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DA8. Predictive Analytics

Statement

Predictive analytics play an important role in discovering the underlying reasons and findings in any big data analysis project.

Discuss any two uses of predictive data analytics with big data. How are these two uses important to businesses’ operational planning?

Answer

Introduction

Predictive analytics is a form of advanced analytics that helps in understanding what is likely to happen in the future. It uses data mining, machine learning, and other techniques to predict future events or outcomes (Ahmed et al., 2021).

Machine learning models are important for predictive analysis as they are trained on historical data and try to understand it, and then extract insights and present them to experts to make informed decisions.

There are many use cases for predictive analysis ranging from customer behavior prediction, predictive maintenance, fraud detection, supply chain management, workforce planning, product development, crime analysis, epidemic intelligence, and many more. In this answer, we will discuss supply chain management and predictive maintenance (Bello-Orgaz et al., 2016).

Supply Chain Management

Supply chain management is at the heart of every business operation; it ensures a stable and enough supply of raw materials, resources, and products for every single business process, production line, or point of sale.

Predictive analysis can be used on the historical data of the supply chain to find correlations that may help in predicting any future disruptions so that decision-makers can be ready to secure different supply routes, find alternative suppliers, or even change their order quantities.

After training the ML model on historical data, the information for this year -or the interested period- is fed into the model and it will flag any potential correlations between variables and any potential disruptions. For example, it may predict a shortage in a specific product if historical data indicates shortages contemporaneously with a specific event (if a similar event is expected to happen in the future).

Predictive Maintenance

Maintenance is key to ensuring that all equipment is running smoothly when you need it; every machine needs maintenance at some point, and following the manufacturer’s maintenance schedule is only good in ideal conditions. Failing to maintain equipment can lead to unexpected downtime, disruptions in production, and financial losses.

Predictive analysis can be used to predict (with good accuracy) the time a machine will fail based on the actual usage data, the environmental conditions, and the machine’s history. This gives the maintenance team enough time to allocate resources and schedule maintenance at times when business operations are not affected.

After training the ML model on historical data, the model can correlate variables such as the number of hours the machine has been running, the number of times it has been turned on and off, the temperature, the humidity, and many more. The model can then predict when the machine is likely to fail and alert the maintenance team to take action.

Conclusion

Predictive analysis is a powerful tool in the set of business analytics set that includes descriptive, diagnostic, and prescriptive analytics. While predictive analysis may give enough insights to make informed decisions, it is rarely used alone; however, a combination of these analytics can improve the operational planning of businesses to a great extent.

References