The supply chain, and issues within it, have been a topic of much discussion over the last year and a half. For many in the retail and manufacturing sectors, the pandemic highlighted a lack of supply chain agility that meant they were unable to scale and rapidly respond to changing consumer demands.
This has spurred a large number of organizations to reassess their supply chain and the technology they leverage within it. As a provider of SaaS for supply chain applications, at snext we’ve been eager to help businesses obtain the resilience, agility and consumer-focus needed to overcome their current supply chain challenges and future-proof their infrastructure. After all, we’re a company built on a vision of supply chain operations maximizing customer experience and profitability while conserving resources.
Using InterSystems IRIS as the foundational platform, our cloud-based supply chain applications are enabled by Machine Learning (ML) and span a variety of supply chain processes, including product categorization, inventory control, and demand monitoring.
In this blog, I’ll be exploring how innovative, AI-enabled supply chain tools can optimize a range of supply chain processes.
To alleviate the uncertainty of demand, forecasts are supplemented by safety stock. Yet, unlike forecasting demand, safety stock doesn’t tend to be subjected to the same practices. Instead, it tends to be arbitrarily defined.
However, using AI, our safety stock sizing service is able to transform data into insights and triggers decision workflows which can be applied to the sizing of safety stocks. This approach guarantees optimal configuration that is understandable by all and easy to maintain.
As safety stocks are dimensioned as accurately as possible, stock levels are reduced and cash is freed up. Implementing this solution also increases product availability as safety stocks are sized to absorb variability in demand where it occurs, and it saves time for stock managers who can then turn their attention to more value-added tasks.
Categorization of products is a vital part of improving supply chain performance as it allows suppliers and planners to focus resources where they are needed. Yet, in my experience, this is challenging for many businesses as the criteria for categorizing products is often unclear.
This poses the question, ‘how do you choose an appropriate stock management method for each SKU in the supply chain? Beyond that, in order to meet the customer's demand to considerably increase the number of products in the catalog, how to choose the items to have in stock?’
Today, AI-enabled solutions can help procurement officers, flow pilots and other inventory managers to make the most profitable decisions. By leveraging data, supply chain tools can measure and simulate the impact of decisions on the service rate, as well as on the operating margin.
AI can immediately detect anomalies in the behavior of demand and flag this for maximum reactivity.
For example, an exceptional sale with a high risk of breakage on an item should be detected automatically and reported immediately in the form of a notification or a workflow. In this scenario, the system isn’t responsible for making the decision for the user. But this information can be extremely effective in indicating to the user that a supply order must be placed urgently or any other action to react immediately to any event that will always occur in the business world.
As you can see, AI and interoperability can play a significant role in the optimization of supply chain processes and deliver significant value to both the supply chain industry and their end customers. Find out more about how we’re leveraging InterSystems technology to develop solutions for the supply chain of tomorrow here.
About the Author
Richard Viot Coster
Richard Viot Coster is a Supply Chain professional with nearly 20 years of experience. As an expert in logistics operations, he defines the supply chain as the production of customer service. Business-oriented, he focuses on the levers for optimizing the operating margin. Ambitious, he aims to combine this profitability with a better use of resources. Today, he is dedicated to the design and implementation of tomorrow's Supply Chain management tools. https://www.snext-solutions.com.