How will Big Data help manufacturers in maintaining supply chain quality?

big data in supply chain

The manufacturing sector churns out stacks of data every single day. The challenge for organizations is to figure out how this data can be applied to their business to optimize supply chain performance.

How does a supply chain work? 

The supply chain flow in a manufacturing company involves purchasing of raw materials, completing sale, delivering goods, invoicing, and handling returns of sold goods. This process can be enhanced with new technologies. Investing in the right technology to get insights about regulations, and customer expectations, can help you to deliver goods at reasonable cost and be competitive. 

Having a multi-tier supply chain will help organizations to take decisions efficiently. The top tier involves strategic decisions such as the location of manufacturing sites, supplier partnerships, products and sales markets. Decisions at the operational level such as changes in production, purchasing agreements with suppliers, approving customer orders and moving products in the warehouse affect how your products move along the supply chain. Adopting cost-effective strategies and best practices in transportation, purchasing and storage can improve the bottom line of a company.

But many a times, volatile customer demand and unreliable sub-tier deliveries may cause inefficiencies in the way the products move along the supply chain.

How can big data help? 

Automation, integration of cloud-based technologies, and utilization of data management tools are all modern methods that bring positive impact to supply chain operations. Big Data can streamline communication among all the tiers to improve planning, reduce inventory levels, bring transparency in the system, and collaborate to reduce waste throughout the supply chain.

Manufacturers can have a huge competitive advantage by collecting data using sensors from different areas within their business. Deciphering this data can facilitate extraction of valuable insights through algorithms. Analyzing simulation models for predictive analytics can help make smart real-time decisions for growth and profitability.

There are whole lot of benefits for manufacturers – from simple indexing, process monitoring, and measuring the performance of machines or people, to maintenance. By integrating data from products that their customers use, and applying advanced analytical techniques, firms make innovations or improve the quality of the product and bring efficiency in productivity in the newer versions. They can use latest technology to customize products according to a specific need.

The Alan Turing Institute and Warwick Analytics together conducted a study on how big data and analytics will transform high value manufacturing.

The study illustrates seven areas how big data analytics can benefit manufacturers: 

  • Pinpoint the problem easily: By analyzing analytics, plant managers will be able to locate the root cause of the issue that is otherwise not easy to detect.
  • Improve yield: By capturing data from the machines, it will be easy to track the time required to generate meaningful output from each.
  • Better after sales service: By collecting service feedback from customers, companies can better the warranty process and manage customer experience.
  • Increase in production: Production can be segmented to better understand what widgets are easier to produce.
  • Speeding up time to launch: The environment can be fine-tuned to bring out the best outcome and to maximize production.
  • Predicting maintenance: To control wear and tear of machinery, manufacturers are alerted when maintenance and replacement is required.
  • Optimize supply chain operations: To optimize supply chain, advanced analytics can be used to infer value from the rich data.

Having an eco-system fortified by big data can enable manufacturing supply chains to connect, collaborate and take the company to new heights.

The way forward

If through data management tools, a manufacturer could anticipate the needs of its customers before the competitors do, it certainly could enhance the position of the company.

The main barrier for businesses to adopt big data analytics is the lack of specific IT/data curating skills. Big Data is maturing very quickly and there are tools in the market that are aiding in predictive analytics.

If you are a manufacturer, and wish to know Suyati’s big data capabilities and how we can customize analytics, write to us

Author : Deepa Nishant Sinha Date : 30 Aug 2017