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How is data analytics transforming supply chain management in the manufacturing sector? 

  • By Kanish S
  • March 14, 2024
Big data in SCM
Big data in SCM

In today’s manufacturing world, executives are under a lot of pressure to make more money, get products out faster, and make their processes run smoother. This includes all industries starting from manufacturing companies making chocolates to drugs or chemicals, where even small issues can slow things down. 

To help with these challenges, more and more companies are using big data, which means they’re using computers to analyze huge amounts of information. Surprisingly, even though companies are trying to save money, a lot of bosses are still planning to invest in big data tools. Why? Because using data analytics helps them find and fix problems faster, which means less time and money wasted. 

By using data analytics, companies can figure out what’s going wrong in their operations and make smarter decisions to fix those problems. This helps them run more smoothly and make more profit. So, it’s becoming important for manufacturing executives to use data in their decision-making to stay competitive and successful. 

What are big data analytics? 

Big Data Analytics is the process of examining large and complex sets of data to uncover patterns, trends, and relationships, especially regarding processes, behaviors, and interactions. It involves using advanced statistical methods and tools to delve deep into data, identify cause-and-effect relationships, and predict future outcomes. 

One such method is multivariate data analytics (MVDA), which allows analysis of data with multiple variables simultaneously. This enables manufacturers to conduct sophisticated statistical modeling, scenario analysis, and optimize various aspects of their operations for future success. 

Manufacturers who effectively use data analytics gain valuable insights into their entire business, empowering them to make well-informed decisions and stay ahead of the competition. Despite the importance of this practice, many manufacturers are making avoidable mistakes in their approach to data analytics, leading to negative impacts on their business.  

Here are the five most common mistakes manufacturers make in data analytics: 

Big data is too expensive and hard to understand 

Believing that big data is prohibitively expensive and overly complex is a common misconception. There are scalable solutions and user-friendly tools available to suit various budgets and skill levels. By starting small, focusing on specific business goals, and leveraging cloud-based platforms, companies can gradually harness the power of big data analytics to drive meaningful insights and improvements. 

Kicking-off without business objectives 

Before diving into data analytics, it’s crucial for companies to establish clear business objectives. Without this roadmap, investments in data analytics may lack direction and fail to deliver tangible benefits. Setting specific goals helps ensure that data analytics efforts are aligned with business priorities and ultimately contribute to organizational success. 

No more doesn’t mean merrier in the world of data analytics 

Starting too big can overwhelm companies, leading to inefficient use of resources and slow progress. It’s essential to begin with manageable projects that demonstrate value and build confidence in data analytics capabilities. Gradually expanding initiatives allow iterative learning and refinement, leading to more successful outcomes in the long run. 

Not giving importance to data analytics  

Properly prioritizing data analytics projects ensures that resources are allocated effectively and efforts are focused on initiatives with the greatest potential impact. Failure to prioritize can result in scattered efforts and missed opportunities to address critical business challenges. By carefully evaluating the potential benefits and feasibility of each project, organizations can optimize their data analytics investments and drive meaningful results. 

Not focusing on process mining  

Process Mining involves analyzing event data to discover, monitor, and improve real processes by extracting knowledge from event logs. Not engaging in Process Mining means missing out on valuable insights into process inefficiencies, bottlenecks, and opportunities for optimization. By leveraging Process Mining techniques, organizations can streamline operations, enhance productivity, and drive continuous improvement initiatives. 

Big data in SCM
Big data in SCM

Making use of big data in supply chain 

The first step in getting the most out of your data is to gather it all into one central place. You may have lots of information about how things work, but it’s often not used to make things better; it’s just used to keep things going. It’s time to change that. 

Next, you’ll need to invest in systems and people who know how to use them. That could mean hiring folks who can find patterns in your data and purchasing new technology. 

Here’s a look at some important tools that manufacturers need to use data better: 

  • Centralized data storage: Collect all your data in one place so it’s easier to use 
  • Data cleansing: Make sure your data is neat and tidy so you can understand it better 
  • Data mining: Get tools that help you find the information you need when you need it 
  • Data mapping: Use tools to see how things are connected and where problems might arise 
  • Data analysis: Understand what your data means and how it can help you improve things 
  • Data monitoring: Use tools to keep an eye on your data and make sure everything is running smoothly 
  • Data visualization: Use graphs and charts to see your data in a way that makes sense 
  • Data forecasting: Use tools to predict what might happen next based on what’s happening now. 

By incorporating these tools into your manufacturing process, you can make things run better, make it easier to get things done, and make everything work faster and smoother. 


In summary, embracing big data presents manufacturers with a golden opportunity to enhance operations and boost productivity. By centralizing data, investing in suitable tools and expertise, and utilizing insights effectively, companies can make tangible strides in efficiency and profitability. Embracing data-driven strategies is pivotal for remaining competitive and thriving in today’s ever-changing manufacturing arena.