In the airfreight domain, data analytics can be used to improve load factors, and reduce fuel consumption
In today’s interconnected world, the efficiency of supply chains has a profound impact on global economies. From the moment a consumer clicks ‘buy now’ to the delivery of a product, a complex network of logistics and transportation operations is at work.
However, this intricate system can often be plagued by inefficiencies, delays, and environmental concerns. To address these challenges, businesses must harness the power of data analytics to optimise their supply chains, writes Jadd Elliot Dib, CEO, Pangaea X, in this special OpEd for LogisticsGulf.com.
The journey begins with the consumer. When a customer places an order, a wealth of data is generated: product information, shipping address, and delivery preferences. Businesses can gain valuable insights into consumer behaviour and demand patterns. This information can be used to assess inventory levels, production schedules, and logistics planning.
Reducing carbon footprint
Once an order is placed, the next step is to make the shipping route more efficient. Data analytics can help identify the most efficient routes, taking into account factors such as traffic congestion, weather conditions, and fuel prices. By minimising travel distances and reducing idle time, businesses can significantly reduce their carbon footprint and operational costs.
In the realm of air freight, data analytics can be used to study flight paths, improve load factors, and reduce fuel consumption. Historical flight data and real-time weather information can help airlines make informed decisions about flight routes, altitudes, and speeds. This not only reduces fuel costs but also minimises the environmental impact of air travel.
A prime example of a well-known brand that uses data analytics to stay efficient is Walmart. The retail giant has implemented a sophisticated data-driven system that allows it to track products from the point of origin to the store shelf.
Predictive analytics
According to research, Walmart uses predictive analytics to forecast demand for products. In addition, they also employed real-time inventory tracking systems to monitor stock levels across its vast network of stores and distribution centres. This allows them to identify potential shortages or surpluses and take corrective action promptly.
Another way Walmart uses data to the best of its ability is by analysing historical shipping data, traffic patterns, and fuel costs and how they can opt for shorter routes to reduce delivery times and fuel consumption. Walmart’s data-driven approach to supply chain management has resulted in significant cost savings, improved customer satisfaction, and reduced environmental impact.
As technology continues to evolve, the potential for data-driven supply chain optimization is immense. Emerging technologies such as artificial intelligence (AI) and machine learning can further enhance the capabilities of data analytics. AI-powered systems can predict future demand, optimise inventory levels, and identify potential supply chain disruptions.
Machine learning algorithms can analyse vast amounts of data to uncover hidden patterns and trends, enabling businesses to make more informed decisions.
BOX OUT:
Jadd Elliot Dib is the founder and CEO of Pangaea X, a digital platform that aims to bring together the world’s best data analysts and scientist freelancers from across the world on to a single platform and open them to a multitude of job opportunities.
Jadd holds a Bachelor of Science in Computer Science from Queen Mary University in London, and a degree in Business / Managerial Economics from the London School of Economics and Political Science (LSE). He speaks French, English, and Spanish.
