Jaguar Land Rover Increases Supply Chain Planning Velocity with Graph — And Wins CIO 100 Award for Impressive Results
By Yu Xu, TigerGraph CEO & Founder
I am very pleased to extend my congratulations to Harry Powell, Director of Data and Analytics at Jaguar Land Rover, and his team for winning a CIO 100 Award. The CIO 100 Awards recognizes 100 organizations (and their teams) that use IT in innovative ways to deliver business value, and the award is an acknowledged mark of enterprise excellence.
Jaguar Land Rover, like every automotive manufacturer, makes its sales forecasts years in advance to allow suppliers to tool-up highly specialized production lines. Agreements to purchase specific quantities of parts are often made, based on these forecasts, with hefty fines imposed whenever the actual volumes purchased fall below commitments. The demand for specific vehicles can, however, vary rapidly due to changes in consumer preferences and market conditions.
Harry and his team at Jaguar Land Rover developed a way to offset changes in demand for one type of vehicle by proactively distributing no-longer-needed parts across its fleet of vehicles, and in so doing, minimize the risk of contractual penalties.
Becoming More Flexible
The data necessary to gain transparency across the manufacturing process is distributed across numerous complex data sources from multiple departments, including forecast and supply chain data, parts data from a PLM system, and car configuration data output by a combination of the car-configuration and build-simulation systems. These systems span a diverse array of technology from dedicated mainframe all the way through to dedicated enterprise/manufacturing resource planning platforms and custom distributed car-simulation applications. This diverse combination of data meant it was impossible to query across the data in a timely manner.
By uploading its supply chain data to a graph database with hybrid transactional/analytical processing, Harry’s team was able to combine 12 separate data sources. The information in the graph database includes data about every one of the parts provided by thousands of suppliers, with their relationships to a particular model of the vehicle and the bills of material for each sub-assembly used in it. The database also includes information relevant to the manufacturing build sequencing and order forecast for those vehicles. The flexibility of the graph database enables Jaguar Land Rover to quickly reflect changes in their immediate graph requirements as well as allowing for future expansion.
Innovation is Harry’s Hallmark
Two innovations enabled Harry’s team to construct a connected view of the business from supply to demand, and run powerful analytics, which yielded major benefits:
First, Jaguar Land Rover realized that a car is simply a set of connected features, and a feature is a set of connected parts. This realization led to a new lingua franca, facilitating collaboration between the business units and the data and analytics team.
Second, Jaguar Land Rover understood that graph analytics provides the best perspective to capture and exploit this common understanding of the business. Graphs put equal emphasis on objects and relationships in data structure and analysis. These two realizations enabled Jaguar Land Rover to construct a connected view of the business and answer critical business questions promptly. In doing so, Jaguar Land Rover reaped great rewards by reducing inventory costs and supply chain bottlenecks, improving supplier reliability, and enhancing product profitability.
After realizing that vehicles are simply sets of features and parts, the Jaguar Land Rover team used graph techniques to quickly construct a clear line of sight between supply and demand. They began by representing feature and part layers, then expanded to the car and supplier layers. The result was the Demand-Supply Graph.
When constructing the graph, Harry’s team relied on a key principle: the world is too complex to represent in a top-down manner. Instead, start by representing a car, then a family of cars, then an entire vehicle manufacturing line, and so on. Representing the known business as a minimum viable product, the Jaguar Land Rover team continued to expand their demand-supply graph at the margins. In building this view, the company used bills of materials and fitment conditions stored in manufacturing machines.
Delivering Business Value
Jaguar Land Rover reaped productivity, cost savings, and efficiency rewards from its graph implementation including:
120x acceleration of decision speed—determining the impact of changes to demand for vehicles now takes around 30 minutes and in the past, it would take three weeks if it was possible at all
3x improvement in business value—resulting from decreasing inventory costs, lower working capital, and greater profitability
35% reduction in supplier risk—because of a higher probability that the parts they manufacture will be purchased
I am not aware of any organization within an equally complex industry being able to transform its supply chain planning and obtain these results. Jaguar Land Rover is, consequently, on track to improve its profitability by over £100M annually as a direct result of the success of this project.
Harry and his team deserve this honor for the tremendous work they have done transforming their supply chain practices. Congratulations to everyone involved in this ground-breaking project.
You can learn more about Jaguar Land Rover’s success here. You can also register to hear Harry speak at our Graph + AI Summit, an online conference April 21-22, here.
Let us know if graph analytics sounds like something that can drive business value at your company—you can do that here. Or you can get started immediately with TigerGraph Cloud, and test drive our supply chain starter kit, here.
Dr. Jay Yu is the VP of Product and Innovation at TigerGraph, responsible for driving product strategy and roadmap, as well as fostering innovation in graph database engine and graph solutions. He is a proven hands-on full-stack innovator, strategic thinker, leader, and evangelist for new technology and product, with 25+ years of industry experience ranging from highly scalable distributed database engine company (Teradata), B2B e-commerce services startup, to consumer-facing financial applications company (Intuit). He received his PhD from the University of Wisconsin - Madison, where he specialized in large scale parallel database systems