When all data is readily accessible for analysis, business and engineering end-users can improve current products and design the next generation, based on the insights they can extract themselves.
Manufacturers want to minimize the inventory that they keep on hand and prefer just-in-time delivery of raw materials. On the other hand, stock-outs can cause harmful production delays. Sensors, and RFID tags and IoT in manufacturing reduce the cost of capturing supply chain data, but this creates a large, ongoing flow of data. Hadoop can store this unstructured data at a relatively low cost. That means that manufacturers have more visibility into the history of their supply chains and they are able to see large patterns that might be invisible in only a few months of data. This intelligence can give manufacturers greater lead-time to adjust to supply chain disruptions. It also allows them the connected factory to reduce supply chain costs and improve margins on the finished product.
High-tech manufacturers use sensors to capture data at critical steps in the manufacturing process. This data is useful at the time of manufacture, to detect problems while they are occurring. However, some subtle problems—the “unknown unknowns”—may not be detected at time of manufacture. Nevertheless, those may lead to higher rates of malfunction after the product is purchased. When a product is returned with problems, the manufacturer can do forensic tests on the product and combine the forensic data with the original sensor data from when the product was manufactured. This big data in manufacturing adds added visibility, across a large number of products, helps the manufacturer improve the process and products to levels not possible in a data-scarce environment.
Today’s manufacturing workflows involve sophisticated machines coordinated across pre-defined, precise steps. One machine malfunction can stop the production line. Premature maintenance has a cost; there is an optimal schedule for maintenance and repairs: not too early, not too late. Machine learning algorithms can compare maintenance events and machine data for each piece of equipment to its history of malfunctions. These algorithms can derive optimal maintenance schedules, based on real-time information and historical data. This The use of manufacturing predictive analytics can help maximize equipment utilization, minimize P&E expense, and avoid surprise work stoppages.
Quality compliance costs 100 times more to correct a problem during maintenance than in pre-production. Reduce development timelines up to 50% with an enterprise data hub while responding to performance concerns in real-time and eliminating defects prior to manufacture.