Understanding Predictive Analytics and Predictive Understanding
The term predictive analytics refers to the analysis of existing data to create forecasts that when properly applied, help avoid future problems. While kaizen helps spot problems live on the manufacturing floor, using predictive understanding methods provides an opportunity to:
- recognize problematic issues.
- overcome difficulties.
- prevent difficulties.
Historically, companies implemented predictive analytics to reduce various forms of waste, such as idle time, inventory, logistics and overproduction. An emerging area of implementation, preventative maintenance, includes fault prediction to replace worn or faulty parts before they fail called predictive asset maintenance (PAM).
How Predictives Impact Inventory Planning and Warranty Claims
Until recently, it required formal prediction models to forecast equipment failure. Today, as smart sensors have developed and big data has developed, the two effortlessly combine to feed artificial intelligence (AI) information that erases ambiguities. Today’s companies can install sensors on virtually any part of an auto, airplane, manufacturing equipment, etc. to receive a constant feed of performance data that identifies the moment the part begins degrading. By rule setting a finite percent of acceptable degradation, maintenance personnel can recall the equipment to repair it before it breaks. The level of acceptable degradation can be calculated using time-based or usage-based predictive models. Avoiding breakdowns protects from delays in production and delivery.
Another implementation, inventory management, uses demand forecasting to leverage supply chain data and point-of-sales data combined with externalities such as the economy, markets, availability of raw materials, etc. to determine production, thereby inventory needs. Implementing demand forecasting reduces storage needs since it streamlines production with consumer behavior. The process also improves supplier-manufacturer relations by enabling more efficient stock regulation.
While predictive analysis can identify problems before they occur with equipment, it can do the same with products. Companies can identify the need for a recall before a dire event occurs. Using warranty analysis also examines existing, already identified problems to determine product improvements. It can also lead to new product ideas.
How Maintenance Reduces Warranty Claims
Since PAM helps to accurately predict asset failures, it can help keep products functioning at peak performance. Integrating warranty analytics with recommended product maintenance can reduce warranty claims. Since the analytic process forecasts failure, companies can recommend maintenance before a forecasted failure. This lowers warranty costs since it avoids product replacement and refunds.
For example, automobiles require frequent maintenance. Smart sensors ensure the mechanics know the problem before a vehicle drives onto the lot. Predictive analytics improves parts inventory and enhances technician training programs. Many trucks coming off the line today produce a daily data load of four gigabytes. That provides an ample data set per vehicle and the big data to model and predict across models and manufacturers.
Contact Starr & Associates to learn how predictive analysis can help improve inventory planning and reduce warranty claims. We can help you find simple solutions to develop better business intelligence.