Eight Ways Artificial Intelligence Is Massively Transforming Global Supply Chains
Manufacturing | 6 min READ
    
Supply Chain – Hogging the Limelight
Remember Ever Given? The cargo ship stuck in the Suez Canal and failed to deliver the shipment on time. Or on the other side, companies failing to deliver vaccines due to supply chain and logistics challenges. Imagine, at a time like this, when Black Swan events are ruling the world and predictability has gone for a toss, your business encounters a massive supply chain failure!
Madan Mohan Mewari
Madan Mohan Mewari

SVP, Global Head-Delivery and Operations

(Manufacturing and Services Verticals)

Birlasoft

 
When supply chain disruptions are predominant and a growing need to strengthen them, emerging technologies like artificial intelligence have been of utmost importance. From conjecturing to dispatch and managing deliveries, AI is powering up supply chain operations, improving its efficiency.
According to a Mckinsey report, the value of goods traded globally has tripled to more than $10 trillion annually since 2000. Companies worldwide are aspiring for a well-crafted lean AI model to manage inventory levels, reduce lead times and ensure all-time-in-full deliveries.
Today, we’ll look at eight such examples where artificial intelligence is helping modern organizations transform their supply chains at the speed of light.
Artificial Intelligence In Supply Chain
AI in Logistics and Transportation
Fleet management and optimization are the most underrated applications of AI in the supply chain. Fleet managers amplify the crucial connection between the consumer and the supplier. Therefore they are responsible for the unhindered flow of commerce.
Along with the increasing fuel costs and shortages in resources, fleet managers encounter data overload issues. If businesses do not collect data and process it, it quickly or adequately analyzes the collected data, and it will soon turn into an unproductive swamp.
AI intervenes in such a scenario to ensure efficiency across all activities. With the help of predictive analytics, it assesses truck turnaround time and ad-hoc demands of vehicles. Studies historical demand patterns and, with the help of statistical techniques, predict truck demand per shipping lane. Utilizes powerful multi-dimensional analytics to reduce unplanned downtime of the fleet, ramps up fuel efficiency, detect and remove bottlenecks.
Supplier Risk Assessments
Free up resources from the mundane and unproductive task of assessing supplier performance using AI-driven supplier risk management. Integrate intelligent solutions to get a 360-degree view of the vendors and complete insights into Vendor Performance factors.
Businesses and enterprises can build AI-based and ML-based models depending upon their risk assessment infrastructure. The model can extract deeper insights on real-time data from various sources (like social, news, media, etc.) round the clock across as many categories as you wish.
Using data science-based techniques, AI can run noise reduction, relevance-based normalization to provide actionable insights. Considering the most relevant and meaningful data from the vast pool of big data, it calculates a risk score/index for suppliers. These risk scores alert the organization of any potential supplier failures.
AI in Demand Forecasting and Inventory Management
According to a survey, 90% of the respondents believe that AI will transform the supply chain for the better by 2025. When applied to demand forecasting, AI & ML framework brings about accurate predictions on future demands.
For instance, predicting the decline and end of a product life precisely on the sales channel along with the market growth introducing a new product is easily achievable. Deep Learning deciphers both linear and non-linear dependencies to make demand forecasting more scientific and accurate.
Similarly, in supply chain forecasting, AI and ML ensure material bills and PO data are structured and accurate deductions are made on time. Field operators leverage these data to drive operations and maintain the threshold levels required to meet the current demand.
Maintaining optimum stock levels is one of the biggest challenges faced by supply chain organizations—AI and ML framework work towards maintaining the level while creating a revenue generation path for the businesses.
Use Cases of AI in Supply Chain Management
Use Cases of AI in Supply Chain Management
AI For Warehouse Automation
AI is one such technology that seamlessly integrates with other emerging technologies to modernize processes and better warehouse operations. Computer Vision helps in load & unload automation, reduces handling costs and damage by reducing individual handling.
AI systems will use historical data for optimization and spot opportunities for improved efficiency in inventory management and distribution. Using the data collected, AI would predict accurate demand and finally automate operations and workflows.
Mostly, it’s only a partial integration of AI, and human expertise would still be in demand to supervise and monitor the entire system, probably for error detection and application of preventive measures. If there are tons of data collected, then there’s a fair chance of developing a high-performing algorithm as well.
Higher cost savings and improved efficiencies are inevitable, especially if the integrated technology leads to higher performance.
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Autonomous Vehicles
Warehouse automation is no longer a big deal as most warehouses are receiving autonomous mobile robots (AMRs), helping warehouse employees manage orders faster with more efficiency. AMRs ensure continuous collaboration between different operations and vehicles, enabling workers to be more productive.
Since these days warehouse laborers are harder to find and expensive to upskill, the market for AMRs is exploding. Ecommerce is a booming industry due to the pandemic, and this market is likely to perform well despite the sluggish economy. Broadly, there are two categories of AMRs
  • Fleet management solutions operate with massive payloads, routing the robots from a source to the destination.
  • Pick optimizations to integrate the machine movement with that of the workforce and are designed exclusively to increase the pick throughput. Usually responsible for picking cartons and totes and have a small payload
Reviewing the impact on the workforce
Artificial Intelligence In Warehouse Health And Safety
Bringing together both the traditional and emerging tools, such as telematics, advanced IoT sensors, warehouse managers can get their hands on essential and valuable data to ensure the safety of the warehouse.
Let’s take the example of implementing sensors in material handling equipment. It offers more significant insights to the businesses and warehouses into the operator’s activity and performance by instantly measuring and reporting parameters such as impacts, speeds, and proximity.
When integrated with additional capabilities, the simple application of safety monitoring sensors can transform the safety management strategies of any business warehouse.
Conversational AI For Customer Service
A mix of data analytics, IVR systems, voice assistants, robotic process automation, security, CSAT, and feedback data with conversational analytics, work together in real-time to optimize contact center services for businesses and enterprises. Conversational service automation drives both human-to-machine interactions and also, straightforward conversations between contact center agents and customers.
When integrated with a customer support system, a cloud-based, conversational AI platform is capable of utilizing the potential advances in AI, ML, voice recognition, and natural language processing (NLP) to more precisely comprehend customer queries and respond to them more naturally.
The AI and machine learning capabilities will learn and decipher from earlier errors, improving the responses in the upcoming interactions, thus enhancing customer experiences. With the help of such an automated system, existing customer support agents can now handle higher-level queries, keep customers engaged, and streamline operational efficiency.
Business Process Automation – Account Payables/Receivables
With digital disruption going mainstream, Business Process Automation is increasingly becoming popular and used across operations. According to a survey conducted by Bain & Company, worldwide companies have reported higher cost savings of roughly 20% on an average on automation.
Improved process quality, accuracy, compliance, and reduced cycle times are other benefits reported by the companies.
Business Process Automation
AI is bringing in groundbreaking transformations in the supply chain aiming towards operational excellence. The applications and use cases are the tips of the iceberg. This technology can have a holistic impact on the supply chain and logistics industry. By integrating with other emerging technologies, companies can deliver and spearhead technological innovation.
 
 
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