Data Analytics for Manufacturing

For years, manufacturers have faced intense pressure to increase efficiencies and cut costs. Consumer demand for lower prices, the rising cost of raw materials, and increasingly complex supply chains have prompted manufacturers to seek new ways to meet these mandates. The COVID-19 pandemic has intensified the urgency to become more efficient and agile. Data analytics for manufacturing can help companies realize significant benefits, including more accurate demand forecasting, improved quality control, more efficient inventory management, and true end-to-end visibility. 

How Data Analytics Is Transforming Manufacturing

After decades of  rapid technological advances (often collectively called Industry 4.0), today’s manufacturers can collect mountains of data from each link in the supply chain, from sourcing raw materials to delivery to the end customer. IoT sensors and edge devices mounted on equipment are capable of feeding real-time data into data platforms for analysis. Forklifts operating in warehouses outfitted with sensors are capable of collecting data that can be used to increase operational efficiency. Data collected from online consumer reviews can be analyzed to identify quality control issues and inform future product design changes. The dramatic increase in the quantity and types of data available today represents an enormous opportunity for manufacturers.

Manufacturing Data Analytics Use Cases

Modern manufacturing involves a constellation of complex operations from raw material sourcing to accurately matching production capabilities with demand. Without the help of modern data analysis, it’s difficult to correctly identify where the opportunities lie. Here are just a few examples of how data analytics is helping today’s manufacturers cut costs, improve quality, and boost profits. 

Predictive maintenance

One of the most exciting applications of data analytics for manufacturing is the ability to predict when a piece of critical equipment is likely to need to be taken out of service for maintenance or replacement. Tight production schedules don’t include allowance for unplanned production outages. If a critical piece of equipment fails, it can take a manufacturing line out for days or longer. 

Predictive analytics can be used to analyze vast amounts of historical data to more accurately predict when a piece of equipment needs maintenance or is likely to fail and how. This knowledge makes it possible to have the replacement parts on hand before they’re needed. 

Predictive analytics can also be used to identify the root cause of equipment failure. Data gathered from onboard sensors can be analyzed to identify potential stress factors that cause manufacturing equipment to malfunction. This information can be used to rework the processes needed to extend the life of key parts.

Maximizing yield/throughput

Big data analysis makes it possible to fully utilize each manufacturing asset. Yield-energy-throughput analytics sifts through large amounts of data to uncover practical insights on how to tweak production processes to maximize the efficiency and throughput of each piece of equipment. This type of analysis results in a more energy-efficient manufacturing process capable of increased production.

Supply chain optimization

Smoothing out bumps (or earthquakes) in the supply chain safeguards production schedules, making it possible to meet tight product delivery timelines. Modern data analytics practices are capable of examining complex data sets to proactively identify risks such as adverse weather events, potential logistics bottlenecks, or the impending financial insolvency of a key raw material producer. Being able to anticipate potential supply chain issues before they occur allows manufacturers to put contingencies in place to lessen their impact on production.

More accurate demand forecasts

Knowing which products will be needed and when provides a competitive advantage and ensures that manufacturers are able to accurately align their production capacity with demand. Customer purchasing data, weather trends, availability of raw materials, and many other factors can be analyzed to more accurately predict what consumers are most likely to need or want in the future. 

Warehouse management

Data analytics for manufacturing can be used to streamline warehouse operations. Data gleaned from demand forecasting can be leveraged to better plan for increased staffing needs during busy times. It can also be used to increase the efficiency of warehouse operations and product fulfillment. With this information, companies can identify the manual processes that would benefit from being automated and uncover consistent patterns in order fulfillment mistakes that need to be addressed.

Essentials for Effectively Implementing Data Analytics for Manufacturing 

Realizing meaningful gains from manufacturing analytics requires more than just investing in a modern cloud data warehouse for manufacturing companies. Of course, manufacturers need a data platform capable of ingesting and analyzing complex manufacturing data inputs in real time, but they also must ensure domain experts can participate since relevant insights depend on their domain knowledge. Here are three essentials to focus on when implementing a data analytics initiative.

Data management

Conducting advanced analytics on data gathered from manufacturing operations is a complex process. For this reason, companies need a modern data management system that creates a single source of truth and standardizes data availability. Companies must consider how they will manage the data lifecyle, including best practices for data transformation and the marrying of structured and semi-structured data. 

Getting domain experts integrally involved

For any data analytics program to be meaningful, the expertise of domain-level experts must be engaged. These professionals have an in-depth understanding of key areas in the manufacturing process including equipment maintenance,  supply chain management, and process technology and the data associated with these areas. Domain experts provide data scientists with the domain-specific information they need to ensure that the data analysis yields relevant and accurate insights.

Process and culture change

Adopting a data-driven operational focus represents a significant cultural shift for most companies. Implementing a successful analytics program requires buy-in from everyone from the C-suite to the factory floor. A new approach won’t be successful without thoroughly educating all stakeholders on the role each person plays in implementation, changes that may be required, and how it will benefit everyone. 

Snowflake Empowers Data Analytics for Manufacturing

Using data analysis to streamline operations isn’t a new concept for most manufacturers. But many companies are relying on an on-premises infrastructure that isn’t up to the task of processing, storing, and analyzing the variety and volume of data generated by today’s manufacturing operations. 

The Snowflake Data Cloud offers near-unlimited storage and compute power, allowing manufacturers to aggregate large amounts of data in structured, unstructured, and semi-structured formats. This powerful platform allows teams to quickly access and analyze data without worrying about integration and interoperability issues. Additionally, manufacturers can take advantage of the Snowflake Marketplace for access to live third-party data to supplement proprietary data. 

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.