Estimation of Product Attributes and Their Importances

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Introduction to products, attributes and product types

While confirming some commonly known facts, our findings also show discrepancies between our perception and actual facts in some cases. The objective of this is exercise is to find out input parameters making impact on reliability. Though it is field perception, we have identified them from practitioners and taken as reference for conducting future experiments and literature survey.

We believe that continued research efforts are essential to provide guidelines for reliability estimation process to take care of important but hitherto ignored factors, thus improving relevance and accuracy of reliability predictions. Article :. DOI: Need Help? Density and permeability are both related to wood anatomical features. Chemical composition is directly related to wood physical and mechanical properties. It affects pulp yield and quality.

Conjoint Analysis

Ring characteristics are directly related to aesthetic properties e. They also affect machinability. Moisture content in logs affects many operations throughout the value recovery chain. Moisture content in logs and boards is also related to the formation of mould, decay and sapstain.

Wood density has long been considered the most important wood quality attribute. To a large extent, wood density determines the suitability of a species for a specific end use. High-density wood is usually associated with high lumber strength and stiffness. Some panel producers e. High density wood is usually associated with high pulp yield.

Dimensional stability influences construction efficiency and structural serviceability. Warping may cause serious problems for both structural applications and appearance products. Durability commonly used to mean decay resistance is critical for exterior applications and wood construction. Wood permeability is closely related to wood drying rate and treatability. It may also affect finishing properties. Machinability includes a number of operations considered critical for secondary processing.

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Finishing characteristics are of importance to secondary manufacturing and housing construction. Engineering properties include fastening and gluing characteristics, which are valued by manufacturers of engineered wood products. Other wood characteristics include acoustic, thermal, electrical and other properties that may be of importance to specific end uses. Some wood quality attributes appeared to share a unique link. Stem diameter, stem shape, stem taper and tree age are related to stem and log characteristics; wood anatomical and chemical characteristics constitute basic wood characteristics; gross wood characteristics e.

On the other hand, most wood quality attributes are somehow interrelated, and thus any attempt to group them into categories appears somewhat arbitrary. In general, gross wood characteristics are related to stem and log characteristics e. For example, juvenile wood and heartwood content usually increase with increasing age or diameter. Reaction wood is often associated with stem shapes or straightness. Since gross wood characteristics are related to stem characteristics, the basic wood characteristics of a stem depend, to some extent, on the stem characteristics. This implies that, for a given species, stems and logs of different ages or diameter classes have different wood characteristics at the gross, anatomical and chemical levels, which suggests that these stems or logs may have different physico-mechanical properties and service-related attributes.

Barrett, J. Forest Prod.

Estimation of Product Attributes and Their Importances | SpringerLink

Constantino, L. Forest Sci. Holtzscher, M. Jennings, S. Johansson, G. Jozsa, L. Special Publication No.

Understand Which Attributes Matter Most to Customers

SP, Forintek Canada Corp. Keith, C. Kellison, R. State Univ. Kellogg, R. Kliger, I. Chalmers Univ.

Mitchell, H. Report No. National Lumber Grades Authority. Burnaby, BC. Oberg, J. Southern Plantation Wood Quality Workshop.

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  • June , , Athens, Georgia. Perstorper, M. Part 2. Wood Sci. Steele, P. FPL, Forest Prod. Three-factor indices created were tested for their suitability in the LCM and only the PB index was used because it fitted the model well. Estimation of the LCM to determine the optimal number of segments was based on a balanced assessment of the log-likelihood function and full information maximum likelihood [ 42 ].

    Andrews [ 43 ] noted that AIC and BIC never under-fit the number of segments but may over-fit them leading to larger parameter biases. Since the three-segment model best described the sample, thus the best fitting LCM, consumers were categorized into three homogeneous segments. A multinomial logit model MNL , which gives the unconditional probability for the choice of a product attribute, was run as the starting point to check for parameter fit and as a precursor for further iterations.

    Consumers in segment 1 and 2 have a negative and significant ASC, which represents a preference for the status quo option C over the processed product. Although the respective coefficients of these three attributes differ in magnitudes across segments, suggestive of varying weights, they are important to consumers.

    Consumers would derive utility from nutritious bean products and fuel saving but are sensitive to price changes.

    How to Figure Out Which Product Attributes Matter

    The consciousness toward own health and the need to stay healthy could be the motivation to the high value attached to nutritional enhancement of precooked beans [ 44 , 45 , 46 ]. The significant valuation of fuel saving as a benefit of precooked beans reflects the growing cost of fuel for cooking [ 31 , 32 ] associated with increases in population pressure and urban population.

    Consumers in segment one, derive higher utility from all attributes especially enhanced quality of nutrition attribute. The probability of belonging to this segment was influenced by self-reliance on the supply of beans for consumption, the quantities consumed, sex of respondent and education.

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    For one additional kg of bean consumed per week, the probability of being a member of segment one reduced by 0. This might be because they are very averse to price increases as revealed by the absolute coefficient on price that was second largest within the segment and largest between segments. This segment of consumers derives higher utility from nutrition enhancement, water saving and reduced cooking time attributes. Based on this approach, consumers in segment three rely on beans purchased from the market for home consumption because supply from own production was not sufficient although they consume larger quantities of beans per week.

    It is important to note that consumers in urban areas are heterogeneous and belong to all three segments in almost equal proportions contrary to the expectation that urbanites will switch en masse to consumption of processed beans. This shows a diversity of people with different socioeconomic characteristics including among others; variations in wealth status, incomes, time and cooking constraints, and perceptions. To profile consumers in each segment, we first calculate the probability of a consumer belonging to a segment using estimated LCM coefficients inserted in Eq.