Case Study Data Gartner 
AIMMS
OCTOBER 1, 2019
According to Gartner, 11% of mid and large-sized enterprises currently have some form of prescriptive analytics. Source: Gartner, Forecast Snapshot: Prescriptive Analytics Software, Worldwide, 2019. Source: Gartner, Market Guide for Supply Chain Analytics Technology, May 28, 2019. More investment in data science .
Supply Chain Brain
NOVEMBER 4, 2020
Our businesses are overwhelmed with data. Thanks to digitization, we collect and store data on nearly every activity in our working day. However, just like a traditional filing cabinet full of physical files, this data is only relevant if you know how to use it. Case studies and more. Analyst reports.
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AIMMS
OCTOBER 1, 2019
According to Gartner, 11% of mid and large-sized enterprises currently have some form of prescriptive analytics. Source: Gartner, Forecast Snapshot: Prescriptive Analytics Software, Worldwide, 2019. According to Gartner, 11% of mid and large-sized enterprises currently have some form of prescriptive analytics.
AIMMS
JULY 19, 2017
The list includes the best of our blog and news items, our latest webinars and most recent case studies. Often, teams think they also need plenty of clean and accurate data to do it right. It’s the perfect companion for an inspiring holiday break! . Chris Gordon, AIMMS VP North America, discusses this on our blog.
AIMMS
JULY 19, 2017
The list includes our best supply chain analytics blogs and news items, our latest webinars and most recent case studies. Often, teams think they also need plenty of clean and accurate data to do it right. It’s the perfect companion for an inspiring holiday break! .
AIMMS
MAY 28, 2015
In almost every way this is a large and very professionally run conference with over 1500 attendees, everything you would expect from a Gartner event. What is great innovation via @GuyKawasaki frm Gartner Supply Chain Conference keynote #GartnerSCC pic.twitter.com/prxnv2IXVH. Gartner (@Gartner_inc) May 13, 2015.
AIMMS
JANUARY 9, 2018
Often, teams think they also need plenty of clean and accurate data to do it right. He gathered and looked at the data and would produce a forecast based on previous experiences. He was changing forecasting data upon verification but also based on gut feel. Then we’ll have a more consistent way of getting our data.
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