What Is Manufacturing Analytics?

Manufacturing analytics, also known as smart manufacturing, enables manufacturers to identify the causes of production errors, workflow holdups, and supply chain inefficiencies by analyzing data from IT systems and industrial equipment. This allows businesses to monitor their work processes, track key performance indicators (KPIs), and make improvements.

The global smart manufacturing market is growing at a rate of 14 percent and is expected to be worth $998.99 billion by 2032 [1]. Discover more about the types of manufacturing analytics, who uses them, and how they can benefit businesses.

Types of manufacturing analytics

To best understand how manufacturing analytics works, it’s helpful to break it down into four categories: descriptive, diagnostic, predictive, and prescriptive. You’ll need each of these to build a full picture of what your data means to the business.

Descriptive analytics

When using descriptive analytics, you look at historical data to see what has occurred in the past. In a manufacturing scenario, you may investigate what happens to production pipelines and compare the differences year to year. 

Generally, descriptive analytics answers the question “What happened?” It uses data to examine past events and trends within a business.

For example:

• What was the company’s monthly productivity?

• How many units did it make?

Diagnostic analytics

Diagnostic analytics is similar to descriptive analytics in that you analyze historical data. However, instead of just examining what has happened, you also investigate why it happened. This involves identifying patterns and trends in your data through data discovery, data mining, and finding correlations. 

Diagnostic analytics answers the question, “Why did it happen?”

For example:

• Why did production increase during the third quarter?

• Why has machine efficiency decreased in 2024?

Predictive analytics

Predictive analysis is about uncovering how likely it is that something is going to happen in your business using statistical modeling, algorithms, and machine learning. An example of this is predictive maintenance, where you use data to determine an issue before it impacts your team’s workload and productivity. 

Predictive analysis answers the question “What is going to happen?” 

Examples of outcomes of predictive analysis include:

• Scheduling maintenance on machinery after predicting a mechanical problem

• Identifying a quality control issue and changing the production process

Prescriptive analytics

Prescriptive analytics allows you to use your analytic methods to make changes. This type of analytics supports data-driven decision making. Using analytics in this way may require investing in AI systems that perform this function, which can be costly. As a result, prescriptive analysis is primarily used in specialist industries and to solve complex problems.

Prescriptive analytics answers the question, “What happens next?” 

Examples of cases it could address include:

• Identifying employees in need of training due to an incident report

• Scaling production on a certain line due to sales reports

What does manufacturing analytics do?

Manufacturing analytics provides businesses with a way of ensuring production optimization through the analysis of data at different stages to make important data-driven decisions. This includes increasing factory efficiency by reducing unscheduled downtime, tracking KPIs, increasing production, reducing errors, identifying bottlenecks, and lowering costs. 

Examples of manufacturing analytics in action

Some examples of manufacturing analytics in action include:

Demand forecasting: This involves using manufacturing analytics to predict the demand for a product and the implications that has on manufacturing. Understanding product demand helps you determine how many products to make, which reduces storage costs and helps manage inventory and staffing levels. It also helps determine pricing.

Supplier management: With supplier management, you apply analytics to monitor the work of your suppliers, including quality of materials, delivery time, and responsiveness. 

Predictive maintenance: Utilizing manufacturing analytics enables you to predict when a machine needs maintenance and repairs by using software to monitor performance and identify potential issues. This allows you to schedule downtime at the least disruptive time or to shift workloads to other areas.

Product recalls: When product recalls occur, you leverage data to understand the cause of product dissatisfaction. You can pinpoint the location of production and determine which machine is responsible using analytics. This helps you determine where to start when isolating the problem.

Advantages and limitations of using manufacturing analytics

Manufacturing analytics allows you to optimize production by locating inefficiencies in manufacturing and predicting potential issues so you can establish systems to prevent disruptions. You can also track the performance of your products and suppliers. All of this allows you to streamline your processes and save money by maintaining stock levels, staffing, and the right products to reflect supply and demand. 

However, while manufacturing analytics brings many benefits, you’ll also find limitations to consider. Firstly, these processes are complex. Managing, storing, and analyzing large volumes of data is difficult, and you need to make sure you have effective systems in place. 

Implementing these systems requires skilled and well-trained staff. You may find a skills gap in your workforce and need to recruit analysts or technical professionals. You may also need to offer staff training.

How to make a start in manufacturing analytics

If you have an interest in manufacturing analytics, it’s a good idea to do some research into the types of positions available. You’ll need a degree and relevant skills to stand out against your competition. 

Skills development

To succeed in manufacturing analytics, you’ll need a certain skill set that includes both technical and workplace skills. Some key skills to focus on include:

Technical:

• Excel

• SAP

• MRP

• Manufacturing programs

• Operating systems: Unix, Linux

• Math

• Troubleshooting

• Data integrity

• Production planning

• Process improvement

• Data analysis

• Material management

• Product management systems: MES, ERP, CMMS

• Manufacturing analytics software

Workplace:

• Multitasking

• Attention to detail

• Communication

• Analytical thinking

To build on your skills in this field, consider gaining some experience in manufacturing analysis, process improvement, supply chain management, data collection, or customer service. 

Educational resources

To work as a manufacturing analyst, production analyst, or similar role, start with earning a bachelor’s degree in a relevant field such as manufacturing, business, accounting, computer science, industrial engineering, or computer information systems. Some positions may require a master’s degree if they are more specialist or include leadership responsibilities. 

Next steps in manufacturing analytics on Coursera

Manufacturing analytics helps streamline your business processes by using data to understand where you can improve efficiency, reduce costs, and make informed business decisions. To work in this field, you need analytical skills as well as a solid understanding of the manufacturing process. To make a start, you might consider taking an online course such as Advanced Manufacturing Process Analysis, part of the Digital Manufacturing & Design Technology Specialization offered by the State University of New York on Coursera.