How AI is reshaping Inventory Management

How AI is reshaping Inventory Management
Demand forecasting is not a new concept in the supply chain. In fact, it’s often referred to as ‘guessing between the goalposts,’ and for the most part, that was right.

Now, using algorithms that not only predict demand to drive forecasts but also incorporate external data sources and continuously learn and optimize the algorithm without intervention we have the ability to significantly reduce the size of those goal posts. Using AI we can improve the accuracy of our forecasts and then allow us to predict where and when those products should be deployed ahead of customer demand. The result? Improved forecasts, reduced freight costs, better customer service levels, and optimized working capital.
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Digital Transformation: Practical Steps to Getting Everyone Onboard

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With changing technologies all around us it can be challenging to know where to start when planning your organizations digital transformation strategy. One thing that remains clear across industries though is the need to have the right culture and behaviors within an organization to successfully take on this transformation.
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The Beginner’s Guide to Applying Machine Learning to Incident Response

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There is a lot of hype around Machine Learning (ML) and its application in infosec. Putting the Machine Learning snake oil aside, according to a recent report conducted by the Ponemon Institute, 49% of security professionals are finding that Machine Learning enhances their ability to prioritize threats and vulnerabilities and 47% say it increases the productivity of security personnel. In this webinar, join SANS Analyst, Dave Shackleford and Senior Data Scientist, Sean McNee for an introduction to AI and ML, as well as how to identify technologies that actually use ML, and tips for harnessing the power of ML in Incident Response.
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Real-Time Labor Costs and Unit-Based Budgets

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ForgeRock and Accenture Share How To Enhance Existing Governance Investments with Artificial Intelligence

Global organizations have long used identity governance solutions to address user access and regulatory compliance requirements. Each technology requires a clear identification of which users need access, what resources they need, and how access can be controlled to prevent unauthorized usage. As the size and scope of these challenges has increased, traditional approaches like RBAC have fallen short, for the simple reason that defining and maintaining roles is time consuming and difficult using legacy tooling or even the dreaded spreadsheets. Operational costs are exploding, users are suffering from identity fatigue, and auditors are calling out rubber stamping as a regulatory issue.
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