How Big Data Enhances Supply Chain Visibility

Supply chain data can be analyzed and processed by AI from both structured and unstructured data to provide real-time insights across all stages of a supply chain. This article will discuss that with some examples. Let's begin with the definition.

What is Big Data?

Fundamentally, big data refers to the vast amounts of data, structured and unstructured, that offer companies visibility into their current and future market and customer needs. These insights are interpreted through big data analytics that use a combination of AI and ML-driven tools, algorithms and processing systems to interpret data. Traditionally, supply chains used to rely on ERP and other disparate storage systems to leverage data. However, with supply chain analytics, data is now used to gain visibility and improve decision-making.

How Big Data Bring Increases Supply Chain Visibility

Here is how big data enhances visibility across the supply chain:

Product Quality

Big data is helping companies produce excellent products by managing hyper-responsive supply chains. It does so by giving visibility into not just customer needs and market trends but also into micro-level factors that have a direct effect on the product quality. For instance, a dairy farm deployed RFID and IoT sensors to detect up- and down-stream issues regarding health of the cattle, quality of fodder and even temperature changes, all of which have a direct effect on the milk quality. The result was a more nutritious, homogenized product of higher quality.

Planning and Forecasting

Big data offers critical visibility at the planning stage. Businesses can collect and analyze data across the supply chain network and accurately forecast demand using statistical models. For example, retail owners can leverage data from inventory and replenishment systems to mitigate any out-of-stock scenarios. These models not only factor in historical and real-time data, but also consider industry trends, competitor data and macroeconomic factors while forecasting.

Machine Maintenance

Unexpected issues with machinery caused by malfunctions, improper care or not replacing legacy equipment can cost industries heavily.

Big data combined with IoT can send automatic alerts for faults in machinery and schedule regular maintenance to ensure equipment run at its best. This cuts down repair costs and eliminates unscheduled downtimes significantly.

Order Fulfillment

Big data can allow businesses to offer up-to-date shipping information to customers. Moreover, it can reduce costs by optimizing delivery fleet management by designating the shortest route, delivery schedules, and shipment tracking.

Since 2016, UPS has been reducing approximately 1.6 million gallons of gas used in their deliveries, every year. They exploit supply chain data throughout their shipping process. Using sensors and radars to capture data and data analytics they have full visibility into where their item is in transit. They also optimize routes to ensure efficient deliveries.

Warehousing Efficiency

Some goods have special storage and transit needs based on their unique characteristics, like composition, weight, fragility or non-standard dimensions. Cameras and sensors can record data during product packaging or transit to enhance warehouse efficiency and eliminate the likelihood of improper storage or damage. An analysis of automatically collected images and insights about shelf weight, temperature, etc. can be used to monitor picking accuracy and overall warehouse performance in real-time. Data analytics at this level of supply chain helps companies increase efficiency for storage space usage, manage logistics and tackle issues like fraud and theft.

Conclusion

Businesses can integrate big data into every stage of the supply chain management process. Increased supply chain visibility offered by big data enables better decision-making and helps organizations mitigate risks, cut costs, enhance supplier relations and increase customer satisfaction.

Frequently Asked Questions