The rise of complexity in digital supply networks

By Stephen Laaper

The move to digital supply networks can be daunting, especially when organizations consider how exactly to implement these solutions into their existing supply chain. With so much information and hype about digital, it can be hard for organizations to know what works for them and what might be a hidden roadblock. However, when the digital transformation is implemented correctly, it can also seamlessly enable an organization’s digital operations.

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Change happens: Adopting a digital supply network

By Brenna Sniderman

Sometimes I avoid change. This is only natural; a lot of people do, at least some of the time. I like things that are comfortable and familiar, things that I understand and know my way around. I may steer clear of change because I worry that new can be risky, and that I might result in being worse off in the end–-a tendency known as loss aversion. Beyond loss aversion, however, change can be particularly challenging because it tends to have a ripple effect–one change necessitates another, then another, until you find yourself having to update everything. Anyone who has ever upgraded just one piece of technology in their office, or even updated just one appliance in their kitchen, can understand this phenomenon.

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Five steps to reach Smart Predictive Maintenance

By Chris Coleman and Ryan Manes

Smart Predictive Maintenance accelerates the maintenance journey and has potential to increase machine availability and visibility across an entire asset network.

New techniques can improve plant throughput

Maintenance professionals today can face a number of issues, often including outsourcing, cost cutting, scarcity of experienced labor and increasing complexity of equipment. Whatever the challenge, maintenance and reliability professionals share a common goal – to maximize machine availability. Yet traditional maintenance programs can only take you so far. In fact, machine failures go well beyond statistical time-based failure. Recent studies show that only 20 percent of machine failures are time-based, while the other 80 percent of failures occur either in the infant mortality startup phase or most often due to random or unknown failure.1 But truly, no failure is random, only that the root causes have gone unidentified. Modern maintenance techniques can help detect impending failures before they happen with typically more accuracy than time-based approaches. For manufacturers, exceptional asset maintenance can be a strategic differentiator in improving a plant’s throughput, efficiency, quality and safety.

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