Big Data Analytics for Manufacturing Businesses: How to Leverage Existing Data for Insights?

We are in the midst of a digital economy where businesses are generating vast amounts of data on a daily basis. Specifically, manufacturing companies produce tons of data through their enterprise application systems.

However, without advanced big data analytics to uncover hidden insights to enable smart business decision making, data will be unusable and stay locked in separate silos. 

The Need for Big Data Analytics

Thus, it’s critical that businesses leverage data analysis to discover hidden information, convert historical and real-time data into actionable insights for improving business processes, operation, and driving growth.

Whether it’s inventory management, supply chain, or production, advanced big data analytics can help manufacturers to identify hidden patterns as well as dependencies within their business systems.

Doing so allows manufacturers to make informed decisions and/or optimizing the whole business processes. Here are a number of typical
data analytics use cases in manufacturing that has proven to enhance business processes:

  • Predictive analytics for inventory levels forecast: using traditional methods like spreadsheets usually results in an inaccurate forecast of inventory levels, especially during high season when demands strongly fluctuate. Manufacturers can adopt AI technologies including Machine Learning and Advanced Analytics to monitor customers’ demand and economic trends, then leveraging the information to make appropriate stock levels decisions.
  • Product optimization: advanced data analytics provide an understanding of the factors that affect product quality as well as the ones that causing waste. This helps improve production effectiveness and eliminates waste, maximizing the results of production. 
  • Predictive maintenance: Analyzing historical data on the factors that causing breakdown of machinery, companies can have an overview of their machinery lifetime and make an accurate forecast on the time that an equipment part might break-down or requires maintenance. This helps avoiding downtime and labor waste.  
  • And others

Consider system integration between applications for central data access



To fully benefit from the potential of data analytics, it’s critical that manufacturers break down the separate silos barriers and allow the sharing of data between different business units.

Data from a separate process is a valuable asset on its own, but it’s become even more valuable when using in unified for different processes operations.

For example, by using predictive analytics, manufacturers may know in advance when a machinery requires maintenance and/or replacement, which helps to plan advance production schedule or maintenance workload. Having access to these insights allows production floors to avoid the risk of downtime due to unexpected machinery maintenance.

Hence, in order for manufacturers to fully benefit from advanced big data analytics, it is critical that businesses take into consideration the integration of various enterprise applications for central data access. 

Advanced big data analytics implementation: How to get started?

Artificial intelligence (AI) / Machine Learning Technologies as well as Big Data Analytics applied in Manufacturing business, when done right, can enable cost savings and enhance business operations.

However, applying those advanced technologies across businesses requires a team of specialists with a high degree of expertise in data science and analytics.

In particular, due to its high complexity nature, analytics projects usually involve various specific challenges which manufacturers need to work up in advance in order to avoid failure, as follows:

  • Human resources: as mentioned previously, advanced data analytics requires specialists with the right mix of technical skills and business knowledge. In case organizations just start off with analytics, looking internal for suitable candidates and provide them with in-house analytics training could be a good choice to start with. Manufacturers can also explore the option of hiring Ph.D. data scientists from IT consulting companies for analytics projects with high complexity, in order to work together with the in-house team and complement the existing capacity.
  • Data and Analytics strategy: This is the critical factor ensuring the success of the manufacturing company's analytics project. A clearly defined plan with detailed goals and milestones will allow the manufacturer to govern all the stages of implementations with the necessary effort and resources. Also, as part of the data and analytics strategy, companies also need to consider how to collect and prepare the big data, ensuring data is of high quality and suitable for the analytics project.

Read more about: “Key Factors to consider for a good data strategy

  • IT infrastructure: Specifically, companies need the hardware and big data (software) platform to collect, store data centrally as well as analyzing such data to uncover hidden insights. 
  • Experiment: when it comes to advanced analytics, many companies might expect that there are out of the box (OOTB) solutions meeting all of their specific big data analytics needs, but that is rarely the case, and many applied advanced Big Data Analytics solutions are still somewhat in the “developing” stage. Hence, it’s necessary for companies to have an experimental mindset to effectively identify the most suitable solutions according to their needs. This could be an off-the-shelf solution or a customized one.
  • Apply advanced analytics at a company-wide level: Finally, from successful experimentation, businesses need to apply advanced data analytics across businesses, ensuring a stable and effective solution. 

Getting started

Even though manufacturing companies are already aware of the various benefits of big data analytics, many are still hesitant to take action. This could be due to many reasons including the continuous use of outdated legacy or on-premise applications which are incompatible for data collection and analysis.

Thus some manufacturers might feel that they’re not ready for advanced big data analytics because of the lack of adequate infrastructure. However, such challenges can be overcome by gradually introducing advanced analytics using an MVP (minimum viable product) approach before adopting a full-scale solution.

Read our previous blog post: How To Choose the Right Big Data-As-A-Service (BDaaS) Provider?

Besides, many key employees at manufacturing companies might feel that they are already overwhelmed with their daily activities to adopt big data analytics. As a solution, companies can hire data scientists from external agencies to help reduce the workload, transforming the legacy systems of data storage and recording into a modern system capable of big data analytics.

Advanced analytics can provide tremendous benefits and ultimately improve business profitability. To reap the benefits, manufacturers can start by taking the first step in defining their data analytics strategy, gradually experiment and let data become the new valuable asset.