Predictive analytics is important for the business. As it helps to monitor business assets and it let you allow you to predict failure and avoid unexpected downtime of the asset. It is very helpful to the maintenance department. Predictive analysis also plays a crucial role in increasing return on investment (ROI).
What Is Predictive Analytics?
As the name suggests it is helpful in predict events for the future. For this purpose, historical data is used so that future insights can be generated. Thus, predictive analytics help you in keeping yourself ahead of the curve.
In simple words, you can make better-informed decisions with predictive analytics. Historical and present data are utilized to find the pattern that can help identify a trend which can be helpful in business growth.
Predictive analytics is used for data mining, it uses techniques such as machine learning and artificial intelligence to make future predictions.
According to the report, “the global predictive analytics market was valued at approximately USD 3.49 billion in 2016 and is expected to reach approximately USD 10.95 billion by 2022, growing at a CAGR of around 21% between 2016 and 2022.”
How Predictive Analytics Can Help in Maintenance and Business?
Predictive analytics can help in maintenance and business in several ways:
1. Predicting Asset Failure
Sudden asset failure kills business productivity especially when assets are very essential for business. Especially those businesses dealing in manufacturing, logistics, etc.
As we know manufacturing-based organizations have machines that work continuously for long hours and if machine performance decreased then it will impact the production work. It starts a chain reaction of failure events. It begins with asset failure then production work suffers and delivery does not take place on time. Few events like these can result in customer loss. Nobody wants to lose a customer because of machine failure.
Predictive analytics is very helpful in avoiding an unexpected breakdown. When you use predictive analytics in your business you will notice decreased downtime of assets.
2. Decreasing Maintenance Time
With deep analytics and CMMS reports, maintenance becomes simple and repairing can be done proactively. Technicians are in a better position to know what are the activities that need to be performed in the maintenance. It is helpful to technicians as they do not have to look for the issue. Thus, it saves time for technicians and it also results in decreased maintenance time.
3. Decreasing Maintenance Cost
As we told you above predictive analytics help avoid asset failure. Sudden asset failure is more expensive than regular maintenance. Sudden failure of assets interrupts daily operations that impact the productivity of the whole organization. For instance, if machine halts suddenly then it slows down the process of manufacturing and it results in delayed shipment, and customer disappointment also occurs.
Thus, an organization needs to utilize predictive analytics specifically when decreasing maintenance cost is one of your main concerns. Maintenance cost is one of the reasons why data & predictive analytics are used. Maintenance can be very expensive especially in case of asset breakdown because it can be time-consuming and inventory is also required.
Predictive Analytic in Maintenance
When you understand that an equipment failure is in its early stages. It is vital to predicting the probable outcome to ensure that the correct decision is taken on “how to proceed”.
At present, it is commonly believed that the outcome of each incipient failure is set and hence predetermined if it is permitted to progress to functional failure. In practice, however, for any particular case of failure, there are a variety of potential consequences of that failure and these can change over time. For instance, high wheel bearing temperature on a train could easily lead to collapse and possible derailment during the bearing seasons with subsequent damage to the wheel and track or it could lead to wearing collapse.
In this space, there is a lot less work being done. More work is being done to develops more sophisticated methods to assess if asset failure occurs. However, less work being done to develops more sophisticated methods of determining if equipment failure. It can improve possible business outcomes.
How Predictive Analytics Helps Business Operations?
Predictive analytics helps business operations in many ways:
It assists the organization in improving business operations as it can help predict inventory usage. Predictive analytics is helpful to organizations in predicting when their business sales are low or high for industries such as travel, hotel, etc. When sales are low marketing can be done in a way to increase sales. Predictive analytics help organizations to function effectively. They can also help in determining the customer response.
Predictive analysis is helpful to business by predicting pitfalls and when you have detailed data then you can use the data for overcoming or avoiding those pitfalls. The analytic is the key to business growth.
Overall, predictive analysis can enhance the efficiency of machines and the productivity of an organization. As it makes you more proactive and it predicts the data history and allows you to prepare for possible outcomes.
We know analytics is very important for the business. Several businesses need real-time analytics and updates for improving the efficiency of the business. As managers can change the priority of work order when a maintenance work arrives on high priority.
Asset management software assists organizations as it provides analytics that is helpful in the business decision-making process. Predictive analytics can show the possible future event which can be very useful especially in maintenance as it can play a crucial role in saving maintenance expenses or avoiding asset failure.
Frequently Asked Questions
It can help avoid fraud as it can help to detect & reduce fraud. Banks which are issuing credit card can detect areas which have a bad reputation for paying credit bill so you can avoid those areas.
Big brands such as Netflix, Amazon, and all the digital marketing organizations use predictive analytics. Almost every business utilizes analytics for business development and to reach out to their customers.
Predictive analytics can be helpful to industries such as
1. Oil & Gas Industry
2. Government industry
3. Manufacturing industry
4. Retail industry
5. Health industry
6. Travel & Hotel industry
7. IT Industry