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Like many of you, I experienced the disrupting effects introduced by external forces such as weather, geopolitical instability, and the COVID-19 pandemic. To improve supply chain resilience, organizations need visibility across their supply chain so that they can quickly find and respond to risks. This is increasingly complex as their customers’ preferences are rapidly changing, and historical demand assumptions are not valid anymore.
To add to that, supply chain data is often spread out across disconnected systems, and existing tools lack the elastic processing power and specialized machine learning (ML) models needed to create meaningful insights. Without real-time insights, organizations cannot detect variations in demand patterns, unexpected trends, or supply disruptions. And failing to react quickly can impact their customers and operational costs.
Today, I am happy to share that AWS Supply Chain is generally available. AWS Supply Chain is a cloud application that mitigates risk and lowers costs with unified data, ML-powered actionable insights, and built-in contextual collaboration. Let’s see how it can help your organization before taking a look at how you can use it.
How AWS Supply Chain Works
AWS Supply Chain connects to your existing enterprise resource planning (ERP) and supply chain management systems. When those connections are in place, you can benefit from the following capabilities:
- A data lake is set up using ML models that have been pre-trained for supply chains to understand, extract, and transform data from different sources into a unified data model. The data lake can ingest data from a variety of data sources, including your existing ERP systems (such as SAP S4/HANA) and supply chain management systems.
- Your data is represented in a real-time visual map using a set of interactive visual end-user interfaces built on a micro front-end architecture. This map highlights current inventory selection, quantity, and health at each location (for example, inventory that is at risk for stock out). Inventory managers can drill down into specific facilities and view the current inventory on hand, in transit, and potentially at risk in each location.
- Actionable insights are automatically generated for potential supply chain risks (for example, overstock or stock outs) using the comprehensive supply chain data in the data lake and are shown in the real-time visual map. ML models, built on similar technology that Amazon uses, are used to generate more accurate vendor lead time predictions. Supply planners can use these predicted vendor lead times to update static assumptions built into planning models to reduce stock out or excess inventory risks.
- Rebalancing options are automatically evaluated, ranked, and shared to provide inventory managers and planners with recommended actions to take if a risk is detected. Recommendation options are scored by the percentage of risk resolved, the distance between facilities, and the sustainability impact. Supply chain managers can also drill down to review the impact each option will have on other distribution centers across the network. Recommendations continuously improve by learning from the decisions you make.
- To help you work with remote colleagues and implement rebalancing actions, contextual built-in collaboration capabilities are provided. When teams chat and message each other, the information about the risk and recommended options is shared, reducing errors and delays caused by poor communication so you can resolve issues faster.
- To help remove the manual effort and guesswork around demand planning, ML is used to analyze historical sales data and real-time data (for example, open orders), create forecasts, and continually adjust models to improve accuracy. Demand planning also continuously learns from changing demand patterns and user inputs to offer near real-time forecast updates, allowing organizations to proactively adjust supply chain operations.
Now, let’s see how this works in practice.
Using AWS Supply Chain To Reduce Inventory Risks
The AWS Supply Chain team was kind enough to share an environment connected to an ERP system. When I log in, I choose Inventory and the Network Map from the navigation pane. Here, I have a general overview of the inventory status of the distribution centers (DCs). Using the timeline slider, I am able to fast forward in time and see how the inventory risks change over time. This allows me to predict future risks, not just the current ones.
I choose the Seattle DC to have more information on that location.
Instead of looking at each distribution center, I create an insight watchlist that is analyzed by AWS Supply Chain. I choose Insights from the navigation pane and then Inventory Risk to track stock out and inventory excess risks. I enter a name (Shortages) for the insight watchlist and select all locations and products.
In the Tracking parameters, I choose to only track Stock Out Risk. I want to be warned if the inventory level is 10 percent below the minimum inventory target and set my time horizon to two weeks. I save to complete the creation of the insight watchlist.
I choose New Insight Watchlist to create another one. This time, I select the Lead time Deviation insight type. I enter a name (Lead time) for the insight watchlist and, again, all locations and products. This time, I choose to be notified when there is a deviation in the lead time that is 20 percent or more than the planned lead times. I choose to consider one year of historical time.
After a few minutes, I see that new insights are available. In the Insights page, I select Shortages from the dropdown. On the left, I have a series of stacks of insights grouped by week. I expand the first stack and drag one of the insights to put it In Review.
I choose View Details to see the status and the recommendations for this out-of-stock risk for a specific product and location.
Just after the Overview, a list of Resolution Recommendations is sorted by a Score. Score weights are used to rank recommendations by setting the relative importance of distance, emissions (CO2), and percentage of the risk resolved. In the settings, I can also configure a max distance to be considered when proposing recommendations. The first recommendation is the best based on how I configure the score.
The recommendation shows the effect of the rebalance. If I move eight units of this product from the Detroit DC to the Seattle DC, the projected inventory is now balanced (color green) for the next two days in the After Rebalance section instead of being out of stock (red) as in the Before Rebalance section. This also solves the excess stock risk (purple) in the Detroit DC. At the top of the recommendation, I see the probability that this rebalance resolves the inventory risk and the impact on emissions (CO2).
I choose Select to proceed with this recommendation. In the dialog, I enter a comment and choose to message the team to start using the collaboration capabilities of AWS Supply Chain. In this way, all the communication from those involved in solving this inventory issue is stored and linked to the specific issue instead of happening in a separate channel such as emails. I choose Confirm.
Straight from the Stock Out Risk, I can message those that can help me implement the recommendation.
I get the reply here, but I prefer to see it in all its context. I choose Collaboration from the navigation pane. There, I find all the conversations started from insights (one for now) and the Stock Out Risk and Resolution recommendations as proposed before. All those collaborating on solving the issue have a clear view of the problem and the possible resolutions. For future reference, this conversation will be available with its risk and resolution context.
When the risk is resolved, I move the Stock Out Risk card to Resolved.
Now, I look at the Lead time insights. Similar to before, I choose an insight and put it In Review. I choose View Details to have more information. I see that, based on historical purchase orders, the recommended lead time for this specific product and location should be seven days and not one day as found in the connected ERP system. This can have a negative impact on the expectations of my customers.
Without the need of re-platforming or reimplementing the current systems, I was able to connect AWS Supply Chain and get insights on the inventory of the distribution centers and recommendations based on my personal settings. These recommendations help resolve inventory risks such as items being out of stock or having excess stock in a distribution center. By better understanding the lead time, I can set better expectations for end customers.
Availability and Pricing
AWS Supply Chain is available today in the following AWS Regions: US East (N. Virginia), US West (Oregon), and Europe (Frankfurt).
AWS Supply Chain allows your organization to quickly gain visibility across your supply chain, and it helps you make more informed supply chain decisions. You can use AWS Supply Chain to mitigate overstock and stock-out risks. In this way, you can improve your customer experience, and at the same time, AWS Supply Chain can help you lower excess inventory costs. Using contextual chat and messaging, you can improve the way you collaborate with other teams and resolve issues quickly.
With AWS Supply Chain, you only pay for what you use. There are no required upfront licensing fees or long-term contracts. For more information, see AWS Supply Chain pricing.
Mitigate risk and lower cost of with increased visibility and ML-powered actionable insights for your supply chain.
— Danilo
