In today’s rapidly evolving market, the supply chain industry is undergoing a significant transformation, driven by the increasing demand for real-time data, seamless global operations, and sustainable practices. Enterprises are eagerly adopting digital technologies to enhance operational efficiency and resilience, yet they face substantial technical challenges. Chief among these are the integration of disparate data systems, managing the complexity of real-time data analysis across the global supply network, and ensuring robust data security and compliance with international standards.
These hurdles underscore the urgent need for advanced solutions that can not only address these challenges but also scale according to the changing dynamics of the industry. This blog explores how an intelligent supply chain platform, particularly through AWS, can meet these demands, offering the agility and insight that modern enterprises require to stay competitive.
Industry Trends
What we are seeing across the industry, according to the latest data from Gartner as reported by Supply Chain Digital, is a notable trend towards hyper-automation and the use of composite AI. These technologies combine multiple AI techniques to solve complex problems more efficiently, significantly boosting the performance and adaptability of supply chain systems.
These insights highlight the dynamic nature of the supply chain industry and the pivotal role of emerging technologies in shaping its future. Enterprises seeking to remain competitive must navigate these trends by investing in advanced technological solutions and adapting to the evolving regulatory and cyber threat landscapes.
Customer Challenges with Supply Chains
Customers are continually seeking innovative solutions, yet each innovation initiative is strategically aligned with specific business outcomes. Consequently, many enterprises are focusing on achieving measurable benefits such as:
Improve Customer Satisfaction: Enhancing the overall customer experience by ensuring products are delivered as expected and handling customer queries and issues more efficiently.
Improve Customer SLAs Metrics: Increasing the rate at which service level agreements are met, which directly contributes to higher customer satisfaction and trust.
Pick-up and Delivery: Boosting the efficiency and reliability of order pick-ups and deliveries, which is crucial for maintaining high service levels.
Improve Communication: Strengthening the communication channels within the supply chain as well as with customers to ensure all parties are informed and can make timely decisions.
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Improve in Operating Metrics:
- Number of Empty Miles: Reducing wasted resources and improving cost efficiency by minimizing the number of miles traveled without carrying a load.
- Tractor Utilization: Optimizing the use of available tractors to ensure they are being used efficiently, reducing idle times and increasing profitability.
- Driver Satisfaction: Focusing on the welfare and satisfaction of drivers, which can lead to improved retention rates and less turnover.
Each of these outcomes not only contributes to the direct operational efficiency of the supply chain but also helps in building a more responsive and agile system. Implementing an intelligent supply chain platform can leverage technologies such as AI, machine learning, and advanced analytics to monitor these KPIs and drive improvements across all levels of the supply chain operations.
Challenges with Current Architectures
Many organizations encounter significant barriers within their existing architectures that prevent them from fully leveraging their supply chain capabilities. Key challenges include:
Inconsistent Coding Across Systems: Different systems within the same supply chain use varying codes, which disrupts the seamless flow and access of information. This inconsistency extends from customer and vendor master data management to modern systems that employ APIs.
Data Integration Issues: The lack of consistent port codes across applications, especially with older mainframe systems still in use, complicates the integration of data. Many systems rely on manually entered order numbers, making it difficult to find common data elements that link different bookings across applications.
Manual Data Entry and Quality Issues: Manual data processes not only increase the risk of errors but also lead to data quality issues that affect the organization's ability to analyze and utilize information effectively.
Tribal Knowledge in Data Interpretation: Data interpretation heavily relies on individual knowledge and experience, which is not scalable or easily transferable within the organization.
Operational Delays and Communication Gaps: Challenges in tracking and communicating key operational metrics like shipment dwell times, delay notifications, and demurrage charges are prevalent. Systems that do not integrate real-time data sharing, such as GPS tracking for cargo, exacerbate these issues by failing to provide timely updates to all stakeholders.
These architectural inefficiencies underscore the need for upgraded systems that are integrated, automated, and capable of providing a unified view of the supply chain to enhance decision-making and operational effectiveness.
Why Customers are choosing AWS
Enterprises are increasingly choosing AWS Supply Chain to unlock significant value in their supply chain operations. This platform distinguishes itself by using advanced machine learning models and a robust data integration framework, offering deep insights into potential supply chain risks like overstock or stock-outs.
AWS Supply Chain enhances decision-making by providing real-time predictive insights on vendor lead times and maintenance schedules, which are critical for optimizing inventory levels and reducing operational downtime. Additionally, the ability to customize insight watchlists and receive immediate alerts about potential disruptions ensures that supply chain leaders can proactively manage risks and maintain continuity. These capabilities make AWS an attractive option for businesses looking to streamline their supply chain management and achieve greater efficiency and reliability.
Lets review a potential architecture
In the future state architecture, AWS Lake Formation will centralize management of the customer in a data lake, enhancing security and accessibility through a meticulously maintained data catalog that specifies data sources, access rules, and security policies.
In the data collection phase of our architecture, we employ Amazon Kinesis Data Firehose for its reliable capabilities in loading streaming data directly into the data lake. We establish Kinesis Delivery Streams to efficiently transport data from the customers data platform into the unified storage within the data lake.
AWS Data Exchange (ADX) to facilitate the subscription and integration of third-party data, such as weather information. This external data is crucial, as it enriches our predictive models, making them more accurate and reflective of real-world conditions.
Automated Glue Crawlers will facilitate dynamic data discovery and metadata extraction, populating the AWS Glue Data Catalog to make the data immediately searchable and ready for analytics. This catalog, structured logically with databases and tables, will include detailed metadata and custom attributes to streamline data discovery and usage.
Further integration with Active Directory will streamline user and permission management as customers expands the data lake to democratize data access, employing SAML assertions for secure user authentication.
In the storage phase of our architecture, the data storage strategy encompasses critical aspects such as the security approval for storage, required data volume, schema definition, compliance adherence, permission management, and data quality control.
Utilizing AWS Lambda, we initiate step functions and trigger AWS Glue jobs that facilitate the movement and processing of data from RDS/Aurora databases. This data, integrated with additional inputs from AWS Data Exchange (ADX)is then consolidated into a curated area designed specifically for enhanced reporting and analytics.
Management of these storage areas is efficiently handled using S3 Buckets and AWS Lake Formation, ensuring both security and accessibility, and supporting a streamlined data lifecycle tailored to organizational needs and regulatory requirements.
This description helps underline the strategic use of AWS services in ensuring that data not only is stored securely but also remains readily accessible for analysis and business intelligence purposes.
For data processing, AWS Lambda will activate workflows and Glue jobs to amalgamate data from various sources, including RDS/Aurora and the Service Platform, into a curated area managed by S3 Buckets and Lake Formation for advanced reporting and analytics.
ML using Amazon SageMaker supports the material demand planning process by overlaying the following:
Historical material consumption to predict material demand for future campaigns
Consumption-based data in the enterprise resource planning (ERP) software system for equipment as well as the equipment-installed base
This allows for calculating equipment and material failure rates (in other words, mean time between failure) using analytics. This also provides maintenance planners with an automated forecast for maintenance activities, material demand, and visibility in historical material consumption.
It also makes it easier for them to identify relevant task lists and bill of materials for future MOs.
Conclusion
This detailed vision of an advanced AWS-based architecture represents just one of the many possibilities for leveraging AWS to enhance supply chain operations.
Each customer's journey will be unique, shaped by specific business needs and operational contexts. AWS offers a versatile and powerful platform that can be tailored to meet diverse requirements, ensuring that every organization can find the right set of tools and technologies to drive their supply chain success.
As we all know, AWS is always looking for ways to continue to innovate and expand their services and capabilities, AWS remains committed to providing scalable, secure, and efficient solutions that empower businesses to realize their supply chain goals in increasingly effective ways that allow customers to unlock new features to enhance their customers experience with supply chain and innovation.
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