Key Points
  • Amid increasing complexity and accelerated change, big data analytics can drive logistics and supply chain management companies towards increased agility, efficiency and profitability

  • Insights from big data can not only help companies streamline sourcing, but enable them to accurately predict demand and improve customer experience

  • Big data analytics is pivotal for enterprises to leverage the synergized value of commercial, finance and supply chain operations, and achieve robust business outcomes

A leading Supply Chain Management (SCM) company recently analyzed its data, only to discover an interesting phenomenon. The company realized it was incurring significant costs through wasted fuel, delays in delivery and higher risks when its trucks turned left into incoming traffic. By changing routes to ensure that its trucks turned left less than 10 percent of the time, the company not only reduced its fleet strength by 1100, but delivered 350,000 more packages every year.

This is the advantage that the logistics and SCM industry unlock with big data analytics today – especially in an era of increasing complexity, and dynamic demand and supply environment. Across route planning, fleet management, inventory and maintenance, let us see how big analytics can transform the way supply chains are managed.

Transforming Demand Forecasting

As a predictor of the what, when and how, big data analytics plays a vital role across supply chain operations. Amidst changing marketplace factors, it enables accurate demand forecasting and reduces supply chain waste — leading to customer loyalty, minimal lost sales and improved profitability.

New generation big data analytics platforms, driven by Artificial Intelligence (AI) and Machine Learning (ML) algorithms, are enabling enterprises to achieve exponential improvements in demand planning productivity, revenue optimization, inventory management, and distribution network efficiency. For instance, a global logistics company’s predictive network management system analyzes 58 internal data parameters to minimize shipment delays. Additionally, introducing automation into this process enables organizations to respond with agility to demand-supply market fluctuations in real-time. A convenience store chain in the U.S. monitors customer reactions as they browse products on their retail store shelves and integrate this with purchase data to create accurate profiles of individual shopping preferences.

Streamlining Sourcing and Supplier Management

Customer reviews, profitability, location, quality of service, level of compliance — big data tools allow these key performance metrics and more to be computed for supplier assessment across commodities, locations and capabilities.

From a sourcing perspective, big data analytics enables companies to integrate their supply chains with real-time information on weather, traffic and other similar disruptions. For example, an analysis of shopping patterns conducted by an U.S. multinational retail corporation revealed that the sale of strawberry pop-tarts increased seven times ahead of a hurricane, while beer was the pre-hurricane top-selling item. Another leading online retailer uses its finely-honed data analytics capabilities to initiate fulfilment and shipping processes even before customers place their purchases in digital shopping baskets.

Big data analytics also helps organizations be aware of and prepared for deviations from established delivery patterns for efficient re-routing. In the area of pricing, which varies with market changes, big data analytics can analyze the simultaneous impact of multiple variables for optimal costing.

Raising the Bar for Customer Experience

Consumer mandates of speed, transparency and ease of purchase (and returns) are driving supply chain dynamics like never before. Big data analytics improves customer relationships by providing access to accurate information across every touchpoint in the supply chain. It can also help companies create newer business models and solutions for changing needs — such as flexible fulfillment networks and proximity-based cost models for shipping.

The scenario of mass production of customized products may sound like an oxymoron, but is very much a reality. ‘Batch size one’ production, as it is called, leverages data analytics to bring production close to the consumer, thereby reducing lead times. A global sports brand has leveraged ML algorithms to assess multiple predictor variables (such as social media data and purchase patterns) and map purchases to customer behaviors. Such personalized analysis across specific geographies has helped the company make effective decisions on the locations of its stores and shelf content.

As commercial, finance and supply chain operations come together as inseparable components of an organization’s cross-functional success, big data and advanced analytics capabilities will be crucial. Capitalizing on the value of big data analytics through combined data pools will enable companies to make the right customer-centric decisions with a perfect balance of cost, revenue and profits.

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