Three ways AI is improving manufacturing operations
Industry 4.0 has become more than a buzzword in the world of manufacturing; it’s the new reality. A reality that has been accelerated by the Covid-19 pandemic. During the first three months of the pandemic taking hold, digital advanced the equivalent of ten years, as both businesses and consumers adjusted to an online world.
The key tenets of a future-ready business – agility, resilience and innovation – can all be helped by investment in digital solutions. With the pressure on to streamline operations, reduce costs and maximise revenue, digital transformation has become an imperative.
“The winners in digital transformation are utilising disruptive technology to tackle business challenges, driving improvement through practical application. Digitising operational processes is critical for manufacturers to cope with the demands and challenges of the crisis, and is considered a natural step forward in the evolution of manufacturing,” said Jonathan Whiteside, Principal Technology Consultant at Dept.
“Luckily, innovation has now reached a point where emerging technologies such as AI and the Internet of Things (IoT), are widely available, helping to accelerate the transformation of traditional manufacturing businesses. There are three key areas where manufacturers can implement AI to become more resilient and improve the bottom line: forecasting, conditional maintenance and communication.”
Machine Learning has the ability to automate analysis and detect patterns of data at a rate that would be impossible for humans to achieve. It can take data segmentation beyond simple keyword clusters and opens up the opportunity to glean information from new data sources. When applied to forecasting models, the results are impressive. According toMckinsey Digital, AI-powered forecasting can reduce errors by 30 to 50% in supply chain networks. The improved accuracy leads up to a 65% reduction in lost sales due to inventory out-of-stock situations and warehousing costs decrease around 10 to 40%. Theestimated impact of AI within the supply chain is between $1.2T and $2T inmanufacturing and supply chain planning
With those stats in mind, it’s no wonder that machine learning is being embraced by demand planners across the industry. The speed and accuracy of machine learning forecasts provide multiple benefits, with the improvement to customer experience as a result of improved availability being just the tip of the iceberg. When there is confidence in the forecast, buffer stock levels can be lowered, thereby reducing working capital and freeing up valuable space. Better forecasts can deliver savings across the value chain from improved transport planning to optimised labour schedules.
Improving demand forecast accuracy is demonstrating solid results across all industries with Consumer Packaged Goods manufacturers leading the pack. French multinational Danone Group is improving planning coordination across marketing, sales, account management and supply chain with machine learning. Through AI-driven demand modelling, Danone is better placed to meet demand from product promotions and achieve its target service levels for channel or store-level inventories. The system led to a 20% reduction in forecast error, a 30% reduction in lost sales, a 30% reduction in product obsolescence, and a 50% reduction in demand planners’ workload.
Machine learning and AI are not only influencing howcompanies manufacture, but also whatthey manufacture. With the power to identify changing consumer tastes, through analysing data and spotting trends, packaged-food companies can react by switching up ingredients to create limited-time specials that will resonate with consumers. There are also major opportunities to identify possible areas for product expansion while speeding up the process for a more timely launch.
