Predictive Analytics and its use in the Manufacturing Industry

Business forecasting is important to create winning strategies and ensure profitability. A well-rounded business planning touches all the areas that impact profits – costs, demand, and sales. At the end of the day, businesses depend on forecasts to accurately foretell supply costs, demand for products and services, and estimate fluctuations in sales with changing times. 

The more accurate your forecasts are, the better prepared your business will be to meet future goals while avoiding unexpected problems. 

With that said, it wouldn’t be an exaggeration to conclude that adopting predictive analytics in manufacturing is no longer an option if decision-makers want to improve the accuracy of their forecasts.

What Is Predictive Analytics?

Predictive analytics employs AI and machine learning technologies to collate, refine, and examine historical and current data with the goal of monitoring and optimizing business processes. An analytic approach keeps manufacturers on top of trends, potential problems, and possible opportunities to maximize production or performance. 

In the manufacturing sector, the process data will first need to be collected, stored, and organized if you are to leverage predictive analytics software. This data can be generated by devices, machines, and systems being operated on the factory floor. Factories need to have at least 3-6 months of historical data to obtain valuable outcomes from analytics, though the time frame can vary as per your objective of analyzing data. 

Now this collection of historical data can help you derive key information to be able to make effective predictions. The process will involve correlating different variables (like line speed, product quality, and more) against each other. As a result, the decision-makers will be able to identify areas requiring attention and take early action to boost efficiency and prevent waste.

Top 4 Uses of Predictive Analytics in the Manufacturing Industry

1. Predictive Maintenance Analytics

[Use Case] Reduce downtime and cut down on maintenance demands

Instead of depending on standard maintenance programs or assumptions, collecting data allows you to predict the actual need for maintenance. Accurate predictions give you ample time to plan the maintenance or make necessary adjustments to sustain a machinery failure. As a result, your production uptime increases. 

The accuracy and possibility of predictive analytics in manufacturing expand as more data is obtained, which allows you to correlate data and spot patterns – all contributing to quality decision-making.

2. Quality Analytics

[Use Case] Minimize scrap, Alerts to performance or quality issues

Performance tracking makes it possible to get timely alerts of derailed processes or a threat to quality. Being able to quickly bring a situation under control can significantly reduce or even prevent material waste or rework. 

Early projection of maintenance requirements and potential quality problems increase applications’ value, particularly for applications involved in producing an end product with unstable prices or unpredictable market fluctuations.

3. Predicting Demand Analytics

[Use Case] Determining and benefiting from KPI and ERP

Material requirements and production demands can be volatile. However, some understanding can be obtained by tracing each process and overall lead times. With the use of connected real-time devices, KPI can be determined to increase the ability, accuracy, and value of ERP and other such software tools. 

Predicting shipping and consumption rates through data will also help streamline supply chain management. Similarly, forecasting timelines, market demand, and volume will help manage cost and economics for provisioning new products, equipment, or processes.

4. Workforce Analytics

[Use Case] Predicting workforce demands and their education

Predictive demand analytics can also be leveraged for effective labor management and talent acquisition. Through data, manufacturers are able to foresee the future requirements of skills and labor, thereby overcoming the biggest challenge of skill-gap in the manufacturing industry through predictive analytics

Predictive Analytics Improves Forecasts

Mass-producing goods are capital-intensive everywhere in the world. However, manufacturers can manage these costs properly when they know future demands. Forecasts help businesses plan better by striking a perfect balance between costs, sales, and consumer demand. Predictive analytics in manufacturing can greatly add accuracy to forecasting and improve the bottom line. 

By the use of advanced statistical algorithms, predictive analytics can solve the complex web of factors like raw material availability, consumer buying habits, and supplier issues. It can even account for weather-related shipping conditions, trade war impacts, and other unseen disruptions.

Therefore, predictive analytics can identify unknown correlations between different variables. This information empowers manufacturers to evolve their supply management practices. They will then be better equipped to prevent shortages or excess inventory, improve market strategies, and anticipate difficult scenarios.

Difference Between Data Analytics and Predictive Analytics

Data Analytics involves inspecting and refining data by applying traditional algorithmic and mechanical processes. They help achieve required transformations and models necessary to identify logical sets and patterns and draw meaningful conclusions. The objective of data analytics in the manufacturing industry is to make data-driven decisions.

On the other hand, predictive analytics is an approach toward making predictions based on patterns identified by examining current and past data trends. The practice makes use of advanced computational models and algorithms (including data modeling, data mining, machine learning, and deep learning algorithms) to build a prediction platform or foundation. This helps extract insights from data and project patterns. The objective of predictive analytics in the manufacturing industry is to evaluate potential risks and forecast future outcomes. 

Role of Predictive Analytics in Manufacturing Industry Businesses

Enhance and Maintain Quality 

Manufacturing analytics powered by machine learning can enable manufacturers to evaluate heavy datasets and trace signs of future failure or errors. With real-time predictions on quality fluctuations across production stages, you can institute a corrective plan in time. Being aware of warnings gives you ample time to take action and avoid loss caused by poor quality.

Prevent Downtime with Predictive Maintenance

Signs of a potential machinery breakdown can be hard to notice and can lead to massive production downtime. Through predictive analytics, you can tell when a breakdown can happen based on the insight obtained by examining the historical performance record of machines. Manufacturers can, thus, plan maintenance cycles as well as get alerts on possible failures and get pre-emptive checks done.

Manage Demand and Supply Chains

Predictive analytics empowers manufacturers to efficiently make the most important decision – how much of a product they should produce. They can access historical and current data and obtain real-time insights on inventory status and market demand to determine production quantities.

Also, analytics offers a clear picture of demand trends, especially around special events, which can tell what products will see an increased or reduced demand. This knowledge will dictate the changing priorities of the production line, ensuring that the production never stops. 

Combine Insights from Multiple Data Sources

With the introduction of IoT-driven connected devices and processes, industries are seeing an overwhelming influx of data from multiple devices or systems. Manufacturers can utilize predictive analytics to unify data from diverse sources and achieve centralized access to all KPIs. 

Manufacturers can also work with their data through self-serve reports or dashboards to draw deeper insights from multiple perspectives.

Conclusion

Are you ready to build a predictive model that checks all boxes and boosts your plant’s efficiency? Trust the analytics professionals at Vlink to match you with the right solutions for your needs. 
We welcome you to have a friendly, one-on-one whiteboard session where we will help you consider your options. Our professionals will guide you on leveraging predictive analytics tools to bring efficiency to your operational performance and increase profit margins. Contact data analytics expert to schedule a consultation today.

January 11, 2022


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