How analytics-driven transformation is reforming the manufacturing industry?

Manufacturers have been able to reduce waste in manufacturing processes and enhance product yield and quality during the past 20 years by using Six Sigma and Lean techniques. However, the volatile nature of today’s manufacturing market, particularly in chemicals, pharmaceuticals, and mining, has necessitated a more detailed approach to finding and resolving defects in the process.

One such approach is big data in manufacturing. And there is evidence to back it up, such as the fact that the worldwide big data in the manufacturing area is expected to reach $9.11 billion by 2026. The technology’s use case has played a significant impact in driving market expansion. In this article, we will look at the function of big data analytics in manufacturing and how it is helping to make the whole industry smarter and more efficient.

Big Data is characterised as high velocity, high volume, and high variety data sets that aid in the processing of information, which improves insights, aids in decision making, and automates procedures.

Big data refers to very big data sets, which might consist of millions or even billions of rows and attributes. Big data in manufacturing may refer to any data gathered during the entire production process, including information from machines, operators, and other equipment and people.

Gathering data

In addition to the broad sources of generating data, such as loyalty programmes, online marketing analysis, and social media monitoring, the industry is adopting a variety of tools to gather the information. In the manufacturing environment, the various software packages, such as CRP, MES, and CMMS, amongst others, are coupled with the machines in order to gather large quantities of data. The data sets that are generated as a result of these pieces of software and hardware may then be put to use to discover patterns, zero in on problem areas, and develop solutions that are supported by data.

Big data

Advanced analytics refers to the process of using statistical analysis and other mathematical methods to big data pertaining to a company in order to evaluate and enhance business procedures. Utilizing advanced analytics, factory operations managers are able to conduct an in-depth investigation of historical process data, discover patterns and linkages among discrete process stages and inputs, and then improve the elements that show to have the biggest impact on yield. A great number of global manufacturers operating in a diverse range of markets and locations now have access to a wealth of real-time data gathered from their production floors and the capacity to carry out such complex statistical analyses. They are merging the data sets that were previously kept separate and then evaluating the combined data in order to get valuable insights.

The application of sophisticated analytics may uncover further chances to boost yield, even within manufacturing processes that are already deemed to be among the best in their class.

• Minimizing supply chain risk: Any industrial business is extremely vulnerable to supply chain disruptions. In order to mitigate danger, businesses should make use of the insights provided by big data analytics to detect and prepare for any disruptions to product deliveries. In addition, it may employ cloud computing to improve its connections with its suppliers by sharing relevant data with them.
• Maximizing productivity and enhancing quality: Access to big data may improve a manufacturer’s capacity to leverage quality as a competitive advantage. Companies will be able to easily combine quality and compliance, enabling them to adopt a proactive rather than a reactive approach to quality management. Manufacturing companies will be able to use sensor-provided data from their equipment to decrease waste and increase process efficiency.
• Enhancing preventative maintenance practices: Manufacturers spend a lot on equipment maintenance. Companies may buy “smart” machinery with predictive analytics. This data helps manufacturers track equipment performance and health and can take more of a preemptive approach to preventative maintenance and detect minor issues to avoid more expensive problems.
• Improving attentiveness to consumer preferences: Big data analytics may give more information on what customers want and need, regardless of whether the items that are being manufactured are for corporate or consumer usage. For instance, manufacturers that allow consumers add bespoke features to a product are in a position to learn more about the preferences of their customers. They will subsequently be able to use this input into their ongoing efforts to further enhance their products. Customers will realise that the firm is attentive to their requirements, which may result in an increase in their loyalty to the brand.
• Improving the efficiency of manufacturing processes: Manufacturers must be able to react swiftly to market variations and changes in the business environment these days. The availability of large amounts of data enables manufactuers to make quicker, more informed choices. They’ll be able to use their assets to make real-time changes to production capacity as required, resulting in maximum efficiency. This enhanced adaptability might also provide a competitive edge in the market.