Digital Supply Chain Management (SCM) is seen as the next logical step in the evolution of the manufacturing industry. The traditional approach is no longer in contention, considering the influx of information that needs to be managed between suppliers, vendors, and customers. From the distribution of raw materials to the creation of the end-product, each step in the supply chain journey requires cohesion across regulatory policies, quality controls, pricing dynamics, labor contracts, and vendor relationships.
- Time-consuming, error-prone, and expensive due to siloed processes across disparate departments
- Unable to keep pace with the global market due to lack of scalability
- Unable to meet surging customer expectations due to complex order and delivery management
- Decreasing market share due to lack of product innovation
- Micro-optimization due to lack of real-time data
Today, future-ready manufacturers are easily scaling such labor intrinsic tasks by utilizing new-age technologies like Robotic Process Automation (RPA), without complicating processes or incurring high expenses. Through automated workflows, they are able to reduce manual effort while enhancing operational efficiency and workforce productivity. RPA not only helps them in tying together any number of cross-functional processes, but it also automates the unification of interactions that normally take place between siloed enterprise systems. This makes it easy to ensure compliant, high-quality, and error-free processes in any part of the SCM.
- Order and Payment Processing
- Inventory Management
- Vendor Management
- Communication Automation
- Shipping and Logistics Management
- Customer Relationship Management
- Strategic Supply Planning
Order and Payment Processing: RPA can take control of the order-to-cash cycle by connecting all the manual touchpoints in the supply chain. From product selection and payment processing to order confirmation, it automates the back-office tasks required to manage these interconnected parts. It also provides predictive data analytics that leads to enhanced decision-making for inventory restocks. Hence, the workforce gets more time to focus on customer-centric activities without being burdened with ensuring timely delivery and fulfillment accuracy.
Shipping and Logistics: RPA unlocks the ability to scale shipping and logistics operations in real-time. It provides an agile platform to increase distribution channels and ensure inter-warehouse connectivity by centrally managed all the business processes. Other benefits include automation of shipping labels, RFID and GPS-based tracking systems, and e-signature capture facilities. RPA also helps in automating returns management processes, which reduces liability for the business and increases customer delight.
Inventory Management: RPA can help detect inventory movement and automatically alert the concerned departments in case the warehouse is full, or there is a need to halt orders. It also assists in finding another warehouse where the goods can be transferred. They can also determine stockpiles in warehouses to help facilitate the entire inventory management process and avoid problems like delayed orders from factories.
Vendor Management: Manually selecting the right vendor can be an arduous and time-consuming task. It may involve deciphering loads of paperwork to ensure operational legitimacy and creditworthiness, waiting for quotations, and vendor enrollment. Through RPA implementation, these activities can be carried out without human intervention. It can automatically evaluate vendors, execute credit checks, and validate documentation before onboarding them seamlessly into the system.
Strategic Supply Planning: RPA plays a significant role in improving the overall supply-and-demand planning in the manufacturing sector. It puts the power of real-time data in the hands of the SCM owners to make decisions that matter in the long run. Through AI and ML technologies, RPA collects, analyzes, and integrates historical and current data to predict futuristic insights with a high degree of accuracy.