Predictive analytics is a branch of advanced analytics which is used to make predictions about unknown future events. Predictive analytics use data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
The technology is being highly adopted in many sectors including manufacturing. The manufacturing radar undergoes various challenges on daily basis. The adoption of predictive analytics is seen as a saviour that addresses manufacturing pain points.
Machinery breakdown in a manufacturing unit is a common happening. Machines collapse or malfunction due to extreme pressure, temperatures or range of motion. Unfortunately, it can cost a fortune at times when the machine’s abnormality is not noticed initially. Predictive analytics uses the aggregate data from real-time sensors on machines to anticipate when they need replacement or service.
Creates efficient operation
Fluctuations in raw material, machinery components and supply costs are big trouble for the manufacturing sector. When the raw materials cost spikes, the company has to endure the increased price without putting them on customers. Predictive analytics can counteract this encroaching profit erosion by creating a more efficient operation. The technology can help reduce mistakes that result in unavoidable waste.
Consumer’s demand change based on behavioural and environmental circumstances. Producing less selling products at large scale might slip the company revenue into a deep pit. Fortunately, by using predictive analytics, the past history of demands can be analysed. This analysis gives an insight on consumer buying habits, raw material availability, trade war impacts, shipping barriers, supplier issues and other unseen disruptions.
Improves workforce management
Even though machineries do a lot of work in manufacturing, we can’t deny the fact that humans are the initial accelerators. However, unlike machines, humans tend to go under different circumstances which affect productivity. The issue of multiple workforce management barriers can be addressed using predictive analytics. The technology leverages the facility to obtain deep insight into industry hiring trends, internal employee engagement, safety incidents, employee productivity, contract negotiations, etc.
Upgrades warranty policies
As consumers’ expectations increase, the chances of them not liking the product and applying for return also spikes. Using predictive analytics, the manufacturing house can analyse historical data related to returns and add the learning to features and products on their roadmap. From there, the company will get an opportunity to build a better warranty policy for their products.
For applications such as predictive quality and predictive performance, predictive analytics is necessary. It enables manufacturers to detect problems at their very preliminary stages, so they can be worked with before problems begin to escalate.
Some of the benefits of predictive analytics in manufacturing sector are,
- Predictive analytics track machinery and use past data to anticipate possible breakdowns which help cut cost and lazy working hours.
- Predictive analytics rummages much faster and more reliably through large quantities of historical data than a person would.
- Predictive analytics, in addition to ensuring machine health, can also guarantee better conditions in factories and throughout the supply chain.
- Predictive analytics can allow factories to detect quality issues and take remedial action more quickly to mitigate the effect and reduce waste-related costs.
- Eventually, predictive analytics offers real-world data to manufacturers to enable them to refine their processes to achieve excellence.