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Predictive Analytics in Manufacturing – How It Works [Use Case]

Manual operations in manufacturing often result in increased costs and decreased growth. Manufacturers face 4 critical challenges: optimizing operations, saving costs, improving production quality, and forecasting demand.

Digitizing one or two processes can only work to a certain extent and only a complete digital solution could prove useful. In particular, critical challenges like forecasting demand require a robust prediction system based on analysis of operating data and without it, manufacturers can never plan for the future.

Predictive Analytics in Manufacturing – Why It Matters and How It Works

So what would be the best way to meet these challenges?

An interesting but best way to overcome this challenge is to automate the process with predictive maintenance solutions.

Let’s start with the applications of predictive maintenance in manufacturing through improving operations and production quality at a reduced cost and forecasting demand for the future in detail in the sections below.

What is predictive maintenance?

“Predictive maintenance (PdM) is maintenance that monitors the performance and condition of equipment during normal operation to reduce the likelihood of failures. Also called condition-based maintenance, predictive maintenance has been used in the industrial world since the 1990s.

The goal of predictive maintenance is the ability to first predict when equipment failure could occur (depending on certain factors) and then prevent the failure through regular and corrective maintenance. (Source: Reliable Factory)

Manufacturing Predictive Analytics Market Outlook 2018 to 2026

The size of the manufacturing predictive analytics market was valued at $ 535.0 million in 2018 and is expected to reach $ 2.5 billion by 2026, with a CAGR of 21.7% from 2019 to 2026 The advent of Industry 4.0 spurs substantial recent innovations in manufacturing. (Source: Allied Market Research)

How the whole predictive maintenance system works

A predictive maintenance system includes the Internet of Things (to collect data from any surface); Cloud (to process data); Mobile applications (to send notifications based on data); AI / ML (to analyze and predict information using data); Web application (to share all operating data under one roof).

The system works that way. Initially, the data will be collected by IoT devices installed on machines or assets.

The data will be processed in the Cloud or shared with relevant personnel in the form of notifications / warnings or alerts.

The processed data will be fed into the AI ​​/ ML system to analyze and predict the results of the data accumulated over a certain period (generally historical data of at least 1 year is recommended).

The prediction reports will be shared with the respective stakeholders to take the necessary actions or decisions.

Image credit: Hakuna Matata Solutions

(To note: The image above illustrates how predictive maintenance works in a manufacturing plant)

Benefits of predictive maintenance for manufacturing

  • Accurately capture real-time data collection based on conditions
  • Anticipate and predict machine downtime early
  • More transparency
  • Reduced production times
  • Improve the expected production rate
  • Reduced maintenance costs
  • Predict machine breakdowns
  • Reduce repair costs
  • Increase the life and use of the equipment
  • Improve employee safety
  • Increase in overall profits
  • Expected demand

You have now gathered the basics of predictive maintenance and its benefits.

Let’s deepen the discussion of how predictive maintenance is transforming manufacturing operations and growth.

Predictive maintenance for improved operations

Operational efficiency plays a key role in production rate and build quality. As it involves people, machines and technology, optimizing everything is important to enjoy hassle-free production that matches expected results.

Before embarking on operations, it is essential to understand the challenges that impact operational efficiency.

It is essential to analyze the performance of machines operated at different levels (peak, average or normal). Machine efficiency is very important when it comes to improving operational efficiency. Maximum efficiency can only be achieved if the machines are used to the maximum and function optimally.

To do this, it is essential to monitor the performance of every machine and every possible movement. The IoT is used to collect data and based on the analysis of historical data, flaws or inefficiencies in operations are identified and rectified.

Not only the problems that may arise in the future can be predicted with the IoT enabled predictive maintenance system.

Typically, OEE (Overall Equipment Efficiency) is calculated using IoT data and it is analyzed and improved to make overall operations efficient and rewarding.

“OEE = Availability * Performance * Quality”

Another scenario would be the performance of resources against machines. It must be identified and corrected to improve staff efficiency. By digitizing the process with Industry 4.0 solutions like IoT, it is easier to improve the efficiency of the overall operation.

Predictive maintenance for the use and management of machines

Unplanned machine maintenance is more expensive for most manufacturing companies and this must be monitored and controlled for maximum returns.

Malfunctions or faulty machines impact manufacturing in two ways: first, it will reduce the quality of production, and second, it will lead to frequent repair costs.

It is therefore essential to find a way to detect inefficient machines and improve their performance before failure occurs, which costs you an arm and a leg.

With a predictive maintenance system, the data collected with each movement of the machine will provide a large volume of data which can then be analyzed using an AI / ML program to identify faults and malfunctions of the machines.

A predictive maintenance system provides data on the current state of the asset, its availability, defect information to help you rethink your production plans.

With such an approach and data trends, predict and predict machine failures as early as possible, resulting in lower maintenance and labor costs. This could potentially save your business millions.

Predictive maintenance for production quality

Even though predictive maintenance or IoT does not have a direct impact on the quality of production or its rate, the combination of these two elements can really create a big impact on the overall production on the ground in a significant way.

As the IoT can help streamline the machine, people and technology. A predictive maintenance system will take care of improving machine efficiency – expecting improved quality and production rate is never a challenge for manufacturers.

Predictive maintenance for forecasting demand

An exclusive benefit of predictive maintenance for manufacturers is demand forecasting.

Since manufacturers have tons of data but have no idea, the process of improvement and planning ahead is always slipping. With a predictive maintenance system in place, it’s easy to predict what can be done in the coming years based on historical data.

As the predictive maintenance system limits data silos and creates 100% transparency across the entire manufacturing plant, it is never impossible to realize where you are now and what is going on. wait in the future.

With a plan and knowing what to expect, manufacturing managers can plan well in advance to meet customer requirements. Not only does this allow you to easily identify the efficiency of machines, personnel and repair costs to plan for future goals, which will be practical.

Predictive Maintenance Use Case – Asset Management

Predictive maintenance has a large number of use cases in the manufacturing industry, particularly in condition monitoring of assets.

There may be scenarios where the assets will be operated at different temperatures and monitoring their performance under different conditions is essential to maintain the quality and rate of production.

These types of assets need to be constantly monitored to keep them in good condition and even minor malfunctions or flaws can cost the business millions of dollars.

With a predictive maintenance system, monitoring of the asset under different conditions is transparent and the historical data obtained will help predict how the asset will perform in the future and when it will need to be replaced or maintained.

Predictive maintenance helps to discover

  • When the asset needs to be replaced
  • When asset maintenance is required
  • How long it will be effective
  • When it can fail
  • What causes failure
  • What is the risk associated with failure
  • What maintenance would be practical to improve asset utilization

ROI of predictive maintenance

The implementation of a functional predictive maintenance program can give remarkable results: a tenfold increase in return on investment, a 25% to 30% reduction in maintenance costs, a 70 to 75% reduction in breakdowns and a 35-45% reduction in downtime.

When savings are expressed per man-hour, predictive maintenance costs $ 9 hourly wage per year, while preventive maintenance costs $ 13 hourly wage per year. (Source:


From what we have discussed above, predictive analytics is a boon for manufacturers as it will reduce maintenance costs while improving operational efficiency and production quality and help you plan future programs. .

Predictive analytics is evolving and the latest addition to predictive analytics, prescriptive analytics is gaining ground in the industrial landscape.

The latter is a subcomponent of predictive analytics and provides data on the cause of equipment failure and recommendations to improve the failure or fault.

With too many companies investing in predictive maintenance systems, it’s high time you decided to go with the competition. Start now before any of your competition.

Gengarajan PV

Gengarajan PV

CEO of Hakuna Matata Solutions Pvt Ltd

Gengarajan PV is CEO of Hakuna Matata Solutions, a leading company in the field of digital transformation. He has over 14 years of experience in the information technology industry. He spends his time reading about new technologies in manufacturing, distribution and logistics.



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