Seize the AI Future: Be a Logistics Market Leader with Deep Network Analytics (Graph)
The logistics sector, often hailed as the heartbeat of modern commerce, plays a pivotal role in ensuring that products seamlessly reach consumers - timely and in optimal condition. As we navigate through an era punctuated by burgeoning logistical complexities, market front-runners are tapping into the immense potential of Artificial Intelligence (AI) and, notably, graph analytics, to foster unparalleled operational efficiencies. 'Seize the AI Future' amplifies the criticality for logistics organizations to weave AI meticulously into their strategic tapestry, enabling them to not just stay abreast of shifting market dynamics but also carve out a future wherein they deftly steer the logistics market with acumen and anticipatory foresight.
Now, Pivoting to Graph Analytics - What Is It, Exactly?
Think of graph analytics as the contemporary compass designed to traverse the complex network of relationships embedded within your business data. In layman's terms, it dissects data into 'nodes' (such as suppliers or products) and 'edges' (the relationships or interactions between them), interconnected much like the neural nodes in our brain. While conventional data tools zero in on isolated data points, graph analytics delves into the networks between them, working its magic when bolstered by advanced data science techniques. Not only deciphering existing patterns, but it also astutely predicts upcoming scenarios.
For those in the logistics arena, this translates to a profound understanding of interactions among suppliers, distribution hubs, and transport pathways, intertwined with cost, Service Level Agreements (SLAs), and regulations. This foresight enables the anticipation of potential disruptions and the uncovering of new efficiencies, presenting graph analytics as a strategic ally in industries where swift, informed decisions are paramount. Let’s delve deeper into its transformative potential within the logistics sector.
Embarking on a Transformative Journey: The Top 10 Graph Analytics Use Cases to Revolutionize Your Logistics Business:
(1) Route Optimization
Traditional mapping systems may provide a shortest path, but graph analytics dives deeper. It analyses routes in real-time, accounting for variables like traffic, weather conditions, associated costs and cargo-specific requirements. This allows for real-time optimization, minimizing delays and reducing costs. When there is a disruption, graph analytics can do a 360-impact assessment and provide recommendations on next best options.
Take an example of a logistics firm who frequently transports goods overland from Mumbai to Bangkok. While traditional maps suggest the most direct route, graph analytics elevates their approach. It dynamically analyses real-time variables: a sudden monsoon in Myanmar, unexpected border delays in India-Nepal, or a festival-induced traffic surge in Bangkok. On one occasion, when a political protest disrupted a major checkpoint, the system assessed the total impact, predicting a 48-hour delay. Within moments, it recommended an alternative route via Laos, factoring in cargo specifications, associated costs, and the current weather pattern. This adjustment not only saved time but also ensured timely delivery, showcasing the profound benefits of combining real-time analytics with logistical expertise.
(2) Network Design & Optimization
Logistics companies manage vast networks of distribution centres, suppliers, and transportation routes. Graph analytics help visualize and optimize these networks, highlighting inefficiencies and suggesting optimal placement of hubs and routes.
Imagine a major logistics provider in Southeast Asia manages numerous distribution centres across Thailand, Vietnam, Indonesia, and the Philippines. With graph analytics, they pinpoint a bottleneck: goods from Vietnamese suppliers often get delayed at a busy Jakarta hub. By analysing their network, they identify an underutilized centre in Surabaya. By rerouting shipments through Surabaya, they not only speed up deliveries but also reduce operational costs, enhancing efficiency across their expansive network.
(3) Demand Forecasting
By analysing graphs that include historical data, current market trends, and other variables, companies can anticipate demand spikes and adjust their logistics operations accordingly.
Consider a logistics provider specializing in transporting agricultural products within Asia. As the Lunar New Year approaches, there's traditionally an increased demand for certain commodities like rice, fruits, and vegetables. By analysing graphs containing historical shipping data from previous years alongside current market trends and factors like weather forecasts, the company can predict a surge in demand for transportation of oranges from regions in China to urban centres throughout Southeast Asia. With this predictive insight, the provider can proactively allocate additional cargo space, reroute transportation, or even negotiate timely contracts with farmers, ensuring they meet demand efficiently while maximizing profit.
(4) Inventory Management
Graph analytics can be used to track the movement and storage of inventory across a supply chain. This helps in predicting stock-outs, optimizing inventory levels, and reducing holding costs.
Take an example of an Asia-based electronics manufacturer sources components from suppliers in Taiwan, South Korea, and Japan. Using graph analytics, they trace the flow of these components across their storage warehouses. They spot a recurring issue: capacitors from a Taiwanese supplier consistently run low in the Bangkok warehouse, leading to production delays. Simultaneously, an overstock of these capacitors sits idle in their Manila warehouse. Armed with this insight, they adjust their inventory distribution, shifting excess capacitors from Manila to Bangkok. Additionally, they refine their procurement strategy, ensuring a balanced and cost-effective inventory in the future.
(5) Supplier Relationship Management
By mapping out supplier networks and analysing performance data, companies can identify which suppliers are the most reliable, who provides the best rates, and where potential vulnerabilities lie.
Consider an Asia-based automobile manufacturer sources parts from China, India, South Korea, and Japan. Using graph analytics, they gain a comprehensive view of their supplier network, assessing transaction rates, punctuality, costs, and quality metrics. The analysis reveals that a transmission supplier from China consistently excels in delivery and low defect rates. In contrast, an electronics supplier from South Korea, besides frequent delays, has seen a surge in warranty claims recently. With this knowledge, the manufacturer strengthens ties with the Chinese supplier, while considering a quality review and supplementary sourcing for the South Korean supplier to maintain product quality and delivery schedules.
(6) Fraud Detection
Unusual patterns in shipping and receiving, sudden changes in supplier behaviour, or unexpected routing changes can be flags for fraud or theft. Graph analytics can quickly identify these anomalies and trigger alerts.
Take an example of an international electronics distributor with operations across various Asian cities, handling shipments of high-value items like smartphones, tablets, and laptops. Using graph analytics, the company consistently monitors patterns in shipping, receiving, and routing data. Over time, the system recognizes a stable pattern for how shipments move, the duration between dispatch and receipt, and the common routes taken. One month, the graph analytics tool flags an anomaly: a batch of smartphones, instead of taking the usual direct route from Seoul to Singapore, made unexpected stops in two other cities and took twice as long to reach its destination. Furthermore, the weight of the shipment decreased slightly during one of these stops. Upon investigation, it's found that a middleman had rerouted the shipment to siphon off some units for illegal resale. The unusual routing and the change in weight were the primary indicators detected by the graph analytics system, leading to the discovery of the fraudulent activity.
By leveraging graph analytics, the company was able to quickly identify and respond to this security breach, thereby safeguarding its assets and reputation.
(7) Capacity Planning
By understanding patterns and flows in transportation and storage, graph analytics can help logistics providers anticipate when and where they'll need additional capacity, be it in warehousing or transport.
Imagine an intricate logistics network spanning Asia, weaving maritime, rail, road, and air transport. A container ship arriving at Mumbai's port carries high-priority electronics destined for a Delhi product launch. These are swiftly transferred to trains but are interdependent with road transport in Delhi, which distributes them to North Indian retail centres. Simultaneously, this network intertwines with textiles from Dhaka flown to Bangkok and auto parts from Nagoya railed to Seoul. A single delay at Mumbai's port can trigger a cascade affecting deliveries in Delhi, Bangkok, and Seoul, given the tight interlinking of schedules, capacities, and routes. Graph analytics untangles and visualizes these multifaceted relationships, enabling optimal capacity planning and minimizing disruptions.
(8) Customer Experience Enhancement
Graphs can map out customer touchpoints and feedback loops. Understanding the journey from order to delivery allows logistics companies to identify areas for improvement and ensure timely delivery, enhancing customer satisfaction.
For example, a global automobile manufacturer, spread extensively across Asia, orchestrates a vast network — importing electronics from Malaysia, tires from Vietnam, and metal parts from India to a central assembly hub in Thailand. Through graph analytics, the intricate interplay between suppliers, component quality, assembly sequences, and distribution routes is visualized. A revealing pattern emerges: occasional delays in the electronics delivery from Malaysia have a downstream effect, correlating with feedback about infotainment system issues in cars sold in Indonesia. Armed with this insight, not only do they streamline supplier coordination and enhance quality checks, but they also proactively communicate with Indonesian dealers and customers, updating them about potential delays and offering extended warranties on the infotainment system. Such pre-emptive communication fosters trust, ensuring customers feel valued and informed, amplifying their overall satisfaction.
(9) Collaboration Networks
Logistics often involves collaboration between multiple entities, from suppliers to third-party logistics providers. Graph analytics can visualize these collaborations, making it easier to identify bottlenecks and streamline communication.
For example, a global electronics manufacturer based in Taipei orchestrates a vast supply chain involving suppliers in Japan and South Korea, assembly plants in Taiwan and China, and third-party logistics partners in Singapore and Hong Kong. Applying graph analytics, they unveil crucial intricacies: a pivotal component's timely arrival from South Korea is interlinked with a sub-supplier in Japan, the Singaporean logistics partner often subcontracts to a Vietnamese firm during peak periods, and direct communication between their two factories is suboptimal, causing occasional component-sharing delays. Armed with these insights through graph analytics, the manufacturer bolsters direct factory communications, instigates clearer protocols with the Korean supplier regarding its sub-supplier, and fosters more transparent dialogue with the Singaporean logistics entity, streamlining their entire collaborative framework.
(10) Environmental Impact Analysis
As sustainability becomes a priority, logistics companies can use graph analytics to assess the environmental impact of their routes, transportation modes, and warehousing practices. By analysing these graphs, they can make data-driven decisions to reduce their carbon footprint.
Take an example of a prominent shipping conglomerate operating across the South China Sea seeks to minimize its environmental footprint amidst growing concerns over climate change. With an extensive fleet and multiple routes connecting ports from Shanghai to Jakarta, the company turns to graph analytics for a holistic environmental evaluation. The graphical data reveals that certain routes, especially those navigating through the busy Malacca Strait, often experience significant congestion, leading to vessels idling and consequently higher emissions. Furthermore, older vessels in the fleet, despite being less frequent in operation, contribute disproportionately to CO2 emissions. Additionally, their major warehousing facility in Hong Kong, due to its outdated cooling system, consumes energy at a rate higher than global standards. Using this interconnected data, the company decides to reroute some of its schedules away from high-congestion areas, initiate a phased retirement of older ships, and invest in eco-friendly cooling solutions for the Hong Kong warehouse. The outcome is a significant reduction in their carbon footprint, aligning their operations with sustainable goals.
Logistics stands as a labyrinth of intertwined processes and dependencies. As such, the infusion of graph analytics brings a clarifying lens to this maze, enabling logistics firms to pinpoint inefficiencies, bolster sustainability, and enhance overall operations. Using graph analytics alone won't fix everything. You get the best results when you combine it with solid logistics expertise. Hence, it is important to work with an implementer who has strong logistics industry knowledge and the technical capabilities of graph analytics.
Speak to us if you are interested to explore Graph for your Logistics operations.