There’s no denying that data analytics has revolutionized many industries, and the manufacturing sector is no exception. Thanks to these technologies, companies are given incredible opportunities to analyze information from every area of their operations, from distribution and procurement to production. While the variety and volume of data come with their fair share of challenges, the gains in terms of profitability and productivity are potentially massive.
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Businesses that can integrate manufacturing software solutions into their workflows and processes can generate considerable competitive advantages in areas including but not necessarily limited to production planning and inventory management. Conversely, those who choose to delay their adoption are likely to fall behind. With that in mind, here are a few ways data analytics can help manufacturers operate and plan smoothly.
Discrete-event simulation
Discrete-event simulation, or DES, is a valuable tool to represent the growing complexity of modern manufacturing workflows. Its models are characterized by processes as event sequences that occur at discrete points in time. It recreates a manufacturing facility in a digital world. These simulations are as versatile as they are flexible, with different applications like scenario analysis, bottleneck identification, resource allocation, capacity planning, and scheduling. One example of the use of DES is in accurately characterizing production uncertainties. By handling these uncertainties, you can generate optimal schedules for production.
Quality analytics
Quality analytics refers to data analytics’ targeted application of enhancing a product’s attributes. The supply and delivery of quality offerings is the foundation on which a business’s long-term success is based. Through quality analytics, it’s possible to speed up quality checks. More importantly, any potential quality problems can be determined to warn the operators well in advance. Moreover, it can make recommendations for resolving and troubleshooting issues more efficiently.
Productivity analytics
Productivity analytics is the utilization of big data to help manufacturing facilities operate smoothly. It includes the avoidance of factors like unplanned equipment downtimes that can significantly impact the enterprise’s profitability. Other factors that may be detrimental to the business are the inefficient utilization of the machines or poor allocation of resources for labor. With the implementation of analytics tools like demand forecasting, simulation, optimization, and inventory management, it’s possible to run a facility optimally.
Throughput optimization
Manufacturing throughput is the quantification of intermediate or finished goods that facilities can produce within a specific time frame. Factories that appear to be running smoothly and have minimal downtimes might not still be operating as optimally as they should. With the increase in data, manufacturers will be able to isolate bottlenecks and inefficiencies that may be limiting or obstructing throughput. You can identify the appropriate adjustments and tweaks in your production parameters through data analytics for better operations.
Conclusion
With all of the advantages that come with its use, it isn’t surprising that a growing number of manufacturers are starting to adopt data analytics solutions to boost their efficiency and productivity levels. And if you haven’t done the same, it’s highly recommended that you do too, or you’ll miss out on opportunities that your competition will surely take advantage of.