The commercial vehicle manufacturing industry has been held back by the limitations of enormous on-premises systems for far too long. And now, since the outset of the global pandemic, and the war in Ukraine, the sense of urgency to maintain availability, adapt to the changing supply chain landscape, and meet customer demands, is at an all-time high.
As a result of global disruptions, there’s no denying that the future of business as we know it will be changed forever. With mass migrations to digital operations, and the number of digital interactions rising daily, an explosive growth of captured data has also become available. Extracting maximum value from big data is now a driving factor for forward-thinking manufacturers to adopt cloud-native smart manufacturing analytics platforms to enable a 360° view of customers and enterprise.
What is a smart manufacturing analytics platform and how are automotive manufacturers leveraging analytics capabilities to drive value? Let’s explore.
The majority of manufacturers (83%), regardless of their geography or business domain, already have a robust cloud strategy in place. In fact, cloud-enabled services are expected to make up almost 50% of all enterprise-level software usage among industrial companies by 2023. The top five manufacturing sub-sectors relying on the cloud include heavy machinery (92%), automotive/OEMs (87%), industrial & assembly (87%), automotive suppliers (81%), and chemicals (81%).
Now that cloud adoption is on the rise and business leaders continue to embrace and optimize strategies for digital transformation, cloud migration is no longer a matter of “whether” or “not”. These days it’s the question of maximizing the value of cloud-native solutions that is high on the agenda.
According to McKinsey, 95% of the cloud’s value potential is in business-related functions. At the same time, most industrial companies (59%) misplace their focus and concentrate solely on IT optimization. Digital transformation, however, is not merely an IT project – it requires a complete overhaul of the manufacturing ecosystem from application architecture to production operations and the entire development life cycle.
If we look at the statistics on the tech-enabled value of the cloud in the same report, we can see that nearly one-third of the cloud’s $700 billion economic capacity lies in manufacturing (30%), closely followed by procurement function (25%). A significant portion of that financial pie is related to marketing and sales (15%), while the supply chain accounts for 10%. Fifth place is a tie between IT, aftersales, general and administrative, and R&D functional domains, each amounting to a 5% share.
These figures are not just numbers. They actually translate into tangible benefits automotive manufacturers can achieve through transformational changes in business processes, organization, product development, or sourcing. Successfully deployed cloud-native analytics platforms can help manufacturers overcome the most common business process challenges.
Cloud-native analytics can provide numerous benefits to the manufacturing of commercial vehicles, particularly heavy lorries, trucks, and buses. Here is a summary of benefits:
How are these benefits realized in the automotive manufacturing industry? Read on to find out.
Cloud-native analytics can help automotive manufacturers collect and analyze large amounts of data from various sources, including IoT sensors, telematics, and other connected devices. This data can be used to gain insights into various aspects of vehicle manufacturing, such as production efficiency, quality control, and supply chain management. By using data to make decisions, manufacturers can improve their operations, reduce costs, and increase customer satisfaction.
|Learn more: Analytics platform for smart manufacturing: A Jabil case study Turn Data Into Insights Faster With Grid Dynamics Analytics Platform Starter Kit on AWS Cloud Loss Prevention with AI-powered IoT analytics platform on AWS Building an IoT Platform in GCP: A Starter Kit IoT Platform: A Starter Kit for AWS Analytics Platforms and MLOps Smart Manufacturing and IoT|
|Real-time anomaly detection
With cloud-native analytics, manufacturers can monitor their vehicles in real-time, allowing them to detect anomalies and take corrective action quickly. For example, if a vehicle's engine begins to overheat, manufacturers can use real-time data to diagnose the problem and take steps to prevent it from escalating.
|Learn more: Anomaly detection in industrial applications: solution design methodology Anomaly detection in industrial IoT data using Google Vertex AI: A reference notebook Anomaly Detection for Industry 4.0 Detecting anomalies in high-dimensional IoT data using hierarchical decomposition and one-class learning|
Cloud-native analytics can also enable manufacturers to predict when a vehicle is likely to require maintenance, reducing downtime and maintenance costs. By analyzing data from sensors and other sources, manufacturers can identify patterns that indicate when a component is likely to fail, allowing them to proactively replace it before it causes a breakdown.
|Learn more: Building a Predictive Maintenance Solution Using AWS AutoML and No-code Tools Quality 4.0: reimagining AI-powered quality control for smart manufacturing How anomaly detection, predictive maintenance, and visual quality control save companies time and money|
|Visual quality control
Cloud-native analytics can also help manufacturers improve vehicle safety and product quality by analyzing data from sensors and other sources. For example, data from sensors can be used to identify drivers who are engaging in unsafe behavior, or product defects on the assembly line.
|Learn more: Building a Visual Quality Control solution in Google Cloud using Vertex AI How to identify vehicle tires using deep learning visual models Visual search: how to find manufacturing parts in a cinch How to build visual traffic analytics with open source: car tracking and license plates recognition|
Cloud-native analytics can provide real-time visibility into inventory levels, enabling manufacturers to optimize their inventory management practices. By tracking inventory levels and demand, manufacturers can make more informed decisions about when and how much to order, reducing waste and minimizing costs.
|Learn more: Optimization of order and inventory sourcing decisions in supply chains with multiple nodes, carriers, shipment options, and products Inventory allocation optimization: A Dataiku starter kit How explainable AI helped reduce warehouse order picking time by 1/4|
|Supply chain optimization
Cloud-native analytics can help manufacturers manage their relationships with suppliers by providing real-time visibility into supplier performance, such as delivery times and quality metrics. This can help manufacturers identify and address issues quickly, reducing the risk of supply chain disruptions. Further, by analyzing data from various sources, such as customer orders, production schedules, and inventory levels, cloud-native analytics can optimize production planning. This can help manufacturers reduce lead times, increase efficiency, and minimize waste.
|Learn more: Multi-agent deep reinforcement learning for multi-echelon supply chain optimization Supply Chain and Inventory Reimagining resilience: supply chain optimization for smart manufacturing A rapid response to COVID-19 supply chain and market shocks: Emerge from the crisis stronger|
Until recently, deployment of digitally-powered factories remained elusive due to limitations in tech capabilities. The development of smart manufacturing analytics platforms enabled industrial companies to align their people, assets, and operations to optimize production, mitigate risks, and augment human intelligence with AI.
Modern cloud-based manufacturing platforms utilize cognitive analytics to interpret, comply with, and learn from the information gathered from connected machines. An Analytics Platform leverages that real-time historical data to scale and flex with current business needs. The embedded agile flexibility, therefore, allows applications to tune the equipment to adapt to the schedule and product changes with minimal disruptions.
Data is the lifeblood of digital manufacturing transformation, and automotive companies have ample sources to drive insights – from machines, sensors, and programmable logic controllers to different points in the supply chain. However, 68% of data available to enterprises go unleveraged, either because it is unusable, its quality and security are not sufficient, or there are no protocols for proper storage of the collected information.
A comprehensive analytics platform affords manufacturers the ability to connect and manage data assets, and share insights across the entire organization. Instead of siloed systems that fail to unlock the real value of data, they get an efficient cloud infrastructure that becomes a central repository for all the information extracted from disparate technologies, equipment, and assets in a factory. This will generate insights and predictions that can boost product quality, process safety, manufacturing-line efficiency, as well as economic and environmental resilience across the production system.
In traditional manufacturing, product quality is usually measured at a single control point leaving a huge gap in production flow monitoring. It is quite burdensome to identify the exact human, machine, or environmental causes of quality issues in the complex, multi-staged assembly process.
Product quality analytics, an integral feature of the smart factory, can self-optimize performance across a manufacturing ecosystem by relating in-line sensor data to production process parameters and machine settings. Based on the inputs received from the field devices, IoT cloud platforms can predict and detect quality defect trends sooner, allowing for early-stage intervention. Automated quality control, therefore, not only leads to a deeper understanding of specific process situations but also enables manufacturers to take action proactively.
According to Deloitte, deployment of pre-configured cloud-based process monitoring applications could lower scrap rates and lead times, while improving yield and fill rates. This results in a 30% increase in product quality.
Global fragmentation of production, with manufacturing processes spread among several facilities and vendors located across multiple regions, prompted increased supply chain complexity in recent years. Rapidly evolving sourcing, production, and sales departments require manufacturers to have a 360° view of their partners, suppliers, OEMs, logistics, and distributors to stay on top of the game.
Cloud computing offers intelligent tools that can trace, record, process and visualize the position and movements of any resources across multiple locations and business networks. The positioning data allows advanced analytics to generate heat maps, pathway analysis, utilization of space, and approximation analysis that are used to create intuitive supply chain dashboards.
Gaining end-to-end visibility of their value chains helps manufacturers tackle production, packaging, and dispatch risks in a robust and controlled manner. Real-time asset tracking, coupled with machine learning and optimization algorithms, enables producers to identify the most efficient sourcing and logistics options. It also allows them to dynamically adjust schedules and react quickly to unexpected events, promoting supply chain resilience.
Optimized production traditionally leads to more cost-efficient operations. For one thing, combining data from multiple sources allows manufacturers to improve algorithms that enable predictive maintenance. This, in turn, increases machine uptime, helps eliminate regular and costly replacements, and boosts overall system productivity, leading to lowered warranty and technology costs.
Smart manufacturing solutions built with cloud-native technologies can also offer better access to enterprise inventory. Analytics platforms provide deeper insight into inventory levels, delivery status, and demand cycles, reducing the cost of inventory management and revitalizing the fulfillment ecosystem.
Migrating data and IT resources to the cloud allows automotive companies to channel their funds into projects that differentiate their business, rather than investing in data centers and servers. By using cloud computing, manufacturers can drastically save on data storage and IT infrastructure as they only pay for the resources they use and only for how much they consume.
The rising awareness of and higher demands for data have influenced how the concept of an analytics platform evolved. The journey to data-centricity started with fairly simple enterprise data warehouses (EDWs), which were superseded by next-gen systems called data lakes that later matured into modern cloud-native analytics platforms, such as the ones we build for leading manufacturers.
The Grid Dynamics Analytics Platform Starter Kits provide a set of pre-integrated capabilities covering the end-to-end data lifecycle from ingestion to machine learning including batch processing and streaming data ingestion, data processing, data transformation, data management, catalog and lineage, data pipeline orchestration, data preparation, data warehouses, reporting, as well as an AI platform. It is built on a combination of best-of-breed open-source software, SaaS platforms, as well as cloud-based services. The solution has been battle tested in numerous company-wide deployments for Fortune 5000 companies and technology startups, satisfying the strictest performance and security requirements.
To unlock the true potential of the data that exists within manufacturing enterprises, our advanced cloud-hosted platforms leverage the following capabilities:
A data catalog is a high-level inventory of all data assets in an organization, designed to help factory-floor technicians and back-office engineers quickly discover, understand and manage both technical (sensor data, process data and quality data) and business metadata. Data lineage is a map of the data journey, which helps to understand how and from which data sources a particular dataset originated and what transformations it underwent along the way.
Increasing the quality of data pipelines is the first step to becoming a data-driven organization. Monitoring the situation across different stages of the product life cycle is an absolute must for modern smart manufacturing companies. It allows for making proper adjustments early on in production and therefore creates tangible business impact.
Read this case study to learn more about data observability processes: How a Fortune 500 manufacturer reduced time-to-market for industrial tools using a data observability framework
Data governance helps to prevent data lakes from turning into data swamps. To unlock business value with analytics, companies first have to build a platform and fill it with data. Data governance allows manufacturers to thoroughly cover all source-of-record systems, making data universally accessible across the enterprise. It enforces oversight ensuring availability, usability, integrity and security of the data pipelines in the smart factory ecosystem.
Data quality monitoring and management solutions help spot, prevent, and auto-correct anomalies in data processing pipelines. This feature is crucial as data is used for decision-making and powering predictive models that are only as good as the information behind them. If corrupted data enters the system, it will increase the chances of defects in data sources, cause issues with data import middleware or infrastructure, and undermine product/process quality. This enables manufacturers to gain more relevant insights, make fast decisions based on live data, and earn trust from stakeholders.
Modern AI/ML platforms are capable of merging data from different sources into one consolidated data model and building meaningful labels and features for machine learning processes. Their scope spans such services as:
Leveraging such a powerful technology stack lays the foundation for building more agile smart factories. Data schemas optimized by AI and ML capabilities can support complex, multi-dimensional queries at scale, reduce total cost of ownership, and accelerate innovation. It enables manufacturing companies to get from raw data to business impact faster, connecting the physical and digital world.
For examples of our Analytics Platform in action, check out this case study: Analytics platform for smart manufacturing: A Jabil case study
Grid Dynamics is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. The company has 15+ years of experience in digital transformation and software innovation, most notably open-source cloud-native programs.
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