How does machine learning support better supply chain management?
How does machine learning support better supply chain management, especially when working under the unique constraints and challenges of a global pandemic?
In times of uncertainty, companies are under pressure to make the right decisions for their customers quickly and reliably while staying within budget. The complexity and agility of global supply chains has increased tremendously over the last decade and without the support of smart technology, proper decisions are increasingly difficult to make (Read: How Machine Learning Can Improve Supply Chain Efficiency.)
Prior to the pandemic, our clients were extremely focused on optimizing the supply chain in the pursuit of the holy grail: “just-in-time” sourcing. They wanted to maintain the minimum stock levels to support current and forecasted orders.
This came with an assumption that their upstream suppliers could deliver based on pre-defined contractual schedules. Additionally, they often reduced the number of suppliers for a given set of materials or components in return for higher discounts through volume.
With this came tight integration between our customer’s enterprise resource planning (ERP) systems and these key partners. All these assumptions went out the window with the pandemic. Upstream vendors can no longer guarantee delivery timelines, and, subsequently, companies are searching to find other suppliers to meet demand. (Read: Big Data: Logistically Speaking.)
With this, the current coronavirus crisis has emphasized the importance of proper technological support. Ships around the world are docked at the harbor and trucks are unable to unload due to plant closures. These supply chain disruptions have led to unanticipated difficulties for both retailers and manufacturers in keeping their businesses running.
This is where artificial intelligence (AI) and machine learning (ML) smart technologies come into play. Our manufacturing and logistics customer base at Syntax was facing supply chain disruptions due to the coronavirus pandemic. We identified a need for a solution supported by smart technology to minimize business disruptions and developed the Syntax Crisis Dashboard to meet this need.
The dashboard uses artificial intelligence to uncover the risks for the supply chain and allows them to make decisions by accessing continuously updated COVID-19 information from Johns Hopkins University. (Read: The Impact Internet of Things (IoT) is Having on Different Industries.)
In this case, the AI does not make automated decisions, but rather focuses on decision support – the decision-making authority still lies with humans. The data collected in this dashboard is then applied to our supply chain data, allowing us to evaluate existing supply chains to organize them by availability versus risk. Alongside this dashboard, we use smart ERP technology to propose supply chain alternatives.
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