Machine learning has been successfully applied to demand planning, but leading suppliers of supply chain planning are beginning to work on using machine learning to improve supply planning. But architecturally and culturally, this is a much tougher problem than machine learning applied to demand planning.
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Artificial intelligence (AI) is hot. Over $4 billion in venture capital has been invested in AI firms just in the US. But supply chain planning software companies, with their cadre of operations research Ph.Ds who have been modeling complex problems for decades, may be better poised to solve many complex business problems than the hot new Silicon Valley firms.
A “self-healing” supply chain needs to be based on a robust supply chain digital twin model that is updated with near real-time alerts.
Isn’t any model not based on paper and pencil a digital twin? What value does it add to call a supply planning model a digital twin? I thought it was hype. Supply chain planning executives disagree.
There are some young supply chain technologies that are getting a lot of buzz. But how mature are these technologies? Do they have a proven ROI? Are they worth piloting? Or can we safely ignore them for a few more years?
Oracle and SAP are the two largest suppliers of enterprise applications are also leaders in the supply chain planning (SCP) market, but the leadership is being contested. Their arguements for why customers should choose their planning solution go beyond their applications functionality. Interestingly, over time most enterprise applications markets consolidate. This is not happening in the SCP market.
Trying to understand and implement artificial intelligence (AI) for your supply chain network during your digital transformation efforts, or let alone finding the right partner in your journey, is like walking in an enchanted forest full of myths, ogres, and lost souls trying to find a way out. Finding the right path in there to an eternal bliss starts with knowing what is not right. Implementing AI is not a small feat and you may get bombarded with vague definitions and stories about how AI makes the world a better place. Let’s look at a few common myths you may come face to face with in your quest to transform your supply chain network by using AI while going digital in your organization.
There are twenty things companies need to know if they are contemplating a digital supply chain transformation. One thing that is very important, is that they learn about a new set of solutions called Supply Chain Collaboration Networks.
One area of interest in today’s end-to-end supply chain is machine learning. And this is certainly a topic that we have written about quite often. Over the last few months, Steve Banker, Clint Reiser, and I have written about artificial intelligence and machine learning in a number of contexts and how it impacts the supply chain. These topics have included transportation management, warehouse management, and supply chain planning, among others. This technology continues to be […]
Bonds had a yield curve inversion on August 14th. This often often signals a recession within about 14 months. Demand planning is difficult in these circumstances. This article discusses how supply chain teams can do better in these circumstances.
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