Big Data

In the same way that demand sensing can be used to monitor and mitigate on-shelf availability during extraordinary conditions, so can it be used for day-to-day operations.

5 questions every enterprise should ask to avoid getting stuck in the past.

A new paradigm is needed – one which brings all factors into play, and which enables a proactive approach to solving inventory problems before they occur.

By developing methods for analyzing textual data that optimize social science goals directly, we are able to considerably outperform standard computer science methods developed for a different purpose.

Turns out, what Data Scientists do is really hard - so if you're looking for one of the highest paid professions, data science is hard to beat.

Considering the growing number of use cases relevant to your enterprise, what has changed is that these are no longer confined to legacy data-driven functions such as marketing or finance.

If you drive a car then you know what a dashboard is. There are two main gauges that we are all very familiar with - the speedometer and the fuel gauge. The other gauges we don’t usually take much notice of.

Learn why there is a new sense of urgency to implement Industrial Internet solutions fueled by Big Data analytics.

Manufacturing companies and third-party logistics providers are facing the most complex and volatile business environment in their history.

For mid-size and fast growing companies, keeping track of cash position and cash flow in a meaningful way is difficult!

Go beyond canned KPI’s and pre-built analytics and understand your business the way that makes the most sense to you.

This report captures the highlights of quantitative research conducted by Supply Chain Insights during 2014, with the goal to distill research from 2014 into snippets and into a consolidated research summary.

Savvy, global shippers that capture and analyze transactional transportation management data in collaboration with their third-party service providers have seen hard dollar savings drop to the bottom line. Here’s how they’re getting it done.

So the promise of using statistical algorithms, forecasting and predictive analytics is now added to the list of a company’s number one priorities. There is a sense of urgency surrounding this new high profile initiative. One may ask, “What’s next?” Well, here are a few steps that you will need to take to deploy your forecasts successfully.

In the world of commerce, every business ecosystem has a type of supply chain that is critical to corporate operations. These supply chains rely on a network of plants and facilities to add value to and transform raw materials into a final product. Any disruption or sub-optimization in the supply chain can and will significantly impact a company’s profitability.

Every 10 to 15 years, a tectonic shift comes along in our industry disturbing the status quo, fundamentally changing how technologically is consumed, purchased, and delivered.

Distribution or dissemination of information is probably one of the most critical aspects of supply chain business intelligence and information management.

Collaboration of information and timing and are two extremely critical aspects of supply chain business intelligence and information management.

New Survey from GE and Accenture finds less than one-third of companies have predictive analytics capabilities although big data analytics is a top priority for 88 percent of executives.

Analytics are now an integral part of any supply chain information management system. Modern computing power and advances in analytical software have made powerful analytics not only possible, but practical for most businesses sitting on top of a cornucopia of supply chain data. Analytics run the gambit from historical data dumps to models that suggest the best course of action based on a set of probable outcomes.