As the supply chain leader of a large multisite organization, how do you organize and drive an effective, sustainable process to attack the biggest inventory management problems on a daily basis across complex teams with multiple ERP systems? Have you ever been in a meeting where inventory optimization was brought up as a top priority by leadership without clear direction on how exactly to attack the issue?
The manufacturing and distribution environments are becoming more complex every day. Where 20 years ago it was enough for an aerospace manufacturer to build aircraft that flew efficiently, the same factory is now being asked to accommodate different seat arrangements; a wide variety of cabin interior options; and myriad different colors, media devices, and other fine details. “The number of options has proliferated and added a lot of complexity to the supply chain,” says Richard Lebovitz, LeanDNA’s CEO, “across nearly all manufacturing verticals.”
In a recent Supply Chain Management Review study exploring the information and analytics needs of supply chain professionals, it was immediately clear that a new horizon is in view for supply chain professionals around the world. Leaders see the need for better tools—tools that do more than just provide data about where they’ve been.
In this special edition white paper, the Supply Chain Management Review shows how LeanDNA drives supply chain efficiency by moving manufacturing teams from slow, manual spreadsheet analytics processes into a high-speed, cloud-based platform that is purpose-built to tie together shortage reduction, supplier management, lean principles, and more.
Artificial intelligence, machine learning, Internet of Things, algorithms, cloud-based software—these buzzwords are commonly heard in the industry, but what do these technologies really mean for supply chains? How will advances in supply chain software affect the future for leaders?
Hello from Chicago! I’m here attending the APICS 2018 supply chain conference, and I’m spending time around some brilliant minds in manufacturing and supply chain. I’d like to share a little of what I’ve encountered here through great speaker sessions and conversations with industry leaders.
In this special edition white paper, Zodiac Aerospace reveals how they used LeanDNA to get rid of slow, manual spreadsheet processes and save millions of dollars through better-informed decisions and automated prescriptive analytics.
One of the balancing acts for manufacturers and supply chain leaders is having the right inventory levels. But it’s often difficult to identify ways to reduce inventory without sacrificing on-time delivery performance. LeanDNA is featured in this article in Modern Materials Handling, which highlights how our Factory Analytics solution:
- Provides advanced, collaborative analytics focused on inventory, rather than traditional MRP or factory planning tools
- Performs analysis and sizing parameters automatically
- Recommends action opportunities for increased profitability
- Standardizes best practices across the organization
- Improves on-time delivery performance into the 90th percentile or above
It has taken decades for supply chains to adopt analytics at a large scale. For many years, supply chain leaders relied on instinct and experience to make critical inventory decisions. But over the years, as capabilities have grown more powerful and impactful, leaders have seen how much business value can be delivered by implementing advanced analytics.
This infographic traces the evolution of supply chain analytics—from the 1950s when the first large-scale business analytics program was initiated in the U.S., through the present day and into the future as business leaders seek to bring their data and people together in a collaborative analytics platform. Read the detailed blog here.
I just finished my first day at the Supply Chain Insights Global Summit in Philadelphia and spent the day in the analytics track with manufacturing and technology leaders from across the country. The message from day one was clear: there’s a lot of talk about the technology behind analytics, and not enough focus on the human side that will ultimately yield results.