In an article recently published by Forbes, software expert Louis Columbus discusses 10 ways big data is having an impact on the manufacturing sector. Manufacturers are already using advances analytics to increase yields, reduce costs, monitor production and gain operation insights.
However, big data and advanced analytics also have the potential to streamline manufacturing value chains by finding the core determinants of process performance, and then taking action to continually improve them.
How big data is revolutionizing manufacturing
1. Making ‘smart factories’ a reality
Industry 4.0 or Industrie 4.0 is a German government initiative that promotes automation in the manufacturing industry - its ultimate aim being to develop so-called smart factories. Big data is already being used in this regard to optimize production schedules based on supply and demand, machine availability and cost constraints. However, the ultimate smart factory is one that is multifunctional, diverse and lean – big data and advanced analytics will become critical to its success.
2. Better forecasts of product demand and production
Big data and sophisticated analytics can give manufacturers information in real-time about product success and demand. This information can help manufacturers save time and money, while reducing waste.
3. Understanding plant performance across multiple metrics
Performance is not measured on one metric, but multiple indicators and big data, for the first time allows manufacturers to pool all this data in one place and gain genuine overall insights into performance, efficiency and quality control.
4. Providing faster customer service and support
A happy customer is a loyal customer and that translates directly into a healthy bottom line. Big data analytics can help manufacturers respond quickly to customer complaints or queries.
5. Integrating advanced analytics across the Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) framework to fuel continuous improvement
Big data will give manufacturers greater insight into how each phase of a DMAIC-driven improvement program is working. The use of big data in this way shows great potential to make production workflows more customer-driven than ever before.
6. Greater visibility into supplier quality levels, and greater accuracy in predicting supplier performance over time
Using big data and advanced analytics, manufacturers are able to view product quality and delivery accuracy in real-time. Armed with this information, they can give preference to top suppliers and thus improve quality overtime.
7. Equipment monitoring and preventative maintenance
More and more frequently, manufacturing machines are being fitted with sensors to monitor their activity and performance. These sensors report back activity and can send alerts for preventative maintenance.
8. Measuring compliance and traceability
Using sensors on all machinery in a production center provides operations managers with immediate visibility. Advanced analytics can provide insight into quality, performance and training variances by each machine and its operators. This is invaluable in streamlining workflows in a production center, and is becoming increasingly commonplace.
9. Making quality a top priority
Manufacturers, for a long time, have viewed quality control as a standalone concern, however big data and predictive analytics have helped make it a top-level priority. The use of software and big data means that quality can be integrated throughout the entire supply chain, rather than at the end of the production line. This level of knowledge will increase quality while reducing waste.
10. Financial performance and visibility
Big data and advanced analytics are delivering the missing link that can unify daily production activity to the financial performance of a manufacturer. Being able to know to the machine level if the factory floor is running efficiently, production planners and senior management know how best to scale operations. By unifying daily production to financial metrics, manufacturers have a greater chance of profitably scaling their operations.