To say that the pandemic made 2020 a very challenging year would be a gross understatement. Because of service disruptions – not being able to buy toilet paper, for example – the supply chain has been more discussed than ever before. It was also a very, very tough year for supply chain professionals working in […]
COVID-19 has put supply chain organizations under great strain. But there has been one nice side effect in the consumer goods supply chain. SKU proliferation has been reversed.
P&G is respected for having one of the best supply chains in the world. They rarely speak about their approach to supply chain management in any detail. So, it was a treat to hear Bob Herzog, the Global Planning Digitization Leader for Supply Chain, and Pedro Noriega, Planning Director, North American Product Supply, speak at Kinaxis’ Kinexions 2019 user conference. P&G has developed one of the world’s most agile supply chains. To accomplish this, P&G embraced digital tools, a new network, and citizen developers.
Digitalization requires learning. Pilots of supply chain planning software can cost millions. How can companies test-and-learn cheaper and smarter? A conversation with the leadership team of Solvoyo suggests some answers. Solvoyo is in a test-and-learn with a very large consumer goods company.
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.
The subscription retail model is not a new concept. The first basic model I can think of is a magazine subscription – a new magazine came every month unless you canceled your subscription. The first subscription service I used was Netflix, way back when the company only shipped DVD’s to your house. However, as the […]
If demand management teaches us anything about learning to leverage the Big Data generated by sensors, it is that leveraging IoT data is likely to be a lengthy maturation process.