Re-shaping manufacturing with analytics

With the help of the right analytics, manufacturers can closely monitor supply chains in exact detail and aim at modifying every section of the production process. This capability lets manufacturers identify production bottlenecks and reveal underperforming components throughout the entire process. Therefore, manufacturers can spot their dependencies and generate an alternative plan to strengthen production processes and eliminate potential pitfalls

- Ali Hyder

Efficiency, productivity, and profitability have been the quintessential mantras for manufacturing industries. We have collectively worked for centuries, beginning with the first Industrial Revolution, to optimise these three critical factors. With the onset of Industrial Revolution 4.0, we can fairly say that we are optimising efficiency, performance, productivity, and, ultimately, profitability like never before. We have incorporated some of the most in-demand technologies, like Artificial Intelligence (AI), Machine Learning (ML), Data Science, Industrial Internet of Things (IIoT), Cloud Computing, and more, into our manufacturing processes & workflows for precision outcomes and reduced overhead expenses. We even have real-world examples. A France-based food producing company, Danone Group, shared that with the implementation of Machine Learning, they were able to reduce forecasting errors by 40%, reduce lost sales by 30%, and decrease the workload of demand planners by around 50%. Not just this, even our very own 184-year-old FMCG giant, Procter & Gamble Co, is taking the smart manufacturing route by implementing advanced algorithms, predictive analytics, and IoT in its paper towel manufacturing division. While this is happening across manufacturing companies at scale, there is still a haze around the benefits of AI in digital manufacturing and how it has been key in driving the transformation from conventional manufacturing to digital.

Digitalisation in manufacturing