Data and AI are key enablers of supply chain excellence

Discover how AI can transform supply chains and enhance efficiencies to drive innovation and growth.

Avoiding tech and supply chain disruptions

Could a situation similar to the Suez Canal blockage occur in the tech and supply chain industry where a single point of failure causes widespread disruption? Certainly. To avoid such a scenario, organizations must enhance their operations by optimizing supply chains and connectivity in the transportation value chain, especially regarding data, insights, and reports. Today, they are increasingly recognized as strategic channels that drive innovation. With this comes regulatory scrutiny to lower costs, improve operational efficiencies, and realize value faster. With rising inflationary transportation costs and geopolitical turmoil, it has become imperative for businesses to optimize and improve operational margins to a greater degree. How exactly can technology serve as a crucial enabler and accelerator to solve this problem?

Just-in-time and just-in-case supply chains, which once represented significant advancements for many, may no longer be the only efficient way to ensure customer satisfaction and profitability. Companies are starting to realize the importance of AI and how to extract tangible value through specific use cases of data and AI in supply chains. Some of the current challenges that drive the need for supply chain network optimization are as follows:

• Complex, siloed, and demand-based supply chains: Fragmented data streams from multiple locations, a lack of centralized sources to view insights, and reduced visibility to proactively resolve issues and identify opportunities, leading to increased turnaround time and higher costs.
• Competitive pressures and the need for hassle-free fulfillment: To stay competitive, companies must offer seamless, hassle-free fulfillment experiences. This means providing fast, reliable delivery options, easy returns, and exceptional customer service.
• Adapting to and recovering from disruptions: Natural disasters, geopolitical events, and channel blockages (e.g., the Suez Canal incident), all pose significant challenges.
• Improper management and inefficiencies: Sudden spikes in demand can lead to wastage and inefficiency, leaving companies to struggle to scale operations quickly enough to meet the increased volume, resulting in overstocking or understocking.
• Increased strategic importance of sustainable supply chains: Balancing sustainability with cost, profitability, and fulfillment time is becoming more crucial.
• Slower or minimal real-time decision–making: Without access to real-time data and actionable analytics, businesses cannot promptly identify and respond to issues such as inventory shortages, transportation delays, or sudden changes in demand.
• Consumers are becoming brand–agnostic: Customer loyalty is shifting toward retailers offering speed, convenience, and other value-added services, such as one-day delivery.

Optimizing siloed supply chains

How do manufacturers go about optimizing the entire process? Multiple data points generally affect supply chains, including inventory levels, raw material availability and costs, price elasticity, sales and seasonality data, logistics metrics, pricing information, and economic indicators. By leveraging this data, manufacturers can optimize and transform each value chain component, ultimately enhancing the overall supply chain. Some critical functions where data plays a pivotal role include:

1. Sourcing: Supply chain analytics predict disruptions, solve issues proactively, flag anomalies, help maintain robust operations, and contribute to business continuity readiness plans more effectively.
2. Warehousing: The usage of IoT devices and sensors can help track various physical metrics required to monitor storage conditions and maintain safety. These sensors act as local data hubs, reporting inferences and insights, which contribute to:
   • Better, faster decision-making
   • Improved inventory management, avoiding stockouts and reducing waste
   • Increased labor productivity
   • Superior machine and process failure prediction and mitigation
3. Point of sale (POS): Enhanced merchandizing and product clustering in physical stores, driven by insights from POS trends.

Retailers then use millions of data points across multiple channels and data sources across the value chain. Some typical data sources that help retailers make informed decisions are:

1. Master product data: Information about each product and how the products relate to other products/portfolios.
2. Inventory data: Details involving the value, volume, and location of current inventory across multiple distribution points.
3. Production data: Information on the entire product line and current production capacity for each product and automation data.
4. Demand data: Historical sales information and data from demand forecasting and projections for future sales.
5. Warehouse data: Insights including warehousing contracts, available storage space, equipment, and staffing.
6. Logistics data: Details regarding all areas of transportation and movement of inventory.

Delving into the core of using data analytics for supply chain excellence, the following steps ideally help convert a supply chain optimization strategy into actionable post-data collection and analysis:

1. Identification of KPIs: These are performance benchmarks specific to the strategic goals of retailers, such as improving customer satisfaction, reducing delivery time, minimizing returns and wastage, preventing stockouts, reducing costs, and optimizing the inventory-to-sales ratio.
2. Real-time analytics: Facilitates real-time decision-making and proactive resolution of potential issues to prevent disruptive problems later in the value chain.
3. Demand forecasting using predictive analytics: This scientific approach optimizes inventory levels, production schedules, and resource allocation while reducing wastage. It is one of the most strategically important use cases in the value chain.
4. Identification of bottlenecks and operational inefficiencies: This critical step improves and optimizes the supply chain by pinpointing potential errors, delays, and resource constraints that may hamper schedules.
5. Automation opportunities: The vast amounts of collected data, analytics, and machine learning algorithms help identify areas where automation can be beneficial. This reduces the need for human effort in repetitive, low-value tasks and minimizes human error.
6. Performance monitoring: Data also helps monitor and identify supply chain partners who consistently meet or exceed performance expectations. Maintaining strong relationships with these suppliers is crucial for sustaining supply chain excellence and quality standards. Additionally, performance monitoring helps uncover insights and trends within the data that can assist in root cause analysis when potential issues arise.

All this data can be leveraged through multiple technologies to optimize supply chains, such as control towers, digital twins, automation and robotics, IoT, and RFID. These technologies help drive meaningful and actionable insights for decision-makers.

Advancing supply chain efficiency

Uncovering insights does not automatically guarantee an optimized supply chain. So, what should retailers do differently? Refreshing the way insights are interpreted, fine-tuning parameters that are not contributing as expected, and innovating new use cases for data are essential steps. While data and AI can unlock valuable insights, achieving true data-driven decision-making to streamline operations requires the proper implementation of AI/ML algorithms.

Adopting emerging technologies like Generative AI (with use cases such as anomaly detection, price optimization, route optimization, and demand forecasting) and integrating them effectively within operations will drive supply chain excellence and efficiency. This approach will ultimately enhance profitability and improve the customer experience.