions. We can build neural networks or apply ML algorithms, such as hierarchical clustering and multi-class support vector machines, to evaluate your suppliers and assess the risks associated with each of them.

Improved production efficiency

We can help you fight low overall equipment effectiveness (OEE) by identifying the root causes for availability, performance and quality losses. We apply machine learning techniques to achieve predictive maintenance, undisrupted functioning, and enhanced product quality.

• Predictive maintenance
  Our data scientists can analyze the data from sensors installed at monitored machinery parts to understand the patterns in machinery functioning so that you could plan its maintenance more efficiently. One of the ways to solve this task is to apply Naïve Bayes algorithm to classify normal and pre-failure events. To get more insights, we can further classify the cases based on the time left until a breakdown. For instance, a failure that can happen within 24 hours gets a red color, a failure within 24h – 72h – yellow, a failure in more than 72h – green color.
• Operational intelligence
  Even if the monitored parameters seem to be fine when considered individually, their particular combination can still witness an upcoming breakdown. With the help of data science, we can identify such hidden interdependencies and their potential consequences. As a result, operators can receive real-time alerts and solve issues themselves or escalate them to the attention of the maintenance team.
• Enhanced product quality
  Machine learning techniques are helpful when you need to identify process disruptions at each production stage. We compare the actual and expected duration of each operation, as well as check temperature, vibration and other parameters. All this, to move beyond simple thresholds to really complex dependencies and identify the deviations that may affect product quality as early as possible. Having learned at some early stages that raw materials or parts are defective, you won’t waste your time and resources on continuing with them at all the remaining production stages. This can also save the entire manufacturing process from being affected by a defective part or component.

Personalized customer experience

Applying machine learning techniques, such as collaborative or content-based filtering (or both of them combined), we can design a recommendation engine to boost the sales of your ecommerce store. Such an engine can help you make your customers happier with relevant product offers. A webpage showing personalized content, a mobile app with promo offers that spark customers’ interest, as well as relevant email campaigns are also among the gains that you can get with data science.

Sales effectiveness

We can implement machine learning-based lead and opportunity scoring so that you’re sure that your sales team adheres to the chosen business strategy, as well as wisely prioritizes their efforts.

Besides, we can create a machine learning model to make your communication with customers stellar. Trained to detect attitude markers and recognize your customers’ mood, the model will signal your sales team if a particular customer experiences negative emotions.

We can provide you with a machine learning model to help you streamline the sales process by providing your sales team with clever next-step recommendations.

Customer behavior prediction

We’ll apply machine learning algorithms to provide you with accurate predictions of your customers’ behavior. For example, you’ll be able to assess whether it’s likely that your customer is a late payer, how they will react to price changes or to promotions. We can also help you identify potential churners so that you can design the strategies to prevent their loss.

Image analysis

Using machine-learning-based analysis, our data scientists can turn images or videos into meaningful info. You can use these insights to solve various business tasks, such as automated visual inspection, facial or emotion recognition, grading, and counting.