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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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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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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Supplier Performance Management Tools for Manufacturers

As part of a company-wide initiative to reduce working capital and inventory costs, your leadership has just asked you to analyze your supply base and determine the top 10 suppliers requiring improvement. You need an action plan and KPIs to measure performance improvements for those suppliers—and fast. Your supplier development team is struggling to come up with streamlined ways to track supplier performance, understand the root cause of performance issues, and actually drive improvements. Now it’s time to scramble as a team to develop and implement an improvement strategy. But where do you start? You need the right supplier performance management tools to achieve these objectives.

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Why is predictive analytics one of the top disruptors for supply chain?

Supply chain executives are always on the hunt for improvements to efficiency that lead to cost savings. Improving processes is often low-hanging fruit that can be done quickly with the right tools in-hand. In this post, I’d like to show you how our customers have saved millions through inventory optimization and cost reductions by leveraging new supply chain predictive analytics technology that leverages AI and cloud computing. I’ll also touch on Deloitte’s 2018 MHI Annual Industry Report, which discusses trends of next-generation supply chains, and the tools most leaders believe will disrupt the industry. Automation is right at the top of the list.

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It All Starts with Buyers: Supplier Performance and Inventory Optimization

Of the thousands of moving parts and hundreds of different roles across the supply chain, few are more directly impactful to the bottom line than that of the buyer. If buyers are successful, on-time delivery improves and inventory capital is reduced. If they fail, the company can experience enormous financial losses due to shortages, work stoppage, and days of “firefighting.” And like many critical business roles, buyers’ days are so full that work often bleeds into evenings and weekends.

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