The secret to smoother transport – Data-driven supply chains–By Jadd Elliot Dib
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.
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.
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 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. 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.
