Only 45% of manufacturers can access and analyze data from across their value chain, according to Capgemini’s Smart Factories at Scale report. While manufacturing plants are inundated with a myriad of data sources (sensors, machine logs, equipment usage historical data, and building or warehouse management systems, for example), there’s a disparity between having that data and having the capabilities to act upon the data to make a more sophisticated manufacturing plant.
Industry 4.0, which refers to the fourth industrial revolution, involves advanced manufacturing technologies that fuel operational efficiencies and enterprise-wide growth by combining AI in unison with the Internet of Things (IoT). Although AI can in fact drive tangible impact across a company’s entire value chain (i.e., finance, sales and marketing, customer service), manufacturing-specific use cases are heavily distributed throughout R&D, production, and logistics.
High-Value Use Cases
In R&D-related manufacturing processes, data science and analytics can help save time and improve processes. For example, with development and life cycle management, R&D teams can use data captured by AI-enabled technology in order to continuously improve the product development process with a feedback loop. They can identify key factors influencing performance, predict failures more accurately, and expedite time to value by taking historical information into account.
With logistics, manufacturing teams can minimize stock waste and production disruptions. In the industry, it’s tremendously difficult to plan how many spare parts will be needed, making it challenging for manufacturers to accurately schedule repairs based on the inventory level of specific parts.
Dataiku can help organizations navigate both of these problems by helping teams consolidate and manipulate a wide variety of data sources in one, collaborative environment. The platform allows teams to combine various techniques (time series forecasting or according to business rules or stock specificities, for example) and offers a visual or code interface for building machine learning models, including those built on geospatial data.
Next, production represents a significant portion of manufacturing processes. Here are some key production-specific use cases that will help organizations save both time and money:
1. Predictive Maintenance
In its simplest form, predictive maintenance combines data from a myriad of sources (IoT — sensors, machine logs, and so on) and uses machine learning techniques to anticipate equipment failure before it happens, minimizing both repair costs and time spent unnecessarily doing maintenance. While IoT data plays an important role, other data sources can be included such as external data from APIs (like weather), geographical data, manual data from human inspection, and much more.
2. Improving Quality and Reducing False Error Rate
Understanding the impact of production parameters on quality allows the optimization of settings and the real-time monitoring of production conformity. Thanks to process and quality information, machine learning makes it possible for manufacturing companies to understand the key factors that influence their final product in order to optimize the recipe and/or process for production.
Once that is done, teams can put a quality assurance (QA) strategy and team in place, which can come together to reduce false errors. Here, Dataiku can help teams preempt errors and update models to prevent false error rate deterioration.
3. Cross-Functional Process Optimization
Manufacturers make upgrades to the machines they use, but even without these improvements things like minor inter-machine variability, site-to-site variability (i.e., temperature, humidity), physical limitations of the layout, and the number of people that need to interact with the machines come into play.
Traditional optimization methods can only account for the factors that are tested and are only run when the process seems off. Ongoing testing ensures that the process stays within acceptable limits. With cross-functional process optimization, the plant can use a digital twin, or a mathematical model of the process. The model can compare to an ideal process, which the AI can use to detect changes early on, adjust accordingly, and course-correct once the adjustments have been made.
During those adjustments, it is paramount that the original developers of the process are in the loop, a step that can be tricky when the decisions may be made months or years before with people who are no longer involved or have visibility to the current process.
The advancement of smart sensors combined with the rapid evolution of data science and machine learning techniques and technologies generate a massive amount of potential for manufacturers to embrace. Whether they are seeking insights into their production processes, reduced maintenance costs, easy visualization of data for better decision making, real-time analysis to help improve manufacturing processes, or something else, Dataiku can help. It provides access to data in one single environment for collaboration across IT, data scientists, analysts, process engineers, machine operators, plant managers, and more.