In 2023, retailers are likely to continue to focus on improving their supply chain operations through the use of artificial intelligence (AI). It can upgrade inventory management, forecast demand, optimize logistics and transportation capabilities, increase customer satisfaction, and even more.
According to a McKinsey study, adopters of AI-powered supply chain management reduced their logistics costs by 15% while increasing inventory levels by 35% and customer service by 65%.
Isn't that an inspiring result? Here we will examine the key AI trends that will shape supply chain management in 2023. But first, let's discover the most pressing concerns retailers are having about the 2023 supply chain.
Main supply chain management needs
The following are some potential areas of supply chain management that will be of interest in the near future:
- Resilience and agility. With ongoing geopolitical and economic uncertainty, companies may need to focus on building more resilient and agile supply chains that can quickly adapt to changing conditions. This applies to both inventory management and logistics.
- Inventory management. Managing stock levels may present a challenge for retailers who are concerned with meeting customer demand and avoiding stockouts and/or overstocking.
- Logistics and transportation. Efficient logistics and transportation management is crucial for meeting customer demand and reducing costs at the same time.
- Environmental sustainability. A company may need to reduce its environmental footprint and implement more sustainable practices in its supply chain.
- Collaboration and partnership. Companies focus on building stronger partnerships and collaborations with suppliers, customers, and other stakeholders to manage risk and optimize supply chain performance.
In the age of AI, it’s possible to achieve these goals with minimal human intervention, at an affordable cost, and with great accuracy. Our next step is to look at specific AI-based solutions that can address these challenges.
Key AI Trends to Improve Your Supply Chain Management
As retailers participate in the AI supply-chain revolution, the following solutions are a must.
In recent years, supply chains have changed dramatically, and so have the capabilities of predictive analytics. Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to determine the likelihood of future outcomes based on historical data.
Supply chain managers use predictive analytics to analyze data on past sales, inventory levels, and customer behavior. As a result, retailers can reduce shortages and overstock and accurately forecast demand.
What’s more, as of 2021, 68% of supply chain executives say they have spent the last three years responding to serious disruptions. The majority of them say they haven’t had time to recover before the next disruption occurs. However, predictive analytics can prevent supply chain disruptions. AI analyzes past disruptions, such as natural disasters or supplier issues to identify potential risks.
A machine learning system is one that learns from experience and improves without being explicitly programmed.
Using AI in retail can optimize transportation routes based on traffic patterns, delivery times, and other logistics-related information. Machine learning allows retailers to reduce travel times, fuel consumption, and other transportation costs.
The supply chain can also benefit from machine learning through predictive maintenance. In this way, supply chain managers anticipate when equipment or machinery will fail, thus scheduling preventative maintenance. As a result, downtime and interruptions can be reduced.
Robotics and intelligent machines
It seems that the future is already here. AI plays a significant role in our lives and business operations, including inventory management. It’s already possible to manage inventory automatically.
Robotics and intelligent machines can automate repetitive and time-consuming tasks such as picking and packing, inventory counting, and replenishing stock. In addition, smart machines and robots can accurately scan and identify products. This reduces the risk of human error. They can also handle heavy loads, which are dangerous for humans, reducing the risk of workplace injuries. It’s a very efficient way to cut labor costs while improving accuracy and safety.
Computer vision systems understand and interpret visual information from the world, such as images and videos.
A computer vision algorithm can inspect and assess product quality and detect defects in manufactured goods. Reducing waste and improving quality control can both benefit from this.
Natural language processing
AI-assisted natural language processing (NLP) is the use of AI techniques to analyze, understand, and produce human speech. A few NLP examples include tasks such as language translation, text summarization, speech recognition, and sentiment analysis.
In the context of supply chain management, NLP can be used to automate and streamline various tasks, such as analyzing and extracting information from unstructured data sources, such as emails and customer feedback. Using this information will improve forecasting, inventory management, and customer service.
Furthermore, NLP can improve communication and collaboration between manufacturers, suppliers, and logistics providers in the supply chain.
AI-based carbon footprint analysis
Carbon footprint analysis evaluates the number of greenhouse gases emitted into the atmosphere as a result of a company's activities. AI helps to collect and process data on energy consumption, emissions, and other environmental factors from various sources such as suppliers, logistics providers, and internal operations.
Even though AI handles a large part of carbon footprint analysis, it still requires a great deal of expertise to properly interpret the results.
AI-powered chatbots and virtual assistants
Chatbots and AI-powered virtual assistants can automate routine communications with suppliers, including order confirmations, shipping updates, and invoice processing.
Chatbots can also handle basic customer service tasks, such as answering frequently asked questions and providing order status information. In this way, customers will be able to track their orders and get real-time updates on their status without having to contact customer support.
The result will be a reduction in workload for customer service representatives and an increase in efficiency in general.
Implementing AI in supply chain management can bring significant benefits to companies in any area, from automating inventory management, logistics, and customer service requests to improving sustainability and partnerships.
Predictive analytics, robotics and intelligent machines, machine learning, computer vision, NLP, carbon footprint analysis, and chatbots are the key trends to watch out for in 2023.
While there can be some challenges to implementing AI in supply chain management, the potential benefits make it worth considering for companies looking to improve operations and stay ahead in today's fast-paced business world.
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