AI Supply Chain

Build and manage an agile, intelligent, and cost-efficient supply chain

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Overview

Overview

Alibaba Cloud AI Supply Chain solution leverages data intelligence and artificial intelligence (AI) technologies to provide end-to-end real-time monitoring for your supply chain, and helps you quickly diagnose and optimize your inventory, generate an accurate forecast of the future demand with deep learning models trained on your business data, and avoid stockouts and supply chain disruption by performing inventory simulation based on business objectives and constraints, so you can plan and streamline your supply chain proactively, reduce sales losses, and improve capital efficiency.

Why Alibaba Cloud

  • Recognized Supply Chain Solution

    Listed as a representative solution of 2023 Gartner Market Guide for Analytics and Decision Intelligence Platforms in Supply Chain for its cohesive and end-to-end supply chain A&DI capabilities.

  • Out-of-the-Box Solution

    Deploy this solution in virtual private clouds (VPCs) or as a SaaS solution without replacing or changing your existing technology infrastructure for better time efficiency and total cost of ownership (TCO)

  • Timely Supply Chain Synchronization

    Synchronize data across the entire supply chain in seconds to accelerate upstream and downstream response, facilitate full-link collaboration, and prevent problems such as stockout and supply chain disruption

  • Accurate Demand Forecast

    Improve forecast accuracy by over 20% with a time series algorithm. It's fine-tuned to your business requirements and deep learning models based on industry best practice and your supply chain data.

Learn more about Alibaba Cloud AI Supply Chain solution

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How AI Supply Chain Works

Alibaba Cloud AI Supply Chain solution provides end-to-end monitoring and management of your supply chain with Control Tower, examines inventory health for optimization, performs accurate demand forecasts, and generates replenishment suggestions based on forecast results. All these help you build and manage an agile, intelligent, and cost-efficient supply chain.

Control Tower
Inventory Health
Demand Forecast
Intelligent Replenishment

Control Tower - Dashboard

Control tower provides end-to-end real-time data visualization on the business content, operation links, and operation indexes of your supply chain and analyzes these key factors in different steps from stock preparation and production to outbound performance, to help you gain comprehensive insight into the entire supply chain.

Control Towner - Risk Prediction

Control tower performs data mining on operation links and operation indexes to proactively discover potential risks besides system-defined failures and real-time alarms, to avoid demand/supply fluctuations and risks of failure to achieve operation indexes.

Control Tower - Intelligent Troubleshooting

Control tower leverages AI technology and machine learning algorithms to provide intelligent troubleshooting services for abnormal indexes, including problem association, anomaly release, multi-indicator combination, historical data comparison, and disassembly and attribution within the organization for abnormal indicators.

Control Tower - Decision Recommendation

Control tower provides recommended optimization strategies for network, transportation, supplementary adjustment, and clearance based on index performance and risk prediction and analysis, to help you adjust automated supply chain operations for optimal results.

Inventory Health: Dashboard

This solution diagnoses your inventory based on key factors such as the supply chain health index, capital backlog, and out-of-stock loss. It performs optimization simulation to analyze the root cause of the problem so you can quickly identify the optimization objectives and choose optimization strategies to balance your stock level and eliminate stagnation in your supply chain.

Demand Forecast: Dashboard

This solution simulates and analyzes market demand, and pre-processes the analysis data based on data intelligence and time-series forecasting algorithms such as historical time-series smoothing. It leverages machine learning models to train these algorithms based on the processed data. The algorithms provide trend analysis for demand forecast.

Demand Forecast - Trend Analysis

The machine learning models are fine-tuned with parameter optimization and cross validation, to transform market demand analysis into accurate forecasts of the future demand by week, month, or year, so you can optimize core operational processes such as logistics, production planning, and inventory replenishment, to streamline the entire supply chain.

Demand Forecast - Trend Graph

The detailed analysis and trend graph reveal hidden patterns in historical demand in customizable time periods and help you discover issues proactively to prevent supply chain disruption, and adjust replenish plans and sales/marketing strategies accordingly.

Intelligent Replenishment: Dashboard

This solution combines omnichannel forecasting results with business optimization objectives such as inventory turnover days and out-of-stock rate, and performs inventory simulation based on key factors such as safety stock and other parameters related to buying period (BP) and vendor lead time (VLT), to provide replenishment recommendation.

Intelligent Replenishment - Parameter Information

You can view detailed information of key parameters such as purchase lead time and minimal order quantity (MOQ), and refer to parameter recommendations such as safety inventory to improve decision-making in complex scenarios such as large-scale promotions and multi-echelon inventory networks, reducing sales losses while improving capital efficiency.

_Alibaba Cloud_

How AI Supply Chain Works

Alibaba Cloud AI Supply Chain solution provides end-to-end monitoring and management of your supply chain with Control Tower, examines inventory health for optimization, performs accurate demand forecasts, and generates replenishment suggestions based on forecast results. All these help you build and manage an agile, intelligent, and cost-efficient supply chain.

Control Tower - Dashboard

Control tower provides end-to-end real-time data visualization on the business content, operation links, and operation indexes of your supply chain and analyzes these key factors in different steps from stock preparation and production to outbound performance, to help you gain comprehensive insight into the entire supply chain.

Control Towner - Risk Prediction

Control tower performs data mining on operation links and operation indexes to proactively discover potential risks besides system-defined failures and real-time alarms, to avoid demand/supply fluctuations and risks of failure to achieve operation indexes.

Control Tower - Intelligent Troubleshooting

Control tower leverages AI technology and machine learning algorithms to provide intelligent troubleshooting services for abnormal indexes, including problem association, anomaly release, multi-indicator combination, historical data comparison, and disassembly and attribution within the organization for abnormal indicators.

Control Tower - Decision Recommendation

Control tower provides recommended optimization strategies for network, transportation, supplementary adjustment, and clearance based on index performance and risk prediction and analysis, to help you adjust automated supply chain operations for optimal results.

Inventory Health: Dashboard

This solution diagnoses your inventory based on key factors such as the supply chain health index, capital backlog, and out-of-stock loss. It performs optimization simulation to analyze the root cause of the problem so you can quickly identify the optimization objectives and choose optimization strategies to balance your stock level and eliminate stagnation in your supply chain.

Demand Forecast: Dashboard

This solution simulates and analyzes market demand, and pre-processes the analysis data based on data intelligence and time-series forecasting algorithms such as historical time-series smoothing. It leverages machine learning models to train these algorithms based on the processed data. The algorithms provide trend analysis for demand forecast.

Demand Forecast - Trend Analysis

The machine learning models are fine-tuned with parameter optimization and cross validation, to transform market demand analysis into accurate forecasts of the future demand by week, month, or year, so you can optimize core operational processes such as logistics, production planning, and inventory replenishment, to streamline the entire supply chain.

Demand Forecast - Trend Graph

The detailed analysis and trend graph reveal hidden patterns in historical demand in customizable time periods and help you discover issues proactively to prevent supply chain disruption, and adjust replenish plans and sales/marketing strategies accordingly.

Intelligent Replenishment: Dashboard

This solution combines omnichannel forecasting results with business optimization objectives such as inventory turnover days and out-of-stock rate, and performs inventory simulation based on key factors such as safety stock and other parameters related to buying period (BP) and vendor lead time (VLT), to provide replenishment recommendation.

Intelligent Replenishment - Parameter Information

You can view detailed information of key parameters such as purchase lead time and minimal order quantity (MOQ), and refer to parameter recommendations such as safety inventory to improve decision-making in complex scenarios such as large-scale promotions and multi-echelon inventory networks, reducing sales losses while improving capital efficiency.

Industrial Use Cases

Sales Forecast for Seasonal FMCG Products

A well-known FMCG brand used an AI Supply Chain to identify seasonal products with curve fitting, increasing the demand forecast accuracy by 10% and reducing the out-of-stock rate by 1%. Predictions were based on historical data back-testing and saved up to tens of millions of dollars in potential losses. In addition, the turnover time was estimated to have reduced by 10%, saving annualized capital costs of millions of dollars.
Reshaping Product Demand Forecasting
· Analyze and identify noise in historical sales and provide noise rejection and refilling configurations to generate baseline sales based on denoised historical sales.
· Perform baseline forecasts with multiple algorithm models, which can be trained and selected to produce baseline sales forecasts

Sales Forecast for Durable Consumption Goods

A leading brand of small home appliances faced a growing demand for data-driven operations. The brand leveraged Alibaba Cloud AI Supply Chain Solution to help manage challenges in the digital transformation of its supply chain.
Inventory Diagnosis and Optimization Simulation
· Reduce forecast cycle from ten days to one day by quickly locating products and taking actions based on the inventory index warning
Forecast Accuracy Increase
· Improve forecast accuracy by 10%-30% with deep learning forecasting algorithms, and complete marketing optimization by enhancing delivery explainability through a whitebox feature for increased accuracy attribution
On-Shelf Rate Increase
· Increase the on-shelf rate by 2% by breaking down forecast results and transforming them into executable and effective replenishment recommendations with inventory simulation algorithms

Human Resource Scheduling

Hourly or minute-level demand forecasts for industrial scenarios that require human resources can ensure accurate scheduling and reduce manpower costs by tens of millions of dollars. Examples include logistics and delivery, online customer service, content auditing, etc.
Fine-Grained Forecast Time
· Reduce forecast time to hours or even minutes with time-series forecast algorithms 5%-10% more accurate than open-source algorithms (hourly accuracy rate 90%, minute-level accuracy rate 85%).
Long Prediction Timespan
· Plan fine-grained forecast days, weeks, or even months in advance
Fast Deployment
· Deploy rapidly based on configurations from zero, within 3 man-days

Sales Forecast for E-Commerce

A famous e-commerce platform increased its sales forecast accuracy (5% higher than the open-source algorithms) and product on-shelf rate, reducing inventory turnover days. This helped them move pre-sales and hot products to Front-End Logistics Centers (FDCs), significantly shortened delivery time, improving customers experience and reducing logistics costs.
Accurate Forecast
· Ensure forecast accuracy and stability with built-in time-series forecast algorithm
· Benchmark against major historical fluctuations to increase forecast accuracy by annotating and updating the marketing campaign calendar with the assistance of algorithms
High Efficiency
· Run millions of samples in parallel on multiple servers and GPUs to ensure high forecast efficiency
· Reduce memory consumption by three to five times with optimized time-series data, saving cloud resource costs

Learn more about Alibaba Cloud AI Supply Chain solution

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