Slim profit margins, difficulties in managing supplier relationships, and iffy availability of raw materials — these are just a few of the issues facing today’s manufacturers. Stiff competition from manufacturing operations in developing nations, along with a complex labor situation here in the US also play a part in making the manufacturer’s life more difficult than it probably should be.
Big data can’t lower American labor wages or fix the rising costs of commodities, but it can help reduce waste, improve manufacturing efficiency, and even predict trends that might affect operations in the future, such as economic conditions that are conducive to higher prices of raw materials or shortages of qualified labor. Here are several ways that big data is helping bolster the manufacturing sector.
1. Better Supply Chain Management
Manufacturers are using big data on numerous factors that affect the supply chain, including weather and natural occurrences like earthquakes and hurricanes, to predict possible delays in the supply chain in order to reduce risks of running into shortages of raw materials. This is an example of big data used for predictive analytics. This allows manufacturers to produce a map and use the associated data to predict probabilities of delays in the mining, refining, or delivery of raw materials.
Knowing where shortages or delays might occur ahead of time allows manufacturers to find backup suppliers and make other contingencies so that production isn’t disrupted. This is just one example of how manufacturers are using big data to manage their supply chains more effectively.
2. Improving Product Quality
Every manufacturer has to put their products through some sort of quality testing. In some cases, such as that of Intel processor chips, that can mean as many as 19,000 different quality tests for each of their chips! Using big data and its predictive analytical capabilities, manufacturers can significantly lower the number of tests they have to run their products through for QA.
For example, Intel used historical data on chip quality to focus only on certain specific tests that actually needed to be done. Cutting down QA testing saved Intel about $3 million for a single product line. When they expanded this predictive analytics into their testing across all product lines, it resulted in a savings of more like $30 million.
3. Improving Manufacturers’ Operations & Processes
Big data is useful in many aspects of production, particularly for identifying problems and helping discover and eliminate inefficiencies. For instance, industrial IoT devices can detect patterns that indicate a problem with one of the machines, so that maintenance can fix the machine before it results in quality issues or shutting down the production line.
Another common use for big data in manufacturing is to identify parts of the manufacturing process that are inefficient or redundant. One biopharmaceutical manufacturer tracked more than 200 variables in their process, and identified a process that was 50 to 100 percent more efficient. The changes made resulted in a 50 percent savings to the company, slashing operational costs by $5 to $10 million each year.
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