Top 3 Use Cases for Advanced Analytics

Implementing modern tools and technologies like AI and Machine Learning can empower businesses to derive profound insights from their data, enabling them to make informed decisions, streamline processes, or develop new products. But how can this be effectively applied in practice? We offer inspiration and introduce our top three use cases based on advanced analytics

Data Analytics

Advanced Analytics, a subset of data analysis, involves using modern tools and technologies like AI and Machine Learning to gain deeper insights from data. Unlike conventional data analysis, which focuses on summarizing and presenting data, Advanced Analytics aims to derive complex patterns, trends, and predictions. Key features include:

  • Statistical analyses such as regression or cluster analyses reveal relationships and correlations in business data.
  • Data Mining uncovers hidden patterns and connections in large datasets using algorithms and models.
  • Machine Learning in Advanced Analytics generates predictions and recommendations.
  • Predictive Analytics focus on predicting future events or trends based on historical data and statistical models.
  • Text and Sentiment Analyses identify moods, opinions, and topics in unstructured data.
  • Data Visualization complements Advanced Analytics, simplifying decision-making.

Predictive Maintenance: A prime example from industry

Unplanned production downtimes can cost companies enormously. According to a report by AI specialist Senseye, costs for a single hour of downtime range from $39,000 for FMCG companies to over $2 million in the automotive sector. Predictive Maintenance, a flagship application of Advanced Analytics, increases the availability and productivity of equipment and plants, safeguarding against unnecessary operational, maintenance, and repair costs. Companies like Feintool and KUKA already benefit from predictive maintenance. Find out here how it’s achieved through the analysis of operational data.

With modern data platforms like Databricks as the foundation for Advanced Analytics, companies can comprehensively monitor overall equipment effectiveness. They can make forecasts at the component level, further optimizing production. A digital twin can enhance operational efficiency and decision-making.

Resilient supply chains thanks to Advanced Analytics

Empty supermarket shelves, shortened production cycles, and soaring raw material prices – the COVID-19 pandemic has highlighted the fragility of global supply chains across industries. To bolster resilience, supply chain management has long relied on digitization. However, according to researchers at the Fraunhofer Institute, logistical processes pose a special challenge: The data collected is not always comprehensive and requires specific mathematical methods for targeted analysis.

The Data Lakehouse concept, providing a coherent data platform for Advanced Analytics, certainly offers a solution. Various logistics sectors stand to benefit, such as: 

  • Intralogistics, where Advanced Analytics can shorten lead times from order receipt to shipment, a crucial metric in lean manufacturing. Lead time is a crucial metric in lean-oriented manufacturing. Reducing it makes the company more flexible, enabling better responses to market changes – a significant advantage in uncertain times! With Advanced Analytics, lead time components can be examined and then optimized based on the results.
  • Trade logistics, where algorithms provide precise forecasts for upcoming demands and sales volumes. Demand planning often occurs quarterly and, if it is to be precise, requires processing a large amount of data. Additionally, those responsible should consider external factors such as seasons or economic data. This mammoth task is hardly manageable without tools like Data Mining and Machine Learning.
  • Transport Logistics, where Advanced Analytics enables proactive adjustment of transport capacities and early detection of systematic changes. Today, goods need to reach their destination quickly, cost-effectively, and as sustainably as possible. This requires excellent route planning.

Smart energy management with Advanced Analytics

In energy management, Advanced Analytics is used to monitor energy consumption, create forecasts, and employ energy more efficiently. This is beneficial for both providers and the industrial sector. Traditional energy management quickly reaches its limits when companies aim to reduce energy consumption, save costs, and increase energy efficiency simultaneously. The challenge often lies in the lack of data or the fact that data from various sources can’t easily be analyzed.

The innovative combination of IoT and energy data can cut energy costs by up to 30% – partly thanks to Advanced Analytics methods. Learn more about this third application in our Solution Paper.

Advanced Analytics: Added value from data

Predictive Maintenance, Supply Chain Management, and smart energy supply – these are our top 3 application fields for Advanced Analytics. Moreover, modern data analysis can be employed in almost every industry and various business areas. It enables companies to make data-driven decisions, achieve efficiency gains, and secure competitive advantages. Advanced Analytics is a versatile tool that helps extract valuable insights from large datasets. Perhaps you too have an idea where you and your company can benefit?

+++ With our IoT expertise, we at Device Insight actively implement the advantages of the Lakehouse concept for our customers. We integrate machine data with Databricks services to realize use cases in Advanced Analytics and Machine Learning. +++ +++

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