Demand Forecasting

Demand patterns can fluctuate due to changing customer preferences, prices, and seasonal requirements. Predictive analytics of big data helps to predict demand accurately. Moreover, it makes sense to club it with capacity forecasting, and labor spending optimisation.

 

Supply Forecasting

Supplier data on production and delivery is important to streamline activities. However, AI and ML in the supply chain can track metrics, develop benchmarks, and recommend vendor selection.

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Inventory Planning

Both understocking and overstocking are serious threats to the business. For instance, if you go through companies using AI in supply chain case studies, you will find they manage to strike the right balance and shorten lead time.

 

Production Planning

Meeting the delivery date every time is a challenge for the production department. Digital resource mapping seen across multiple case studies in the supply chain ensures optimization. Digital twin technology is becoming popular to augment production performance, while computer vision defect detection is picking pace in manufacturing companies.

 

Warehouse Management

A warehouse operation collects a lot of data in a controlled environment. Simulations based on activity-level data reduce the manual measurements of product, layout, and labor effort to boost performance. Innovative applications like Intelligent Appointment Scheduler and Smart Task Allocation have been introduced to improve supply chain scheduling with AI.

 

Logistics Management

Traffic congestions pose a major threat to logistics. This hampers delivery timelines, which in turn results in hefty fines from clients to supply chain vendors. Route selection and scheduling can be optimized with the help of AI.

 

Pricing Management

Time-based pricing linked to market demands and competitor plans can help companies remain competitive. Thus, insights from AI in supply chain case studies analyze the impact and suggest dynamic pricing based on customer psychology, perceived value, and other factors.

Text Analytics

Web scraping, social media (SM) listening, and translation can help to track data from internal (shipment, supply, partner) and external (news, SM platforms, compliance) sources. AI and supply chain users, therefore, employ Natural Language Processing (NLP) techniques.

Customer Management

As automation, virtual assistance, and facial recognition technologies enhance customer experience, businesses need precise customer analytics. So, the use of AI in the supply chain is becoming necessary to increase customer engagement.

Artificial Intelligence collects real time data points and helps the business owners improve supply chain visibility to better manage their inventories, reduce delays, and offer better customer service.

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Workforce Management

Apart from administrative functions, intelligent allocation of tasks through data analyses promotes AI and supply chain uses, as it operates at the intersection of data, people, and processes