Enhancing supply chain agility with AWS Supply Chain Vendor Lead Time Insights

Introduction 
Supply chain agility is essential for organizations to remain competitive. Consumer demands, new technologies, and economic conditions can disrupt the balance between supply and demand. Supply planning, or accurately estimating the quantities of products, raw materials, or components needed to meet customer demand, represents one key supply chain challenge. Effective supply planning helps organizations avoid stockouts, minimize excess inventory, and optimize resource utilization. This challenge affects a wide range of industries. In healthcare, siloed data and limited visibility across clinics, hospitals, and medical distribution networks make it problematic to identify consumption and stock levels accurately. This can lead to costly investments in additional warehouse space or the use of recalled or expired products due to poor inventory management. The retail sector also faces difficulties in building a normalized, end-to-end view of true customer demand and placing the right product inventory at the right locations, partly due to a lack of preparedness against dynamic consumer behavior and demand shifts. In manufacturing and automotive industries, global supply chain networks with components and finished goods traversing various systems operated by different partners introduce time lags, data fragmentation, and inconsistent data formats. This obscures accurate inventory insights, making it extremely difficult to determine optimal inventory levels and positioning. The impacts can be severe, with shipping delays, parts shortages, and transportation bottlenecks causing negative profit impacts and disruptions.

A medical technology company faced business challenges with inaccurate contractual vendor lead time data used in their supply planning, negatively impacting inventory levels, supply planning accuracy, and customer order fill rates. While internal initiatives to improve vendor lead time detection were underway, these manual efforts required substantial time investments and were untested. Aligning with their AI-based business strategy, the company deployed AWS Supply Chain Vendor Lead Time Insights, which quickly provides a tested and proven solution leveraging machine learning (ML) models to identify vendor lead time issues impacting their operations.

This medical technology company selected AWS Supply Chain Vendor Lead Time Insights to improve supply chain operations visibility. The clear, ML-based insights highlighted the most problematic vendors, enabling focused actions to enhance supply planning. They discovered that, on average, a significant percentage of sourced products breached expected contractual lead times, with these products being delivered later than anticipated. This invaluable insight empowered the company to identify the top vendors responsible for a substantial portion of instances where contractual lead times were exceeded. Equipped with this data-driven intelligence, this company can take targeted actions, including updating their planning system’s master data and conducting more impactful vendor negotiations.

This medical technology company will continuously refresh their Vendor Lead Time Insights by appending the latest transactional data quarterly to maintain accurate and up-to-date information for their supply planning process. Additionally, they are exploring the expansion of AWS Supply Chain to provide similar visibility into internal transfer lead times, thereby further strengthening their supply chain resilience.

This blog post describes how AWS Supply Chain Vendor Lead Time Insights enables improved visibility into vendor lead times, increases supply planning accuracy, and streamlines supply chain operations. It also includes a brief overview of the Vendor Lead Time Insights capability, highlighting the solution’s role in paving the way for optimized inventory management and enhanced customer satisfaction.

Vendor Lead Time Insights to improve supply chain planning
AWS Supply Chain is a cloud-based application that improves planning accuracy by detecting lead time variability across the supply chain. This empowers organizations to implement better cost management strategies without compromising customer satisfaction. By analyzing historical order data, shipments, inventory movements, and other relevant factors like seasonality patterns, product characteristics, supplier performance, origin/destination locations, and transportation modes, AWS Supply Chain Vendor Lead Time Insights generates data-driven insights into real-world vendor lead times. The ML models continually learn from this data to identify deviations from expected lead times and provide updated projections tailored to specific sites, products, and transportation modes instead of relying solely on contractual estimates.

Traditionally, planners have used average lead times from suppliers along with forecasted demand to determine required inventory levels. However, this method fails to account for real-world fluctuations due to transportation delays, port congestion, or supplier disruptions. To compensate, planners often add arbitrary safety stock buffers, forcing companies to carry excess inventory, which is an inefficient and costly approach. Relying on averages leads to inaccurate plans, regardless of whether lead times are longer or shorter than expected. Vendor Lead Time Insights’ ML-powered capabilities empower supply chain professionals with granular insights to make informed decisions and improve their planning processes. For instance, if historical data reveals that a vendor consistently delivers five days later than quoted for shipments to a specific distribution center, the ML model will recommend adjusting the lead time estimate accordingly. Organizations can generate and export these vendor lead time recommendations for their entire product-site-location combinations with a single click, enabling data-driven supply chain planning and execution.

Please visit our earlier blog to learn about AWS Supply Chain instance creation prerequisites and initial setup steps. Please also refer to the Insights user guide for detailed module setup information. The following screenshot shows the Lead Time Insights dashboard that will alert you on product lead time deviations, focusing on critical factors such as the vendor’s transportation mode and source locations. The application also enables an exportable file with visibility into all product-site-vendor lead time recommendations (configured within a particular insight watch list) for consumption. You can select any of the rows on the dashboard to get more information.

Double-clicking on a row takes you to the following screen that displays additional details on the different purchase order delivery performance of the part and supplier. The application also provides ML-generated lead time recommendations tailored to the product, site, and vendor and based on the historical performance. With AWS Supply Chain Lead Time Insights, you gain the ability to detect lead time variances with greater precision. 

Conclusion
In today’s ever-evolving business landscape, effective supply chain management has emerged as a critical differentiator. Organizations must adapt to rapidly changing consumer demands, technological advancements, economic volatility, and intense competition. Traditional supply planning methods that rely on static data and average lead times often fall short in addressing these dynamic challenges. However, by integrating machine learning (ML) into their supply planning processes, businesses can leverage AWS Supply Chain Lead Time Insights that empowers data-driven decision-making and enhances operational efficiency.

The medical technology company mentioned earlier has realized significant results from AWS Supply Chain Vendor Lead Time Insights, enabling focused efforts toward more effective supplier negotiations and accurate lead time data in their planning systems. They also expect continuous improvements in the following areas:

• Inventory optimization and working capital efficiency: Inventory level improvement and excess stock reduction, freeing up working capital for other strategic investments by accurately accounting for real-world lead time variability.
• Supplier contract compliance and performance management: Proactively identify and address non-compliance issues based on data-driven insights into vendor lead time performance, fostering better supplier relationships and accountability.
• Customer service levels through increased plan reliability: Improved customer service levels because ML-generated lead time projections enhance forecast accuracy and reliability.

AWS Supply Chain Vendor Lead Time Insights represents a transformative approach to supply chain planning. It applies powerful machine learning algorithms to the available data to reduce organizational reliance on lead time averages-based planning. Instead, organizations can embrace a more accurate, data-driven approach to lead time predictions. This approach optimizes inventory levels and fosters stronger vendor relationships through improved visibility and accountability. Organizations can increase profitability by reducing excess inventory and associated costs. Ultimately, the improved supply chain performance enabled by AWS Supply Chain Vendor Lead Time Insights leads to increased customer satisfaction.

Getting started with AWS Supply Chain is simple and doesn’t require any upfront licensing fees or long-term commitments. Begin your journey with the following three steps:

1. Learn about AWS Supply Chain: Visit the AWS Supply Chain website to understand the product’s features and capabilities.
2. Get a technical overview: Explore the AWS Workshop Studio for a self-paced technical walkthrough. You’ll learn how to create an instance, ingest data, navigate the user interface, create insights, and generate demand plans.
3. Start using AWS Supply Chain: Once you’re ready, access the AWS Console and begin streamlining your supply chain operations with AWS Supply Chain’s efficient, data-driven management tools. You can also access the user guide for detailed setup instructions and additional guidance.