In previous articles, we discovered the relevance of advanced analytics for businesses, explaining why it is crucial for businesses and the three main areas that concern business management. We also discussed the key challenges related to data quality, ethics and social responsibility, as well as training and education. Today, we will focus on use cases of advanced analytics.
The possibilities offered by advanced analytics are so extensive that any industry and business model can gain numerous advantages, from retail and transportation to construction,automotive, healthcare, pharmaceuticals, manufacturing and more.
Use cases of advanced analytics: Compilation of relevant scenarios
While there is an infinite number of use cases, we present below compilation of interesting use cases structured into eight major categories applicable to a wide variety of industries. They have been selected based on the areas with the greatest impact on businesses and the activities within them that advanced analytics can optimize.
Use cases of advanced analytics in supply chain management
Demand forecasting: by using historical data, it is possible to predict future demand with greater accuracy. This helps reduce excess inventory or avoid product shortages, allowing for more efficient customer service. Advanced analytics models take these predictions to the next level by determining demand for specific materials or components, customer groups, geographical regions, and other variables. These models are not generic; they can adapt to the specific characteristics of each case.
Supply chain optimization: models can be established to identify bottlenecks, determine supplier effectiveness, and optimize transportation logistics. This results in lower costs and increased efficiency throughout the supply chain process.
Risk management: analytics also enables us to detect patterns and trends that could indicate future risks, such as potential disruptions in the supply chain. With this information, we can proactively mitigate these risks, minimizing costs as well.
At Clariba SEIDOR, we recently collaborated with clients on projects involving demand prediction for portfolios of thousands of unique references. Through automation, we enabled the client to streamline processes up to result generation.
Use cases of advanced analytics in maintenance
Anomaly detection: models can be created to recognize the normal behavior of a system and alert when deviations occur. This method is particularly useful for detecting infrequent incidents or breakdowns that are difficult to predict using historical data.
Fault classification: going further, it is possible not only to detect a fault but also to classify the type of error based on data patterns. This helps technical staff identify and resolve problems more efficiently.
Maintenance scheduling optimization: The resulting information also indicates the optimal timing for preventive maintenance based on real-time historical data patterns. This approach helps prevent potential failures while minimizing maintenance team downtime.
Use cases of advanced analytics in industrial processes
Quality control: through Machine Learning technology, we can identify any type of variation or error in products, allowing for early intervention to rectify any issues before they become more serious problems. This improves product quality and reduces associated costs related to returns and claims.
At Clariba SEIDOR, we collaborated with clients like Almirall, deploying a cloud-based automation solution that accelerates quality control processes. In such scenarios, reductions in control preparation time of over 90% have been observed.
Use cases of advanced analytics in call centers and customer service
Resource optimization: in terms of customer service, there are two challenges: providing timely responses to customers and determining the necessary personnel without increasing costs unnecessarily. Advanced analytics allows us to optimize and adjust staff scheduling, among other things. Additionally, with complete customer information, we can offer personalized recommendations for products and services, have access to incident or order history, identify potential interests, and redirect calls automatically.
At Clariba SEIDOR, we collaborated with clients such as the emergency service of the Generalitat de Catalunya to optimize their call centers, enhancing the aforementioned processes. As a result, they were able to maintain their strict service levels.
Use cases of advanced analytics in logistics
Fleet management: advanced analytics models provide the benefit of optimizing fleet management. This includes vehicle assignment to routes, maintenance scheduling, and predicting potential vehicle performance issues. These approaches also contribute to reducing environmental impact and energy costs.
Fraud detection: models can be developed to identify patterns of fraud in logistics, such as delivering packages to unusual locations or unexpected changes in delivery routes. They can even detect fraud patterns that are undetectable by humans in advance.
Use cases of advanced analytics in warehouses and physical spaces
Warehouse layout optimization: advanced analytics models can analyze sales data and product movement patterns within the warehouse. These systems suggest product organization that minimizes unnecessary movements and improves efficiency during picking.
Dynamic pricing strategies: by analyzing sales, inventory, and demand data, we can determine optimal and timely pricing strategies for products in the warehouse based on specific configurations, order types, etc.
At Clariba SEIDOR, we collaborated with clients like Frit Ravich to optimize warehouse scenarios using digital twins. This enabled the detection of improvements in delivery times throughout different campaigns of the year.
Use cases of advanced analytics in marketing
Programmatic advertising: advanced analytics allows us to generate models for making real-time decisions about which ads are ideal to show to potential customers at any given moment. Based on factors such as online behavior, location, time of day, or the type of device being used, the most suitable type and format of advertising can be determined.
Ad creative optimization: taking it a step further, it is possible to analyze which elements of an ad are most effective (combinations of colors, words, images, sounds, etc.) for each audience segment. This enables the creation of more effective personalized experiences.
Use cases of advanced analytics in user experience and virtual assistants
Personalization of interactions: learning from individual user preferences based on their past interactions allows virtual assistants to adapt their responses and suggestions to specific user needs and preferences. For example, they can focus the conversation on the customer's preferences by analyzing the user's browsing history.
Prediction of user needs: virtual assistants can anticipate what a user may need based on the context of the conversation or past interactions. This allows for proactive assistance and suggestions, significantly improving the user experience.
Embrace Advanced Analytics with Clariba SEIDOR's Solutions
Advanced analytics is becoming a true revolution, especially with the advent of advanced technologies like natural language models.
We have the opportunity to make the most of our data and make informed decisions. However, it is essential to incorporate best practices and reference methodologies to ensure success during and after the implementation of these tools.
The Data & Analytics team at Clariba SEIDOR consists of professionals who are prepared to tackle these new technologies and cutting-edge solutions and apply them to your business. With over 25 years of experience, we have assisted more than 2,500 clients.
If you want to benefit from advanced analytics and have the support of experts in the field, please contact us. We would be delighted to help you maximize the potential of your data and lead your company to success in today's competitive business landscape.