In the manufacturing industry, the goal may be zero waste, but the reality is won’t be easy. Waste estimated by the Eurostat Statistics Database, is a staggering 9.8%, over 256 million tons. Beyond social responsibility, finding a better way is also the path to better profits as the 1000 Ventures Business Guide on Lean Manufacturing puts into clear perspective: Lean manufacturing can reduce production costs and manufacturing cycle by 50%, cut inventory by 80% and increase customer service levels as they do.
To reach zero waste, companies must improve defect tracking and forecasting abilities to optimize supply chains and gain overall operational efficiency improvements. The rewards make it well worth finding a way, because by eliminating waste, companies really benefit: Huge savings, improved product quality and services, higher profits, happier customers happy, as well as earning good will and positive PR. For all that, it’s easier said than done.
Complexity is at the heart of the problem. As manufacturing companies become larger and more diverse, the volume of waste generated by different systems becomes both more significant and complex to manage. The data that is the key to better management, and a more stabilized process less likely to slow down, or be interrupted, until now has been largely untapped and underutilized. Until that changes, the volume of waste increases and along with it, the cost of manufacturing. We have smart, proven solutions.
Using predictive analysis such as statistical models and forecasting based on historical data lowers production costs, controls inventory spikes that can appear unexpectedly and detect where underperformance is likely. Datatics also uses predictive analytics to help determine supplier delivery reliability to avoid raw materials waste and excess inventory.
As sensors continue to become more prevalent, Datatics curates and analyzes sensor production line data to eliminate waste, increase infrastructure efficiency and predict downtime (predictive maintenance and condition monitoring). Additionally, machine learning transforms client data into profits by building precise models to identify opportunities to increase profits with lower costs. Data mining, and other descriptive analytics techniques, broadens understanding with deeper customer feedback, helping to further eliminate waste and improve the quality of products.