The AI Supply Chain Advantage
How Businesses are Using Technology to Stay Ahead of the Competition
Supply chain management refers to the planning, coordination, and control of the movement and storage of goods, services, and related information from the point of origin to the point of consumption. It involves managing the entire flow of materials, funds, and information within a company and between a company and its suppliers, partners, and customers.
Table of Contents
- Introduction
- Predictive analytics for demand forecasting
2.1. Case study: Predictive Analytics for Demand Forecasting at Walmart
2.2. Case study: From Data Silos to Data-Driven Culture: P&G’s Success with Azure - Optimization of inventory levels and transportation routes
3.1. Case Study: Improving Inventory Planning at Amazon
3.2. Case Study: Optimizing E-Fulfillment with IDEA at DHL - Conclusion
Introduction
Effective supply chain management is critical for businesses of all sizes and industries. It helps companies reduce costs, increase efficiency, and improve customer satisfaction by ensuring that the right products are available at the right time and place.
Poor supply chain management, on the other hand, can lead to delays, shortages, excess inventory, and other issues that can negatively impact a company’s bottom line.
With the increasing complexity and global nature of supply chains, businesses are turning to artificial intelligence (AI) to help manage and optimize their operations.
AI can analyze vast amounts of data and make decisions faster and more accurately than humans, enabling businesses to improve their forecasting, inventory management, transportation planning, and other key aspects of supply chain management.
Predictive analytics for demand forecasting
Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
In the context of supply chain management, AI can be used to analyze data about past sales, customer behavior, economic indicators, and other factors to make accurate predictions about future demand for a company’s products or services. This can help businesses better plan and prepare for changes in demand, and make more informed decisions about production, inventory, and other key aspects of their operations.
Many industries are using AI for demand forecasting, including retail, manufacturing, logistics, and healthcare. For example, a retail company might use AI to analyze data about past sales, weather patterns, and other factors to predict demand for certain products at different times of the year. A manufacturing company might use AI to predict demand for its products based on data about economic indicators and customer behavior.
There are several benefits of using AI for demand forecasting, including reduced costs, increased efficiency, and improved decision-making.
By accurately predicting demand, businesses can avoid overproduction or underproduction, which can save them money on materials, labor, and other costs.
AI can also help businesses respond more quickly to changes in demand, allowing them to adjust their operations in real-time and increase their competitiveness. Additionally, using AI for demand forecasting can help businesses make more informed and accurate decisions about their operations, which can lead to improved performance and customer satisfaction.
Case Study: Predictive Analytics for Demand Forecasting at Walmart
Walmart, one of the largest retailers in the world, is leveraging the power of Graphics Processing Units (GPUs) to optimize various aspects of their supply chain and forecasting operations. This has allowed them to see significant improvements in both performance and accuracy.
One of the areas where Walmart has seen the most benefits is in their pack size optimization. By using GPUs, they are able to better understand how different pack sizes can impact costs of goods, inventory, and labor costs across their distribution centers and stores. This has allowed them to make more informed decisions when ordering products from vendors and distributing them to stores.
Another area where Walmart has seen benefits from using GPUs is in their shelf space optimization. By using GPU-accelerated algorithms, they are able to more accurately forecast demand for different products and place them on shelves in a way that maximizes efficiency and minimizes labor costs.
Finally, Walmart is also using GPUs for their forecasting algorithms. This has allowed them to generate more accurate weekly demand forecasts for over 500 million item-store combinations in the US alone, with a goal to forecast daily demand for everything, everywhere. This has helped with inventory control and production planning for vendors.
Walmart’s use of GPUs has allowed them to speed up complex computations, improve the accuracy of their forecasts, and make better decisions throughout their supply chain. This has ultimately led to cost savings and increased efficiency for the company.
Case Study: From Data Silos to Data-Driven Culture: P&G’s Success with Azure
Procter & Gamble (P&G) has long been a leader in the fast-moving consumer goods (FMCG) industry, with a range of household products that can be found in nearly every country. However, in order to stay ahead of the competition and continue to delight consumers, P&G knew it needed to take a more data-driven approach.
P&G developed a multi-cloud-driven data strategy that aimed to unlock the insights from its vast amount of data, which was spread across geographically dispersed silos. With the help of Microsoft Azure data and AI solutions, P&G was able to gain a single view of its business data and foster a data-driven culture.
With the powerful data solutions provided by Azure, P&G was able to tackle three key challenges:
- creating a more resilient supply chain
- improving retail execution
- delivering superior products and packaging
By using AI and machine learning algorithms to analyze past sales data and other key information, P&G was able to make more accurate predictions about demand and optimize its supply chain accordingly.
P&G used granular analytics to improve its retail execution, ensuring that the right products and in-store displays were in front of consumers. And by using data and analytics in its products and packaging, P&G was able to shift from a point-in-time assessment to a real-time, sense-and-respond approach, which helped to speed up the innovation process and optimize costs.
P&G’s use of data and algorithms has enabled them to act with both agility and precision, helping to drive growth and stay ahead of the competition.
“With Azure and the co-innovation we did together, we’re able to get positive outcomes on both fronts, performance and scale”, said Guy Peri, Chief Data & Analytics Officer for P&G.
Optimization of inventory levels and transportation routes
AI can help businesses optimize their inventory levels and transportation routes by analyzing data about these aspects of their operations.
For instance, AI can analyze data about current inventory levels, sales trends, and supplier lead times to determine the optimal levels of inventory to maintain at different times.
Similarly, AI can analyze data about transportation costs, delivery times, and other factors to determine the most efficient routes for transporting goods.
By using AI to analyze this data, businesses can make more informed decisions about their inventory and transportation, which can lead to reduced costs and improved efficiency.
Many industries are using AI to optimize their inventory and transportation, including retail, manufacturing, and logistics. A retail company might use AI to analyze data about past sales and customer behavior to determine the optimal levels of inventory to maintain at different times of the year. A manufacturing company might use AI to optimize its transportation routes based on data about delivery times, transportation costs, and other factors.
There are several benefits of using AI for inventory and transportation optimization, including:
- reduced costs
- improved efficiency
- increased customer satisfaction
By optimizing their inventory levels and transportation routes, businesses can reduce their costs by avoiding overstocking or understocking, and by choosing the most efficient routes for transporting goods.
AI can also help businesses respond more quickly to changes in demand, allowing them to adjust their operations in real-time and increase their competitiveness.
Using AI for inventory and transportation optimization can help businesses improve the efficiency of their operations and can increase customer satisfaction by providing faster and more accurate service.
Case Study: Improving Inventory Planning at Amazon
As Amazon’s customer demand grew and they introduced same-day delivery services such as Prime Now, the company’s existing automated inventory planning system could not meet the ever-growing expectations of its customers. The company needed to find a new system that would help Amazon fulfill delivery promises made to customers.
Amazon’s Supply Chain Optimization Technologies (SCOT) organization developed a first-of-its-kind multi-echelon system for inventory buying and placement. This system included forecasting models that predict the demand for every product, buying systems that determine the right level of product to purchase from different suppliers, and large-scale placement systems that determine the optimal location for products across Amazon’s global fulfillment network.
By redesigning the inventory planning system from the ground up, Amazon was able to better meet the needs of its customers by positioning inventory closer to them and pivoting from a national network to a local one. This helped the company meet the increased customer demand for a diverse variety of shipping options and the globalization-localization imperative.
Key Takeaways:
- Amazon’s Supply Chain Optimization Technologies (SCOT) developed a first-of-its-kind multi-echelon system for inventory buying and placement to improve the inventory planning system and meet the increasing demand of customers.
- The new system includes forecasting models, buying systems and placement systems that optimize the inventory levels and product locations.
- With the help of this new system, Amazon was able to position the inventory close to customers, resulting in better delivery times and meeting customer’s expectation in terms of shipping options and meeting the globalization-localization imperative.
This case study provides insight into how Amazon leveraged data, advanced algorithms and optimization models to optimize its inventory planning system.
By analyzing customer demand, sales trends, and other factors, Amazon’s SCOT team was able to develop a first-of-its-kind multi-echelon system for inventory buying and placement that improved inventory positioning, delivery times and shipping options. This led to improved customer satisfaction, better efficiency in the supply chain and helped the company meet customer needs more effectively.
This highlights the importance of using advanced technology, data and optimization techniques to optimize inventory planning in e-commerce industry.
Case Study: Optimizing E-Fulfillment with IDEA at DHL
As e-commerce sales continue to grow, DHL Supply Chain faced the challenge of fulfilling online orders in a timely and efficient manner.
The pandemic increased the demands for online orders for everyday staples such as food and medicine, but the size of shipments became smaller. Additionally, customers expect fast, same-day delivery with high transparency and traceability.
DHL Supply Chain needed a way to optimize the warehouse and processing of online orders to meet these e-commerce challenges.
To tackle this challenge, DHL Supply Chain implemented IDEA, an in-house, plug-and-play software solution that uses algorithmic analysis and data science to optimize picking routes and warehouse staff allocation.
IDEA was developed by DHL Supply Chain’s in-house team of engineers and solutions designers to help better anticipate demand fluctuations, optimize the allocation of manpower and accelerate the throughput of goods and order cycles. The solution can integrate with most traditional warehouse management systems and be integrated into existing IT infrastructure.
The IDEA algorithm was able to significantly improve order-picking processes in DHL-operated warehouses. The software reduced distance traveled by warehouse employees by up to 50% and increased productivity of individual DHL locations by up to 30%.
IDEA also reduces error rates and the time required for employee training. This helped DHL Supply Chain to provide more stress-resilient, reliable algorithms and tools to better anticipate demand fluctuations, optimize the allocation of manpower and thereby accelerate the throughput of goods and order cycles.
AI-based tools like IDEA can help logistics companies to optimize warehouse and processing of online orders. IDEA algorithm optimizes picking routes and warehouse staff allocation, leading to improved efficiency and cost-effectiveness.
By developing and designing IDEA in-house, DHL Supply Chain was able to tailor the solution to their specific needs in the e-fulfillment industry. Investing in AI technology and data analysis can help logistics companies stay competitive and adapt to the growing demands of e-commerce.
This case study highlights how DHL Supply Chain leveraged their in-house AI solution, IDEA, to optimize their e-fulfillment operations.
By utilizing algorithmic analysis and data science to optimize picking routes and warehouse staff allocation, DHL Supply Chain was able to improve efficiency and reduce costs, ultimately leading to improved customer satisfaction and competitiveness in the logistics industry.
Conclusion
Supply chain management plays a critical role in the success of businesses by ensuring the right products are available at the right time and place. As the supply chains have become increasingly complex and global, businesses are turning to AI to optimize their operations.
AI can analyze vast amounts of data and make decisions faster and more accurately than humans, enabling businesses to improve forecasting, inventory management, transportation planning, and other key aspects of supply chain management.
One of the specific use cases of AI in supply chain management is demand forecasting which can help businesses better plan and prepare for changes in demand, and make more informed decisions about production, inventory, and other key aspects of their operations.
The implementation of AI in supply chain management can lead to a lot of benefits such as reduced costs, increased efficiency, improved decision-making, and improved performance and customer satisfaction.
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