The Supply Chain Matters blog highlights some current signpost examples in the deployment of artificial intelligence and predictive analytics in supply chain planning process needs.
There are obviously many viewpoints among multi-industry supply chain teams on the applicability or technology readiness for deploying artificial intelligence and predictive analytics to overall decision-making. That is to be expected.
Organizations will always have various levels of process readiness, change management and people skill development perspectives.
It is important to consider that while particular technologies have a certain maturity cycle, organizations themselves need to consider their own maturity readiness and deployment timelines to have readiness to leverage such capabilities.
For our part, Supply Chain Matters will attempt to highlight developments that we believe can help teams to make their determinations regarding these specific technologies.
The Wall Street Journal’s CIO Journal blog recently profiled efforts by German based KGaA healthcare division’s plans to deploy AI and predictive analytics throughout the division’s entire supply chain (Paid subscription or metered view) by the end of 2019. We elected to highlight this report because the division’s CIO communicates what we feel are very important overarching goals of this deployment effort, essentially stated as a means to augment the jobs of the company’s supply chain planners and reduce often tedious and repetitive work.
In other words, the end point is described in capability vs. technology.
According to this blog commentary, by the end of next year, the division’s 100 supply chain planners will likely be utilizing these technology tools. Further indicated is that there are plans in-place to reskill and train planners to become supply chain architects or data scientists to focus on information insights and more predictive decision-making regarding positioning inventory and resources to service customers and patients.
Once more, this CIO stated an insightful conclusion, that within the next 2-3 years supply chain automation will become more mainstream and that competitive advantage comes from firms that invest and develop required competencies in both people and technology dimensions by that timeframe.
The technology itself is noted to be Silicon Valley based Aera Technology.
Preparing Supply Chain Data Architects and Scientists
In this same vein of people preparedness and skills pipeline, we further call reader attention to an announcement from Fusemachines Inc.
This provider of AI software and services announced the global launch of an Artificial Intelligence Scholarship Program geared to provide early training to students from underserved communities around the world. The 10,000-seat scholarship program serves as a precursor to a highly selective Fusemachines AI Fellowship program.
The AI Fellowship is a one-year long fellowship program that turns math/programming students into Machine Learning engineers. Fellows get hands-on experience in a wide range of AI techniques from machine translation to robotics. They are encouraged to develop projects of their own, leveraging the high potential of AI to address real-life situations and social issues.
The precursor scholarship program grants select students access to a proprietary AI platform and content, along with a virtual community of AI experts and student mentors. It enables budding students the ability to acquire hands-on exposure and experience to a wide range of AI techniques in various student projects.
According to the tech provider, both programs enable the building of a pipeline of future AI engineers. We believe that could well include future business operations or supply chain data scientists.
Readers seeking additional information can view: www.fuse.ai
We continue to reiterate that teams should establish their supply chain capability needs in the context of process and competency needs. Selection of the most likely technology to enable such goals is derived from an assessment of the capabilities and maturity of targeted technologies. The goal is matching-up both timelines. In some cases, while the mainstreaming of the technology is a few
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