月別アーカイブ: 2023年6月

INTERVIEW: How AI can prevent supply chain disruptions

ChatGPT and Artificial Intelligence in Logistics Top 30 AI Use Cases in Logistics in 2023

ai for supply chain optimization

Such precision optimizes inventory levels, reducing excessive stockpiling and storage costs. Advanced manufacturing companies are seeing their order books recover post-Covid-19 and are having to ramp up production to cope with rising demand and backlogs. In these circumstances, standard production scheduling tools are often not fit for purpose. In addition to incorporating AI into their production processes, companies may improve their supply chains by utilizing AI-driven inventory management solutions.

How McKinsey succeed in the AI supply chain revolution?

McKinsey's Succeeding in the AI Supply Chain Revolution estimates that AI and ML's ability to support better decision-making through the analysis of massive volumes of data has enabled early adopters to improve logistics costs by 15%, inventory levels by 35% and service levels by 65%.

Data input and customer service are just two examples of the kinds of mundane but necessary chores that may be automated with the help of AI. The usage of AI in areas such as supply chain management and pricing strategy has the potential to further enhance a company’s productivity and profitability. As AI and data science are increasingly becoming recognised as the most revolutionary technologies of our times, I https://www.metadialog.com/ decided to put together the AI Case Studies Bible, a document that covers close to 30 industries. It is an extensive collection of exciting applications of AI and data science, spanning many different sectors and companies including the supply chain and logistics industry. Ever since the COVID-19 pandemic hit the globe, it has wreaked havoc and posed significant challenges for supply chains worldwide.

Supply chain optimisation

The realm of logistics, being a pivotal backbone of global commerce, is no exception. The complex interplay of global supply chains, local distributions, and customer interactions is being transformed by the innovative capabilities brought forth by AI. Organizations can collaborate with their suppliers through customized and contextualized responses to fulfill high-priority customer orders via alternate distribution centers, which ultimately streamline operations and save time. By harnessing the power of generative AI and collaboration, Copilot in Microsoft Supply Chain Center helps Supply Chain managers maintain optimal supply chain performance while also mitigating potential disruptions.


Industries leading the charge in AI and machine learning adoption span from retail and ecommerce to manufacturing, with sectors like healthcare and logistics also following suit. All companies involved in advanced manufacturing and composite parts manufacturing use production planning in order to maintain an overview of the factory floor and the whole manufacturing process. It is crucial to have a plan against which performance is monitored and that takes into account stations capacity and availability, materials, tools, labor, timescales, work orders, and other factors. Companies may benefit from picture recognition in a number of ways, one being the usage of AI-driven image processing and analysis tools. Image recognition is one method that may be used by organizations to automatically extract data from pictures.

Using the Supply Chain Modeler

It builds up knowledge from a local perspective, making local customers in local stores the center of its forecast. The tool also considers specific influencing factors like festival shopping patterns, weather, season, and so on. Are operational inefficiencies and external disruptions resulting in underperformance of your supply chains? Unfortunately, businesses across the globe have felt the strings tighten due to supplies unable to meet the demand as they adjust to the new normal. Artificial intelligence, with its ability to analyze vast amounts of data and identify complex patterns, can be crucial in navigating these uncertainties.

  • The application of AI in the supply chain has brought significant benefits, including improved efficiency, reduced costs, and enhanced customer satisfaction.
  • In the life sciences and pharma industry, AI-driven demand forecasting has shown promise in managing the supply of active components.
  • Dmitriy Solopov, Business Development Manager (Advanced Analytics), and Nataliia Dranchuk, Data & AI Specialist Microsoft.
  • As a Retail Blockchain Advisor, he is helping retail companies to explore blockchain technology in Retail.

With so many competing priorities and demands on their time, they need the best possible tools to assist them in performing their role. Using AI to analyze data and make predictions about future trends and customer behavior. Data, statistical algorithms, and machine learning techniques are used in predictive analytics to determine the likelihood of future events given existing data. When ai for supply chain optimization applied to consumer data, predictive analytics may help businesses foresee how their clientele will act in the future. With this information, firms will be able to better determine how to allocate resources in the areas of advertising, sales, and new product development. I’ll be talking about artificial intelligence (AI) and the role it can play in preventing supply chain disruptions.

Instead of pulling a heavy cart through the entire warehouse, the pickers are assigned to a specific area, while the robot moves optimally from station to station according to the order to be picked. The dialogue between robot and picker happens through user-friendly interfaces and the recognition of the employee is handled by the machine. This approach has enabled GEODIS to double the productivity of its teams while reducing employee fatigue. Employees may find themselves working alongside AI-powered tools and systems, requiring effective communication and coordination. Organizations will need to foster a culture that embraces AI, promotes collaboration, and encourages continuous learning and adaptation. Organizations must ensure transparency, fairness, and accountability in AI systems to prevent biases, discrimination, and privacy breaches.

Artificial intelligence systems excel at analyzing vast quantities of inventory data, discerning patterns, and predicting incoming consumer demand forecasts with remarkable accuracy. Plataine’s Practimum-Optimum™  AI algorithm is a breakthrough in optimizing production scheduling. It has an AI core and built-in machine-learning algorithms to combine unprecedented levels of optimized KPIs with a practical, robust planning application. The patent-pending algorithm builds optimal schedules that integrate practical considerations, including trade-offs between competing goals and patterns of demand sets. It adapts its planning capabilities and improves its performance over time, and the schedules produced are optimal and practical to execute. Supply chain management is one area that can benefit from the implementation of AI-powered technologies by enterprises.

Discover the potential of artificial intelligence in supply chain network design

To optimize prices and boost revenue, firms may use AI to evaluate data on market circumstances, consumer behaviour, and rival pricing, for instance. Artificial intelligence (AI) may also be used to tailor prices to individual customers or to adapt to fluctuations in the market. Automation may also be employed in inventory management through the application of artificial intelligence to forecast demand, restock supplies, and maximize stock utilization. Predictive analytics may be used by businesses to study client information including purchase patterns, web surfing habits, and demographics. The results of this study will help the company determine which consumers are the most likely to buy, which goods will be the most popular, and when customers will be most likely to make a purchase.

ai for supply chain optimization

In modern enterprises, supplier data is handled by multiple departments, and manipulated by siloed processes and disparate systems of governance. Within procurement, supply chain professionals are faced with the challenge of ingesting the enormous amount of data they have available to them. The primary tool to help fuel success in extending operations to mobile devices is Microsoft’s ai for supply chain optimization low-code app-building platform PowerApps. PowerApps help employees in the supply chain industry quickly develop a multitude of apps for various cases, with little or no coding knowledge needed. Below, we’ll have a look at how modern technologies can drive agility and opportunities for growth in the supply chain industry, improving real-time monitoring of your end-to-end supply chain.

Artificial intelligence brings this capability to your fingertips, enabling accurate, up-to-the-minute monitoring of stock levels across various locations. This real-time visibility eliminates guesswork, drastically minimizes human error, and facilitates swift, informed decision-making. With our AI-based production scheduling software, the scheduler sets the rules and receives AI-driven alerts and updates. The solution minimizes risk and runs 1000s of updates and scenarios before helping to select the best one. Using AI for image and speech recognition to improve customer service and automate tasks. Artificial intelligence (AI) includes the subfields of image and speech recognition, which provide computers the ability to process and make sense of visual and aural data.

ai for supply chain optimization

What is the disadvantage of AI in logistics?

Overall, AI has many disadvantages in transport, including high startup costs, expensive equipment, costly maintenance, training requirements, risk of cyberattack, human job loss, and unequal access to technology. These disadvantages will make applying ‘human intelligence’ to any operation necessary.