Revolutionizing Supply Chain Management with Automation: Improving Efficiency and Performance

Supply chain management has constantly been a critical element of any enterprise, as it plays a major role in ensuring an efficient flow of goods and services from manufacturers to end consumers.

However, with the increasing complexity of the logistics and transportation industry, traditional techniques of managing supply chains can no longer keep up with the ongoing digital disruptions and increasing customer demand levels. As such, businesses must embrace emerging automation technologies to streamline their supply chain operations, drive better business outcomes, and stay competitive in today’s globalized economy.

Main Aspects of Automating Supply Chain Management

A supply chain is a complex logistics network that encompasses all the organizations, individuals, resources, technologies, and activities involved in the entire process of producing and selling goods–from procurement and supply of raw materials to manufacturing, quality inspection, distribution, storage, delivery to end consumers and recovery.

Supply chain management enables enterprises to acquire raw materials more easily, improve the efficiency and productivity of their manufacturing processes, reduce quality issues, deliver finished products to end users more quickly, and navigate product returns with ease, ultimately driving business growth and boosting customer satisfaction. Hence, optimizing the existing resources in your supply chain can help maximize its efficiency and performance, thereby improving value, both within the enterprise and for the end customers. An effective way to improve the efficiency and performance of supply chain management is automation.

Supply chain management automation refers to deploying several digital technologies that automate traditionally manual activities in a supply chain to connect applications, streamline workflows, and improve efficiencies. These digital technologies include:

Robotics

Robotics technology aims to design and develop intelligent machines (robots) that assist human workers in various ways. This technology is rapidly transforming supply chain management through the automation of repetitive and labor-intensive manual tasks, such as data entry, quality inspection, sorting, classifying of products, storing, and material handling, resulting in cost reductions as well as increased efficiency and productivity.

In supply chains, different types of robots like Automated Guided Vehicles (AGVs), Cobots, articulated robots, and Autonomous Mobile Robots (AMRs) are being used to pick and pack products, assemble orders, sort and track packages, load and unload containers or vehicles, and for transportation within smart warehouses.

Amazon Retail, for example, has largely implemented logistics robots–robots that automate and optimize the process of moving and storing commodities across the supply chain–to transport products within its warehouses, thereby simplifying logistics operations and reducing delivery times. Integrating robots in supply chain management gives businesses a competitive edge by increasing their operational efficiency and allowing for more effective utilization of human resources, further propelling their market growth.

Robotic Process Automation (RPA)

Robotic Process Automation uses software robots or bots that mimic human actions and communicate with digital systems to automate routine, repetitive tasks in various industries.

In supply chain management, RPA automates low-value, repetitive, time-intensive, and clerical tasks such as data entry, inventory management, compliance reporting, and customer order processing, freeing human workers to perform high-value tasks. This also streamlines logic-based processes within the supply chain by enhancing speed and eliminating human error. Essentially, RPA-based solutions allow supply chains to scale up faster and readily meet supply requirements with increasing product demand.

Artificial Intelligence (AI)

Artificial Intelligence (AI) is the theory and development of digital computer systems or computer-controlled robots able to perform tasks normally requiring human intelligence and skills, like visual perception, decision-making, speech recognition, and problem-solving capabilities.

AI technology can be used in supply chains to process and analyze large amounts of supply chain data and develop cognitive predictive models to help businesses anticipate future customer demands and make well-informed decisions regarding supply chain management. This is possible as AI-generated models can identify common buying patterns and trends of customers as well as when they are likely to change. Also, by analyzing data at different levels of the supply chain, AI algorithms can detect potential risks or problems and generate actionable insights to prevent disruption.

In addition, companies can use AI-powered solutions to monitor login activities, network traffic, and any irregular operations on their servers. Moreover, AI-based automation tools can be used to ensure efficient warehouse management and accurate inventory control, as these tools are helpful in preventing inadequate stocking, unexpected stock-outs, and overstocking. AI algorithms can also analyze workplace safety data and alert the appropriate personnel about possible risks, consequently enhancing worker and material safety.

Overall, integrating AI-driven solutions into supply chain management allows companies to predict customer demand trends, optimize inventory levels, streamline warehouse operations, enhance workplace safety, execute smarter transportation planning, optimize delivery routes, improve efficiency, attain higher productivity levels, and reduce operations costs.

For example, DHL Supply Chain has developed and implemented an AI-powered software solution–known as IDEA–to address its e-commerce challenges. This software solution is specifically used in DHL-operated e-fulfillment warehouses to optimize workload distribution, improve warehouse staff utilization, optimize route selection within the warehouse, and prioritize more time-critical shipments, thereby reducing product delivery times and enhancing customer satisfaction.

Also, the company uses AI-powered robots to classify and sort items for shipment based on pre-defined characteristics, which has significantly improved order-fulfillment processes in DHL-managed warehouses. In addition, DHL’s AI-driven systems featuring powerful data analytics capabilities are being used to monitor shipment movements andidentify any real-time issues within the supply chain.

Machine Learning (ML)

Machine Learning (ML) is an Artificial Intelligence technique that focuses on using past data and algorithms to teach computer systems to imitate the way humans learn, gradually improving accuracy. It simply refers to the algorithms and technologies that enable computers to identify patterns, make decisions, predict outcomes, and improve themselves through experience.

A major challenge in supply chain management is accurately forecasting the future demands for production. However, Machine Learning algorithms can analyze huge amounts of diverse data sets and quickly pinpoint new patterns in the supply chain while continually learning in the process. For example, these algorithms can precisely analyze market trends, customer behavior, and historical sales data to forecast production demands. This is greatly improving the accuracy of demand forecasting in supply chains.

For instance, McDonald’s Supply Chain has implemented a Machine Learning-powered decision technology to forecast what menu offerings will likely drive the most sales in its drive-through restaurants. This technology evaluates purchases made by other customers and accordingly updates the menu offerings presented on the McDonald’s ordering digital displays. It also updates the menu displays based on current order selections, weather conditions, time of the day, popularity of the items on menus, and current restaurant traffic.

Internet of Things (IoT)

The Internet of Things (IoT) is a collective network of interrelated physical objects, such as computing devices, digital and mechanical machines, etc., that are provided with unique numerical identifiers (i.e., IP addresses) and the ability to transmit data over wired or wireless networks without the need for human-to-computer or human-to-human interactions. This allows digital monitoring or even control of the physical world.

IoT plays a key role in automating several aspects of supply chain management, including optimizing space and resource utilization and automating warehouse operations. It combines the power of Internet networks, mobile and cloud computing, and analytics to change how organizations manage their logistics and supply chain operations.

For instance, IoT in smart warehouses and logistics centers enables automation of processes whereby IoT-connected devices such as sensors and trackers are embedded in products and containers to track their location, humidity levels, temperature, and other essential parameters. This helps to streamline the flow of goods in the warehouses, increase visibility into stock availability and inventory levels, optimize logistics operations, minimize material waste, and reduce delivery times.

Maersk Line, the biggest container shipping company in the world, has largely implemented IoT solutions in supply chain management. By using containers and vehicles equipped with IoT-enabled sensors, which are then connected to computer systems through GPS, Wi-Fi networks, and other similar technologies, the company can monitor product conditions during transportation, track the location of its containers in real-time, and optimize transportation routes for maximum efficiency. In doing so, Maersk Line has improved the operational efficiency of its supply chains, thereby achieving greater customer satisfaction and setting a precedent for the shipping industry.  

Predictive Analytics

Predictive analytics is aggregating and analyzing multiple datasets to forecast future behavior. The process uses statistical models, data analysis, Machine Learning algorithms, and Artificial Intelligence (AI) techniques to identify patterns likely to predict future outcomes based on raw historical data.

As the volume of supply chain data grows, it is becoming more and more challenging to identify new trends and patterns in supply chains. However, with predictive analytics, businesses can now analyze the vast amounts of available data and make better-informed decisions on improving supply chain management performance. For example, supply chain managers can use sophisticated statistical models to determine the optimal inventory levels to satisfy customer demands while minimizing warehouse stock.

Also, predictive analytics allow businesses to anticipate possible anomalies within their supply chain networks and determine the most appropriate courses of action given past conditions. This technology supports supply chain management through network design, supplier management, accurate inventory control, and warehouse optimization. Hence, by forecasting customer demand, optimizing inventory levels, and improving shipment planning with predictive analytics, businesses can improve the efficiency and productivity of their operations, enhance customer service, and reduce operational costs.

FedEx Corporation is an example of a supply chain that has embraced predictive analytics. The conglomerate combines predictive models and near-real-time performance data to enable its customers to optimize transportation routes and maintain timely deliveries of high-value packages.  Also, it uses Machine Learning techniques to navigate robotic deliveries; these techniques enable FedEx’s autonomous robots to detect obstacles en route and change direction accordingly to provide same-day deliveries.

Cloud-based Collaboration

Cloud computing is the on-demand delivery of different IT resources –including data storage, Artificial Intelligence, software applications, analytics, databases, networking, servers,  and processing power– over the Internet(“Cloud”) without physically installing and maintaining these resources on-premises.

Cloud computing technology is revolutionizing supply chain management by providing safe and secure centralized storage of massive volumes of data without requiring dedicated hardware resources prone to expensive downtimes. As such, supply chain managers can access the decentralized data and other applications remotely, allowing them a more holistic view of the supply chain operations in real time. This helps streamline communication in the supply chain while enhancing real-time decision-making.

In addition, Cloud-based solutions can readily integrate with other business software systems such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP), allowing easier access to the data stored in such systems. This, in turn, enables managers to make better-informed decisions and reduce redundancies in supply chain networks. Integrating cloud computing solutions into supply chain management can improve decision-making and enable companies to predict market changes, streamline shipping processes, and better anticipate possible disruptions across their supply chain networks.

For example, United Parcel Service, Inc. (UPS) has adopted a cloud-based computing platform–the UPS Order Watch – to increase its customers’ visibility of shipments, improve transportation planning, and cut operational costs. This Cloud solution uses Google’s flexible Cloud bandwidth and advanced data analytics to enable UPS customers to collaborate more efficiently with international suppliers and better manage their inventories and other supply chain operations.

Blockchain Technology

Blockchain is a securely shared ledger or database of decentralized data. This technology allows secure and transparent sharing of information within a business network.

In supply chain management, implementing Blockchain technology provides enhanced transparency and security in multistep transactions that require verification and traceability.  This distributed digital ledger system can secure transactions, reduce fraudulent activities, significantly improve traceability, speed up data transfer processing, reduce compliance costs, and facilitate better collaboration between multiple supply chain networks.

For example, Walmart uses a decentralized digital ledger system called the IBM Food Trust platform that runs on the IBM cloud. This platform leverages the power of Blockchain technology to enhance food safety and traceability of food products throughout the farm-to-store journey.

Also, Walmart Canada has implemented a Blockchain-based system called DL Freight. This innovative Blockchain platform was created to facilitate transparent and secure data sharing and seamless communication and to address the complex challenges associated with payment processing between Walmart Canada and 70 of its third-party freight carriers. So far, DL Freight has enhanced the overall efficiency of supply chain operations in Walmart Canada.

Ways in which Automation is Transforming Supply Chain Management

Supply chain management is undergoing a dramatic transformation due to automation, with the adoption of cutting-edge technologies streamlining processes, increasing efficiency, and bolstering the capacity for making strategic decisions. Here are various ways in which the aforementioned automation technologies are revolutionizing supply chain management.

Enhancing end-to-end Visibility

Combining Machine Learning and Artificial Intelligence with big data analytics, IoT-connected sensors, real-time monitoring, and cloud computing allows businesses to see deeper into their supply chains than ever. And since more supply chain data is now available, firms can predict future consumer demands more accurately and make better decisions based on hard facts.

The use of RFID (Radio-Frequency Identification) chips and other sensor technologies with Internet connectivity in supply chains is a classic example of automation-driven transparency. The RFID sensor technology allows businesses to gather data at every logistical touchpoint, from the procurement of raw materials to the distribution of finished products. As such, companies can save money and effort by keeping track of their stock levels in real-time and acting on any changes immediately. In addition, innovative sensor technologies also aid in preventing revenue losses and operational inefficiencies often caused by missing inventories and shipments.

Improved Demand Prediction

Demand forecasting is a particularly difficult task in today’s fast-paced corporate world. But businesses can now accurately anticipate consumer demand using various automation technologies like Predictive Analytics, Machine Learning, and Artificial Intelligence. These technologies enable supply chain managers to understand demand drivers and variations better by analyzing large volumes of data from multiple supply chain networks. They can then use the analysis reports to improve production planning, optimize inventories, and maximize resource distribution.

Notably, Starbucks uses information from its mobile ordering app to forecast consumer demand for particular menu offerings, which helps the firm plan its inventory and labor needs better.

Accurate Inventory Control

Hyper-automation is greatly improving inventory management in supply chains by leveraging leading-edge automation technologies, including Machine Learning (ML), Advanced Analytics (AA), Artificial Intelligence (AI), and Robotic Process Automation (RPA),  to provide end-to-end automation of tasks such as tracking of inventory levels, logistics management, and re-ordering of products as necessary. Also, using predictive analytics, companies can forecast consumer demand, optimize inventory levels, and prevent stock-outs.

In addition, customized hyper-automation solutions such as barcode and RFID scanning for automatic invoicing and location-wise stock management are assisting in optimizing inventory control across warehouses or storefronts and automating order fulfillment. Generally, these innovations boost operational efficiency in supply chains, improving customer service by processing orders more accurately and quickly.

Improved Warehouse Optimization

Artificial Intelligence of Things (AIoT) –the convergence of Artificial Intelligence (AI) capabilities and the Internet of Things (IoT) infrastructure– allows real-time tracking of inventory levels, detection of stock shortages, and precise location-tracking of commodities within warehouses. This has significantly improved warehouse optimization and inventory management while ensuring the timely delivery of end products to consumers.

Better Fleet Management

Using the location-tracking capabilities of Artificial Intelligence of Things (AIoT), organizations can optimize transportation routes and track the location of their delivery vehicles in real-time. This can, in turn, speed up deliveries, reduce fuel usage and related costs, and enhance customer gratification. Also, equipping delivery trucks with sensors monitoring fuel usage can greatly reduce greenhouse gas emissions and support sustainable logistics.

Increased Waste Reduction

One of the greatest positive impacts that supply chain automation can have on waste reduction is to minimize the waste before it occurs. For example, using advanced design and modeling tools such as Computer-Aided Manufacturing (CAM) and Computer-Aided Design (CAD) software has enabled companies to identify eco-friendly materials they can integrate into their digital product designs.

In addition, the development and implementation of last-mile delivery vehicles, such as the drones and bots used by FedEx and Amazon for same-day deliveries, significantly reduce supply chains’ environmental footprints. Moreover, the proliferation of 3D printing technology has enabled companies to produce goods locally, reducing waste and carbon emissions while expanding consumers’ ability to tailor their purchases to their tastes.

In Summary

By adopting cutting-edge automation technologies such as RPA, robotics, Artificial Intelligence (AI), cloud computing, Internet of Things (IoT), Blockchain, Machine Learning, and predictive analytics, businesses can improve their ability to manage the intricacies of today’s globalized supply chains, boost productivity, reduce operational costs, and provide better customer service.

This entry was posted on January 8th, 2024 and is filed under Automation, Electrical, Technology. Both comments and pings are currently closed.