Enterprise AI® Powered Supply Chains
Supply chains today are in the forefront now more than ever. The resiliency of supply chains is being severely tested by unpredictable change, uncertainty and increasing complexity. One thing is for sure though – the ability to accurately predict supply and demand anomalies, coupled with speed of action, is what will separate great supply chains from good supply chains. It is no longer sufficient to just have great product. It is equally important, if not more so, to have supply chains that deliver the right amount of inventory at the right place and also at the right time, globally.
Is this an impossible proposition? Yes. Especially if we are talking about a complex supply chain with thousands of SKUs, high variability in demand and supply, and that too on a global scale. Most supply chain leaders will say this can only be achieved if we could actually peek into the future and know what was likely to happen.
Would a supply chain executive make better supply chain decisions if they knew what was likely to happen during the supply chain execution planning horizon of eight to twelve weeks? Consider the following real-world vignettes:
- If a supply chain planner knew which SKU was going to spike in demand in a particular geography within the next 12 weeks, would they act now to capture the demand?
- Fill rates are in the 90% range and yet inventory is deemed to be high. Should a supply chain planner increase fill rates of future orders that would otherwise be lost?
- The ERP system is generating thousands and thousands of supply chain alerts. Many are false positives and false negatives. Can a supply chain planner cut through all the noise and focus on solving the real problems?
- Can a supply chain executive have the foresight to de-expedite a shipment in spite of the field screaming for additional inventory?
If the desired answer to these questions is a “Yes”, imagine the likely business impact on costs, margins and revenue of actions coming out of these answers! Stated simply, the impact can be significant. For a $10 billion-dollar global consumer products company, the benefits can run into eight figures, annually.
Undoubtedly, if a supply chain executive or planner knew what was likely to happen, they would clearly make better business decisions to drive better outcomes.
History has shown that deep innovation occurs when we face the impossible or when we experience a major crisis. Today, supply chain leaders and planners face both. The accelerated pace of change, unpredictability and uncertainty, further exacerbated by COVID-19, has created a burning platform for ground-breaking innovation in supply chains. This is where Enterprise Artificial Intelligence (AI) comes into play.
Fundamentally, machine learning algorithms are able to learn from the trends and patterns in data and predict likely outcomes. The more data you feed the algorithms, the better they become in discerning the interaction of variables and in predicting the outcome for the dependent variable.
Given today’s supply chains must deal with high variability not only in demand but also in supply, traditional deterministic, rules-based enterprise software is just not able to properly support the needs of today’s supply chain planners. This is where Enterprise AI shines.
While AI has been around for a long time, Enterprise AI is relatively new but its applicability to the world of supply chains couldn’t be more exciting. Most, if not all, Fortune 2000 companies already have the most important component of Enterprise AI in place – multiple years of transaction data from traditional ERP systems (such as SAP and Oracle). To get going on their Enterprise AI journey, supply chain executives need to first define the scope/use-case, establish the business case for the initiative and acquire the appropriate Enterprise AI application to power the work.
Noodle.ai has built one of the world’s foremost Enterprise AI applications for Supply Chain – the Athena Supply Chain AI Suite. Based on its proprietary algorithms, the Athena product suite provides predictive answers to forward-looking questions over a 12-week planning horizon, questions such as:
- What will the fill rate be for the top SKUs by distribution center?
- Which orders are at risk for not being filled?
- What is the total value at risk by product by geography?
- Which SKUs need increased production?
- Should we be expediting or de-expediting certain shipments?
- Which supply chain hotspots will emerge in the next 8-12 weeks?
Operationalizing the insight from Enterprise AI needs a shift in how decisions are made and can deliver a very compelling value proposition through increased revenue and reduced costs. Traditional enterprise software offers a deterministic solution. For example, it will show a supply chain planner the precise amount of inventory on-hand by SKU and distribution center on almost real-time basis. No judgment is required to make a backward-looking decision. Enterprise AI on the other hand, will provide a likelihood of what the inventory on-hand could be in the future. So, the supply chain planner will have to use judgment to make a forward-looking decision. Using Enterprise AI, planners can look to the future and solve issues before they become issues. Imagine the incredible power of future supply chains when inventory related issues are identified and solved before they become issues.
This is not just future think. Enterprise AI for Supply Chains is real and here! The Noodle.ai Athena Supply Chain AI suite of applications is live and delivering value for our customers today. And it has the distinct potential to dramatically change how companies operate and make supply chain related decisions. While the financial benefits are sizable and compelling, Enterprise AI is differentiated in one very important category from traditional enterprise software. As the algorithms become smarter over time, they will tune the supply chain to be highly optimized and therefore become more efficient than anything that traditional enterprise software can drive. This efficiency has the highly desirable outcome of maximizing the utilization of assets and therefore minimizing waste in supply chains, which has a direct connect into sustainability of our planet.
The adoption and usage of Enterprise AI for supply chains is inevitable; companies are already upgrading their technology capability to compete on the strength of their AI powered supply chains. Unlike traditional enterprise software, the early adopters will always have an advantage as their algorithms will get more learning under their belt and more importantly, the humans will learn how to harness the incredible power of Enterprise AI. When Enterprise AI and humans learn to work with each other and trust each other, the outcome will represent enormous value creation.
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