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Analytics has traditionally helped companies achieve improvements in all areas of their business. Today, the boundaries of analytics are being thrust forward by the application of Data Science and Artificial Intelligence. Deep Learning and other algorithmic methods are opening up new possibilities and disrupting the current status quo, allowing for new and exciting problems to be solved for businesses of all sizes and verticals.
For example, an efficient supply chain is vital to the success of many companies. Without a properly functioning supply chain, manufacturers, retailers, and other businesses would not be able to get the materials required to produce their goods, let alone deliver a product to a customer on time. As such, many businesses are seeking a competitive edge by relying on sophisticated algorithms over human intuition and basic statistics alone.
Today’s supply chains are far more complicated than those of the past. Companies are transitioning from having one customer-facing distribution center, which is also connected to its suppliers, towards having multiple levels. This involves having customer-facing distribution centers that are connected to regional distribution centers, which are in turn connected to suppliers.
Traditionally, each node of the supply chain is locally-optimized, meaning they maintain their own safety stock to protect themselves against fluctuations in lead times and demand. This is a very risky situation. Having excess inventory removes capital that can be allocated towards other things. Moreover, excess inventory takes up limited warehouse space and can become obsolete, both of which drive up costs. When each facility attempts to optimize its own decisions without taking other parts of the supply chain into consideration, the entire supply chain ends up having high inventory levels and low returns.
More advanced methods of optimizing inventory across the entire supply chain are required for future competitive success. Rather than optimizing each node of the supply chain locally, a multi-echelon approach allows an enterprise to look at its entire supply chain holistically.
In a traditional single echelon supply chain, inventory can be optimized by using several methods. These methods typically involve starting with a fundamental assumption, such as constant demand or assuming well behaved probability distributions for demand by SKUs, and then optimizing to find minimal inventory. However, when moving to a multi-echelon system, one can no longer simply rely on the basic assumptions that make a single echelon system work.
While there are many ways of dealing with inventory optimization in a multi-echelon supply chain (stochastic versus deterministic, guaranteed service model, etc.), they all have drawbacks and short comings. Using these methods, businesses can still be subject to the Bullwhip effect (as you move up the supply chain away from the end-customer, forecast accuracy decreases) or end up with excess stock at regional distribution centers.
More advanced capabilities can be achieved by leveraging Artificial Intelligence. For example, traditional models for handling inventory in a multi-echelon supply chain can be used in conjunction with sophisticated algorithms to increase the speed of computation. Artificial Intelligence can also augment this process by generating new features to run these models on.
This can be further enhanced by adopting recent advancements in Deep Learning. With Deep Learning, a Neural Network is used to take the initial input data from the supply chain to generate optimal inventory levels. This is accomplished by building an incredible amount of non-linearity into the data and taking into account all of the constraints, risks, and variables that can affect a supply chain, all while imitating the functionality of a human brain. This leads to reduced inventory costs and more effective allocation of warehouse space.
Multi-echelon supply chain optimization is just one example of how companies can increase profitability and improve customer satisfaction. Soothsayer Analytics is actively pushing the boundaries of Data Science and Artificial Intelligence, and re-imagining the complexity and diversity of problems that can be solved. Reach out to us to discuss how we can help your business explain the unknown, optimize outcomes, and predict the future.