With so much data being generated in real-time from smart sensors and IoT devices in manufacturing plants across the country, manufacturing companies are feeling the pressure to embrace big data analytics as part of their standard operating procedure. 

Thanks to the pandemic, the pace of technological advancement in manufacturing has become more of a quantum leap than a gradual trickle. In May 2020, Forbes predicted that due to COVID-19, manufacturing will experience five  years of innovation in the next 18 months. What’s more, the big data analytics manufacturing industry, which was valued at $904.65 million in 2020, is expected to reach $4.55 billion by 2026. 

While there is enormous potential for big data analytics in manufacturing, most companies are still playing catchup. A study by IBM found that manufacturers are lagging behind their cross-industry peers in their ability to create a competitive advantage from analytics. Only 53% of industrial manufacturers report that the use of big data and analytics is creating a competitive advantage for their organizations compared with 64% of cross-industry respondents. 

Despite this setback, manufacturers are working hard to develop data maturity and generate gains from big data. The study also found that three-quarters of industrial manufacturing companies have either started developing a big data strategy (45%) or are piloting and implementing big data projects (32%) on par with their cross-industry peers. 

In this article, we’ll look at the types of data generated in the manufacturing industry, why big data matters to manufacturers, and the most common use cases for big data in manufacturing. 

So where does all this big data come from?👀

Data is constantly generated from assets like sensors, pumps, motors, compressors, and conveyors. Data can also come from outside partners, vendors, and customers (eg: customer feedback, supply chain and logistics data). In fact, IoT sensors enable manufacturers to track data points from non-computerized machines as well. This is known as condition monitoring—the process of monitoring the condition of an asset in real-time to anticipate its maintenance needs. These sensors enable global manufacturers to collect real-time shop floor data (business intelligence) that allows them to continuously adapt their processes. 

Collectively, this data feeds into dashboards, scorecards, and databases. The data is used to generate reports as well as real-time business intelligence. 

The sheer quantity of data generated by factories today requires modern storage and processing tools in order to mine the data. In many cases, manufacturing data is stored in data lakes via the cloud and is processed on GPU clusters rather than traditional CPU processors. 

Sounds good. But what can I do with all this data? 🤔

Big data matters because companies are increasingly competing on minute differentiators like speed, consistency, and customer service rather than competing on a product. In critical industries, the insights generated by data analytics can spell the difference between life and death. Automakers, high-precision parts suppliers, medical device manufacturers, and F&B companies know that maintaining high-quality standards is essential for safety and compliance. 

Manufacturers must be ready to apply AI and machine learning to discover patterns and build models to make predictions and continuously improve their business processes. Data can be used to look for signals such as defects, downtime, or yield, with dashboards and applications that can monitor key KPIs in real-time. Manufacturers can also build models to make advanced predictions regarding production volume, equipment failure, and product quality.

Did you know?

A McKinsey study found that the appropriate use of data-driven techniques by manufacturers “typically reduces machine downtime by 30 to 50 percent an increases machine life by 20 to 40 percent.”

What are the top use cases for big data in manufacturing? 🤓

1. Predictive maintenance

Manufacturing profits depend largely on maximizing asset yield, so performance increases from reduced asset breakdowns can lead to massive productivity increases. Preventive maintenance—performing maintenance on an asset ahead of anticipated failure—isn’t always optimal because it hinges on doing maintenance earlier than needed, which reduces Overall Equipment Efficiency (OEE). Maintenance engineers are increasingly moving towards predictive maintenance, which is more accurate. Maintenance tasks are scheduled only when warranted, which keeps costs down. 

Predictive maintenance uses historical data to determine an optimal maintenance schedule. It also involves using sensing equipment to collect data in real-time. If the software detects an anomaly in your operations or a potential equipment defect, a work order is automatically triggered. Predictive maintenance keeps the maintenance frequency as low as possible because a work order is only triggered under specific conditions. 

Over time, the predictions grow more accurate based on the data generated by the real-time monitoring of an asset (condition monitoring), work order data, and benchmarking MRO inventory usage. Predictive maintenance sensors can perform vibration analysis, oil analysis, thermal imaging, and equipment observation. Examples include:

• Monitoring the temperature of computers and machinery to prevent overheating or using smart HVAC units to control building temperature and save energy. 
• Monitoring pressure in a water system to predict when a pipe could fail. 
• Monitoring oil particles in construction or fleet vehicles 

Tip: Predictive maintenance hinges on processing multiple datasets associated with different sensors and other maintenance detection devices. However, combining data from multiple sensors requires additional investments in data processing tools. For example, you might need to integrate data stored in a dedicated sensor database with data stored in your CMMS. 

2. Anomaly detection 

Anomaly detection means identifying data points that lie outside of the norm. There are three types of anomalies: 

• Point anomalies - a single datapoint that deviates from the rest of the material
• Contextual anomalies - abnormalities in a specific context (eg: time delay due to environmental influences) 
• Collective anomalies - a collection of data points is anomalous relative to the rest of the data. 

Manufacturers can use anomaly detention to determine where and when abnormal behavior has occurred. Isolating the anomalous data points helps with performing a root cause analysis to determine why a particular asset failed or why product quality did not pass muster.

3. Supply chain management 

Supply chain analytics uses data to improve decision-making across the entire supply chain. It expands the dataset for analysis beyond the internal data held on ERP (Enterprise Resource Planning) and SCM (supply chain management) systems to include point-of-sale (POS) data, inventory data, and production data. Aggregating data points from different junctures of the supply chain gives managers insights into every facet of real-time operations. 

Amazon, for example, has patented an “anticipatory shipping” process in which orders are packaged and pushed to the delivery network before customers place an order.

Other use cases:

• Optimizing delivery systems

Delivery routes must account for variables like changing fuel prices, road closures, and changing weather conditions. Sensors on delivery trucks, weather data, road maintenance data, fleet maintenance schedules can all be integrated into a system that looks at historical trends and makes recommendations accordingly

• Cold chain monitoring

Cold chain monitoring technology supports temperature-sensitive product logistics through data logging. In industries like F&B, pharmaceuticals, and chemical processing, even a slight change of a few degrees in product temperature can render the product unusable. Monitoring technology allows logistics professionals to track temperature situations in real-time and adjust heating and cooling remotely. 

4. Demand forecasting

Demand forecasting is the process of making predictions about future customer demand based on historical trends. Doing so helps businesses make informed decisions about pricing, business growth strategies, and market potential. Here are some other wins you can achieve with demand forecasting:

• Optimize inventory management and reduce holding costs
• Forecast upcoming cash flow for more accurate budgeting 
• Improve production lead times (the time between an order being placed and the manufacturer completing the order) 

Most businesses forecast demand by performing a time series analysis to identify seasonal fluctuations in demand and key sales trends. If you don’t have a lot of historical data on hand, you can use qualitative data—expert opinions, market research, competitor analyses— for demand forecasting until you gather enough data to make reasonable predictions. 

5. Product life cycle management (PLM)

PLM is the process of managing a product from inception to retirement. Businesses use PLM software to track and share data long the product value chain, from design to manufacturing and sales. 

Research from MIT shows that the introduction stage— when you first launch a new item on the market— is where 70-90% of product lifecycle costs accumulate. Sales are slow as you work to build product awareness. At the same time, your organization is spending a lot of money on marketing the product. 

This is where data insights prove most useful: finding opportunities to reduce waste and choosing marketing channels with the highest ROI. Market data shows you which kinds of products generate the biggest ROI and what kind of pricing structure is necessary to turn a profit. 

As you move through the product life cycle, you’ll start collecting more data about customer preferences, which you can use in your decision-making strategy. For example, you might discover that customers are willing to buy at a slightly higher price point, or that the ideal demographic for your product isn’t the audience you’ve targeted thus far. 

6. Quality assessment 

Quality assessment is the process of collecting and analyzing data to determine the degree to which the final product conforms to predetermined standards. This is key to ensuring customers receive quality products devoid of defects. If the quality is unsatisfactory, then you must perform a root cause analysis to determine why. 

Manufacturers can reduce variability using standard operating procedures (SOPs) and keeping equipment in good condition via an effective maintenance strategy. 

Data enables manufacturers to track the most important quality assurance KPIs, including:

• Specification compliance
• Low percentage rate of defects
• On-time shipping
• Shipping results in delivery without damage to the product or packaging
• Speed of response from customer service (response times, first-call resolution, etc.)