According to the survey conducted by Honeywell, unscheduled downtime is one of the major causes behind the loss of revenue for manufacturers. Besides, other key factors such as equipment breakdowns, unscheduled maintenance, safety incidents, supply chain management issues are also some of the major threats faced by the manufacturing sector.
Data Analytics has emerged as a core component of IIOT (Industrial Internet of Things) and offers viable solutions to the numerous problems and challenges faced by the manufacturing industry today. As a result, several manufacturing companies are seen investing in predictive analytics to minimize their business risks, reduce wastage, predict downtime risks and make informed decisions for their business.
Let’s understand the key benefits of predictive analytics and take a close look at how it is being effectively used by some of the major companies across the world.
Top reasons why predictive analytics is vital to manufacturing
The early 1970’s saw the implementation of lean principles and Six Sigma techniques to manage issues related to wastage, overproduction, idle time, inventory and logistics in the factories. With the information technology age, there was a rise in automation with industrial robots resulting in mass and flexible manufacturing methods to provide superior products to customers. In this new era, predictive analytics is emerging as an effective solution to boost manufacturing operations, improve efficiency, reduce maintenance costs, and increase profitability for the companies.
Predictive analytics is helpful in demand forecasting
Efficient demand forecasting, which involves predicting the future demand for goods and services based on past events and trends, is key to the growth and survival of every business. With the use of predictive analytics, manufacturers can forecast the future sales possibilities depending on their past sales activities. Analytics also provides insights into additional factors which might have influenced sales in the past for the business and apply them in their future forecasting models.
Predictive analytics helps in preventive maintenance
One of the main purposes of preventive maintenance is to reduce the incidence of risks associated with devices by triggering calls or alerts from machines depending on the data which has been captured inside the machines. For example – Preventive maintenance may involve providing automatic signals for repairing of the broken belt, reduced demand for the products, or load on the machine and even identify the patterns from the machines. This helps in ensuring that the machines are operating at their peak efficiency. It can also be used to identify equipment manufacturer defects which can save lots of money and time and result in greater efficiency.
Predictive analytics increases equipment uptime and enhances the quality of service delivery
Predictive analytics allows for real-time monitoring of the health of equipment so that services can be performed accordingly to rectify the issues at the earliest. It also helps in enhancing the productive uptime by identifying the problems to do the repairs at the scheduled production downtime instead of peak times of operation. This helps manufacturers in avoiding expensive breakdowns that can hinder the production process and ensure optimum performance of the equipment at all times. By avoiding catastrophic failures, companies can achieve significant savings and deliver superior quality products to their customers.
Predictive analytics can improve the safety of workers
In all the manufacturing setups, worker safety is of utmost importance. Employees are exposed to occupational risks and hazards while dealing with industrial equipment. By leveraging predictive analytics and machine learning techniques, manufacturers can monitor the equipment for any major faults or damage which can ensure greater safety of the workers. The machine may be able to issue an alert signaling danger or provide countermeasures to sustain any human injuries or risk to their lives.
Predictive analytics helps in optimizing supply chain
With consumers becoming more and more demanding about product delivery timelines, predictive analytics can help manufacturers have a positive impact on their supply chain. By using a combination of structured and unstructured data, analysis, and predictive analytics, companies can reduce potential delays and speed up the delivery schedules. With accurate forecasting of demand and future events, using predictive analytics, companies can avoid inaccurate stocking and delays in deliveries. By optimally managing the resource-intensive inventory management processes, companies can have a positive impact on their bottom line.
Conclusion
Manufacturing companies looking to make their factories function at optimal levels with maximum uptime, high efficiency, and best quality products have already started leveraging predictive analytics. Apart from the manufacturing quality, it is helping them improve their equipment effectiveness (OEE) and increase the overall RoI over their equipment. Predictive analytics can help in transforming the future of manufacturing by providing all the necessary tools and technologies to create automated factories which are equipped with high capabilities to maximize efficiency and production in manufacturing.