As a child, I was mesmerized by beauty pageants, especially Miss World, where women from all around the world strutted in gorgeous fashion. I vividly remember the moments when the top winners declared their lofty goals for humanity: solving world hunger!
Since the emergence of AI as the shiny object of our times, we see similar grandiosity: that AI will solve world hunger. Consequently, everyone asserts they are utilizing AI, and those in the supply chain world are no exception. However, the question remains: how? How will it address “the world hunger problem” in supply chains, particularly in the context of supply chain planning? How can AI contribute to end-to-end decision automation?
Here, let’s explore 6 essential elements of AI-powered automation in supply chain planning & analytics, culminating in a powerful solution.
1) Streamlined Data Flow and Process Automation Is all about AI
At the heart of effective supply chain automation lies the seamless flow of data across various sources and digital platforms, akin to a well-constructed highway for data. This ensures the secure, high-capacity, and bi-directional transfer of essential information such as master data on products, customers, production-distribution infrastructure, transactional data on sales, inventory status and position, transportation execution data, external data e.g. competitor pricing, weather, recommendations, action triggers.
Process automation is pivotal in providing end-to-end visibility across the supply chain. It gives planners and managers a holistic workflow view, offering insights within their operational domain and broader supply chain processes. This comprehensive view enables more informed decision-making and enhances the ability to adapt to changing conditions.
In a world characterized by dynamic supply chains, plan automation means dynamic planning and re-planning of executable steps based on real-time data and alterations in upstream and downstream processes. For instance, adjustments in order volumes trigger immediate updates to demand, inventory, supply, production, and transportation plans.
2) AI-Infused Data Quality Assurance
Ok, we built the proverbial highway. Now, we must ensure cars continuously run on clean and safe roads. In tech speak, this means effective AI-driven decision-making based on high-quality and internally consistent data. Consequently, AI-based data diagnostics capabilities have been developed to maintain data quality continuously. How?
AI validates Master Data and maintains internal consistency with transactions such as:
- Checking and identifying unreasonable records in Master Data (e.g., Incremental Order Quantity > Minimum Order Quantity, delivery cycle > shelf life and transactions (e.g., outliers, product with active sales but no forecast, sales in an inactive product or customer).
- Cross-checking consistency between Master Data and transactional data
- Replacing missing or unreasonable records
AI techniques, particularly Machine Learning (ML), are pivotal in enhancing decision-making at every stage of the supply chain. This results in more accurate recommendations and efficient process automation.
These advancements are reflected in impressive User Acceptance Rates (UARs), leading to planner efficiencies that are significantly higher than industry norms.
3) AI in Forecasting
One of the critical areas where AI shines is forecasting. AI techniques, particularly ML, enable organizations to:
- Identify demand drivers in historical data
- Quantify the impact of demand drivers such as promotions, stockouts, competitor pricing, weather, or special events, improving demand forecasting accuracy
- Identify specific patterns in historical demand data and match them with the best forecasting algorithms
- Compete with other common statistical forecasting techniques, enhancing the precision of future predictions.
4) AI in Inventory Management
AI also plays a crucial role in optimizing inventory management by:
- Automatically segmenting products into groups based on sales volume, unit price, total profit, and COV (Coefficient of Variation) to establish service level and investment targets.
- Tracking the sales behavior of each product throughout its life cycle and adjusting group membership based on changes in demand patterns.
- Supporting optimal strategies for inventory investment and allocation to support your sustainability goals.
5) AI in Fulfillment and Transportation/ Network Design
Effective fulfillment and transportation planning, and Network Design are vital for any supply chain operation. AI techniques help by:
- Efficiently matching available inventory to open orders, considering dynamic rules and priorities, whether based on channels or customer preferences.
- Optimizing transportation routes and modes based on real-time data and factors such as cost, capacity, and delivery times.
- Supporting what-if scenarios by estimating transportation rates on lanes (i.e., origin location, transportation mode, required service, destination location) for which you do not have commercial rate data.
- Evaluating multiple strategy scenarios in multiple dimensions to determine whether the differences among them are significant (i.e., the recommended strategy is within the error bounds of the inputs)
6) AI supports organizational learning by creating a corporate memory within an organization:
AI tracks every input, output, and change in data at a high level of fidelity. For instance, you can see the full history of imports, exports, edits at the userID level, stage in the workflow, time of transaction, and reason codes for changes. AI algorithms process this vast amount of data for root-cause analyses and alerts, for example, lower UARs than expected, high forecast bias, and low forecast value-add.
These insights result in input data corrections, algorithm enhancements, process improvements, and KPI target changes. AI effectively facilitates continuous improvement in supply chains!
Ah, if I ever make it to a beauty pageant, that will be my declaration.
Nilufer Durak is the Chief Operating Officer, Head of Customer Success at Solvoyo. Nil is a highly motivated technology executive, passionate about implementing Solvoyo’s bold autonomous supply chain vision with clients. With over two decades of experience in Corporate America, Nil has developed a deep understanding of customer success and operational excellence. She is best known for her boundless energy and ability to get things done. Currently, she is COO and Head of Customer Success at Solvoyo, a leading supply chain planning and analytics SaaS company based in Boston. Nil has also been an active member of women’s professional networking groups, advocating for women’s leadership roles in the technology and entrepreneurship fields.
