When their products don’t perform as intended, manufacturers can incur losses in customer goodwill, future sales, brand equity, regulatory penalties, and claims against warranty reserves. Worse yet, when a product endangers customer health and safety, the resulting mandatory recalls increase losses even more. In some industries, annual warranty costs are as high as five percent of product revenues.
Manufacturers seek to improve product quality and reduce the number of claims by shortening the time it takes to identify, correct, and learn from customer safety and quality experiences.
Recognizing potential quality and safety problems before they affect customers starts with predictive models and tools. Defects don’t arise in a vacuum, and there’s almost always data somewhere — in the supply chain, in procurement, in the sales channel, with customers — that accompanies them. Predictive models are instrumental in blending data from multiple sources so that analysts can mine the data for indicators of product quality.
By collecting data on production and consumption patterns then applying analytics to it, manufacturers can develop a system for notifying customers before claims and recalls come into play. If a component supplier is found to be the cause, manufacturers can also notify teams in procurement to explore alternatives, without disrupting production.