Manufacturing Analytics: Top Use Cases and Examples

Manufacturing analytics

The manufacturing industry is highly competitive, with every company striving to outflank its opponents. How can manufacturing analytics help businesses gain a competitive edge?

In the industry 4.0 era, manufacturing companies need to implement the latest technologies to modernize their workflows and efficiently cater to their clients’ needs.

However, no innovative tech can bring great results on its own, without proper data analytics.

More and more businesses are recognizing the benefits of manufacturing analytics, leading to the rapid growth of the global market, valued at $11.98 billion in 2023.

In this article we will explore the benefits businesses reap by leveraging data analytics, and discover the most interesting manufacturing analytics use cases. In addition, we will provide insights on what to take into account when introducing manufacturing analytics into your business.

What is manufacturing analytics?

Manufacturing analytics is the process of collecting, analyzing, and interpreting data that allows manufacturing companies to make more informed decisions and enhance their production pipeline.

Manufacturing analytics makes use of different technologies and techniques, including statistical analysis, data mining, machine learning, the Internet of Things, and cloud computing.

To leverage big data analytics in manufacturing, companies need to ensure consistent collection, storage, and processing of data from all departments as well as accumulation and analysis of industry news and trends. Then, with the help of machine learning, manufacturers can work this all-encompassing data to address their business challenges.

Get acquainted with our IT consulting services designed to address your business challenges

What are the benefits of data analytics in manufacturing?

Benefits of manufacturing analytics

Businesses gain a number of substantial benefits from the introduction of manufacturing analytics into their processes. These include boosted efficiency, improved trend and customer demand awareness, smarter equipment management, better quality control, and cost reduction.

Here is a more detailed overview of each benefit.

Boosted efficiency

With the valuable insights derived from data analytics, manufacturing companies can optimize their strategies and workflows, resulting in improved speed and quality throughout various stages of production, from ideation to product manufacturing and distribution.

Improved trend and customer demand awareness

Data analytics can process customer data to identify popular and unpopular features, as well as missing qualities and functionality in the product.

Manufacturing companies can also use analytics to better navigate the ever-changing trends and tailor their products and marketing to appeal to as many customers as possible.

Smarter equipment management

Manufacturing businesses can leverage predictive analytics to detect potential machinery malfunctions and overloads and take proactive steps to avoid them. This will prevent downtime and expensive equipment repair.

Big data analytics in manufacturing can also provide valuable insights into optimizing machinery performance while avoiding excessive strain on systems.

Better quality control

By analyzing data from production processes, manufacturers can identify and address quality issues in real time. This helps companies improve product quality, enhance customer satisfaction, reduce waste, and avoid warranty claims.

Cost reduction

As mentioned above, manufacturing analytics makes it possible to cut down the expenses associated with warranty claims and equipment repair. Moreover, businesses can leverage data analytics to analyze their production processes and identify inefficiencies that lead to financial waste. They can also identify processes that can be automated and streamlined to improve productivity and profitability.

Explore our data analytics portfolio featuring solutions from contrasting industries

Top 5 use cases of manufacturing analytics

Manufacturing analytics use cases

Big data analytics in manufacturing industry brings vital improvements to different aspects of production, allowing manufacturers to optimize their supply chain and logistics, offer better pricing, forecast demand effectively, execute predictive maintenance, and perform more elaborate product design and development.

Let’s examine each of the use cases in more detail.

Supply chain and logistics optimization

Manufacturing analytics offers significant optimization opportunities for supply chain and logistics, enhancing transparency and control. Companies can manage the smooth delivery of materials and components to and from manufacturing sites, as well as efficient distribution of products to customers, by leveraging real-time and historical data. This includes factors such as weather forecasts, traffic conditions, required delivery conditions (e.g. temperature or humidity), etc.

Big data analytics can also provide real-time tracking of goods in transit, enabling timely interventions to prevent any potential damage, loss, or delays.

Learn more about predictive analytics in supply chains in our article

Price optimization

Adequate pricing strategy is crucial for manufacturing businesses, since it largely determines whether or not the business will be profitable and to what extent. That’s why it is a good idea to leverage manufacturing analytics to accurately calculate product pricing.

Numerous factors influence the price of a product, ranging from production costs to client purchasing power and market competition in a specific location or niche.

By leveraging big data analytics, businesses can collect and analyze these factors to make data-driven pricing decisions that maximize profits while remaining fair to customers.

Demand forecasting

Manufacturing analytics enables more precise demand forecasting, helping companies to tailor their production strategy in accordance with expected customer behavior.

By knowing exactly what products will be in most demand during a specific period of time, manufacturers can:

  • Purchase the necessary materials in advance and, potentially, for a more reasonable price
  • Work out a production schedule which will provide a sufficient amount of the most desired goods in stock at all times
  • Customize supply chain processes to ensure timely delivery of products to the locations where they are in high demand

As well as identifying the most sought-after goods, manufacturing analytics can help determine what products are low-performing. This allows companies to reconsider their offer and either stop producing these items or make changes to their marketing.

Predictive maintenance

Equipment malfunction can impact enormously on the entire production cycle and lead to downtime and high repair costs. With the help of data analytics, companies can schedule equipment checks based on the equipment’s usage intensity and the tasks it performs. By adopting this approach, manufacturers can significantly reduce potential accidents and breakdowns by identifying and addressing them before they escalate into major problems.

Furthermore, implementing a scheduled maintenance plan allows for downtime reduction since staff members are informed in advance about repair works and can redirect tasks, either fully or partially, to alternative machines.

Product design and development

By analyzing precisely which features or capabilities of a best-selling product makes it so desired by customers, a manufacturing company can understand how to upgrade its other offerings.

In addition, businesses can conduct thorough analysis of competitors’ products within the same category, both successful and unsuccessful, to identify what to do and what not to do. Empowered by these insights, a business can choose to rethink its product design and development strategy and change certain aspects of it, such as materials, features, and packaging.

How are digital twins used in manufacturing? Find out in our article

What companies use manufacturing analytics?

Some of the world’s best-known companies, including Ford, Foxconn, and Unilever, have already harnessed the power of manufacturing analytics. Let’s take a look at how they do it and what benefits these businesses stand to enjoy.


One of the biggest automobile manufacturers in the world, Ford successfully leverages manufacturing analytics to execute predictive maintenance. The manufacturer uses specific sensors, called miniterms, that are integrated into plant machinery. These sensors continuously monitor the performance of equipment and send alerts to engineers whenever they detect a decrease in machine speed. Since its introduction in early 2019, this manufacturing analytics solution has helped Ford save more than €1 million.

In addition, Ford has teamed up with Google to work out a solution that will collect and store the data from more than 100 key machines located at two Ford plants — 25 million records per week!

“The growing amount of sensor data generated on our assembly lines creates an opportunity for smarter analytics around product quality, production efficiency and equipment health monitoring, but it also means new data intake and management challenges” — Jason Ryska, Director of Manufacturing Technology Development, Ford Motor Company.


Foxconn, an electronics manufacturer and one of Apple’s oldest and largest suppliers, utilizes manufacturing analytics in its supply chain and logistics operations. For instance, the company assesses real-time data on traffic congestion, weather, and other factors to create the most efficient routes or make alterations to the current ones.

Foxconn also leverages data analytics to predict the likelihood of supply chain disruptions and assess the reliability of its contractors.


Unilever, a giant consumer goods company, uses manufacturing analytics to tailor their product range. The company has an AI-based solution that runs through tons of product-related data such as profitability, consumer behavior, and retailer benefit. This results in an accurate and actionable view of Unilever’s portfolio, allowing it to invest more effort into their best-selling goods and avoid wasting resources on unsuccessful products.

What to consider before implementing manufacturing analytics in your business

To adopt manufacturing analytics efficiently and achieve the best results, we recommend considering these four questions:

Which aspects and workflows do you want to improve with the help of manufacturing analytics?

Even though data analytics can be successfully applied to any department or process, it is best to start off by defining what exactly you want to improve, whether it’s product development, distribution, or something else.

This way you can get acquainted with the technique and see how it can specifically benefit your business. Moreover, since manufacturing analytics relies on technologies like machine learning and the Internet of Things, it is often more cost-effective to initially introduce it to a limited number of tasks or processes.

Is your business infrastructure ready to embrace and incorporate data analytics?

Gathering, storing, and analyzing all the data within a manufacturing enterprise requires high computing power, adoption of the newest technologies, and established connectivity between different departments.

Before implementing manufacturing analysis, you will need to determine how well your company’s infrastructure is prepared for this novelty and what changes need to be done to the existing tech stack and processes.

Do your employees understand the need for utilizing manufacturing analytics?

To unleash the full potential of big data analytics in manufacturing, your staff should understand the benefits of the technique and how they can contribute to its seamless implementation. This can be accomplished by conducting training sessions and arranging informative lectures.

You will probably also need to hire in-house data analysts or seek assistance from outsourced big data consultants to set up the environment for manufacturing analytics and interpret the output.

How will you ensure data security?

To leverage manufacturing analytics, you will need to store your enterprise data digitally, so cybersecurity is top priority. Think about the most suitable and reliable storage solutions that can effectively safeguard your data against hacking attempts, detect any suspicious activity, and prevent unauthorized access.

Learn more about most common cybersecurity threats and how to avoid them


There is every indication that manufacturing analytics will soon be an integral part of any successful business strategy, with companies who fail to utilize it ultimately losing out competitively.

Manufacturing analytics empowers businesses to make informed decisions and drive continuous improvement in their production operations by providing insights into how to boost efficiency and productivity, reducing expenses, preparing for possible risks, and more.

Our big data consultants will readily answer all your questions regarding manufacturing analytics and suggest the most suitable ways of implementing it in your company.

Contact us and let’s see how data analytics can drive your figures up.


Valeria Serebryantseva


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