6 Ways Big Data Can Enhance Supply Chain Management

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    6 Ways Big Data Can Enhance Supply Chain Management

    Imagine transforming supply chain management with the power of big data. This article answers the question, 'Can you describe six instance where you used big data to enhance supply chain management or logistics? How did data-driven insights improve efficiency or reduce costs?' The first insight reveals how integrating data for real-time monitoring can revolutionize operations, while the final insight highlights the importance of making quick decisions with data insights. With a total of six actionable insights, this read promises to be both informative and impactful.

    • Integrate Data for Real-Time Monitoring
    • Enhance Visibility with Real-Time Tracking
    • Employ Predictive Analytics for Continuity
    • Optimize Inventory with Demand Forecasting
    • Streamline Procurement Through Automation
    • Make Quick Decisions with Data Insights

    Integrate Data for Real-Time Monitoring

    An efficient and robust supply chain for retail operations involves various complex decisions that depend on each other. Viewing the supply chain comprehensively requires integrating large volumes of data from multiple sources (sales, suppliers, market trends, etc.) and implementing real-time monitoring systems to track inventory, supplier performance, and transportation to enable quick responses to any issues. Big data helps with these decisions by identifying potential risks and optimizing supply-chain operations.

    A recent example of using data-driven insights is when COVID-19 significantly changed the last-mile delivery network for B2B retailers. Due to work-from-home policies, delivery locations were more spread out in the suburbs, with one to two boxes per stop, compared to a larger number of boxes in more centralized office locations. This exposed a general underlying inefficiency in last-mile delivery when (total route time) demand exceeds (temporal) deliverQy capacity, where the challenge is to deliver to all customers on the promised delivery day with the retailer-driver (RD) staying within the regular shift hours.

    Evolving industry practices include outsourcing some deliveries to on-demand drivers (ODDs), such as Uber and Lyft. Big data and complex AI models help determine the route of the RD, the locations that the ODDs will deliver to, and the drop-off locations where the RD will hand over packages to the ODDs.

    Debdatta Sinha Roy
    Debdatta Sinha RoyPrincipal Research Scientist, Oracle

    Enhance Visibility with Real-Time Tracking

    Utilizing real-time tracking in big data enables supply chain managers to have a clearer view of their operations, enhancing overall visibility. This improved visibility allows for proactive measures to be taken, which leads to better management of risks. With timely interventions, businesses can mitigate potential issues before they escalate.

    Furthermore, it supports continuous monitoring of products from manufacturing to delivery. Therefore, businesses should invest in real-time tracking technologies to stay ahead of disruptions.

    Employ Predictive Analytics for Continuity

    By employing predictive analytics, companies can foresee and address potential disruptions within the supply chain. This foresight allows organizations to put preventative measures in place, minimizing risks and ensuring continuity. Predictive models can analyze patterns and trends to warn of issues like delays or component shortages.

    This preparation leads to fewer interruptions and more reliable operations. Embracing predictive analytics is essential for maintaining a resilient supply chain.

    Optimize Inventory with Demand Forecasting

    Demand forecasting through the analysis of big data helps in optimizing inventory levels and significantly reducing waste. Accurate predictions about product demand ensure that stock levels are adequate to meet customer needs without overproducing. This balance not only cuts costs associated with excess inventory but also improves customer satisfaction.

    Additionally, it allows for more efficient resource allocation and planning. Companies should adopt demand forecasting tools to enhance supply chain efficiency.

    Streamline Procurement Through Automation

    Streamlining procurement processes through automation reduces the likelihood of human error and speeds up the ordering process. Automated systems can handle repetitive tasks, freeing up personnel to focus on more critical activities. This efficiency leads to cost savings and faster turnaround times for product orders.

    Furthermore, it ensures more consistent procurement practices. Investing in procurement automation is crucial for any modern supply chain.

    Make Quick Decisions with Data Insights

    Leveraging data-driven insights allows supply chain managers to make informed decisions quickly, increasing their agility in the market. These insights are derived from analyzing large volumes of data, pinpointing areas for improvement. Swift decision-making can lead to better performance and faster responses to changing market conditions.

    This flexibility is vital in today’s fast-paced business environment. Companies must utilize data analytics to gain a competitive edge in supply chain management.