Big Data has become a buzzword in recent years as businesses have discovered new ways to collect valuable information about their customers and processes.
The advent of mobile tech coupled with the Internet of Things (IoT) has given companies new ways to collect data, while machine learning has given analysts the tools needed to discern patterns and insights from it.
Popular stories about Big Data have focused on the innovations it has brought to retailers as they learn to predict consumer behavior to a degree that wasn’t possible in the past.
From customers walking through store layouts to seasonal trends in product sales, Big Data has made the retail sector more efficient.
The efficiencies gained from Big Data analytics aren’t limited to customer behavior or market insights, though.
Manufacturers can apply Big Data technologies to their own operations.
Put simply, Big Data is a set of technologies that collect and analyze data sets far larger than have been possible before.
Mobile devices, network-connected appliances and remote controllers provide new sources of real-time data that’s valuable to business analysts.
These streams of data accumulate into enormous data stores that machine learning applications can process.
The resulting insights give analysts and decision-makers a better understanding of their businesses.
Decisions that were once educated guesses are made more precise with objective metrics.
How can Big Data help manufacturers improve the profitability of their operations?
In this article, we cover six important ways manufacturers can apply Big Data.
Big Data disrupts traditional supply chain management techniques by allowing more control over delivery schedules.
Shipping companies are able, for example, to factor weather and road conditions into routing software to shorten delivery times.
Large enterprises with multiple locations and complex inventory systems can apply artificial intelligence coupled with Big Data to discern seasonal patterns and discover intermittent problems that cause costly stock-outs.
Supplier quality is also another way that Big Data is making supply chains more efficient for large manufacturing enterprises.
Centralizing vendor records and delivery patterns makes it possible for buyers at different locations to evaluate vendors holistically. They can also formulate better risk management plans with more precise information about suppliers.
The Internet of Things provides manufacturers with new ways to automate their production equipment and respond faster when sensors indicate malfunctions.
It also taps a wealth of data that can be collected and analyzed by decision-makers in real-time.
When RFID is implemented to track the location of materials and finished products, it’s possible to have a real-time view of a production operation from a single desktop computer.
This helps manufacturers and companies stay lean and quick to adapt to changes.
Lean manufacturing is nothing new, going back to the Toyota Production System (TPS), which is its precursor, and that enabled the auto-manufacturer to persevere by adapting to changes in the industry quickly.
The TPS is a completely integrated socio-technical system that brings together manufacturing, logistics, but also all interactions with suppliers and customers.
The result of implementing Big Data to such operations helps decision-makers from the C-suite down to floor supervisors make informed decisions instead of relying on guesswork.
Miscommunication and inaccurate production planning are reduced, which eliminates waste and missed deliveries.
The Build-to-Order (BTO) movement in manufacturing has made strides in reducing the cost of inventory and improved customer satisfaction with configurable products.
It’s difficult to manage a BTO system without Big Data giving decision-makers the ability to optimize production.
Otherwise, lead times can lengthen when production materials aren’t available at the right time and place.
Big Data helps manufacturers implement BTO production by analyzing customer buying patterns and organizing inventory more efficiently across multiple locations.
With the multiplication of possible products with different configurations, analysts can leverage Big Data to monitor all possibilities and organize inventory appropriately.
Quality assurance is a mature discipline in manufacturing for a good reason: Defects are the primary complaint that they receive from customers.
Big Data has ushered in a new era in QA by making more data available for analysts to diagnose process problems.
With the addition of networked sensors and greater automation, there’s less guesswork about what happens on the production line.
Big Data adds predictive value to quality assurance as well.
Defective parts can be rejected sooner in a complex production process by automated systems and sensors, which minimizes the cost of a defect.
Problematic products can be analyzed with greater accuracy as well, allowing QA analysts to design better testing processes.
With greater computerization and connectivity of manufacturing equipment, it’s becoming possible to capture and store any level of operating data desired to analyze a machine’s performance.
The implications for equipment maintenance are that tasks can be issued to engineers and maintenance personnel by automated systems.
When a machine’s oil reaches an abnormal temperature, for example, a preventative maintenance order can be issued.
The cost savings go beyond that of repairs.
Critical equipment breakdowns are costly when they halt a production line. Early detection and prediction of breakdowns is an important way to minimize downtime.
Finally, Big Data does for large manufacturing enterprises what it has done for other large businesses in other sectors of the economy: Provide greater awareness of business patterns across multiple locations.
Business analysts in large organizations have enormous amounts of granular data available that’s collected from multiple locations.
When Big Data analytics is applied to that data, they can arrive at real-time assessments of the entire enterprise’s financial and operational health.
When Big Data is applied to historical data, previously unpredictable patterns are detected that allows better overall operating efficiency.
Logistics problems can be discovered at the corporate level that wouldn’t be apparent to analysts working at individual production facilities.
Comparing the performance of individual facilities also becomes more accurate with shorter reporting lead times.
The C-suite can rely on Big Data to detect problems at individual locations and respond to be faster.
Big Data innovations are ushering in a new era of business management.
By accurately analyzing the amount of data that’s available today, executives, production managers, process engineers, and buyers can make better decisions.
The result is leaner production operations and a better understanding of market patterns.
Manufacturers who implement Big Data into their operations will become more profitable and competitive in today’s market.
Joe Peters is a Baltimore-based freelance writer and an ultimate techie. When he is not working his magic as a marketing consultant, this incurable tech junkie devours the news on the latest gadgets and binge-watches his favorite TV shows. Follow him on @bmorepeters