Archive for Machine Learning

Digital Transformation of your Supply Chain Network in the Age of AI

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.

Read More →

Transportation Execution Snapshot

I am finishing up my latest market study on global transportation execution and visibility systems. Transportation Execution Solutions (TE) allow shippers to connect to multiple carriers and then tender, track, and pay in the system. Visibility solutions allow real-time asset tracking across the entire distribution network. This enables improved estimated arrival times of goods. Visibility solutions are playing a larger role in the market, as real-time tracking of assets becomes more important. Suppliers of visibility […]

Read More →

How to Put Data Science to Work for Your Supply Chain

Driving operational growth and increasing supply chain efficiency in today’s ever-changing business world is a top priority for many companies. In order to grow in a rapidly evolving marketplace, organizations must possess the ability to use advanced algorithms and data analytics to generate the next wave of supply chain value. With the abundant data available and the advanced technologies now accessible to help harness it, organizations must establish effective strategies to curate data, produce and […]

Read More →

Machine Learning in the Supply Chain

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 […]

Read More →

Achieving Energy Savings through a Digital Transformation: The Vopak Case Study

Vopak, a leading operator of tank terminals, is undergoing a digital transformation. One of their projects occurred at their Savannah terminal. New sensors, machine learning, and optimization drove significant energy savings.

Read More →

Transportation Management and the Promise of Machine Learning

Transportation Management solutions have a proven ROI. But the additon of Machine Learning opens up some exiciting possibilities. ARC is excited about the promise of machine learning to allow a TMS to better handle competing objectives and discover nonobvious impacts on performance.

Read More →

Bridging the Gap Between Supply Chain Planning and Execution

New solutions have emerged that bridge the gap between supply chain planning and execution. These solutions have a middleware layer that is critical. The middleware allows for a feedback loop that can allow the system to get smarter.

Read More →

Machine Learning in Today’s Warehouse Management Systems (WMS)

While speaking with a number of WMS product managers, I began to notice that machine learning was called out as a focal point for WMS product development efforts. In general, machine learning is a hot topic in the world of supply chain technologies. Just last week, Chris Cunnane wrote about machine learning for transportation execution. And Steve Banker recently wrote  about Vecna Robots use of machine learning to improve its vision system. Naturally, I wanted […]

Read More →

Machine Learning Limitations: The Need for a Clear Measure of Success

Demand planning is a good application for machine learning because the measure of success – the forecast accuracy – is clear. To learn, an application needs a clear measure of success. Having a clear measure of success sounds easy. But often, defining success in not easy. Consider a situation where a manufacturer learns of a shortage of a key component.  Customers have already been promised products that depend upon that key production input.  The supply […]

Read More →

Applying Machine Learning to Supply Planning is Tough

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.

Read More →