Manufacturers have traditionally been very successful using data to increase efficiency and quality but are finding that lean production and cost cutting are no longer enough to remain competitive. The goal today is to integrate and gain insights from data across their complex global and often fragmented supply chains.

Manufacturers generate and store data from many sources across the supply chain, including process control instruments, supply chain management systems, and systems that monitor the performance of products after they’ve been sold. Being able to access hidden data and integrate all of this data across multiple sources provides valuable insights and competitive advantage. These insights can lead to improvements in design and production, product quality, forecasting, more targeted products and distribution, and identify hidden bottlenecks in the production process.

Why Big Data Use Cases in the Manufacturing Industry?

Before looking at some specific big data use cases in the manufacturing industry, let’s address the role use cases play in big data analytics.

Unless you narrow your query to a specific business challenge that can be revealed by patterns or examples, you won’t get much value from big data. Just having vast quantities of data at hand for analysis doesn’t mean you can extract the insight you need. Use cases force you to narrowly define the question.

A big data use case provides a focus for analytics, providing parameters for the types of data that can be of value and determining how to model that data and perform analytics.

These are some example use cases that illustrate how big data is being used in manufacturing, helping to optimize operations, improve quality and reduce costs.

Use Cases

Preventive Maintenance

Minimize Non-Productive Time (NPT) by monitoring equipment or product utilization in a live environment to identify patterns that indicate imminent failure. For revenue-generating operations equipment, downtime results in significant lost revenue as well as costly repairs. MapR Distribution for Hadoop enables ongoing analysis of an entire system and lets businesses predict when failure might occur, so preventive maintenance can avoid the failure. For consumer products, failures or need for replacement will depend highly on usage patterns, and tracking those patterns help manufacturers to alert customers when their products need specific maintenance.

Supply Chain and Logistics

Managing and optimizing supply chain and logistics is key to success for every manufacturing company on this planet. There is tremendous amount of information that is generated from the Planning and Raw Material procurement to Distribution/Warehousing stage of the process. This information is very valuable to organization to simulate for potential breakdowns and delays in the process. Also every RFID data can now be tracked and assessed to know exact location of product that will allow planning for optimum usage of warehouse space, distribution and delivery methods.

Customer Care Call Centers

Most of the manufacturing companies have been collecting call center data records (CDR) for warranty and customer complaints. With Big data tools and technology, companies can use the Call Records to know immediately customer discontent using voice/speech analysis or text analytics. In addition, this information can be used to correlate with social media and internal reports from quality and customer/product surveys and competition analysis.

Customer Sentiment

Customer sentiment analysis has crossed boundaries using feeds from Tweeter, Facebook, Google, Blogs, Reviews?. Eminem is officially the first person ever to get over 60 million Facebook fans. Start of this year, Coco-Cola had the most facebook fans = 35M+. The point is fans are generating major brand awareness for companies by talking to their friends and families and indirectly acting as extended sales team for your organization. Customer Sentiment analysis provides potential to reduce the loyalty decay rate, increase sales by providing vital consumer feedback on products including packaging and distribution.