Big Data Analytics Tools for the Manufacturing Industry

Consulting firm PwC reports that the global demand for manufactured products will only grow by 3.4% in 2017 following an almost similar growth of only 3.1%…

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September 20, 20177 minute read

Consulting firm PwC reports that the global demand for manufactured products will only grow by 3.4% in 2017 following an almost similar growth of only 3.1% in 2016 according to the International Monetary Fund. This demand is not expected to pick up any time soon. The increasing nationalist movements around the world are leading to possible changes in trade agreements which could further impact the growth of the industry as many global manufacturers take a “wait and see” approach to capital expenditures, says PwC.

In response to these trends, manufacturing companies are recognizing the importance of increasing their productivity and efficiency, thereby offsetting any slow growth in demand.

In today’s technology-driven business environment, Big Data stands as one of the key drivers of productivity and efficiency for manufacturers. Big Data analytics can enable manufacturers to take a granular approach to improving the manufacturing process. It can allow manufacturers to go deeper into supply chains, further investigating variabilities in production processes, and going beyond lean manufacturing programs such as Six Sigma.

However, the advantages that Big Data offers manufacturers are not limited to achieving productivity and efficiency gains.

Big Data in Manufacturing

In 2016, Forbes reported that 68% of manufacturers are already investing in data analytics. 48% of manufacturers also believe that utilizing Big Data analytics is no longer optional. As more manufacturing companies are beginning to understand that Big Data and Big Data analytics are required to compete in today’s business landscape, they are also beginning to realize its key business advantages:

Improving Asset Performance and Efficiency

Manufacturing is an asset-intensive industry. A little improvement in asset performance can lead to significant improvements in overall production. Avoiding asset breakdown can also minimize inefficiencies and even losses. Thus, it is important for manufacturing companies to properly maintain and optimize the performance of their assets.

Asset performance is usually recorded in machine logs. The rise of the Internet of Things (IoT) also includes network-enabled assets and equipment that can measure, record, and transmit their performance. Today, however, manufacturers have more data than they can manage and analyze. Big Data analytics tools enable manufacturing companies to capture, clean, and analyze these machine data to generate insights on their performance and optimization.

Aside from analyzing historical data, the predictive capabilities of Big Data analytics tools also enable manufacturers to perform predictive maintenance and prevent asset breakdowns and unexpected downtime. According to Forbes, Big Data analytics can reduce breakdowns by as much as 26% and unscheduled downtime by as much as 23%.

Streamlining the Production Process and Supply Chain

The manufacturing process and supply chain are dauntingly long and complex. Streamlining them requires diving deep into every part of the process and every component of the supply chain. Big Data allows manufacturers to do just that.

Big Data analytics enables manufacturers to segment the production process and supply chain up to the most specific task or activity. This allows manufacturers to narrow down each problem to the smallest component and to identify specific processes or components that are under-performing or causing bottlenecks. Identifying dependencies also helps to enhance critical production processes and create contingency plans to minimize the effects of possible downtimes and inefficiencies.

Enabling Product Customization

In the past, manufacturing and customization did not go hand in hand. Manufacturing focused on scale while leaving customization to niche markets. Customization also entailed additional time, effort, and costs for only a few customers.

Big Data analytics enables manufacturers to reasonably and effectively predict the demand for customized products by identifying patterns in customer behavior. Customized products can now be manufactured at near mass production levels, costs, and efficiencies. Big Data analytics tools also enable product engineers to get real-time access to customer data so that they can further customize products based on customer preferences. Some tools even equip product engineers to collaborate with customers by quickly gathering, analyzing, and visualizing customer feedback.

Big Data analytics also empower manufacturers to take customization to a whole new level via made-to-order production. By taking a granular approach to analyzing manufacturing processes, manufacturers can determine up to what point of their production process they can postpone to allow for customization or made-to-order requests. According to Deloitte, “businesses are postponing production until the latest point possible to allow individual customization.” Deloitte further adds that “postponing production in this way can help reduce inventory levels and ultimately increase plant efficiency.” Streamlining the manufacturing process also helps manufacturers maintain production efficiency in spite of customization.

Big Data Analytics Tools for Manufacturing

To improve asset performance, streamline production processes, and enable product customization, Big Data analytics offer several tools for manufacturers:

Data Storage

  • Gathering data and having the capacity to store data are the first steps in utilizing Big Data analytics. Data storage allows manufacturers to keep equipment, production process, and supply chain data for analysis.


  • As Big Data comes from numerous structured and unstructured sources, it is critical for manufacturers to ensure the quality and integrity of their data for analysis. Big Data analytics tools enable this by cleaning and transforming data into readable, unified data sets for multiple users. Cleansing also involves standardization and parsing data into consistent formats that are usable by different enterprise applications and systems.


  • Profiling tools provide greater visibility into a manufacturer’s production and supply chain. Profiling tools capture information up to the metadata level, enabling manufacturers to create a comprehensive inventory of their critical data so that they can make the most of the information they have.


  • Data Discovery or Data Mining tools enable manufacturers to quickly identify and access the information they need to make production and supply chain decisions.


  • Data Mapping tools help manufacturers understand the flow of data within data environments, production processes, and supply chains. These tools enable manufacturers to identify dependencies and address potential problems at the cause. At the same time, they help identify potential data risks and leakages in the data environment.


  • Data Analysis tools enable manufacturers to identify patterns, measure the impact of those patterns, create actionable insights, and even predict outcomes. By breaking down equipment, production, and supply chain data, analysis tools help manufacturers drive outcomes through better decision-making.


  • Visualization tools communicate the results of analytics to manufacturers and other professionals. It transforms data in spreadsheets and SQL databases into user-friendly graphs and charts, making it easier for manufacturers to generate insights and make data-driven decisions regarding their production processes and supply chain.


  • Monitoring tools ensure that compliance with data quality standards are met on an ongoing basis. They also help ensure the good performance of equipment and the efficiency of the production process. Monitoring tools also enable manufacturers to automate quality assurance processes.

By utilizing these Big Data analytics tools, manufacturing companies can make the most of their machine, production, and supply chain data and significantly raise their productivity and efficiency.

Preparing Manufacturers for Big Data

OpenText’s solutions for the manufacturing industry prepare manufacturers for Big Data adoption. The OpenText ALLOY™ Platform seamlessly integrates and manages manufacturers’ mission-critical data with Big Data analytics solutions enabling manufacturers to improve efficiencies in forecasting, inventory management, material procurement, stock replenishment, order fulfillment, supply chain, and other key manufacturing processes. By utilizing data as strategic assets with ALLOY, manufacturers can improve their bottom line and strengthen customer, partner, and supplier relationships.

OpenText’s ALLOY Platform supports the storage, integration, and syndication activities that enable manufacturers to grow a repository of quality data from their production process and supply chain to identify new market opportunities, improve efficiency and gain a competitive advantage. Contact us to learn more about how OpenText can help you succeed for Big Data.

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OpenText, The Information Company, enables organizations to gain insight through market-leading information management solutions, powered by OpenText Cloud Editions.

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