By: Henry Jackson
Technology development is moving rapidly, assisted by advances in semiconductor technology and the evolution of computer science, specifically data science. The miniaturization of electronic components and the ability to communicate over various protocols have ushered in an unprecedented era in the supply chain world. Equipped with the power of AI Supply chain advancements and the agility of IoT Supply chain dynamics, the Supply chain is witnessing new levels of visibility, real-time decisions, and efficiency.
Unlocking Growth in Logistics: The Symbiotic Role of AI and IoT in Supply Chain Excellence
AI and IoT have disrupted the domain of the supply chain. They have reinvented how goods are transported from point A to point B. Not only have they successfully improved the overall efficiency and speed of transportation, but they have also intervened promptly to enhance the quality of the delivered goods. This results in delivering safer products, whether perishable goods or vaccines. Advancements in the AI Supply Chain are often considered a salve to solve all the issues related to any operational task. While it can be true to some degree, the success of AI and machine learning in logistics largely depends on the data it has access to. Thanks to IoT and the ability to generate data points across the value chain, we have thousands of metrics and data points on which you can train new-age AI systems. The decision-making is only as good as the data available for the systems to train upon. So IoT Supply chain dynamics and AI go hand in hand where IoT, along with its umpteen number of widespread sensors, whether those sensors can detect temperature or humidity, relay real-time GPS coordinates, or report any significant displacement or shock that the delivered package has gone through. In all cases, the treasure trove of data points is what any AI-based system consumes. While high speed and efficiency are essential factors, they also directly determine the enterprise’s profitability. Mckinsey says, “Successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent, compared with slower-moving competitors.”
Navigating the Future: Revealing the Impact of AI in Supply Chain Optimization
The full force of AI Advancement in the Supply Chain is displayed when applied to logistics. Whether it’s monitoring the fast-paced conveyor belts of a warehouse or determining the most optimized traffic route factored for disruptions and weather, AI determines and displays innate authority to make the right calls. The business of the supply chain demands precision and time sensitivity. Both these traits are enhanced for performance when you bring them in. University of Cambridge, Department of Engineering, IfM has commented on how data-driven analytics offers potential in supply chain reducing disruption and improving efficiency. It highlights that “A combination of three emerging developments is changing the game: The first is computational power, enabling us to do calculations in real-time; secondly, powerful new algorithms can automate analysis and decision-making; and crucially, new and previously untapped data sources are emerging. “. AI brings unparalleled benefits, but the benefits can be grouped into the following major areas.
Inventory insights and AI-powered systems can analyze warehouse layouts and determine the most efficient process to manage inventory tracking using IoT, picking, and packaging. It can analyze the route patterns of various incoming supply chains and advise on the proper inventory placement, thereby improving productivity and channeling the right cost savings. Inventory insights, with their capability to analyze large datasets using machine learning in supply chain dynamics, bring the necessary supply chain automation. Efficient Operations: hundreds of hours are potentially wasted with goods waiting in the loading docks when we try to ascertain how best to manage inventory using manual methods. AI-powered algorithms can go beyond analyzing inventory levels and get ahead and predict potential demand. This analysis gives businesses a leg up on getting ahead of perceived demand and staying ahead of the competition. Logistics companies face many scenarios in which they need to make critical decisions. One is rooted in season-based demand; internationally, there are multiple holiday calendars based on the country or region they operate in. AI-based algorithms help analyze the key trends and understand the ebb and flow of demand.
Using multiple region-focused parameters, AI software can assist in real-time decision-making. Organizations worldwide are developing tools to analyze hundreds of data points generated by their end users and systems operational in their warehouses and outlets. These insights create and predict demand for a robust supply chain. Demand Management is another area in which AI has a significant impact. For instance, one of the biggest retailers in the world, Walmart, who also happen to employ over 2 million employees, uses AI and machine learning techniques in logistics to predict when their customers may choose to shop, what products they may be interested in or whether they will decide to pick up or have it delivered. They also use AI to figure out when they might have to order replacements ahead of time to avoid scenarios when they run out of popular merchandise. However, things are only sometimes perfect; moving parts of a supply chain traverse well beyond a specific organization or a supplier; it’s linked across multiple companies and carries the potential to disrupt a fundamental node way down the value chain. This phenomenon, known as the Bullwhip effect, is known to cause major havoc in supply chains if not controlled and predicted within reasonable levels. The idea is to avoid this effect from taking place. Small changes in the supply chain add up and magnify significant fluctuations down the value chain. This article in MIT Solan Management Review covers how the Bullwhip effect was discovered by companies like P&G when they observed a high degree of variability in demand for their products and could not put a finger on the reason for such variance.
Unlocking Efficiency: Exploring the Role of IoT in Transforming the Supply Chain
Recall that we mentioned AI without any meaningful data is practical and of little use; in fact, it can be misleading. IoT is the reason that all nodes of a supply chain are sparkling with beacons of data. Think of it as a meshed network where all participating entities contribute to the system’s overall health while generating an overall time-stamped journey map for a shipment from manufacturing to delivery to an end user, whether it’s the IoT-enabled factories, shops, airports, mobile devices, Edge, ships, planes, or even cities. All are enabled to relay real-time data on the key metrics that matter to the node’s health in the supply chain.
The development of crucial semiconductor technologies has enabled sensor miniaturization and easy implicit placement along the supply chain. RFID tags, sensors, smart sensors, and beacons are a few examples. They cater to multiple use cases, whether there is a dense area of various sensors or communicating over long remote distances. IoT sensors help deliver the precise location and health of an asset. Therefore, asset tracking is a critical use case that relays not just location data but also environmental data like temperature, humidity, and shock/impact on the cargo, as the use case might be. IoT dynamics in manufacturing and supply change have revolutionized how the maintenance of critical components is looked at. Usually, manually troubleshooting an issue, which happens after the failure, leads to unnecessary delays and losses in the overall scheme. Predictive Maintenance allows you to analyze data collected by various IoT equipment and predict a potential failure. This analysis enables engineers to take up due proactive maintenance and avoid a potential shutdown.
The use of IoT also allows automated Inventory Management. It will enable real-time data collection and monitoring, thereby timely reporting of inventory levels at the warehouse or production facility. Innovative use of this tech allows you to avoid glut by over or under-ordering critical equipment, thereby avoiding wastage. As per Gartner, “Real-time visibility of stock availability across channels to support BOPIS (buy online, pickup in-store) and BOPAC (buy online, pickup at the curb) and store returns of online orders will be crucial for sustaining operations.” Inventory Management is the first step towards this fulfillment channel, particularly relevant to the retail industry. IoT is crucial to implementing the Cold Chain monitoring use case. This type of monitoring in the supply chain requires monitoring the temperature/humidity of the cargo in question, as constant reporting is needed at each supply chain step. If there is a threshold breach, perishable food items or vaccines can go bad, leading to a direct adverse impact. To have superior control of the data and metrics in an IoT environment, partnering with a firm known for maintaining very high benchmarks is crucial.
EElink is a well-established player in this industry. Eelink carries high-quality sensors that are compatible with multiple LTE/LTE-M/NB-IOT/satellite/Wi-Fi/BLE devices, come with long battery lives (exceeding five years), and the company is also capable of building custom solutions right from design inception to manufacturing and shipping, and kitting. The company is diverse and operates in a variety of verticals and across multiple use cases. IoT transparency is vital, and the ability to make real-time decisions based on this accurate, transparent data is the catalyst that can catapult any logistics company or third-party logistics payer into the next level of growth in its industry.
In conclusion, while the development of IoT and AI have impacted multiple businesses, their impact is particularly profound in the supply chain business. With the world moving toward doing business at the speed of thought, ideas, and goods are exchanged across vast continents at considerable speed and delivered at a better error rate. Industries are going beyond just moving equipment from point A to point B. Still, they are also constantly optimizing their operations to seek more efficiency and the necessary competitive Edge to survive in this more intelligent, more adaptive supply chain. The supply chain becomes more resilient and less prone to downtimes when organizations make suitable investments in AI and IoT. In short, the nexus of AI and IoT is set to upend the complex supply chain world and revolutionize how business is done.
