Businesses are undergoing a radical transformation – to navigate and grow in the new market scenario and to meet the evolving customer expectations. As a result, supply chains have become vastly complex, with a growing number of partners and physical interlinkages. Yet, the supply chain is rarely a priority when it comes to the management electing functions for transformation. Typically, a business does not look to transform its supply chain until something monumental goes wrong. But given the expansive network of processes, technology, activities, and people that make up the function, the supply chain has a vast untapped potential to reveal strategic business insights. And so, an increasing number of companies are adopting AI technologies to create an intelligent data-driven supply chain. Mckinsey’s report on ‘Succeeding in the AI supply-chain revolution’ reveals that successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent, compared with slower-moving competitors.
What is data-driven supply chain management?
A data-driven supply chain is a system for quality management based on gathering and analyzing information from every point in the chain, internal and external to the organization. This includes third-party suppliers, logistic partners, and even customers. Thus the dataset used for analysis extends way beyond the traditional internal data. The entire supply chain is transformed into a digital function where all decisions are enabled and actioned by data. A data-driven supply chain brings invaluable data and provides the management with a bird’s eye view. The new insights generated enable quick and efficient decision-making – from front-line operations to selecting the optimal supply chain model and the right service providers to partner with.
Data-driven supply chain and demand visibility
The traditional supply chain mechanism is business-centric. It is built inside out, with the organization at the center and then moving outwards to channel partners and the customer. This creates inertia in the business’s response towards customer demand. Any change in the actual demand vs. the forecast manifests as overstocking or stock out, which translates as an incremental buffer stock. Channel partners too buy on the basis of their speculation and not the actual market demand, so overstocking happens across the channel as well.
A data-driven supply chain, on the other hand, is customer-centric. It keeps customer demand at the center of the process, and the mechanism is built outside in – from the customer to channel partners and then the company. The system gathers data inputs from within and outside the organization using AI tools, machine algorithms, and IoT to run advanced analytics and create real-time demand visibility. Then, the business procures, produces, and distributes against this demand. This makes the supply chain more agile, responsive, and lighter on stock inventory, with a remarkable ability to meet customer demand.
This also transforms how supply chain management is viewed – from an expense center where you try to minimize costs to a strategic function that constantly provides intelligent insights on revenue generation, cost control, and ways of meeting customer expectations.
Key benefits of a data-driven supply chain
Data-driven supply chains deliver material, cost, and time benefits across levels. Here are a few key benefits that businesses can expect –
- Real-time visibility of stock levels across the supply chain
- Better recognition of and catering to the seasonality of demand
- Identification of dead stock at all stakeholder levels
- Better on-time performance and reduced variability in lead time due to demand shocks, production stoppages, and transportation disruptions
- Capping of unauthorized distribution
- Reduction in damages
- Freeing up working capital across production, warehousing, and distribution
- Greater supply efficiency to support market activities like pricing changes, product promotions, the addition of new products/product lines.
- Reduction of carbon footprint and creation of sustainable alternatives for stakeholders
Supply chain functions are increasingly complex, dynamic, and competitive. Data-driven supply chain strategies based on smart analytics can help navigate those challenges. In addition, with the advancement of data analytics and IoT, businesses have an opportunity to invest in cutting-edge solutions to gain demand visibility and provide next-generation differentiated services. For more information on what a data-driven intelligent supply chain can mean for your business, reach out to DforD experts today.