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Product information consists of product attributes and the relationships between products.
It aims at improving the environmental efficiency of a product using artificial neural networks, which consist of high-level product attributes and LCA results.
It was also aimed to seek high quality level of product and process characteristics for meeting the desirability of attributes and for health care.
Level of Product and Service Integration.
Extrinsic product attributes are not physical parts of the product, but product-related cues (e.g., price, brand, and level of advertising), while intrinsic product attributes are physical properties of the product (e.g., color and textures).
Throw in cognitive computing and increased image recognition capability, and you'll get the rise of imputed product attributes that go to a level we don't see currently – and retailers scrambling to adopt them.
Historically, the relationship of product quality to product attributes has not been well defined or understood.
For compositional preference measurement, single characters – each attribute and level – of a product or service are directly valued by the respondent and composed ex-post into the overall utility (direct utility measurement using e.g., self-explicated approaches and analytic hierarchy process [AHP]; see Table 1).
The product attributes are either of sensorial or analytical nature.
Product attributes captured 84% of the total WFCs, while service attributes captured 16%.
The fill-level was found to be an important factor in assessment and development of twin-screw granulation processes as it impacted process and product attributes enormously.
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