As supply chains and logistics become increasingly digitalized, organizations are looking to develop capabilities for a digital ecosystem to make their operations more resilient and agile. It is estimated that in the coming times, at least one-third of organizations will have established a digital ecosystem to enhance visibility and collaboration with external partners. By 2026, over 65% of supply chain planning decisions made in the short-term horizon will be automated or autonomous using hyperautomation.
In addition, as a part of sustainability initiatives, at least half of leading supply chains are expected to achieve net-zero carbon emissions through green initiatives and circular economy practices by 2030.
Hyperautomation ranked #1 on Gartner’s list of Top 10 Strategic Technology Trends, is rapidly becoming an essential strategy for IT and business leaders to consider in their organization’s digital transformation efforts.
Hyperautomation leverages next-generation technologies, including Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), and advanced analytics (AA), to deliver end-to-end automation that goes beyond RPA. While traditional automation improves work efficiency, hyperautomation provides an extra edge to analyze, design, measure, and reaccess, resulting in quicker and error-free operations.
The current business landscape’s uncertainty highlights the need for supply chain and logistics organizations to future-proof their operations, and hyperautomation provides the edge they need to achieve digital operational excellence. Since hyperautomation is seen as the future of “intelligent automation,” businesses are adopting it to increase efficiency and adaptability in the post-pandemic era.
How can Supply Chain and Logistics Enterprises benefit from Hyperautomation?
With the growing complexity of the transportation and logistics industry, organizations are increasingly turning to hyperautomation to streamline their operations and drive better business outcomes.
Hyperautomation enables process efficiencies, alleviates tight margins, and increases visibility across the entire supply chain ecosystem. Organizations can leverage AI-driven process models to streamline workflow between different departments, human workforce, RPA bots, and other intelligent tools to drive better business outcomes.
Here are some examples of how automation with AI & ML can transform the transportation and logistics industry:
Route Optimization: Hyperautomation can automate tasks such as optimizing routes for delivery vehicles and reducing travel time and costs using AI-driven process models. Trigent is a proud silver partner with HERE Technologies & SAS Partner, allowing us to provide integrated Telematics for better control and route management for your fleet. With deep expertise in transportation and logistics, Trigent can also provide comprehensive support for route optimization.
- Inventory Management: Hyperautomation can automate tasks such as tracking inventory levels, reordering products when necessary, and managing logistics, using AI and advanced analytics to forecast demand and optimize inventory levels. Trigent Software Inc. can help with Inventory Management using Hyperautomation by developing custom applications that suit your needs and requirements in the supply chain and logistics industry. Some of the real-world applications of Hyperautomation for Inventory Management are:
- Barcode & RFID Billing facility: This application can help automate billing by automatically scanning barcodes or RFID tags on products or packages and generating invoices.
- Location-wise stock management: This application supports optimizing inventory levels by tracking the location and quantity of goods or materials across different warehouses or stores.
- Order fulfillment: This application automates the order fulfillment process by matching orders with available inventory, assigning delivery routes, notifying customers, etc.
- Load/Tender Processing: Hyperautomation uses RPA and AI to streamline load tendering operations by automating data entry, validation, and formatting.
- Carrier Management: With AI and advanced analytics, hyperautomation optimizes carrier operations by automating carrier relationship management, performance tracking, and carrier discovery
- Load Matching: Using advanced analytics and AI, hyperautomation streamlines tasks such as matching available loads with suitable carriers, tracking carrier capacity, and identifying new carrier options, to optimize the entire load-matching process.
- Rate/Quote Management: By leveraging AI and ML, hyper-automation streamlines processes like obtaining and analyzing carrier shipping quotes, negotiating costs, and managing rate contracts.
- Contract Management: Hyperautomation streamlines contract management by automating document processing, invoicing, and payment tracking using RPA and intelligent document processing.
- Accurate Forecasting: Accurate forecasting plays a vital role in supply chain management with approximately 50% of organizations shifting investment towards applications that support enhanced analytical capabilities by 2024. Furthermore, advanced analytics can help to accelerate logistics competencies.
The use of real-time data from digital twins and IoT devices and the transformation of that data into AI and machine learning algorithms results in the most accurate data forecasting.
This predictive analysis can best be utilized to understand demand and supply fluctuations, product pricing, and malfunctioned machines. Intelligent bots observe these data from AI models and send alert information based on prediction to employees to take immediate action.
These predictions offer real-time insights on:
- Budget planning
- Inventory management of particular locations and times.
- Maintenance of machines
- Analysis of market expansion.
Automation Trends
Let’s take a look at the following trends in Automation that can shape and drive supply chain logistics management:
- Data Analytics: In the current digital landscape, businesses have volumes of data from different sources. Accurate data analysis allows companies to derive meaningful insights, and AI and ML can help in predictive and prescriptive analytics.
- Hyperautomation: The cumulative usage of AI, ML, and RPA to automate not only repetitive processes but also deliver real-time data insights for warehousing, production, logistics, demand forecasting, and a lot more.
- Enterprise-driven Robots: Robots have revolutionized supply chain management by focusing on different functions and improving adaptability to scale as needed. Furthermore, the enterprise-centric robots and the streamlined, automated equipment in the supply chain will communicate with each other, removing blockheads.
- Digital Supply Chain Twin: The digital twin of the supply chain will support the end-to-end supply chain by providing real-time insights for accurate decision-making.
- Autonomous Systems: Autonomous systems like drones, robots, and vehicles are going to play an indispensable role in the supply chain. These technological capabilities will streamline supply chain management with greater visibility and efficiency.
- Cybersecurity Mesh Architecture: Cybersecurity mesh architecture refers to the ecosystem of tools and systems that controls the devices, tools, and systems. The access point to each device, tool, and system is adequately secured and controlled by a centralized authority point, ensuring centralized procedures and policies support enterprises operating from anywhere.
- Sustainability Factor: As the reliance on technology grows, the automated supply chain model needs to be sustainable. All the aspects of supply chain planning, procuring, manufacturing, and distribution need to be agile to accelerate digital transformation.
Benefits of Hyperautomation
Hyperautomation can bring several benefits to the Transportation and Logistics industry, including:
- Improved operational efficiency: Automating repetitive and time-consuming tasks such as inventory management, route optimization, and shipment tracking, hyperautomation can help organizations optimize their operations and increase productivity.
- Enhanced supply chain visibility: Digitization of crucial business processes through hyperautomation can provide end-to-end supply chain visibility. It allows organizations to identify bottlenecks and take preventive measures to avoid disruptions in goods clearance. This visibility can also enable faster responses to changing global trade dynamics.
- Better customer experience: Hyperautomation can help transform the customer experience journey by providing real-time visibility of shipment status and delivery, anticipating customer needs, and improving support processes.
- Improved compliance: Hyperautomation can automate tasks such as monitoring transactions for compliance with regulations, such as GDPR and FACTA, reducing the risk of non-compliance.
- Reduced operational costs: By automating tasks such as load/tender processing, document management, and rate/quote management, hyperautomation can help organizations save costs, reduce errors, and improve efficiency.
Conclusion
Overall, hyperautomation enables supply chain logistics to become more agile, efficient, and competitive in the face of growing challenges and demands. Human augmentation and matured AI will automate the supply chain with minimal human involvement in the future. The human-centric tasks for supply chain management in the future will cater to defining strategies, driving innovation, decision-making, customer service, and managing AI data to optimize digital acceleration. Therefore, logistics and supply chain management can leverage advanced technologies to optimize operational efficiency and sustainability to stay relevant in the long run.
Leverage hyperautomation with disruptive technologies to deliver automation that goes beyond RPA.
