There is terrific payback from supply chain design solutions. At GM, the supply chain design team is an in-house consulting group. Annually, the projects they take on save between 5 to 10 percent of the in scope spend. They have done many projects in the logistics area, some on an ongoing basis. Logistics costs as a percentage of revenue decreased every year from 2013 to 2018. That is a remarkable feat in today’s freight environment. Considering the size of GM’s transportation spend, $6.5 billion, and the length of time they have been at this, it would not surprise me at all if this small team has saved GM over a billion dollars.
This article describes the five most common fallacies surrounding supply chain management. Some of these are fallacies that have been around a long time and never seem to go away. Others are more recent in origin.
I recently attempted to read a white paper from McKinsey that talked about the value a digitization program can provide; the problem was that after reading the article for ten minutes I still had no idea how they defined “digitization.” Sometimes I get fed up with theory. What follows are ten supply chain case studies […]
I am kicking off some new research on the Transportation Execution and Visibility Systems market in the next week or so. This will be an update on a study Steve Banker did a few years ago, which looked mostly at the execution space, and how the Uber for Freight model was disrupting the market. According […]
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 […]
There is an arms race to incorporate artificial intelligence in demand planning solutions. Many new data sources, features, and tools are being explored. A new demand modeling tool has been introduced which will make it easier to analyze new data sources to see if they can be used to improve forecasts.
What was hot in supply chain technology? A look at 3D Printing, robots, autonomous trucks, the use of Social-News-Event-Media data for risk management, and the Uberization of Freight with ARC’s assessment of the maturity of these technologies.
Machine learning engines can take data on forecast accuracy and use that data to automatically improve the forecast model. However, three is a rub. Lost sales is a key piece of data on the accuracy of the forecast, but lost sales is generated with a demand forecast. This is circular reasoning. But that does not mean this analysis is without value.
Machine learning has become hot this year. Supply chain software suppliers are investing in improving their software’s capabilities by using machine learning. But machine learning in supply chain software is not new.
JDA is working toward providing off the shelf solutions that allow companies to use SNEW data – social media, news, event, and weather data – to improve their supply chain capabilities. In short, they are working to leverage Internet of Things (IoT) to improve supply chain management.