5 Areas where Big Data is Playing a Crucial Role in Effective Supply Chain Management

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Planning logistics and supply chain management has always been a very complicated affair. The need for sourcing raw materials, transporting goods, delivering the end customers, and managing the returns, etc., all comes under the realm of supply chain management. There are a lot of parties coming into the supply chain management lifecycle, including vendors, suppliers, manufacturers, logistics providers, etc., and all are crucial in the chain.

The traditional methods of Supply chain management or SCM using the ERP software are becoming outdated now for the time consumption and cost involved in all these approaches. As the conventional supply chain practices are not delivering the needed results, modern enterprises are looking for advanced technology solutions based on big data and data analytics, etc., to streamline supply chain operations. Many such applications are also helping out in automating the recurring processes in supply chain management and thereby help improve the efficiency of the process.

The data generated through the supply chain process belongs more to enterprises, especially when the manufacturers use logistic services for the movement of goods. A huge volume of data is generated every minute, and it is necessary to use the latest technologies to analyze the data. Big data solves this problem.

Big data in enterprise data handling

Big data deals with the larger and complex data sets and handles it effectively than any traditional way of data processing. In order to store and analyze such huge stores of structured, semi-structured, and unstructured data, enterprises now need to bring in advanced analytical methods. The use of advanced analytics using big data is known as big data analytics.

In order to facilitate Big Data analytics, data is collected in real-time from various sources in various forms.  The nature of this data is defined with 3 V’s as high volume, wide variety, and high velocity. This is processed using advanced artificial intelligence and predictive analytics methods. All these come as the subsets of AI, machine learning, and NLP. Data analytics effectively help to cover the big data into smart data. Many services providers are offering big data consulting for enterprises of all sizes to gain insights from their data stores.

Big Data Analytics in the supply chain sector

As we have seen the relevance of big data, let us explore whether big data analytics can help in the supply chain management process. Whether Is it ideal to invest in data analytics for SCM and logistics purposes now?

As we have seen above, a huge volume of data is produced both within and outside the enterprises related to the supply chain process. All these data needed to be collected, cleansed, stored, and analyzed to gain actionable insights from it. One has to handle both real-time data and historical data for gathering accurate insights and making informed decisions. There are many such decisions related to weather conditions, demand and supply, the impact of seasonal changes, transport conditions, and so on. Quick and real-time decisions are needed to make based on these insights gained from big data.

Along with big data analytics, the IoT also comes into the picture to gather data through various sensors and data sourcing devices connected to the network. IoT helps to leverage the data from all possible data points and use this for effective supply chain tracking. When IoT is combined with the computing powers of big data analytics, the users can create a comprehensive network for live data exchange and real-time management of the supply chain system. To actualize this, it is also essential to have a solid data store for the big data system, and providers like RemoteDBA.com can help to fine-tune this.

  1. To speed up planning

By effectively integrating the data throughout the supply chain management, organizations can adopt various statistical models and predictive analytics methods to understand forthcoming market trends and developments. This will further help in effective planning, warehousing, production, and delivery of the finished goods. Latest data management practices are used to process real-time and historical data to derive actionable insights.

  1. Getting raw materials 

Deloitte conducted a Global CPO Survey back in 2016, which showed that there is no such fundamental digital strategy in terms of procurement for about 60% of the businesses. There are many SMEs who try to save costs by sourcing their raw materials based on comprehensive data. There is also real-time data that will help.

  • Understanding the category of raw materials to procure.
  • How and when to reduce cost.
  • How to manage production.
  1. Execution of work plans

It may not be just enough to plan the individual elements in the supply chain. The success of a business is largely based on executing such plans without errors. Effective implementation of big data analytics will make it possible by optimizing resource usage while increasing productivity. 

  1. Delivering final products

Another key area of supply chain management is effective product delivery. This is where a manufacturer gets back their investment and profits. It is made possible by delivering the final product to the customers on time by keeping them happy. In order to avoid delays and to avoid any uncertainties or damage during the delivery, enterprises can effectively use big data analytics.

  1. Handling product returns 

Regardless of the product quality, all manufacturers may have to deal with the return of goods due to the dissatisfaction of customers for various reasons. It is costly to manage reverse logistics, which involves the need for additional warehousing, and also, there are transportation costs involved while returning the customer’s payments. Doing proper data analytics will help organizations reduce the scope of any returns and, even in the case of returns, make it much smoother and hassle-free.

With the use of big data in manufacturing, it is also possible to make custom models based on the needs of the actual users. This will help the enterprises to try out different strategies based on the data and make changes to their existing models to find a better market. In all these ways, big data analytics will help product manufacturers, supply chain managers, and business enterprises to streamline their processes at various levels. Being in a highly competitive market scenario, it is essential to be equipped with the most advanced technologies to place your business on top of the competition.

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