Artificial Intelligence (AI) in Supply Chain & Logistics

Artificial intelligence (AI) in the supply chain, as well as machine learning (ML), are already changing the face of supply chain management, widening the gap between winners and losers. Artificial intelligence in supply chain management drives enterprise-wide insights into all areas of the supply chain, with granularity and methodology that humans just can’t replicate at scale, by weeding out deep-rooted inefficiencies and uncertainty. AI in the supply chain helps in assisting the delivery of the powerful optimization skills necessary for more accurate capacity planning, enhanced efficiency, high quality, lower costs, and increased output, all while encouraging safer working conditions.

When faced with a pandemic such as COVID-19, industrial organisations might benefit from a thorough understanding of the impact on supply networks and contingency measures. Nearly 75% of businesses have experienced supply chain interruptions as a result of coronavirus-related transportation limitations, and that number is projected to climb in the coming weeks. This is only one of many aspects of COVID-19’s worldwide influence, but it is an important one which brought to light the importance of AI in supply chains.

Benefits of AI in Supply Chain

• Accurate Inventory Management: The appropriate flow of commodities in and out of a warehouse may be ensured by accurate inventory management. In general, there are various inventory-related factors, such as order processing, picking, and packaging, which can be time-consuming and error-prone. Overstocking, insufficient supply, and unexpected stock-outs may all be avoided with proper inventory management. Artificial intelligence supply chain management which drives inventory management technologies can be extremely successful due to its capacity to handle large amounts of data. These intelligent algorithms can swiftly evaluate and understand large datasets, offering timely supply and demand forecasting advice. These AI supply chain management systems can also predict and uncover new consumer patterns, as well as estimate seasonal demand, thanks to their clever algorithms.
• Warehouse Efficiency: An effective warehouse is an important aspect of the supply chain, and automation may help with the prompt retrieval of items from warehouses and the smooth delivery of goods to customers. AI supply chain management systems can also address a variety of warehouse problems faster and more precisely than humans, as well as simplifying complex procedures and speeding up labour. Additionally, AI-driven automation initiatives can drastically reduce the need for, and expense of, warehouse employees, in addition to saving important time.
• Enhanced Safety: Artificial intelligence supply chain management-based automated technologies can help with better planning and warehouse management, as well as worker and material safety. AI may also examine data on workplace safety and alert manufacturers to any potential dangers. It can keep track of stocking parameters and operations, as well as provide feedback loops and preventative maintenance. This enables producers to respond quickly and aggressively in order to maintain warehouse safe and compliance with safety regulations.
• Reduced Operations Costs: This is a significant advantage of AI supply chain management systems. Automated intelligent activities, from customer care to warehousing, can function error-free for longer periods of time, lowering the number of mistakes and workplace mishaps. Warehouse robots are faster and more accurate, resulting in improved output.
• On-Time Delivery: AI in supply chain management brings technologies that can assist in reducing reliance on manual labour, making the entire process quicker, safer, and more intelligent. This facilitates prompt delivery to the consumer in accordance with the agreement. Traditional warehouse operations are accelerated by automated technology, reducing operational bottlenecks along the value chain with little effort to meet delivery objectives.

Challenges of AI in Supply Chain

• System complexities: AI in supply chain management systems has systems that are often cloud-based, which need a large amount of bandwidth to power the system. Operators may also require specialised gear to access AI capabilities, and the cost of AI-specific technology might be too expensive for many supply chain partners.
• Scalability factor: The problem here is the degree of initial start-up users/systems required to be more impactful and effective, given that most AI supply chain management systems and cloud-based technologies are highly scalable. Because all AI supply chain management systems are distinct and varied, supply chain partners will need to negotiate this with their AI service providers in detail.
• The operational costs involved: Given that most AI and cloud-based technologies are extremely scalable, the issue here is the number of early start-up users/systems necessary to be more impactful and successful. Because all AI supply chain management systems are unique and different, supply chain partners will need to discuss this in-depth with their AI service providers.
• Training costs: Training, like any other new technological solution, necessitates a major commitment of both time and money. During the integration phase, supply chain partners will need to collaborate with AI providers to develop a training solution that is both effective and cost-effective.

How do AI-powered supply chains bolster operational efficiency and businesses?

1. Better planning and scheduling of activities: Supply chain managers may improve their decision-making by using AI in supply chain management and logistics to identify bottlenecks, unforeseen irregularities, and remedies in order to simplify production scheduling, which is otherwise extremely unpredictable owing to reliance on manufacturing operations management. Furthermore, AI in supply chain management has resulted in precise forecasts and quantification of expected results at various phases of the schedule, allowing for the scheduling of more ideal alternatives when such disruptions occur during execution.
2. Analytics to smoothen the operations: Several firms today lack important actionable data and the following insights that may be used to make timely choices that satisfy expectations quickly and efficiently. Cognitive automation that makes use of AI in supply chain management has the capacity to sift through enormous volumes of dispersed data to find patterns and quantify tradeoffs on a wide scale, far better than traditional systems can.
3. Smart decision making: By employing cognitive predictions and recommendations on optimal actions, AI-led supply chain optimization software magnifies crucial decisions. This can assist improve the overall performance of the supply chain. It also assists manufacturers in determining the potential consequences of various situations in terms of time, cost, and income. It also improves on these recommendations when relative situations change since it is always learning.
4. Better and efficient operations: IoT-enabled physical sensors throughout supply chains now provide a wealth of information to monitor and modify supply chain planning processes, in addition to the gems still mainly buried in disaggregated data system silos at most organisations. With billions of sensors and devices, manually evaluating this pot of gold can waste a lot of operational resources and cause production cycles to be delayed. This is where AI-powered cognitive analytics in supply chain and logistics adds significant value. Radical savings may be obtained when supply chain components become crucial nodes for tapping data and powering machine learning algorithms. Through the use of machine learning in pricing planning, the value can be efficiently achieved.
5. Striking a balance between demand and inventory: Maintaining optimal stock levels to avoid stock-out difficulties is one of the most difficult tasks encountered by supply chain firms. At the same time, overstocking might result in excessive storage expenses, which, on the other hand, do not generate income. Mastering the art of inventory and warehouse management is key to achieving the ideal balance. When artificial intelligence in supply chain management and machine learning techniques are used to demand to forecast, they produce very accurate estimates of future demand. Similarly, in supply chain forecasting, machine learning and artificial intelligence ensure that material bills and PO data are organised and accurate forecasts are generated on time. This allows data-driven field operators to approach maintaining the optimal levels necessary to fulfil current (and near-term) demand.

CONCLUSION

Today, AI in supply chain management can help supply chain optimization achieve the much-needed extraordinary agility and precision. It may also result in a significant improvement in operational and supply chain efficiency, as well as a reduction in costs when repetitive manual processes are automated. Learn how a good AI supply chain optimization software may help manufacturers get the most out of AI-enabled efficiency.