Supply Chain
Case Studies
7-Eleven improved customer marketing and in-store capacity planning in Indonesia and Mexico using machine learning to predict demand variations
7-Eleven decided to use AI to optimise capacity planning and marketing. They established an information analysis environment to analyse patterns and gather valuable insights from point-of-sale data.
Linked use case: Ensure inventory availability by predicting demand and triggering appropriate action
A global transport provider gains a significant competitive advantage by implementing AI for supply chain visibility
Hong Kong-based Gravity Supply Chain is utilising artificial intelligence (AI) and big data to bring e-commerce-style supply chain visibility to international freight.
Linked use case: Optimise supply chain
Accenture redeploys after automating tasks with RPA system
Accenture has developed a system, SynOps, for analysing its various data input sources and automating things like contract review. While it has been using this system internally to automate processes in finance, marketing, accounting, and procurement, purportedly resulting in the redeployment of 40,000 staff, it is now selling it tp clients.
Linked use case: Digitise and automate processes using Robotic Process Automation (RPA)
Alliance Boots achieves inventory savings and improved service levels with machine learning optimisation algorithm
Alliance Boots has deployed Manhattan Solutions'' replenishment and demand forecasting technology, which leverages machine learning to analyse data and produce accurate forecasts. Through the solution, it has been able to reduce inventory and stock levels while also improve customer service and productivity across Europe.
Linked use case: Optimise supply chain
Amazon is testing its Prime Air drone delivery service promising to fly parcels to customers within 30 minutes of ordering.
Amazon has been testing drone delivery to enhance its Amazon Prime offerings through Prime Air, which will be able to autonomously fly packages to customers in less than 30 minutes, given they fulfil requirements, such as weight, size and distance of route.
Linked use case: Automate delivery to customer eg via drone or self-driving vehicle
Aston Martin raises public first time availability target 2% to 97.5% and drives 18% reduction in safety stock value with machine learning engine
Aston Martin required a solution to be able to offer a high first time availability, having spare parts immediately available for customers, without increasing stock levels. The company implemented ToolsGroup''s advanced machine learning engine to analyse historical data on consumer behaviour to better anticipate its customers'' needs. The implementation of the technology has resulted in a reduction of inventory value of its safety stock by 18% and an improvement in FTA service levels of 97.1%, within only two months.
Linked use case: Ensure inventory availability by predicting demand and triggering appropriate action
BMW increases worker safety and productivity and space utilisation through human-robot-collaboration
BMW is leveraging the KUKA LBR iiwa lightweight robots at its plant in Munich, to assist workers and operators with monotonous and physically strenuous tasks. The otherwise endangering collaboration of human and robots (HRC) has become safer as the lightweight robots are enhanced with AI and machine learning technology, enabling them to be capable of working along humans and being easily programmed. BMW has managed to support its employees while also saving roughly 25% more space, as the robots do not require fences or other safeguards within the factory.
Linked use case: Accelerate and support key processes with improved human-robot collaboration
Big River Steel is predicting the availability of scrap steel using a predictive AI engine
Big River Steel has implemented Noodle.ai''s enterprise AI solution aiming to optimise operations at the company''s new metal recycling and steel production facility in Arkansas. Using machine learning models, the company is able to make predictions on the availability of its raw material, scrap steel.
Linked use case: Ensure inventory availability by predicting demand and triggering appropriate action
Big River Steel makes more accurate demand predictions using machine learning
Big River Steel has implemented Noodle.ai''s enterprise AI solution aiming to optimise operations at the company''s new metal recycling and steel production facility in Arkansas. Using machine learning models, the company is able to make demand predictions based on macroeconomic and historical data.
Linked use case: Forecast product / service demand levels
Big River Steel reduces cost of outbound transportation and optimises delivery windows using machine learning
Big River Steel has implemented Noodle.ai''s enterprise AI solution aiming to optimise operations at the company''s new metal recycling and steel production facility in Arkansas. Using machine learning models, the company is able to reduce outbound transportation costs while optimising delivery windows for its customers.
Linked use case: Optimise quality of service and product delivery
Chronopost International ensures on-time deliveries during peak activity with machine learning predictive analytics
Chronopost can ensure on-time deliveries during peak activity with ease of delivery rating for all addresses based on parcel tracking and geographical data provided by Dataiku DSS. The custom solution implemented analyses historical internal delivery and retrieval data using a machine learning interface. The company has increased business intelligence productivity and optimised package delivery operational costs .
Linked use case: Optimise quality of service and product delivery
Coca-Cola Hellenic covers 3.6X more stores by automating in-store inventory audits using image recognition
Coca-Cola Hellenic uses image recognition technology, deep reporting and real-time analytics to keep track of SKUs. Trax''s platform helps accelerate retail execution by automating store audits. Coca-Cola Hellenic has reduced audit times by upto 9 times. The reduction in costs has enabled them to triple their sales execution coverage and reduce out of stock.
Linked use case: Optimise product layout in stores
Coca-Cola achieves 6 percent additional revenue with 15 percent fewer restocking trips by identifying the right product and placement using intelligent vending machines
Using Hivery''s AI solution, Coca-Cola has been able to identify the right product, the right space-to-sales ratio and the right promotional activity for products to be placed in each vending machine. The software also optimises physical product placement in the vending machine. It also analyses sales to predict demand. Coca-Cola estimates intelligent vending machines have helped drive 6% of the revenue increase.
Linked use case: Evaluate and optimise packaging and placement
Danone increased product demand forecast accuracy to 92% with a 55% improvement in net uplift from promotional events using machine learning
Danone has improved the reliability of forecasting increased demand for products as a result of its promotional efforts (uplift) through implementing the ToolsGroup system which analyses demand variability. The system draws in information from point-of-sale feeds in hypermarkets
Linked use case: Forecast product / service demand levels
Danone reduces forecast error and lost sales by 20 and 30 percent respectively and achieves a 10 point ROI improvement in promotions with machine learning
Danone implemented ToolsGroup''s machine learning solution with the aim of identifying how promotional and media events affect their sales. They also needed to secure a more accurate forecast of demand, which is necessary given that their fresh products face volatile demand and a short shelf life. The solution leverages machine learning to predict demand variability and planning. Danone managed to create an efficient planning coordination between various departments such as marketing, sales, account management, supply chain and finance and has also reported a 20% reduction in forecast error, 30% reduction in lost sales, and a 10 point ROI improvement in promotions.
Linked use case: Forecast product / service demand levels
DeepMind increases value of wind power by 20% by predicting supply 36 hours in advance
Researchers from DeepMind and Google develop a neural network machine learning system to better predict availability of wind power 36 hours in the future. This is based on weather forecasts and historic turbine data, allowing for better grid scheduling of wind power supply a day in advance.
Linked use case: Optimise performance and positioning of wind turbines
Didi to tackle traffic congestion and optimise navigation routes with deep learning
Didi has leveraged artificial intelligence to develop a city traffic management system. The company, whose AI research labs are working on deep learning, natural language processing and machine vision, has launched the Smart Transportation Brain to combine video and sensor data from its vehicles and the government for traffic congestion and navigation optimisation. The company aims to facilitate the development of smart traffic lights and monitoring systems to alleviate road congestion.
Linked use case: Predict likelihood of traffic accidents or queues and optimise traffic system to reduce the risk
Domino’s delivers world’s first ever pizza by drone in New Zealand
Domino''s pizza delivered used a drone for delivery in New Zealand. The company aims to use this service to deliver pizzas within 10 minutes covering a 10km radius from a store. Although this is only available in one location in New Zealand at the moment, Whangaparaoa, Domino''s is looking to expand the project trials to Australia, Belgium, France, The Netherlands, Japan and Germany.
Linked use case: Automate delivery to customer eg via drone or self-driving vehicle
Facebook create new location address schemes for developing areas decreasing travel times by 22% in tests
Facebook researchers develop a model of predicting roadways and other human infrastructure from satellite images in order to create an accurate addressing system, particularly for areas where no consistent system exists. The goal is to aid developing countries and improve direction accuracy to help disaster relief. The automatic addressing system covers more than 80% of populated areas.
Linked use case: Create maps from satellite and other remote imagery
Flipkart attempts to resolve address problem in India using machine learning
Flipkart, India''s leading e-commerce company, is trying to fix the ''address'' problem facing many developing nations. The addresses are not standardised and spelled differently, and with varied no. of lines. Flipkart is using to standardise addresses to
Linked use case: Predict appropriate data labelling to support data analytics work
General Electric has saved $80 million over the past few years by integrating supplier data across business units using machine learning
GE has leveraged machine learning technology from Tamr, to integrate supplier data and records across business units. The company''s goal is to identify products that are priced under different names from the same supplier in order to achieve better purchasing power and pricing. GE claims that the TAMR machine learning software has enabled GE to save $80 million over the past few years. According to Emily Galt of GE the use of Tamr has helped the company save $80 million over the past few years.
Linked use case: Capture 3rd party or internal data for price comparison and supplier relationship overview
Gousto, a British meal kit retailer uses genetic algorithms to optimise warehouse to determine factory layout and movement of boxes through the floor
Gousto, a British meal kit retailer, uses proprietary algorithms to optimise floor layout and movement of boxes in the warehouse. Resulting operational cost savings have led to 15% salary hike for warehouse employees.
Linked use case: Optimise building management including space management, health and safety
Granarolo reduces inventory levels by more than 50% and cuts capital and lead time in half using machine learning
Granarolo leverages machine learning technology from ToolsGroup. Using the SO99+ and Trade Promotion Forecasting (TPF) solutions, which use advanced machine learning analytics and deep learning, the company is able to plan demand for perishable products, optimise its inventory and accurately estimate future promotions based on historical data. Granarolo has managed to reduce its inventory levels and delivery time by 50% and increase its average forecast reliability by 5%, resulting in better customer service and fresh products offering.
Linked use case: Optimise supply chain
H&M improve single store sales by improving inventory planning through machine learning to discover trends on social media
H&M is beginning to use machine learning to analyse social media and online content for trend discovery, leading to better inventory and pricing planning.
Linked use case: Optimise product pricing to improve yield management
Hitachi conducted an on-site demonstration of a warehouse management system equipped with its AI technology where results showed efficiency improvement in logistics tasks
Hitachi has announced the development of artificial intelligence technology which provides appropriate work orders, based on demand fluctuation and on-site kaizen activity. It does so based on an understanding of demand fluctuation and on-site kaizen activity derived from big data accumulated daily in business systems and is designed to realize efficient operations.
Linked use case: Optimise supply chain including logistics, procurement timing and inventory distribution across warehouses and stores