Big Data Analytics in Manufacturing | HCLTech

Big Data Analytics in Manufacturing

October 24, 2018
Vikash Kumar


Vikash Kumar
October 24, 2018

Data is all grown up, with new multidimensional capabilities and broader horizons to offer the manufacturing community. Large data pools have turned manufacturers’ attention to Big Data solutions for an altogether new dimension of research and trend analysis. Predicting future events, foreseeing risk, understanding extended value chain, and enhancing the customer experience are all much easier now.

Done right, Big Data management can form the foundation for a variety of new capabilities and ultimately determine the success of customer encounters by identifying correlations between customer data, and scheduling and maintaining data. It even has the potential to unravel hidden patterns to enhance operational efficiency, enable the anticipation of order lead times, shorten machine downtimes, and drive more effective materials purchasing and work-in decisions. What manufacturers then end up with is a means to gain a 360-degree view of operations for timely decision-making.

Maneuvering Variety, Quality, Complexity, and Processes

It is interesting how Big Data solutions have found answers for some of the most common sore points in the manufacturing space. Consider how difficult it has become to cater to volatile customer demands and manage a flexible production environment for mass customization. And then imagine having to continuously minimize costs, optimize the use of resources, and maintain high product quality amidst all through carefully balancing execution needs and product constraints. Then, of course, there’s the need to synchronize orders, purchases, and production, and manage inventory and customers service expectations.

The increase in data velocity, variety, and value naturally drives an increase in data volumes companies must process.

Big Data Analytics Benefits

Big Data management system comes with a wide range of benefits, such as:

  • Improved product accuracy, quality, and production
  • Accelerated integration of IT in manufacturing and operational systems
  • Better forecasts of product requirements and production
  • Continuous improvement across framework with advanced manufacturing analytics such as Big Data
  • Enhanced visibility in quality levels and better accuracy in predicting supplier performance
  • Machine-level traceability and compliance measurement
  • Strategic services that can contribute to customers’ goals by monitoring products and proactively providing preventative maintenance recommendations
  • Identifying and selling profitable customized products that have minimum impact on production

Values for Manufacturers

Recovery – Big Data can help manufacturers save a lot of money by reducing material costs and system capacity recovery.

Big Data can form the foundation for a variety of new capabilities, including identifying correlations between customer data, scheduling, and maintenance.

Product Quality and Safety – With Big Data, resources will be better equipped to understand and eliminate the root causes of product risk.

Removing overlap cost and support – Manufacturing analytics can help identify and remove overlaps in the allocation of resources for people and technologies.

More Efficiency – It provides macro and granular views of information, bridging the gap between management and resources.

Using Best Tools - In manufacturing, Big Data in manufacturing has enabled organizations to look beyond just revenue generation and focus on the actual business.

Use Cases for Analytics

Big Data helps manufacturers to reduce processing flaws, improve production quality, increase efficiency, and save time and money. Some of the use cases in this regard are:

Improvement in Process - helped manufacturers to identify the parameters that had a direct impact on production. After modifying the target process, it also helped companies to increase productivity by 50%, enhancing the profit margin.

Companies historically used data warehouses and business intelligence tools to report on and analyse customer behavior, supply chains, and manufacturing operations.

Product Design- Big Data analytics made understanding repeat customers and their requirements and delivering goods profitably for manufacturers. It also helped many companies to identify products that needed to be scrapped using lean manufacturing principles.

Good Product Quality- Earlier, manufacturers had to run multiple tests on each product, increasing the time to market. However, with Big Data analytics in manufacturing, the required number of tests reduced drastically. Modifying the quality assurance process helped manufacturers to bring the manufacturing cost down.

Maintaining Supply Risk- Big Data in manufacturing helped companies to avoid delays in production even during disasters.

Keys to Using Big Data Analytics Successfully

The decision to incorporate Big Data should come from the company’s business unit as well as the IT unit. Alignment between the business needs, goals, IT design, and deployment plans holds the key to successful implementation of Big Data. Providing the right data to the right people at the right time can drive efficiency and process improvements which are critical for improving margins and being competitive in the market.

Understanding Goals/Visions- Companies need to understand the current state of business, including the gaps correctly to be able to prepare for the future. The changing dynamics of the market make it important for companies to modify their business architecture as and when required. A popular method for success is to deploy quickly in well-scoped increments and tweak the plan as and when required.

IT and Business Alignment-The business unit and the IT unit of a company need to have common goals. Companies need to work on a use case that can lead to meet the objectives of both, business and IT.

Key Performance Indicators- KPIs are unique to each use case, phase, and step of an assignment.

Data Security - Securing access to data, regardless of data management platforms, tools, and data transmission methods used is critical.

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