Mingo Logo Formerly SensrTrx

How is Data Science Used in Manufacturing?

Data science is a multidisciplinary field responsible for the management and visualizing of all types of data, big and small. It is the study of statistics and probability, which when fed enough data into the right data model can provide powerful insights for manufacturers. Fueled primarily by an increase in IoT devices sending productivity and process data to the cloud, data science is used in manufacturing for a variety of reasons.

Here are 8 of the most popular types of data science used in manufacturing and how they affect productivity, minimize risk, and increase profit. Data-driven manufacturers will be leveraging data science for:

  • Performance, quality assurance, and defect tracking
  • Predictive and conditional maintenance
  • Demand and throughput forecasting
  • Supply chain and supplier relations
  • Global market pricing
  • Automation and the design of new facilities
  • New processes and materials for product development and production techniques
  • Sustainability and greater energy efficiency

What are the Top Data Science Applications in Manufacturing?

Real-time Performance Data and Quality

The data collected from machines and operators can provide a set of Key Performance Indicators (KPIs) such as OEE, or Overall Equipment Effectiveness and enable a data-driven root-cause analysis of downtime and scrap. This lays the foundation for a responsive, proactive approach to machine optimization and maintenance and the ability to respond quickly to issues that impact productivity and cause costly downtime.

Data scientists can then provide a predictive model for machine performance and downtime. These models are used to anticipate the impact of changes on the factory floor, including an increase or decrease in yield gains, scrap reduction and quality, and of course, machine downtime. By graphing Pareto charts on downtime, for example, a manufacturer can focus on the top issues that affect performance. 20% of causes usually account for 80% of downtime, so manufacturers use data science to identify and prioritize the issues that most impact productivity.

By tracking metrics like first-pass yield and scrap counts, manufacturers can discover new ways to manage costs and increase quality. Whether through testing of materials or new processes or merely fine-tuning current processes to avoid costly scrap and rework.

Fault Prediction and Preventive Maintenance

In modern manufacturing, production can often depend on a few critical machines or cells. The same data that provides a manufacturer real-time monitoring can be analyzed through data science to improve asset management and prevent machine failure. Understanding why a machine fails is the first step in predicting when a machine may fail.

Big data manufacturing, means process data like temperature and vibration might indicate a problem before it causes failure. Tracking this data against the optimum performance settings indicated by OEMs for particular machines means that condition monitoring might indicate the need for service and act as a check engine light for an engineer, signaling preventative maintenance that could avert a critical failure later on. Data science provides the statistical model used to anticipate failure and thus proactively reducing downtime.

Demand Forecasting and Inventory Management

Filling and delivering a customer order on time is a priority for all manufacturers. Many manufacturers depend on data science to create forecasts of demand and delivery. With the advent of just-in-time (JIT) manufacturing, orders are based on tight timelines and tighter supply chains. Many manufacturers are using data science in order to hedge their inventories, optimize their supply chain, and ensure they can deliver on these orders in a lean manner, avoiding over-ordering inventory and over-producing goods.

Supply Chain Optimization

Managing supply chain risk can be a complicated proposition. Inputs range from fuel and shipping costs, tariffs, market scarcity, pricing differences, local weather, etc., that data science is leveraged in order to manage all of the various data points. By using a data science model that anticipates market changes and minimizes risk, high costs can be replaced with savings.

Supply chains are often called value chains and for good reason. Parts and material manufacturers all form a clockwork system that delivers goods to assembly plants. These relationships depend on forecasting to ensure that every part required is delivered, stocked, and ready for assembly. Late deliveries or scarcity of stock are costly mistakes for industries like electronics, machines, or auto assembly, so increasingly data scientists are being tasked with eliminating this risk in order to provide on-the-money estimates for delivery.

Price Optimization

Prices rise and fall, and for manufacturers using data science to determine the best price, price determines profit and profit is defined by what the market will bear. To do this well, they must take into account a global marketplace of goods and services. The same information that informs a data-driven supply chain management can also be used by savvy manufacturers to anticipate industry pricing changes to optimize profit.

Robotics, Automation and Smart Factory Design

The big push for automation means big investment. Engineers and systems integrators depend on data science to chart the path and make sure this investment will provide significant productivity gains. Data scientists crunch numbers to determine with engineers the best opportunities for cost savings on the line. For manufacturers investing millions in robotics and other automation, ensuring an ROI means they confidently implement industry 4.0 technology.

Digital twinning, championed by global manufacturers like Siemens offers a new method for the design and optimization of state-of-the-art production facilities. It requires complex data sets and advanced data science. The method uses real-world data to simulate how production might be affected by new machinery and production designs.

Product Development and Material Design

Data science can be used to validate design and material decisions. For many contract manufacturers, product development is part of the service they provide, so having data to validate their choices to their customers is crucial. Especially for tool and die design and manufacturing to order companies, data science is used to determine the best way to produce a product or material to the customer’s specifications.

Manufacturers designing a new product to sell also leverage data science, both to understand consumers and broader market trends and to make sure the product delivered meets standards and fulfills customer needs.

Enterprise and Plant-Wide Sustainability

Many manufacturers are setting ambitious goals to reduce costs and save energy, including the complex calculations required for reducing overall carbon emissions. Global food manufacturers like Pepsi Co. have made sustainability and efficiency a key part of their long term strategy. By managing their supply chain and estimating their own energy usage, they use data science to meet and exceed these goals.

What is the Future of Data Science in Manufacturing?

The future of data science in manufacturing is bright with thousands of data science jobs currently filled and thousands more on the horizon. As the number of smart factories grows, so too will the demand for data science to make sense of it all.

Bryan Sapot
Bryan Sapot
Bryan Sapot is a lifelong entrepreneur, speaker, CEO, and founder of Mingo. With more than 24 years of experience in manufacturing technology, Bryan is known for his deep manufacturing industry insights. Throughout his career, he’s built products and started companies that leveraged technology to solve problems to make the lives of manufacturers easier. Follow Bryan on LinkedIn here.