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How Big Data Can Improve Manufacturing Efficiency

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With the growth of technology and services, huge amount of data is generated every second. This produced data are structured, semi-structured and unstructured. We can use these data for our business goals and benefits. Big data analysis is not specific to one industry, it can extend to anything which we may even not heard of. However, this requires some set of experience and expertise.

Here, in this blog we will talk about some of the major aspects which are efficient enough to improve Manufacturing efficiency by analysing big data and incorporate few techniques.

Topics to discuss-

  • What is Big Data?
  • Ways to improve manufacturing efficiency with Big Data.
  • Goals that can be achieved after implementing Big Data.
  • Conclusion

What is Big Data?

Big data is a collection or combinations of massive, complex and varieties of data set which are dynamic in nature. These data can be analysing further to carry out desire outputs.

In simple words, Big data is a technique to solve data problems which are almost impossible to solve using traditional tools.

Ways to improve Manufacturing efficiency with Big data:

In this blog, we cover seven important ways manufacturers can apply Big Data.

  1. Product Quality –

Quality matters for every industry to grow and build reputation into the market. It is well known that the products with high quality has a huge demand regardless to its pricing. Industries acquires many methods to improve the quality. By analysing the past data and statics, we can make a system self-sufficient system to take care for its quality.

For instance, using predictive analytics in Quality Assurance (QA) may provide significant cost savings, but products could require “n” number of tests. This number can be dramatically reduced by analysing past data through pattern recognition and analysis of big data. The analytics will Further define the number and types of testing parameters that are essential to implement.

  1. Real-Time Operations Tracking-

The most problematic part for the manufacturers is the delay in manufacturing line. Production delay may occur due to maintenance downtime, Defects, unskilled labour etc. Here, real time monitoring can be done by smart sensors that can pinpoint where error exist. These sensitive sensorsnot just identified the cause of delay, but also when it started and how long it lasted. Real-time monitoring allows the manufacturers to identify problems quickly so that the quality can be intervene and improve.

  1. Supply Chain Management-

Big data analytics will impact a lot in improving supply chain management. It resolves multiple pain points at strategic, operational and tactical levels. Supply chain analytics implementation can ensure data-driven decisions to reduce costs and improve service.

We are here to look upon some of those-

  • Consumer Behaviour and Usage Pattern:

By tracking the consumers’ needs and behaviours. The information gathered from the analytics reports enable businesses to retain their customers and increase revenue significantly.

  • Inventory Management:

Analysing the past sales data, manufacturers can predict the future sales. Therefore, enables the manufacturers to control their inventory also improvement in customer service and demand fulfilment, faster and effective reaction time to supply chain issues, increase in supply chain efficiency, and greater integration across the supply chain, optimization of inventory and assets productivity and shortened order to delivery time.

  1. Equipment Maintenance-

With the help evolved technology, it has become possible to capture any level of operating data which is desired to analyse a machine’s performance. Thus, the preventive measures can be taken and maintained well before the measures are actually needed. This reduce the downtime and eliminates the cost related to its warranty.

  1. Build-To-Order-

In recent years, we have seen a change in the manufacturing strategies. The companies are acquiring BTO approach. However, to sustain growth from BTO, a manufacturers must construct a platform to efficiently analyse customer behaviour and sales data. To determine profitability, data analyst and manufacturers can incorporate changes in the supply chain based on the analysis that address problems and provides solutions accordingly.

  1. Enterprise Analytics-

Since the Big data has many benefits, it is now widely being widely accepted by the manufacturing companies all across the globe and the insights gained from big data analytics are believed to be one of the effective ways to generate extraordinary results.

Tools such as ERP, CRM, SCM, FSM, and BI are helping manufacturers “identify hidden information which can be used by organisations to provide valuable insights,” the report notes.

Goals that can be achieved after implementing Big Data

Big Data never cares about how much data we have, but how it can be used and utilised. Data can be taken from various sources for analysing it and goals that can be achieved. Industries main goals like,

  • Reduction in cost.
  • Time reductions.
  • New product development with optimised offers.
  • Well-groomed decision making.

When you integrate big data with big data analytics, it is possible to achieve business-related tasks like:

  • Real-time determination of core causes of failures, problems, or faults.
  • Produce token and coupons as per the customer’s buying behaviour.
  • Risk-management can be done in minutes by calculating risk portfolios.
  • Detection of deceptive behaviour before its influence.

Conclusion

Bid Data provides an opportunity for “big Analysis” leading to “Big Opportunity” to advance the quality of the manufacturing process. Big Data Analysis is not bounded to any particular industry or entity, we can apply it anywhere to get the unexpected outcomes.