How to Optimize Supply Chain Management with Big Data

The last few decades have seen the development of big data, a technology applied in collecting data that is complex in nature, fast in velocity, streaming from multiple internal and external sources, huge in volume, and exponentially grows.

Big data technology has its practical application in climatology, epidemiology, healthcare, and other numerous areas. Managing the flow of goods and services also applies big data. Goods are constantly ordered, inventories are prepared, the ordered goods are delivered to consumers, and customer satisfaction is factored in at every step.

All these processes are complex and require big data to result in a strategy supply chain management. This article discusses some ways of optimizing supply chain managing with big data.

 a. Produce contextual intelligence from big data

The supply chain management data is complex and originates from various points such as sales, orders, inventories, customer care, finance, and feedbacks. The stakeholders can unify this data, producing contextual intelligence for entire processes with the market and operations. Such data have a competitive advantage. It helps you preempt any unfavorable situations, communicate better with your customer, lay proper strategies for the business’ development, and make crucial decisions without fumbling.

 b. Categorize your data

In supply chain management, data sources cover multiple providers. Such data are complex, and unifying them gives a business a hell of a time. Why not make things easier using big data?  Classify your data’s into two; what is from vendors and what is from providers. With the two categories, it is easier to manipulate and edit this data’s as need be. Sometimes, the needs are specific to a given group of people in the chain, making data classification a significant element.

 c. Collaborate data all through the chain

When a manufacturer understands the entire chain, including retailers and the customers, he works out things better. Traditionally, though, the retailer is considered while the supplier is left out, creating a gap in the chain. Use big data to eliminate such a gap. Couple big data with IoT and collect information from the whole chain back to the manufacturer and provide rooms for improvement and better strategy reviews. With the improved functionality and better reviews, the processes in the entire chain run smoothly.

 d. Get rid of silos

Those sub-organizations below your central organization that exist independently and do not share data need to be eliminated. The subsidiaries could be so numerous that singling out important metrics and channeling them to a single system becomes an almost impossible task. As large and complex as it is, the data is still viewed independently in silos.  It’s time you stopped viewing things in lined up buckets; use big data to create means for viewing aggregate data.  Information in buckets rather than a single tank provides room for redundancy. It is vital to dismiss the silos and find something that cuts across the entire chain, hence representing all stakeholders, conserving resources, and ensuring efficiency.

e. Use big data’s tools to provide necessary information

We no longer live in the former times when processing data would take weeks or months. Use big data tools like Xplenty, Lumify, MongoDB, Datawrapper, Apache Hadoop, or Cassandra to quickly process information. Using these tools, analyze the information at hand and get what you need to make supply chain management decisions. You will not linger around because of indecisiveness; whether it’s a decision on making orders, revoking orders, trying new products, or considering a different provider, it’s all made easy.

 f. Employ the application of demand forecasting and optimization tools

Business is only business when you can accurately predict your customers’ response. This has become challenging, mainly because of the knowledge gap that exists and the complexity of supply chain management data’s. Big data tools make your work easier by classifying important information in the first-party compartment. This feature enables you to deal with ‘near actual demand’ and avoid losses through backlogs and dead stocks.  Past sales data are peered into by the system and algorithms used to predict future sales. Such a strategy supply chain management is accurate, optimized, and reliable. A good example would be the ‘shipping status tool’ that studies the system and sends alerts when goods are due for arrival.

 g. Take advantage of mobile technology

Another way of optimizing supply chain management using big data’s is the application of mobile technology. Because of the sizes, functionality, storage capacity, and native apps’ usage, smartphones are becoming increasingly crucial in heightening supply chain management. The phones read barcodes to reveal product information and upload this information online simultaneously. With these unique abilities, real-time data is generated. Additionally, smartphones have GPS, making it possible to use them to track shipped products and help drivers find the shortest route through Google maps or using a route optimization app for Android and iOS for fast delivery.

h. Manage your supply inventory using big data

Because of their limitless cloud-based data’s storage, big data’s technologies have effectively coordinated and reconciled supplier data’s. Without such a system, backlogs are constantly experienced because of the constant streaming of multiple data’s from multiple sources simultaneously. System-based credentials and documentation can also be processed instantaneously within all the existing outlets globally using integrated reconciliation tools. Such a system captures every delivery online in real-time, hence no cause for alarm regarding whether the deliveries are correct. The supply chain management is generally improved, and interventions are made as need be.

 i. Create friendly costs

You have to balance cost and quality in the supply chain if you want to stay in business. You cannot overprice your products, for you will have no one to buy them. Neither can you do meager prices; customers will question your products’ legitimacy and quality. Consequently, creating reasonable and friendly costs is a skill for mastery in supply chain management.  Using big data tools, you can factor in the two variables, i.e., cost and quality, and come up with prices that will keep your customers stuck to you.

 j. Align loopholes

Sometimes we do so much, but we still err, a factor that holds for supply chain management too. When errors and loopholes occur, there is a need for alignment. Big data’s technology comes in handy when it offers you suggestions on how to correct the mistake. Besides that, you can prompt big data’s for tips in case you are skeptical about a step.

 

Conclusion

In supply chain management, big data’s application is critical. When properly applied, big data technology optimizes the supply chain management’s functionality. This article looked at various applications of big data to optimize supply chain management. Such applications keep processes flowing smoothly and minimize errors that can be evaded.