From medical supply shortages to consumers hoarding toilet paper, the links in supply chains are under strain. Can we learn valuable lessons for the future?
For example, design of the medical supply chain in the United States has low stress resilience. In early stages of the pandemic, most hospitals are already experience a shortage of personal protective equipment, such as N95 masks. As COVID19 cases spread, so will scarcity.
Normally, most enterprise resource planning (ERP) solutions can reliably analyze things like inventory levels, historical purchasing trends, and discounts to recommend how much of a product to order. During the worldwide disruption caused by the COVID-19 pandemic, many programs a are making fluctuating recommendations, and adjustments to inputs are occurring more frequently. As this becomes more dynamic, the frequency of monitoring by supply chain managers is increasing. Human interventions into automated systems can have adverse impacts on algorithms.
Most retail companies rely on some type of model or algorithm to help predict customer demand, from a basic Excel spreadsheet or a refined, software engineer-built application. Normally, those models are fairly reliable and work well. But as with virtual all other systems, they are being impacted by the pandemic. On the news, we see how customers may be concerned about having enough access to essentials such as paper towels.
One reason for added stress is that a lot of trade is international -- for example, trade between the US and other countries, particularly China, has been under stress due to the virus. So distributors and retailers have had to find new sources for products.
Machine learning-based algorithms are the foundation of the next generation of ERP, particularly around logistics. We will see the most significant cost efficiencies around advanced resource scheduling. Neural net and A.I.-based methods are the foundation of a broad spectrum of next-generation logistics and supply chain technologies under the hood of the best ERP solutions. Significant gains are being made where machine learning can contribute to addressing complex constraint, cost, and delivery problems organizations face. Automated analysis can help provide significant insights into how supply chain performance can be improved, anticipating anomalies in logistics constraints, and matching "pull" performance before shortages occur.
Another example is using a decentralized supply chain for track-and-trace applications. This would improve performance and reduce costs. A study found that in a 30-node configuration when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings appeared to be more than $5 million a year.
Watch this video to learn more, or read this article.
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