Keeping Up With Supply Chain Complexity: Tools You Need Today

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.”

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Supply Chain Analytics Tools: 5 Obstacles to Adoption (and How to Overcome Them)

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. One of the most compelling research findings addresses the obstacles leaders face when implementing new supply chain analytics tools into their systems. Here’s a look at the breakdown of responses.

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The Road From Manual Processes to High-Speed Efficiency

In a new 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.

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LeanDNA In the News: Cutting Inventory For the Factory With 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

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Infographic: The Supply Chain Analytics of Yesterday, Today, & Tomorrow

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.

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The Name of the Game is Adoption: Observations from the Supply Chain Insights Global Summit

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.

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People, Not Data, Will Transform Your Supply Chain

Cut Through the Data Craze With Collaborative Analytics

When business analytics first became available to US companies in the 1950s, capabilities were limited and technology was often prohibitively expensive. It was decades before most companies made analytics a standard practice in their organizations. Business leaders continued to rely on their experience and intuition to make critical decisions. But as analytics technology evolved, insights became more valuable and leaders took notice. By the 1980s, most large companies were employing some form of analytics for decision support.

Fast forward 30 years and companies like General Electric are making multi-billion dollar investments in their analytics infrastructures. It’s more than just Big Data; leaders know that their data is only as valuable as the insights it provides. As analytics have evolved and improved, decision-makers are demanding more from their analytics than ever.

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