There are multiple areas where people can use product data to make better decisions for existing and future products. These areas include, but are not limited to, the following:
1.
Product quality improvement: Based on quality incidents, customer complaints, test results, design scenarios, computer-aided design (CAD) model analysis, and all other relevant information, analytics will help manufacturers determine root causes of product problems and address product quality issues more efficiently.
2.
Product risk management: Compared to product quality improvement, product risk management takes a more proactive approach towards product sustainability. By analyzing historical product data as well as product requirements data, manufacturers will be able to identify risk factors (e.g., underperforming suppliers, high production costs for parts, and non-compliant products) and address them before a product reaches the production stage.
3.
Product portfolio management: Based on quality, risk, and performance analysis, manufacturers are able to determine which products should be pursued. The result may include portfolios of existing and future products, which will affect product innovation investments in project portfolio management.
4.
Part portfolio management: Using standard parts is a long-term practice in various manufacturing areas. Based on part performance and usage data, analytics may help companies build an optimized parts library that contains standard parts (on international, regional, national, industrial, and organizational levels) and non-standard parts.
1.
Product quality improvement: Based on quality incidents, customer complaints, test results, design scenarios, computer-aided design (CAD) model analysis, and all other relevant information, analytics will help manufacturers determine root causes of product problems and address product quality issues more efficiently.
2.
Product risk management: Compared to product quality improvement, product risk management takes a more proactive approach towards product sustainability. By analyzing historical product data as well as product requirements data, manufacturers will be able to identify risk factors (e.g., underperforming suppliers, high production costs for parts, and non-compliant products) and address them before a product reaches the production stage.
3.
Product portfolio management: Based on quality, risk, and performance analysis, manufacturers are able to determine which products should be pursued. The result may include portfolios of existing and future products, which will affect product innovation investments in project portfolio management.
4.
Part portfolio management: Using standard parts is a long-term practice in various manufacturing areas. Based on part performance and usage data, analytics may help companies build an optimized parts library that contains standard parts (on international, regional, national, industrial, and organizational levels) and non-standard parts.
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