5 ways IoT, data science can reshape the automotive sector

Rising operational costs, falling demand and the complexity of the global supply chain make automotive enterprises search for new ways to remain competitive. To overcome these challenges and keep up with the market, automotive companies turn to modern technologies. The technologies that are expected to deliver value in the automotive sector are the Internet of Things and data science.

IoT provides unprecedented visibility into the shop floor and supply chain operations. IoT systems collect data with the help of sensors, radio frequency identification tags and other sources, provide reliable and scalable storage for the collected data, offer tools for basic data analytics, and present the insights obtained with analysis in the form of reports.

To tap into predictive capabilities and realize operational and business value from IoT data, the collected information can be analyzed with machine learning techniques, which are rooted in data science.

Let's have a closer look at the operational improvements that automotive enterprises can get adopting smart solutions based on IoT and data science.

Many automotive enterprises have long used manual, spreadsheet-based methods for monitoring equipment utilization and evaluating production line performance. However, manually collected data is often unreliable — estimates show that 88 percent of spreadsheets contain errors. Moreover, manually prepared utilization reports usually become available after significant delays, which may hinder decision-making.

IoT and data science provide access to near real-time insights about the availability and performance of the automotive manufacturing and assembly machinery.

IoT allows collecting data about equipment operational parameters (e.g., power-on time, in-cycle time, the number of parts produced) and analyzing it for reporting and visualization.

Applying machine learning algorithms to the collected operational data, it is possible to detect factors hindering production line performance, calculate overall equipment effectiveness, reveal best practices for increasing overall equipment effectiveness and perform other nontrivial tasks.

BMW applies IoT and data science to monitor the performance of industrial machines producing vehicle components in real time, which allows the plant to manufacture about 1,100 cars a day because of optimized production line performance.

Many automotive enterprises maintain their equipment using predefined schedules. Usually, the older the machinery, the more frequent checkups it gets. However, 68 percent of industrial machinery shows random failure patterns and breakdown irrespective of age. This makes age-based maintenance a doubtful way to go.

IoT and data science help address the problem of inefficient maintenance.

The technologies allow monitoring equipment conditions to detect problems as early as possible and identify the right time for a checkup.

IoT, in this case, becomes the source of data about the health of machines and their components, as well as their operational and ambient parameters. The role of data science is to reveal hidden correlations in the IoT data and detect those data patterns that can be a signal of potential equipment deterioration.

The analysis of IoT data helps predict whether a machine shows emerging changes that can develop into serious issues, whether a piece of equipment is likely to fail within a certain time frame, as well as estimate how many days/cycles remain until a machine fails if it continues to operate under current settings.

Toyota uses sensors to monitor the condition of factory equipment to come up with an optimal maintenance schedule and has experienced significant cost savings.

Cost-effective inventory management stands on two pillars: sound inventory tracking and reliable inventory forecasting. To carry out these tasks, automotive enterprises tend to use spreadsheet-based methods. However, labor-intensiveness and the high probability of error bound to these methods make manufacturers upgrade their inventory management strategies.

To optimize and automate inventory tracking, IoT pairs with radio frequency identification. Radio frequency identification becomes the source of data about the locations and properties of inventory items, while IoT provides scalable storage for the data fetched from radio frequency identification tags and turns this data into informative insights about the locations and properties of inventory items.

Data science improves the reliability of inventory forecasting. For that, machine learning algorithms take in the data about past weeks' demand for a particular vehicle and analyze it in correlation with multiple factors ranging from vehicle type to customers' locations to assess holding costs against shortage costs and project the optimal inventory figure.

Managing supply chain activities with traditional methods involving manual data collection and preparation, automotive enterprises risk making important business decisions based on fragmented and often outdated information. Leveraging IoT and data science, enterprises make sure their decision-making is backed by accurate, detailed data.

IoT pairs with radio frequency identification to provide manufacturers with information about the locations, properties and conditions of individual details and assembled vehicles in the supply chain, while the key area of data science application in the supply chain management is demand forecasting.

To improve the accuracy of demand forecasts, machine learning algorithms consider diverse data, e.g., the previous weeks' demand, SKUs and promotional campaigns and capture complex nonlinear dependencies in this data.

Technology-driven product quality control relies on monitoring the condition, calibration and ambient parameters of the automotive manufacturing and assembly equipment.

IoT becomes the source of data about equipment health (e.g., engine temperature), calibration (e.g., spindle speed) and ambient (e.g., air humidity) parameters. This data is collected with the help of sensors attached to the equipment or placed nearby.

Machine learning algorithms are applied to this data to identify those combinations of equipment condition and operational and ambient parameters that cause the quality of the produced and assembled components to deteriorate.

In summary, the use of IoT and data science in the automotive industry will bring about better production line performance, shorter manufacturing cycle time, improved quality of the produced vehicles and greater savings realized through increased operational efficiency, reduced waste and lower labor costs.